BlueData and Hortonworks Data Platform (HDP)
0 likes5,207 views
With BlueData, you can spin up instant containerized environments for the Hortonworks Data Platform (HDP) and other Big Data analytics and machine learning workloads — providing your data science teams with on-demand environments for greater agility. You can decouple compute from storage resources, to improve efficiency and reduce costs. And you can ensure the enterprise-grade security and governance that your IT teams require. BlueData has completed certification through the rigorous Hortonworks QATS (Quality Assured Testing Suite) program for deploying HDP in a containerized environment. This certification enables Hortonworks and BlueData to provide best-in-class support and high performance for their customers’ existing and future investments in HDP. “We’ve seen rapidly growing interest in running HDP on containers, therefore it was key that we work closely with BlueData to benefit those users,” said Scott Andress, vice president of global channels & alliances at Hortonworks. “They passed our most rigorous QATS certification tests, validating that BlueData provides complete interoperability and high performance for customers running HDP in containerized environments.”
1 of 2
Downloaded 10 times
Ad
Recommended
What's the Hadoop-la about Kubernetes?
What's the Hadoop-la about Kubernetes?DataWorks Summit There is increased interest in using Kubernetes, the open-source container orchestration system for modern, stateful Big Data analytics workloads. The promised land is a unified platform that can handle cloud native stateless and stateful Big Data applications. However, stateful, multi-service Big Data cluster orchestration brings unique challenges. This session will delve into the technical gaps and considerations for Big Data on Kubernetes.
Containers offer significant value to businesses; including increased developer agility, and the ability to move applications between on-premises servers, cloud instances, and across data centers. Organizations have embarked on this journey to containerization with an emphasis on stateless workloads. Stateless applications are usually microservices or containerized applications that don’t “store” data. Web services (such as front end UIs and simple, content-centric experiences) are often great candidates as stateless applications since HTTP is stateless by nature. There is no dependency on the local container storage for the stateless workload.
Stateful applications, on the other hand, are services that require backing storage and keeping state is critical to running the service. Hadoop, Spark and to lesser extent, noSQL platforms such as Cassandra, MongoDB, Postgres, and mySQL are great examples. They require some form of persistent storage that will survive service restarts...
Speakers
Anant Chintamaneni, VP Products, BlueData
Nanda Vijaydev, Director Solutions, BlueData
Manage Microservices & Fast Data Systems on One Platform w/ DC/OS
Manage Microservices & Fast Data Systems on One Platform w/ DC/OSMesosphere Inc. This document provides an overview of Mesosphere DC/OS and its benefits. It begins with an introduction to the challenges of building data-intensive applications at scale. It then outlines how Mesosphere DC/OS provides a unified platform for containers and data services across infrastructure with automation and architectural control. Key benefits highlighted include speed, cost savings, and ensuring necessary skills. The document concludes with examples of how Mesosphere is powering industry leaders and a demo.
Logical Data Warehouse: How to Build a Virtualized Data Services Layer
Logical Data Warehouse: How to Build a Virtualized Data Services LayerDataWorks Summit The document discusses the emergence of logical data warehouses in response to big data. It describes how a logical data warehouse uses virtualization, distributed processing, and other techniques to provide a unified view of data across different repositories like Hadoop, relational databases and NoSQL stores. It also discusses how organizations can optimize resources by offloading analytical workloads from their enterprise data warehouse to Hadoop clusters to reduce costs while still using existing code and applications.
20150716 introduction to apache spark v3 
20150716 introduction to apache spark v3 Andrey Vykhodtsev Apache Spark is a fast, general-purpose, and easy-to-use cluster computing system for large-scale data processing. It provides APIs in Scala, Java, Python, and R. Spark is versatile and can run on YARN/HDFS, standalone, or Mesos. It leverages in-memory computing to be faster than Hadoop MapReduce. Resilient Distributed Datasets (RDDs) are Spark's abstraction for distributed data. RDDs support transformations like map and filter, which are lazily evaluated, and actions like count and collect, which trigger computation. Caching RDDs in memory improves performance of subsequent jobs on the same data.
Sahara presentation latest - Codemotion Rome 2015
Sahara presentation latest - Codemotion Rome 2015Codemotion The document discusses a presentation on OpenStack Sahara given at a conference in Rome. It begins with introducing the three speakers and their backgrounds. It then provides an agenda for the presentation which includes an introduction to big data, an overview of OpenStack components, and a demonstration of Sahara in action. The presentation discusses what big data is, provides a brief history of MapReduce and Hadoop, and explains how OpenStack is well-suited to host big data platforms through its various components and architecture. It concludes by introducing OpenStack Sahara as a way to simplify deploying and managing Hadoop clusters on OpenStack.
Stsg17 speaker yousunjeong
Stsg17 speaker yousunjeongYousun Jeong Strata Singapore 2017 business use case section
"Big Telco Real-Time Network Analytics"
https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/62797
20150314 sahara intro and the future plan for open stack meetup
20150314 sahara intro and the future plan for open stack meetupWei Ting Chen Sahara is an OpenStack service that allows users to easily provision and manage Hadoop clusters in OpenStack. It currently supports plugins for Hortonworks, Cloudera, and MapR distributions of Hadoop. The Cloudera plugin integrates Cloudera Manager to provision CDH services. Sahara aims to provide analytics as a service and allow data processing directly in OpenStack clusters using technologies like HDFS, Swift, and Hadoop frameworks. Performance overhead compared to bare metal Hadoop clusters is a current limitation being addressed.
Hadoop Virtualization - Intel White Paper
Hadoop Virtualization - Intel White PaperBlueData, Inc. This white paper describes how BlueData enables virtualization of Hadoop and Spark workloads running on Intel architecture.
Even as virtualization has spread throughout the data center, Apache Hadoop continues to be deployed almost exclusively on bare-metal physical servers. Processing overhead and I/O latency typically associated with virtualization have prevented big data architects from virtualizing Hadoop implementations.
As a result, most Hadoop initiatives have been limited in terms of agility, with infrastructure changes such as provisioning a new server for Hadoop often taking weeks or even months. This infrastructure complexity continues to slow down adoption in enterprise deployments. Apache Spark is a relatively new big data technology, but interest is growing rapidly; many of these same deployment challenges apply to on-premises Spark implementations.
The BlueData EPIC software platform addresses these limitations, enabling data center operators to accelerate Hadoop and Spark implementations on Intel architecture-based servers.
For more information, visit intel.com/bigdata and bluedata.com
Unify Stream and Batch Processing using Dataflow, a Portable Programmable Mod...
Unify Stream and Batch Processing using Dataflow, a Portable Programmable Mod...DataWorks Summit Google Cloud Dataflow is a fully managed service that allows users to build batch or streaming parallel data processing pipelines. It provides a unified programming model for batch and streaming workflows. Cloud Dataflow handles resource management and optimization to efficiently execute data processing jobs on Google Cloud Platform.
Interactive query in hadoop
Interactive query in hadoopRommel Garcia This document discusses interactive querying in Hadoop. It describes how Hive facilitates SQL querying over data stored in HDFS. Hive performance is improved through optimizations like using Tez as the execution engine instead of MapReduce, vectorized queries, and ORC file format. Tez is a dataflow framework that allows expressing queries as directed acyclic graphs (DAGs) of vertices and edges, avoiding the multi-step MapReduce approach and improving latency. The document provides examples of expressing Hive queries in Tez and demonstrates its capabilities.
Bootstrapping state in Apache Flink
Bootstrapping state in Apache FlinkDataWorks Summit Apache Flink is a popular stream computing framework for real-time stream computing. Many stream compute algorithms require trailing data in order to compute the intended result. One example is computing the number of user logins in the last 7 days. This creates a dilemma where the results of the stream program are incomplete until the runtime of the program exceeds 7 days. The alternative is to bootstrap the program using historic data to seed the state before shifting to use real-time data.
This talk will discuss alternatives to bootstrap programs in Flink. Some alternatives rely on technologies exogenous to the stream program, such as enhancements to the pub/sub layer, that are more generally applicable to other stream compute engines. Other alternatives include enhancements to Flink source implementations. Lyft is exploring another alternative using orchestration of multiple Flink programs. The talk will cover why Lyft pursued this alternative and future directions to further enhance bootstrapping support in Flink.
Speaker
Gregory Fee, Principal Engineer, Lyft
Hadoop in the Cloud: Real World Lessons from Enterprise Customers
Hadoop in the Cloud: Real World Lessons from Enterprise CustomersDataWorks Summit/Hadoop Summit This document discusses Azure HDInsight and how it provides a managed Hadoop as a service on Microsoft's cloud platform. Key points include:
- Azure HDInsight runs Apache Hadoop and related projects like Hive and Pig in a cloud-based cluster that can be set up in minutes without hardware to deploy or maintain.
- It supports running queries and analytics jobs on data stored locally in HDFS or in Azure cloud storage like Blob storage and Data Lake Store.
- An IDC study found that Microsoft customers using cloud-based Hadoop through Azure HDInsight have 63% lower total cost of ownership than an on-premises Hadoop deployment.
Interactive query using hadoop
Interactive query using hadoopArvind Radhakrishnen Proof of Concept that details differences between Tez, Hive, Impala as it pertains to different complex queries being executed by business.
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and IgniteJoseph Kuo This session aims to establish applications running against distributed and scalable system, or as we know cloud computing system. We will introduce you not only briefing of Hazelcast but also deeper kernel of it, and how it works with Spark, the most famous Map-reduce library. Furthermore, we will introduce another in-memory cache called Apache Ignite and compare it with Hazelcast to see what's the difference between them. In the end, we will give a demonstration showing how Hazelcast and Spark work together well to form a cloud-base service which is distributed, flexible, reliable, available, scalable and stable. You can find demo code here: https://github.com/CyberJos/jcconf2016-hazelcast-spark
https://cyberjos.blog/java/seminar/jcconf-2016-cloud-computing-applications-hazelcast-spark-and-ignite/
Hadoop from Hive with Stinger to Tez
Hadoop from Hive with Stinger to TezJan Pieter Posthuma Presentation given for the SQLPass community at SQLBits XIV in Londen. The presentation is an overview about the performance improvements provided to Hive with the Stinger initiative.
Querying Druid in SQL with Superset
Querying Druid in SQL with SupersetDataWorks Summit Druid is a high performance, column-oriented distributed data store that is widely used at Oath for big data analysis. Druid has a JSON schema as its query language, making it difficult for new users unfamiliar with the schema to start querying Druid quickly. The JSON schema is designed to work with the data ingestion methods of Druid, so it can provide high performance features such as data aggregations in JSON, but many are unable to utilize such features, because they not familiar with the specifics of how to optimize Druid queries. However, most new Druid users at Yahoo are already very familiar with SQL, and the queries they want to write for Druid can be converted to concise SQL.
We found that our data analysts wanted an easy way to issue ad-hoc Druid queries and view the results in a BI tool in a way that's presentable to nontechnical stakeholders. In order to achieve this, we had to bridge the gap between Druid, SQL, and our BI tools such as Apache Superset. In this talk, we will explore different ways to query a Druid datasource in SQL and discuss which methods were most appropriate for our use cases. We will also discuss our open source contributions so others can utilize our work. GURUGANESH KOTTA, Software Dev Eng, Oath and JUNXIAN WU, Software Engineer, Oath Inc.
Large Table Partitioning with PostgreSQL and Django
Large Table Partitioning with PostgreSQL and DjangoEDB With great DB Table comes great responsibility". Our email messages table was growing too much and we needed to do something about it. We will talk about how we integrated PostgreSQL Declarative partitioning with our Django based Customer Portal to solve the problem.
YARN Containerized Services: Fading The Lines Between On-Prem And Cloud
YARN Containerized Services: Fading The Lines Between On-Prem And CloudDataWorks Summit Apache Hadoop YARN is the modern distributed operating system for big data applications. In Apache Hadoop 3.1.0, YARN added a service framework that supports long-running services. This new capability goes hand in hand with the recent improvements in YARN to support Docker containers. Together these features have made it significantly easier to bring new applications and services to YARN.
In this talk you will learn about YARN service framework, its new containerization capabilities and how it lays the foundation for a hybrid and uniform architecture for compute and storage across on-prem and multi-cloud environments. This will include examples highlighting how easy it is to bring applications to the YARN service framework as well as how to containerize applications.
Here's what to expect in this talk:
- Motivation for YARN service framework and containerization
- YARN service framework overview
- YARN service examples
- Containerization overview
- Containerization for Big Data and non Big Data workloads - wait that's everything
Kudu Deep-Dive
Kudu Deep-DiveSupriya Sahay Kudu is popularly referred to as "Fast Analytics on Fast Data" capable of performing both OLAP & OLTP operations. Understand right from essentials to deep-dive into Kudu internals and architecture for building applications based on Kudu and integrating with Hadoop ecosystem.
Read about Kudu clusters, architecture, operations, primary key design and column optimizations, partitioning and other performance considerations.
The Fundamentals Guide to HDP and HDInsight
The Fundamentals Guide to HDP and HDInsightGert Drapers This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
Bare-metal performance for Big Data workloads on Docker containers
Bare-metal performance for Big Data workloads on Docker containersBlueData, Inc. In a benchmark study, Intel® compared the performance of Big Data workloads running on a bare-metal deployment versus running in Docker* containers with the BlueData® EPIC™ software platform.
This in-depth study shows that performance ratios for container-based Hadoop workloads on BlueData EPIC are equal to — and in some cases, better than — bare-metal Hadoop. For example, benchmark tests showed that the BlueData EPIC platform demonstrated an average 2.33% performance gain over bare metal, for a configuration with 50 Hadoop compute nodes and 10 terabytes (TB) of data. These performance results were achieved without any modifications to the Hadoop software.
This is a revolutionary milestone, and the result of an ongoing collaboration between Intel and BlueData software engineering teams.
This white paper describes the software and hardware configurations for the benchmark tests, as well as details of the performance benchmark process and results.
Splunk: Druid on Kubernetes with Druid-operator
Splunk: Druid on Kubernetes with Druid-operatorImply We went through the journey of deploying Apache Druid clusters on Kubernetes(K8s) and created a druid-operator (https://github.com/druid-io/druid-operator). This talk introduces the druid kubernetes operator, how to use it to deploy druid clusters and how it works under the hood. We will share how we use this operator to deploy Druid clusters at Splunk.
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. Druid is a complex stateful distributed system and a Druid cluster consists of multiple web services such as Broker, Historical, Coordinator, Overlord, MiddleManager etc each deployed with multiple replicas. Deploying a single web service on K8s requires creating few K8s resources via YAML files and it multiplies due to multiple services inside of a Druid cluster. Now doing it for multiple Druid clusters (dev, staging, production environments) makes it even more tedious and error prone.
K8s enables creation of application (such as Druid) specific extension, called “Operator”, that combines kubernetes and application specific knowledge into a reusable K8s extension that makes deploying complex applications simple.
Hybrid Data Platform 
Hybrid Data Platform DataWorks Summit/Hadoop Summit The document discusses the need for a hybrid data platform that combines on-premise and cloud-based infrastructure. A hybrid data platform can address issues like mixed workloads, long-tail data usage, and data movement costs. It also allows tapping into the scale of cloud while keeping some data and workloads on-premise. The proposed architecture shows data integration between on-premise Hadoop and cloud services like S3, Redshift, and EMR through private tunnels and security measures. Case studies demonstrate how a large commodities producer built a hybrid analytics environment for productivity, supply chain, and operations.
Bootcamp 2017 - SQL Server on Linux
Bootcamp 2017 - SQL Server on LinuxMaximiliano Accotto SQL Server 2017 on Linux
- SQL Server 2017 will run natively on Linux
- It provides the same features and capabilities as SQL Server on Windows
- It supports the same editions as Windows and can be licensed with the same license
- It has the same database engine and core services as Windows
- Some advanced features like PolyBase and Stretch Database are not yet supported on Linux
- It uses a new platform abstraction layer to run on Linux
MOUG17: DB Security; Secure your Data
MOUG17: DB Security; Secure your DataMonica Li The SQLT utility provides concise summaries of SQL performance and plans. It works by calling the SQL Tuning Advisor and Trace Analyzer to analyze execution plans, profiles, and trace files. The utility outputs comprehensive HTML reports on configuration findings, recommendations, and metadata for troubleshooting SQL performance issues.
Apache Tez -- A modern processing engine
Apache Tez -- A modern processing enginebigdatagurus_meetup The document discusses Apache Tez, a framework for accelerating Hadoop query processing. Some key points:
- Tez is a dataflow framework that expresses computations as directed acyclic graphs (DAGs) of tasks. This allows optimizations like container reuse and locality-aware scheduling.
- It is built on YARN and provides a customizable execution engine as well as runtime and DAG APIs for applications to define computations.
- Compared to MapReduce, Tez can provide better performance, predictability, and resource utilization through its DAG execution model and optimizations like reducing intermediate data writes.
- It has been used to improve performance for workloads like Hive, Pig, and large TPC-DS queries
Red Hat Ceph Storage: Past, Present and Future
Red Hat Ceph Storage: Past, Present and FutureRed_Hat_Storage Ceph is a massively scalable, open source, software-defined storage system that runs on commodity hardware. Get an update about the latest version of Red Hat Ceph Storage, including information about the newest features and use cases, with a particular focus on cloud storage and OpenStack. We’ll also explore the themes and directions for the roadmap for the next 12 months.
Introducing Cloudian HyperStore 6.0
Introducing Cloudian HyperStore 6.0Cloudian Cloudian HyperStore offer 100% S3 compatibility for low-cost, scalable smart object storage.
With HyperStore 6.0, we are focused on bringing down operational costs so that you can more effectively track, manage, and optimize your data storage as you scale.
BlueData DataSheet
BlueData DataSheetGreg Kirchoff The BlueData EPIC™ software platform solves the challenges that can slow down and stall Big Data initiatives. It makes deployment of Big Data infrastructure easier, faster, and more
cost-effective – eliminating complexity as a barrier to adoption.
An Overview of All The Different Databases in Google Cloud
An Overview of All The Different Databases in Google CloudFibonalabs Google cloud platform (GCP) is a high-performance infrastructure for cloud computing, data analytics, and machine learning. Google Cloud runs on the same infrastructure that Google uses for its end-user products like Google Search, Gmail, Google Drive, Google Photos, etc.
Ad
More Related Content
What's hot (20)
Unify Stream and Batch Processing using Dataflow, a Portable Programmable Mod...
Unify Stream and Batch Processing using Dataflow, a Portable Programmable Mod...DataWorks Summit Google Cloud Dataflow is a fully managed service that allows users to build batch or streaming parallel data processing pipelines. It provides a unified programming model for batch and streaming workflows. Cloud Dataflow handles resource management and optimization to efficiently execute data processing jobs on Google Cloud Platform.
Interactive query in hadoop
Interactive query in hadoopRommel Garcia This document discusses interactive querying in Hadoop. It describes how Hive facilitates SQL querying over data stored in HDFS. Hive performance is improved through optimizations like using Tez as the execution engine instead of MapReduce, vectorized queries, and ORC file format. Tez is a dataflow framework that allows expressing queries as directed acyclic graphs (DAGs) of vertices and edges, avoiding the multi-step MapReduce approach and improving latency. The document provides examples of expressing Hive queries in Tez and demonstrates its capabilities.
Bootstrapping state in Apache Flink
Bootstrapping state in Apache FlinkDataWorks Summit Apache Flink is a popular stream computing framework for real-time stream computing. Many stream compute algorithms require trailing data in order to compute the intended result. One example is computing the number of user logins in the last 7 days. This creates a dilemma where the results of the stream program are incomplete until the runtime of the program exceeds 7 days. The alternative is to bootstrap the program using historic data to seed the state before shifting to use real-time data.
This talk will discuss alternatives to bootstrap programs in Flink. Some alternatives rely on technologies exogenous to the stream program, such as enhancements to the pub/sub layer, that are more generally applicable to other stream compute engines. Other alternatives include enhancements to Flink source implementations. Lyft is exploring another alternative using orchestration of multiple Flink programs. The talk will cover why Lyft pursued this alternative and future directions to further enhance bootstrapping support in Flink.
Speaker
Gregory Fee, Principal Engineer, Lyft
Hadoop in the Cloud: Real World Lessons from Enterprise Customers
Hadoop in the Cloud: Real World Lessons from Enterprise CustomersDataWorks Summit/Hadoop Summit This document discusses Azure HDInsight and how it provides a managed Hadoop as a service on Microsoft's cloud platform. Key points include:
- Azure HDInsight runs Apache Hadoop and related projects like Hive and Pig in a cloud-based cluster that can be set up in minutes without hardware to deploy or maintain.
- It supports running queries and analytics jobs on data stored locally in HDFS or in Azure cloud storage like Blob storage and Data Lake Store.
- An IDC study found that Microsoft customers using cloud-based Hadoop through Azure HDInsight have 63% lower total cost of ownership than an on-premises Hadoop deployment.
Interactive query using hadoop
Interactive query using hadoopArvind Radhakrishnen Proof of Concept that details differences between Tez, Hive, Impala as it pertains to different complex queries being executed by business.
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and Ignite
JCConf 2016 - Cloud Computing Applications - Hazelcast, Spark and IgniteJoseph Kuo This session aims to establish applications running against distributed and scalable system, or as we know cloud computing system. We will introduce you not only briefing of Hazelcast but also deeper kernel of it, and how it works with Spark, the most famous Map-reduce library. Furthermore, we will introduce another in-memory cache called Apache Ignite and compare it with Hazelcast to see what's the difference between them. In the end, we will give a demonstration showing how Hazelcast and Spark work together well to form a cloud-base service which is distributed, flexible, reliable, available, scalable and stable. You can find demo code here: https://github.com/CyberJos/jcconf2016-hazelcast-spark
https://cyberjos.blog/java/seminar/jcconf-2016-cloud-computing-applications-hazelcast-spark-and-ignite/
Hadoop from Hive with Stinger to Tez
Hadoop from Hive with Stinger to TezJan Pieter Posthuma Presentation given for the SQLPass community at SQLBits XIV in Londen. The presentation is an overview about the performance improvements provided to Hive with the Stinger initiative.
Querying Druid in SQL with Superset
Querying Druid in SQL with SupersetDataWorks Summit Druid is a high performance, column-oriented distributed data store that is widely used at Oath for big data analysis. Druid has a JSON schema as its query language, making it difficult for new users unfamiliar with the schema to start querying Druid quickly. The JSON schema is designed to work with the data ingestion methods of Druid, so it can provide high performance features such as data aggregations in JSON, but many are unable to utilize such features, because they not familiar with the specifics of how to optimize Druid queries. However, most new Druid users at Yahoo are already very familiar with SQL, and the queries they want to write for Druid can be converted to concise SQL.
We found that our data analysts wanted an easy way to issue ad-hoc Druid queries and view the results in a BI tool in a way that's presentable to nontechnical stakeholders. In order to achieve this, we had to bridge the gap between Druid, SQL, and our BI tools such as Apache Superset. In this talk, we will explore different ways to query a Druid datasource in SQL and discuss which methods were most appropriate for our use cases. We will also discuss our open source contributions so others can utilize our work. GURUGANESH KOTTA, Software Dev Eng, Oath and JUNXIAN WU, Software Engineer, Oath Inc.
Large Table Partitioning with PostgreSQL and Django
Large Table Partitioning with PostgreSQL and DjangoEDB With great DB Table comes great responsibility". Our email messages table was growing too much and we needed to do something about it. We will talk about how we integrated PostgreSQL Declarative partitioning with our Django based Customer Portal to solve the problem.
YARN Containerized Services: Fading The Lines Between On-Prem And Cloud
YARN Containerized Services: Fading The Lines Between On-Prem And CloudDataWorks Summit Apache Hadoop YARN is the modern distributed operating system for big data applications. In Apache Hadoop 3.1.0, YARN added a service framework that supports long-running services. This new capability goes hand in hand with the recent improvements in YARN to support Docker containers. Together these features have made it significantly easier to bring new applications and services to YARN.
In this talk you will learn about YARN service framework, its new containerization capabilities and how it lays the foundation for a hybrid and uniform architecture for compute and storage across on-prem and multi-cloud environments. This will include examples highlighting how easy it is to bring applications to the YARN service framework as well as how to containerize applications.
Here's what to expect in this talk:
- Motivation for YARN service framework and containerization
- YARN service framework overview
- YARN service examples
- Containerization overview
- Containerization for Big Data and non Big Data workloads - wait that's everything
Kudu Deep-Dive
Kudu Deep-DiveSupriya Sahay Kudu is popularly referred to as "Fast Analytics on Fast Data" capable of performing both OLAP & OLTP operations. Understand right from essentials to deep-dive into Kudu internals and architecture for building applications based on Kudu and integrating with Hadoop ecosystem.
Read about Kudu clusters, architecture, operations, primary key design and column optimizations, partitioning and other performance considerations.
The Fundamentals Guide to HDP and HDInsight
The Fundamentals Guide to HDP and HDInsightGert Drapers This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
Bare-metal performance for Big Data workloads on Docker containers
Bare-metal performance for Big Data workloads on Docker containersBlueData, Inc. In a benchmark study, Intel® compared the performance of Big Data workloads running on a bare-metal deployment versus running in Docker* containers with the BlueData® EPIC™ software platform.
This in-depth study shows that performance ratios for container-based Hadoop workloads on BlueData EPIC are equal to — and in some cases, better than — bare-metal Hadoop. For example, benchmark tests showed that the BlueData EPIC platform demonstrated an average 2.33% performance gain over bare metal, for a configuration with 50 Hadoop compute nodes and 10 terabytes (TB) of data. These performance results were achieved without any modifications to the Hadoop software.
This is a revolutionary milestone, and the result of an ongoing collaboration between Intel and BlueData software engineering teams.
This white paper describes the software and hardware configurations for the benchmark tests, as well as details of the performance benchmark process and results.
Splunk: Druid on Kubernetes with Druid-operator
Splunk: Druid on Kubernetes with Druid-operatorImply We went through the journey of deploying Apache Druid clusters on Kubernetes(K8s) and created a druid-operator (https://github.com/druid-io/druid-operator). This talk introduces the druid kubernetes operator, how to use it to deploy druid clusters and how it works under the hood. We will share how we use this operator to deploy Druid clusters at Splunk.
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. Druid is a complex stateful distributed system and a Druid cluster consists of multiple web services such as Broker, Historical, Coordinator, Overlord, MiddleManager etc each deployed with multiple replicas. Deploying a single web service on K8s requires creating few K8s resources via YAML files and it multiplies due to multiple services inside of a Druid cluster. Now doing it for multiple Druid clusters (dev, staging, production environments) makes it even more tedious and error prone.
K8s enables creation of application (such as Druid) specific extension, called “Operator”, that combines kubernetes and application specific knowledge into a reusable K8s extension that makes deploying complex applications simple.
Hybrid Data Platform
Hybrid Data Platform DataWorks Summit/Hadoop Summit The document discusses the need for a hybrid data platform that combines on-premise and cloud-based infrastructure. A hybrid data platform can address issues like mixed workloads, long-tail data usage, and data movement costs. It also allows tapping into the scale of cloud while keeping some data and workloads on-premise. The proposed architecture shows data integration between on-premise Hadoop and cloud services like S3, Redshift, and EMR through private tunnels and security measures. Case studies demonstrate how a large commodities producer built a hybrid analytics environment for productivity, supply chain, and operations.
Bootcamp 2017 - SQL Server on Linux
Bootcamp 2017 - SQL Server on LinuxMaximiliano Accotto SQL Server 2017 on Linux
- SQL Server 2017 will run natively on Linux
- It provides the same features and capabilities as SQL Server on Windows
- It supports the same editions as Windows and can be licensed with the same license
- It has the same database engine and core services as Windows
- Some advanced features like PolyBase and Stretch Database are not yet supported on Linux
- It uses a new platform abstraction layer to run on Linux
MOUG17: DB Security; Secure your Data
MOUG17: DB Security; Secure your DataMonica Li The SQLT utility provides concise summaries of SQL performance and plans. It works by calling the SQL Tuning Advisor and Trace Analyzer to analyze execution plans, profiles, and trace files. The utility outputs comprehensive HTML reports on configuration findings, recommendations, and metadata for troubleshooting SQL performance issues.
Apache Tez -- A modern processing engine
Apache Tez -- A modern processing enginebigdatagurus_meetup The document discusses Apache Tez, a framework for accelerating Hadoop query processing. Some key points:
- Tez is a dataflow framework that expresses computations as directed acyclic graphs (DAGs) of tasks. This allows optimizations like container reuse and locality-aware scheduling.
- It is built on YARN and provides a customizable execution engine as well as runtime and DAG APIs for applications to define computations.
- Compared to MapReduce, Tez can provide better performance, predictability, and resource utilization through its DAG execution model and optimizations like reducing intermediate data writes.
- It has been used to improve performance for workloads like Hive, Pig, and large TPC-DS queries
Red Hat Ceph Storage: Past, Present and Future
Red Hat Ceph Storage: Past, Present and FutureRed_Hat_Storage Ceph is a massively scalable, open source, software-defined storage system that runs on commodity hardware. Get an update about the latest version of Red Hat Ceph Storage, including information about the newest features and use cases, with a particular focus on cloud storage and OpenStack. We’ll also explore the themes and directions for the roadmap for the next 12 months.
Introducing Cloudian HyperStore 6.0
Introducing Cloudian HyperStore 6.0Cloudian Cloudian HyperStore offer 100% S3 compatibility for low-cost, scalable smart object storage.
With HyperStore 6.0, we are focused on bringing down operational costs so that you can more effectively track, manage, and optimize your data storage as you scale.
Similar to BlueData and Hortonworks Data Platform (HDP) (20)
BlueData DataSheet
BlueData DataSheetGreg Kirchoff The BlueData EPIC™ software platform solves the challenges that can slow down and stall Big Data initiatives. It makes deployment of Big Data infrastructure easier, faster, and more
cost-effective – eliminating complexity as a barrier to adoption.
An Overview of All The Different Databases in Google Cloud
An Overview of All The Different Databases in Google CloudFibonalabs Google cloud platform (GCP) is a high-performance infrastructure for cloud computing, data analytics, and machine learning. Google Cloud runs on the same infrastructure that Google uses for its end-user products like Google Search, Gmail, Google Drive, Google Photos, etc.
Big Data Companies and Apache Software
Big Data Companies and Apache SoftwareBob Marcus The document provides an overview of leading big data companies in 2021 and the Apache Hadoop stack, including related Apache software and the NIST big data reference architecture. It lists over 50 big data companies, including Accenture, Actian, Aerospike, Alluxio, Amazon Web Services, Cambridge Semantics, Cloudera, Cloudian, Cockroach Labs, Collibra, Couchbase, Databricks, DataKitchen, DataStax, Denodo, Dremio, Franz, Gigaspaces, Google Cloud, GridGain, HPE, HVR, IBM, Immuta, InfluxData, Informatica, IRI, MariaDB, Matillion, Melissa Data
Hadoop Desktop Cluster
Hadoop Desktop ClusterPaul Morse CriKit is a desktop cluster solution that provides a low-cost way for organizations to test Hadoop and other big data technologies. It contains 4 compute nodes, a 1Gb Ethernet switch, keyboard/video/mouse switch, and an optional management workstation. Each node has an Intel processor, 16GB RAM, and up to 1.2TB of SSD storage, providing enough power for moderate data testing of Hadoop. CriKit's small size and ability to reuse components makes it cheaper than large hardware solutions for evaluating new technologies.
Five Best Practices for Improving the Cloud Experience
Five Best Practices for Improving the Cloud ExperienceHitachi Vantara This document summarizes a report on best practices for improving the cloud experience based on lessons learned from 232 global IT executives. The five best practices are: 1) Ensure cloud providers meet business and IT requirements through service level agreements. 2) Choose the right cloud service model based on needed control over security and data protection. 3) Use architectures that integrate cloud services with existing infrastructure. 4) Consider benefits beyond cost like improved operations and innovation. 5) Define business requirements for IT and have IT act as a cloud broker. The Hitachi Content Platform portfolio aligns with these practices by providing a secure, scalable cloud that meets business needs and accelerates cloud adoption.
Hitachi Cloud Solutions Profile 
Hitachi Cloud Solutions Profile Hitachi Vantara Hitachi Data Systems offers private cloud solutions that provide flexible, scalable cloud storage infrastructures. These solutions allow organizations to lower costs by paying only for consumed storage resources and improving efficiency by reducing management overhead. Key offerings include file tiering services that move inactive files to cloud storage, freeing up resources on primary storage, and fully managed private cloud services where Hitachi remotely manages the on-premises cloud infrastructure.
Performance advantages of Hadoop ETL offload with the Intel processor-powered...
Performance advantages of Hadoop ETL offload with the Intel processor-powered...Principled Technologies High-level Hadoop analysis requires custom solutions to deliver the data that you need, and the faster these jobs run the better. What if ETL jobs created by an entry-level employee after only a few days of training could run even faster than the same jobs created by a Hadoop expert with 18 years of database experience?
This is exactly what we found in our testing with the Dell | Cloudera | Syncsort solution. Not only was this solution was faster, easier, and less expensive to implement, but the ETL use cases our beginner created with this solution ran up to 60.3 percent more quickly than those our expert created with open-source tools.
Using the Dell | Cloudera | Syncsort solution means that your organization can compensate a lower-level employee for half as much time as a senior engineer doing less-optimized work. That is a clear path to savings.
Scaling Data Science on Big Data
Scaling Data Science on Big DataDataWorks Summit Data science holds tremendous potential for organizations to uncover new insights and drivers of revenue and profitability. Big Data has brought the promise of doing data science at scale to enterprises, however this promise also comes with challenges for data scientists to continuously learn and collaborate. Data Scientists have many tools at their disposal such as notebooks like Juypter and Apache Zeppelin & IDEs such as RStudio with languages like R, Python, Scala and frameworks like Apache Spark. Given all the choices how do you best collaborate to build your model and then work through the development lifecycle to deploy it from test into production ?
In this session learn the attributes of a modern data science platform that empowers data scientists to build models using all the data in their data lake and foster continuous learning and collaboration. We will show a demo of DSX with HDP with the focus on integration, security and model deployment and management.
Speakers:
Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM
Vikram Murali, Program Director, Data Science and Machine Learning, IBM
ADB Deployment options_082021.pptx
ADB Deployment options_082021.pptxAhmed Abdellatif The document discusses Oracle's database cloud deployment options. It describes Oracle Autonomous Database which fully automates database management. It can be deployed in Oracle public clouds, dedicated regions, or on-premises. The document also discusses Exadata Cloud services which provide high-performance databases in Oracle data centers or customer data centers. A variety of deployment options are presented that balance control, costs and support for hybrid cloud strategies.
flexpod_hadoop_cloudera
flexpod_hadoop_clouderaPrem Jain FlexPod Select for Hadoop is a pre-validated solution from Cisco and NetApp that provides an enterprise-class architecture for deploying Apache Hadoop workloads at scale. The solution includes Cisco UCS servers and fabric interconnects for compute, NetApp storage arrays, and Cloudera's Distribution of Apache Hadoop for the software stack. It offers benefits like high performance, reliability, scalability, simplified management, and reduced risk for organizations running business-critical Hadoop workloads.
ds_Pivotal_Big_Data_Suite_Product_Suite
ds_Pivotal_Big_Data_Suite_Product_SuiteRobin Fong 方俊强 Pivotal Big Data Suite is a comprehensive platform that allows companies to modernize their data infrastructure, gain insights through advanced analytics, and build analytic applications at scale. It includes components for data processing, storage, analytics, in-memory processing, and application development. The suite is based on open source software, supports multiple deployment options, and provides an agile approach to help companies transform into data-driven enterprises.
Comprehensive Guide for Microsoft Fabric to Master Data Analytics
Comprehensive Guide for Microsoft Fabric to Master Data AnalyticsSparity1 Unlock the power of Microsoft Fabric to master data analytics in 2024 with our comprehensive guide. Elevate your data skills with Sparity's insights
Open Source DWBI-A Primer
Open Source DWBI-A Primerpartha69 This document provides an overview of open source data warehousing and business intelligence (DW/BI). It defines cloud computing and explains how open DW consists of pre-designed data warehouse architectures that are free to use. Open DW reduces costs and risks by shortening design and development time. While the architectures are free, vendors charge for services like customization, support, and maintenance. The document discusses the need for and benefits of open DW/BI, including faster deployment, lower costs, and mitigated risks through rapid development. It also outlines some popular open source databases, tools, and vendors in this space.
Cisco Big Data Use Case
Cisco Big Data Use CaseErni Susanti Cisco implemented an enterprise Hadoop platform using Cisco UCS servers and fabric interconnects to provide a scalable big data analytics solution. This platform allowed Cisco to analyze service sales opportunities in one-tenth the time for one-tenth the cost, resulting in $40 million in additional service bookings. The Cisco UCS infrastructure simplified management of the Hadoop cluster and provided high performance through local storage on the servers.
cisco_bigdata_case_study_1
cisco_bigdata_case_study_1Erni Susanti Cisco implemented an enterprise Hadoop platform using Cisco UCS servers and fabric interconnects to provide a scalable big data analytics solution. This platform allowed Cisco to analyze service sales opportunities in one-tenth the time for one-tenth the cost, resulting in $40 million in additional service bookings. The Cisco UCS infrastructure simplified management of the Hadoop cluster and provided high performance through local storage on the servers.
Achieving Separation of Compute and Storage in a Cloud World
Achieving Separation of Compute and Storage in a Cloud WorldAlluxio, Inc. Alluxio Tech Talk
Feb 12, 2019
Speaker:
Dipti Borkar, Alluxio
The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.
Enter Alluxio, a virtual unified file system, which sits between compute and storage that allows you to realize the benefits of a hybrid cloud architecture with the same performance and lower costs.
In this webinar, we will discuss:
- Why leading enterprises are adopting hybrid cloud architectures with compute and storage disaggregated
- The new challenges that this new paradigm introduces
- An introduction to Alluxio and the unified data solution it provides for hybrid environments
Data Orchestration for the Hybrid Cloud Era
Data Orchestration for the Hybrid Cloud EraAlluxio, Inc. Alluxio Community Office Hour
October 20, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speaker(s):
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
Cisco Big Data Warehouse Expansion Featuring MapR Distribution
Cisco Big Data Warehouse Expansion Featuring MapR DistributionAppfluent Technology The document discusses Cisco's Big Data Warehouse Expansion solution featuring MapR Distribution including Apache Hadoop. The solution reduces data warehouse management costs by enabling organizations to store and analyze more data at lower costs. It does this by offloading infrequently used data from the existing data warehouse to low-cost big data stores running on Cisco UCS hardware optimized for MapR Distribution. This provides benefits like enhanced analytics, improved performance, reduced costs and risks, and competitive advantages from being able to utilize more company data assets.
Best Compute Solutions, Backup Services, and Data Storage Center
Best Compute Solutions, Backup Services, and Data Storage CenterSamidhaTakle1 Stop worrying about your data. SAID Technologies got you covered with the best Compute Solutions, Backup and Data Storage Center services.
For more details, visit : https://saidtechnologies.com/compute-storage-and-backup/
Jak konsolidovat Vaše databáze s využitím Cloud služeb?
Jak konsolidovat Vaše databáze s využitím Cloud služeb?MarketingArrowECS_CZ Prezentace z webináře dne 10.3.2022
Prezentovali:
Jaroslav Malina - Senior Channel Sales Manager, Oracle
Josef Krejčí - Technology Sales Consultant, Oracle
Josef Šlahůnek - Cloud Systems sales Consultant, Oracle
Performance advantages of Hadoop ETL offload with the Intel processor-powered...
Performance advantages of Hadoop ETL offload with the Intel processor-powered...Principled Technologies
Ad
More from BlueData, Inc. (17)
Introduction to KubeDirector - SF Kubernetes Meetup
Introduction to KubeDirector - SF Kubernetes MeetupBlueData, Inc. Presentation from San Francisco Kubernetes Meetup on October 30, 2018
https://www.meetup.com/San-Francisco-Kubernetes-Meetup/events/255431002
What is KubeDirector? - Tom Phelan & Joel Baxter, Bluedata
Kubernetes is clearly the container orchestrator of choice for cloud-native stateless applications. And with the introduction of StatefulSets and Persistent Volumes it is becoming possible to run stateful applications on Kubernetes.
Now the new KubeDirector project allows users to manage complex stateful clusters for AI, machine learning, and big data analytics on Kubernetes without writing a single line of GO code.
KubeDirector is an open source Apache project that uses the standard Kubernetes custom resource functionality and API extensions to deploy and manage complex stateful scale-out application clusters.
This session will provide an overview of the KubeDirector architecture, show how to author the metadata and artifacts required for an example stateful application (e.g. with Spark, Jupyter, and Cassandra), and demonstrate the deployment and management of the cluster on Kubernetes using KubeDirector.
https://github.com/bluek8s/kubedirector
Dell EMC Ready Solutions for Big Data
Dell EMC Ready Solutions for Big DataBlueData, Inc. Dell EMC Ready Solutions for Big Data are powered by the BlueData EPIC software platform - for on-demand provisioning and automation. These integrated solutions enable a cloud-like experience for Big-Data-as-a-Service (BDaaS) while ensuring the enterprise-grade security and performance of on-premises infrastructure.
With Dell EMC Ready Solutions for Big Data, customers can rapidly deploy their analytics and machine learning workloads in a secure multi-tenant architecture, for multiple different user groups running on shared infrastructure. Their users can quickly and easily provision distributed environments for Cloudera, Hortonworks, Kafka, MapR, Spark, TensorFlow, as well as other tools.
The new Ready Solutions include everything that customers need to enable BDaaS on-premises – including BlueData EPIC software as well as Dell EMC hardware, consulting, deployment, and support services.
To learn more, visit www.dellemc.com/bdaas
How to Protect Big Data in a Containerized Environment
How to Protect Big Data in a Containerized EnvironmentBlueData, Inc. Every enterprise spends significant resources to protect its data. This is especially true in the case of big data, since some of this data may include sensitive or confidential customer and financial information. Common methods for protecting data include permissions and access controls as well as the encryption of data at rest and in flight.
The Hadoop community has recently rolled out Transparent Data Encryption (TDE) support in HDFS. Transparent Data Encryption refers to the process whereby data is transparently encrypted by the big data application writing the data; it is not decrypted again until it is accessed by another application. The data is encrypted during its entire lifespan—in transit and at rest—except when it is being specifically accessed by a processing application.
TDE is an excellent approach for protecting data stored in data lakes built on the latest versions of HDFS. However, it does have its challenges and limitations. Systems that want to use TDE require tight integration with enterprise-wide Kerberos Key Distribution Center (KDC) services and Key Management Systems (KMS). This integration isn’t easy to set up or maintain. These issues can be even more challenging in a virtualized or containerized environment where one Kerberos realm may be used to secure the big data compute cluster and a different Kerberos realm may be used to secure the HDFS filesystem accessed by this cluster.
BlueData has developed significant expertise in configuring, managing, and optimizing access to TDE-protected HDFS. This session at the Strata Data Conference in March 2018 (by Thomas Phelan, co-founder and chief architect at BlueData) offers a detailed overview of how transparent data encryption works with HDFS, with a particular focus on containerized environments.
You’ll learn how HDFS TDE is configured and maintained in an environment where many big data frameworks run simultaneously (e.g., in a hybrid cloud architecture using Docker containers). Moreover, you’ll learn how KDC credentials can be managed in a Kerberos cross-realm environment to provide data scientists and analysts with the greatest flexibility in accessing data while maintaining complete enterprise-grade data security.
https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63763
BlueData EPIC datasheet (en Français)
BlueData EPIC datasheet (en Français)BlueData, Inc. La plateforme logicielle BlueData EPIC™ simplifie, accélère et rend plus rentable le déploiement d’infrastructures et d’applications Big Data telles que Hadoop, Spark, Kafka, Cassandra, et plus, dans l’infrastructure locale ou dans le cloud public.
Best Practices for Running Kafka on Docker Containers
Best Practices for Running Kafka on Docker ContainersBlueData, Inc. Docker containers provide an ideal foundation for running Kafka-as-a-Service on-premises or in the public cloud. However, using Docker containers in production environments for Big Data workloads using Kafka poses some challenges – including container management, scheduling, network configuration and security, and performance.
In this session at Kafka Summit in August 2017, Nanda Vijyaydev of BlueData shared lessons learned from implementing Kafka-as-a-Service with Docker containers.
https://kafka-summit.org/sessions/kafka-service-docker-containers
Lessons Learned from Dockerizing Spark Workloads
Lessons Learned from Dockerizing Spark WorkloadsBlueData, Inc. Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed Big Data applications like Apache Spark.
Some of these challenges include container lifecycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. BlueData is “all in” on Docker containers – with a specific focus on Spark applications. They’ve learned first-hand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy Big Data workloads using Docker.
This session at Spark Summit in February 2017 (by Thomas Phelan, co-founder and chief architect at BlueData) described lessons learned as well as some tips and tricks on how to Dockerize your Big Data applications in a reliable, scalable, and high-performance environment.
In this session, Tom described how to network Docker containers across multiple hosts securely. He discussed ways to achieve high availability across distributed Big Data applications and hosts in your data center. And since we’re talking about very large volumes of data, performance is a key factor. So Tom discussed some of the storage options that BlueData explored and implemented to achieve near bare-metal I/O performance for Spark using Docker.
https://spark-summit.org/east-2017/events/lessons-learned-from-dockerizing-spark-workloads
BlueData EPIC on AWS - Spec Sheet
BlueData EPIC on AWS - Spec SheetBlueData, Inc. The BlueData EPIC software platform makes deployment of Big Data infrastructure and applications easier, faster, and more cost-effective – whether on-premises or on the public cloud.
With BlueData EPIC on AWS, you can quickly and easily deploy your preferred Big Data applications, distributions and tools; leverage enterprise-class security and cost controls for multi-tenant deployments on the Amazon cloud; and tap into both Amazon S3 and on-premises storage for your Big Data analytics.
Sign up for a free two-week trial at www.bluedata.com/aws
Lessons Learned Running Hadoop and Spark in Docker Containers
Lessons Learned Running Hadoop and Spark in Docker ContainersBlueData, Inc. Many initiatives for running applications inside containers have been scoped to run on a single host. Using Docker containers for large-scale production environments poses interesting challenges, especially when deploying distributed big data applications like Apache Hadoop and Apache Spark. This session at Strata + Hadoop World in New York City (September 2016) explores various solutions and tips to address the challenges encountered while deploying multi-node Hadoop and Spark production workloads using Docker containers.
Some of these challenges include container life-cycle management, smart scheduling for optimal resource utilization, network configuration and security, and performance. BlueData is "all in” on Docker containers—with a specific focus on big data applications. BlueData has learned firsthand how to address these challenges for Fortune 500 enterprises and government organizations that want to deploy big data workloads using Docker.
This session by Thomas Phelan, co-founder and chief architect at BlueData, discusses how to securely network Docker containers across multiple hosts and discusses ways to achieve high availability across distributed big data applications and hosts in your data center. Since we’re talking about very large volumes of data, performance is a key factor, so Thomas shares some of the storage options implemented at BlueData to achieve near bare-metal I/O performance for Hadoop and Spark using Docker as well as lessons learned and some tips and tricks on how to Dockerize your big data applications in a reliable, scalable, and high-performance environment.
http://conferences.oreilly.com/strata/hadoop-big-data-ny/public/schedule/detail/52042
The Time Has Come for Big-Data-as-a-Service
The Time Has Come for Big-Data-as-a-ServiceBlueData, Inc. The document discusses the rise of Big Data as a Service (BDaaS) and how recent technological advancements have enabled its emergence. It provides a brief history of Hadoop and how improvements in networking, storage, virtualization and containers have addressed earlier limitations. It defines BDaaS and describes the public cloud and on-premises deployment models. Finally, it highlights how BlueData's software platform can deliver an integrated BDaaS solution both on-premises and across multiple public clouds including AWS.
Solution Brief: Real-Time Pipeline Accelerator
Solution Brief: Real-Time Pipeline AcceleratorBlueData, Inc. Get started with Spark Streaming, Kafka, and Cassandra for real-time data analytics.
BlueData makes it easy to deploy Spark infrastructure and applications on- premises. The BlueData EPIC software platform is purpose-built to simplify and accelerate the deployment of Spark, Hadoop, and other tools for Big Data analytics—leveraging Docker containers and virtualized infrastructure.
Our new Real-Time Pipeline Accelerator solution provides the software and professional services you need for building data pipelines in a multi-tenant environment for Spark Streaming, Kafka, and Cassandra. With help from the BlueData team, you’ll also have two end-to-end real-time data pipelines as a starting point.
Learn more about BlueData at www.bluedata.com
Solution Brief: Big Data Lab Accelerator
Solution Brief: Big Data Lab AcceleratorBlueData, Inc. The document describes BlueData's Big Data Lab Accelerator solution which provides software and professional services to deploy a multi-tenant Hadoop and Spark lab environment in two weeks for evaluation of Big Data tools. BlueData's EPIC software simplifies Big Data infrastructure deployment using containers and virtualization. The Accelerator solution includes deployment of the EPIC platform, setup of Hadoop and Spark clusters, data pipeline workshops and implementation of sample use cases to get started with Big Data.
How to deploy Apache Spark in a multi-tenant, on-premises environment
How to deploy Apache Spark in a multi-tenant, on-premises environmentBlueData, Inc. Adoption of Apache Spark in the enterprise is increasing rapidly - it's become one of the fastest growing and most popular technologies in the Big Data ecosystem.
However, implementing an enterprise-ready, on-premises Spark deployment can be very complex and it requires expertise that is generally not available to all.
BlueData makes it easier to deploy Apache Spark on-premises. With BlueData, you can spin up virtual Spark clusters within minutes – providing secure, self-service, on-demand access to Big Data analytics and infrastructure. You can deploy Spark in standalone mode or with Hadoop / YARN. You can also build analytical pipelines and create Spark clusters using our RESTful APIs, and use web-based Zeppelin notebooks for interactive data analytics.
BlueData’s software platform leverages virtualization and Docker containers – combined with our own patent-pending innovations – to make it faster, and more cost-effective for enterprises to get up and running with a multi-tenant Spark deployment on-premises.
Learn more at www.bluedata.com
BlueData EPIC 2.0 Overview
BlueData EPIC 2.0 OverviewBlueData, Inc. This presentation provides an overview of what’s new in the 2.0 release of the BlueData EPIC software platform.
BlueData’s EPIC software platform solves the infrastructure challenges and limitations that can slow down and stall on-premises Big Data deployments. With BlueData, you can spin up Hadoop or Spark clusters in minutes rather than months – with the data and analytical tools that your data scientists need.
The BlueData EPIC 2.0 release leverages Docker containers to simplify Big Data clusters, supports Apache Zeppelin notebooks and other new functionality for Apache Spark, and includes an enhanced App Store that provides one-click access to Big Data distributions and analytics tools.
Learn more about BlueData at http://www.bluedata.com
Big Data Case Study: Fortune 100 Telco
Big Data Case Study: Fortune 100 TelcoBlueData, Inc. This Big Data case study outlines the Hadoop infrastructure deployment for a Fortune 100 media and telecommunications company.
Hadoop adoption in this company had grown organically across multiple different teams, starting with “science projects” and lab initiatives that quickly grew and expanded. Going forward, some of the options they considered for their Big Data deployment included expanding their on-premises infrastructure and using a Hadoop-as-a-Service cloud offering.
Fortunately, they realized that there is a third option: providing the benefits of Hadoop-as-a-Service with on-premises infrastructure. They selected the BlueData EPIC software platform to virtualize their Hadoop infrastructure and provide on-demand access to virtual Hadoop clusters in a secure, multi-tenant model.
Learn more about this case study in the blog post at: http://www.bluedata.com/blog/2015/05/big-data-case-study-hadoop-infrastructure
BlueData Hunk Integration: Splunk Analytics for Hadoop
BlueData Hunk Integration: Splunk Analytics for HadoopBlueData, Inc. Hunk is a Splunk analytics tool that allows users to explore, analyze, and visualize raw big data stored in Hadoop and NoSQL data stores. It can interactively query raw data, accelerate reporting, create charts and dashboards, and archive historical data to HDFS. BlueData's EPIC platform enables running Hunk jobs on Hadoop clusters while accessing data from any storage system, such as HDFS, NFS, Gluster, and others. Hunk supports ingesting large amounts of data and provides pre-packaged analytics functions and intuitive visualization of results.
Spark Infrastructure Made Easy
Spark Infrastructure Made EasyBlueData, Inc. BlueData makes on-premises Spark infrastructure easy.
With BlueData, you can spin up virtual Spark clusters within minutes – providing secure, on-demand access to Big Data analytics and infrastructure. You can use Spark with or without the Hadoop ecosystem of tools – using HDFS, Tachyon, or any shared storage system.
You can also build analytical pipelines and create Spark clusters using our RESTful APIs. BlueData’s software platform leverages virtualization and patent-pending innovations to make it simpler, faster, and more cost-effective to deploy Hadoop or Spark infrastructure on-premises.
Learn more at http://www.bluedata.com
BlueData Integration with Cloudera Manager
BlueData Integration with Cloudera ManagerBlueData, Inc. This presentation provides an overview of the BlueData integration with Cloudera Manager. With this integration, customers of our BlueData EPIC software platform can leverage the power of Cloudera Manager for end-to-end Hadoop systems management and administration.
When the BlueData EPIC platform provisions a virtual CDH cluster, Cloudera Manager can be provisioned as well – so you can easily deploy, manage, monitor and perform diagnostics on your Hadoop cluster. Our customers can take advantage of the Cloudera Manager GUI to monitor their cluster, troubleshoot issues, and administer their Hadoop deployment.
Learn more about BlueData at http://www.bluedata.com
Ad
Recently uploaded (20)
Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdf
Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals.
This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft.
What You’ll Learn in Part 2:
Explore real-world nonprofit use cases and success stories.
Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!
Adobe After Effects Crack FREE FRESH version 2025
Adobe After Effects Crack FREE FRESH version 2025kashifyounis067 🌍📱👉COPY LINK & PASTE ON GOOGLE http://drfiles.net/ 👈🌍
Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
.
After Effects is powerful for creating animated titles, transitions, and other visual elements to enhance the look of videos and presentations.
Visual Effects:
.
It's used extensively in film and television for creating special effects like green screen compositing, object manipulation, and other visual enhancements.
Video Compositing:
.
After Effects allows users to combine multiple video clips, images, and graphics to create a final, cohesive visual.
Animation:
.
It uses keyframes to create smooth, animated sequences, allowing for precise control over the movement and appearance of objects.
Integration with Adobe Creative Cloud:
.
After Effects is part of the Adobe Creative Cloud, a suite of software that includes other popular applications like Photoshop and Premiere Pro.
Post-Production Tool:
.
After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AI
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval.
GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.
Exploring Wayland: A Modern Display Server for the Future
Exploring Wayland: A Modern Display Server for the FutureICS Wayland is revolutionizing the way we interact with graphical interfaces, offering a modern alternative to the X Window System. In this webinar, we’ll delve into the architecture and benefits of Wayland, including its streamlined design, enhanced performance, and improved security features.
Why Orangescrum Is a Game Changer for Construction Companies in 2025
Why Orangescrum Is a Game Changer for Construction Companies in 2025Orangescrum Orangescrum revolutionizes construction project management in 2025 with real-time collaboration, resource planning, task tracking, and workflow automation, boosting efficiency, transparency, and on-time project delivery.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?
How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach.
Read More:- https://www.esofttools.com/nsf-to-pst-converter.html
Secure Test Infrastructure: The Backbone of Trustworthy Software Development
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
How can one start with crypto wallet development.pptx
How can one start with crypto wallet development.pptxlaravinson24 This presentation is a beginner-friendly guide to developing a crypto wallet from scratch. It covers essential concepts such as wallet types, blockchain integration, key management, and security best practices. Ideal for developers and tech enthusiasts looking to enter the world of Web3 and decentralized finance.
Designing AI-Powered APIs on Azure: Best Practices& Considerations
Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions
PDF Reader Pro Crack Latest Version FREE Download 2025
PDF Reader Pro Crack Latest Version FREE Download 2025mu394968 🌍📱👉COPY LINK & PASTE ON GOOGLE https://dr-kain-geera.info/👈🌍
PDF Reader Pro is a software application, often referred to as an AI-powered PDF editor and converter, designed for viewing, editing, annotating, and managing PDF files. It supports various PDF functionalities like merging, splitting, converting, and protecting PDFs. Additionally, it can handle tasks such as creating fillable forms, adding digital signatures, and performing optical character recognition (OCR).
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...
TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
EASEUS Partition Master Crack + License Code
EASEUS Partition Master Crack + License Codeaneelaramzan63 Copy & Paste On Google >>> https://dr-up-community.info/
EASEUS Partition Master Final with Crack and Key Download If you are looking for a powerful and easy-to-use disk partitioning software,
Download Wondershare Filmora Crack [2025] With Latest![Download Wondershare Filmora Crack [2025] With Latest](https://cdn.slidesharecdn.com/ss_thumbnails/neo4j-howkgsareshapingthefutureofgenerativeaiatawssummitlondonapril2024-240426125209-2d9db05d-250419-250428115407-a04afffa-thumbnail.jpg?width=560&fit=bounds)
Download Wondershare Filmora Crack [2025] With Latesttahirabibi60507 Copy & Past Link 👉👉
http://drfiles.net/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Societal challenges of AI: biases, multilinguism and sustainability
Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
Get & Download Wondershare Filmora Crack Latest [2025]![Get & Download Wondershare Filmora Crack Latest [2025]](https://cdn.slidesharecdn.com/ss_thumbnails/revolutionizingresidentialwi-fi-250422112639-60fb726f-250429170801-59e1b240-thumbnail.jpg?width=560&fit=bounds)
Get & Download Wondershare Filmora Crack Latest [2025]saniaaftab72555 Copy & Past Link 👉👉
https://dr-up-community.info/
Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.Dele Amefo Salesforce Data Cloud- Hyperscale data platform, built for Salesforce.
Revolutionizing Residential Wi-Fi PPT.pptx
Revolutionizing Residential Wi-Fi PPT.pptxnidhisingh691197 Discover why Wi-Fi 7 is set to transform wireless networking and how Router Architects is leading the way with next-gen router designs built for speed, reliability, and innovation.
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.
BlueData and Hortonworks Data Platform (HDP)
- 2. BLUEDATA HAS EARNED THE QATS CERTIFICATION BADGE FOR HDP QATS (Quality Assured Testing Suite) is a product integration certification program designed for rigorous testing with HDP. With dedicated engineering resources to continuously and thoroughly test each release of HDP, QATS ensures that solutions are validated for a comprehensive suite of use cases and deliver high performance, under rigorous loads. BLUEDATA IN THE HORTONWORKS MODERN DATA ARCHITECTURE Run HDP in containers whether on-premises, in the public cloud, or in a hybrid cloud. BlueData +1 650 450-4067 +1 877 548-4954 www.bluedata.com BLUEDATA INTEGRATION WITH HDP • HDP is pre-configured with BlueData and can be installed via one-click deployment • Users can create instant containerized HDP compute clusters, using shared HDFS • Out-of-the-box REST API integration with Ambari, Atlas, Ranger, and other tools • Multiple versions of HDP can run on shared infrastructure, for dev/test and easy upgrades • Different groups can run Big Data jobs simultaneously, with policy-based resource controls • Provides enterprise-grade security and high performance for production workloads About Hortonworks Hortonworks is a leading provider of enterprise-grade, global data management platforms, services and solutions that deliver actionable intelligence from any type of data for over half of the Fortune 100. Hortonworks is committed to driving innovation in open source communities, providing unique value to enterprise customers. Along with its partners, Hortonworks provides technology, expertise and support so that enterprise customers can adopt a modern data architecture. Contact Hortonworks +1 408 675-0983 +1 855 8-HORTON INTL:+44(0) 20 3826 1405 www.hortonworks.com Privacy Policy | Terms of Service (c) 2011-2016 Hortonworks Inc. All Rights Reserved