Data in the Cloud Crash Course
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Santhosh B Gowda presents on Cloudbreak, a tool for provisioning Hadoop clusters on cloud infrastructure. Cloudbreak allows for simplified cluster provisioning through prescriptive setups and automation. It supports declarative workload provisioning across multiple cloud providers with flexible topologies and security configuration options. Cloudbreak also enables features like auto-scaling, recipes to customize clusters, and shared services data lakes to provide common metadata and access management across ephemeral clusters. Demonstrations of launching HDP and HDF clusters from the Cloudbreak UI and CLI are also provided.
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Apache Hive's metastore can be used to share the metadata in the same way. It is already used by many SQL and SQL-like systems beyond Hive (e.g. Apache Spark, Presto, Apache Impala, and via HCatalog, Apache Pig). As data processing changes from only data in the cluster to include data in streams, the metastore needs to expand and grow to meet these use cases as well. There is work going on in the Hive community to separate out the metastore, so it can continue to serve Hive but also be used by a more diverse set of tools. This talk will discuss that work, with particular focus on adding support for storing schemas for Kafka messages.
Speaker
Alan Gates, Co-Founder, Hortonworks
Delivering a Flexible IT Infrastructure for Analytics on IBM Power Systems
Delivering a Flexible IT Infrastructure for Analytics on IBM Power SystemsHortonworks Customers are preparing themselves to analyze and manage an increasing quantity of structured and unstructured data. Business leaders introduce new analytical workloads faster than what IT departments can handle. Legacy IT infrastructure needs to evolve to deliver operational improvements and cost containment, while increasing flexibility to meet future requirements. By providing HDP on IBM Power Systems, Hortonworks and IBM are giving customers have more choice in selecting the appropriate architectural platform that is right for them. In this webinar, we’ll discuss some of the challenges with deploying big data platforms, and how choosing solutions built with HDP on IBM Power Systems can offer tangible benefits and flexibility to accommodate changing needs.
What's New in Apache Hive 3.0?
What's New in Apache Hive 3.0?DataWorks Summit Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Lessons learned running a container cloud on YARN
Lessons learned running a container cloud on YARNDataWorks Summit Apache Hadoop YARN is the resource and application manager for Apache Hadoop. In the past, YARN only supported launching containers as processes. However, as containerization has become extremely popular, more and more users wanted support for launching Docker containers. With recent changes, YARN now supports running Docker containers alongside process containers. Coupled with the newly added support for long-running services on YARN, this allows a host of new possibilities.
In this talk, we'll present how to run a container cloud on YARN. Leveraging the support in YARN for Docker and long-running services, we can allow users to easily spin up sets of Docker containers for their applications. These containers can be self contained or wired up to form more complex applications. We will go over some of the lessons we learned as part of our experiences handling issues such as resource management, debugging application failures, running Docker, service discovery, etc.
Speaker
Billie Rinaldi, Principal Software Engineer I, Hortonworks
Using Spark Streaming and NiFi for the Next Generation of ETL in the Enterprise
Using Spark Streaming and NiFi for the Next Generation of ETL in the EnterpriseDataWorks Summit In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speaker: Andrew Psaltis, Principal Solution Engineer, Hortonworks
Containers and Big Data
Containers and Big Data DataWorks Summit As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN.
Ozone and HDFS’s evolution
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Apache Hadoop YARN: state of the union
Apache Hadoop YARN: state of the unionDataWorks Summit Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3
Deep learning on yarn running distributed tensorflow etc on hadoop cluster v3DataWorks Summit Deep learning is useful for enterprises tasks in the field of speech recognition, image classification, AI chatbots, and machine translation, just to name a few.
In order to train deep learning/machine learning models, applications such as TensorFlow, MXNet, Caffe, and XGBoost can be leveraged. And sometimes these applications will be used together to solve different problems.
To make distributed deep learning/machine learning applications easily launched, managed, and monitored, we introduced, in Apache Hadoop 3.x, YARN native services along with other improvements such as first-class GPU support, container-DNS support, scheduling improvements, etc. These improvements make distributed deep learning/machine learning applications run on YARN as simple as running it locally, which can let machine learning engineers focus on algorithms instead of worrying about underlying infrastructure. Also, YARN can better manage a shared cluster which runs deep learning/machine learning and other services and ETL jobs with these improvements.
In this session, we will take a closer look at these improvements and show how to run these applications on YARN with demos. Audiences can start trying running these applications on YARN after this talk.
Speakers
Wanga Tan, Staff Software Engineer, Hortonworks
Sunil Govindan, Staff Engineer, Hortonworks
Hadoop Operations – Past, Present, and Future
Hadoop Operations – Past, Present, and FutureDataWorks Summit Hadoop operations started on-prem primarily driven by Apache Ambari. However, due to the agility and flexibility of the cloud, it has driven many Hadoop cluster operations to the cloud and to hybrid environments. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
Apache Ambari is used by thousands of Hadoop Operators to manage the deployment, lifecycle, and automation of DevOps for Hadoop ecosystem projects. Starting out, Apache Ambari installed a handful of Apache Hadoop ecosystem projects, on a few operating systems, and helped with the most basic Hadoop operational tasks. Today, the product manages over 20 different services, runs on multiple major operating systems and versions, and automates many of the most challenging Hadoop operational tasks in the most secure customer environments.
In this session, we will also take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through a live demo of how the latest from Cloudbreak enables enterprises to easily and securely run Apache Hadoop. This includes deep-dive discussion on Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As part of this talk, will walk you through what we've learned, the challenges we've overcome, and how the Apache Ambari and Cloudbreak community has changed the product to handle them. The future is fast approaching, and with it comes new on-premise and cloud deployment architectures. See how Apache Ambari and Cloudbreak are being re-imagined to handle these new challenges.
Speaker: Santosh Gowda, Principal Solutions Engineer, Hortonworks
Hadoop: The Unintended Benefits
Hadoop: The Unintended BenefitsDataWorks Summit The document discusses some unintended benefits that the Department of Home Affairs discovered from implementing Hadoop. It describes how Hadoop was initially used for passenger systems and the Teradata data warehouse, but then enabled additional uses. Solr was used to enable fast search of billions of rows across Hadoop, improving on slow full table scans in Teradata. A graph database called JanusGraph was also implemented on Hadoop to enable relationship analysis and predictive analytics for security use cases. The document outlines the architectures used and lessons learned around performance, security, and managing expectations for these new capabilities enabled by Hadoop.
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFi
The First Mile – Edge and IoT Data Collection with Apache NiFi and MiNiFiDataWorks Summit Apache NiFi MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately.
Abstract: Apache NiFi provided a revolutionary data flow management system with a broad range of integrations with existing data production, consumption, and analysis ecosystems, all covered with robust data delivery and provenance infrastructure. Now learn about the follow-on project which expands the reach of NiFi to the edge, Apache MiNiFi. MiNiFi is a lightweight application which can be deployed on hardware orders of magnitude smaller and less powerful than the existing standard data collection platforms. With both a JVM compatible and native agent, MiNiFi allows data collection in brand new environments — sensors with tiny footprints, distributed systems with intermittent or restricted bandwidth, and even disposable or ephemeral hardware. Not only can this data be prioritized and have some initial analysis performed at the edge, it can be encrypted and secured immediately. Local governance and regulatory policies can be applied across geopolitical boundaries to conform with legal requirements. And all of this configuration can be done from central command & control using an existing NiFi with the trusted and stable UI data flow managers already love.
Expected prior knowledge / intended audience: developers and data flow managers should have a passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The talk will focus on extending the data collection, routing, provenance, and governance capabilities of NiFi to IoT/edge integration via MiNiFi.
Takeaways: Attendees will learn about opportunities to bring their data flow and capture closer to the "edge" -- sources of data like IoT devices, vehicles, machinery, etc. They will understand the possibilities to prioritize, filter, secure, and manipulate this data earlier in the data lifecycle to enhance their data visibility and performance.
Speaker: Andy LoPresto, Sr. Member of Technical Staff, Hortonworks
Apache Hadoop YARN: state of the union - Tokyo
Apache Hadoop YARN: state of the union - Tokyo DataWorks Summit Apache Hadoop YARN is the latest distributed operating system for HDSF for big data applications and storage. YARN has transformed the Hadoop Compute Layer into a general resource management platform capable of hosting a wide variety of applications.
This lecture begins with the current state how Apache Hadoop YARN is currently used in large scale deployment. The next topic will cover about strengthening YARN 's current and future - like YARN' s excitement - as a top-notch resource management platform for data centers running enterprise Hadoop. Discuss the current state and future of the following functions and initiatives: support of machine learning through strong container placement, global scheduling, GPU and FPGA support and deep learning workload, large scale of YARN federation, on YARN Containerized applications, natural support that does not change to long-running services (along with applications), seamless application upgrades, powerful scheduling functions, operational improvements and better queue management.
The second part of the lecture focuses on the latest enhancement of HDFS. HDFS has several advantages: horizontal scale of IO bandwidth, storage scaled to petabyte storage. In addition, it provides extremely low latency metadata operations and coordinates for over 60,000 concurrent clients. Hadoop 3.0 recently introduced Erasure Coding. One limitation of HDFS is the scaling of multiple files and blocks in the system. I will explain the fundamental change of Hadoop's storage infrastructure using Ozone technology which will be announced soon. This will allow Hadoop to scale billions of files and blocks in the future to a larger number of smaller objects than before.
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...Hortonworks Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges.
Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.
Hive Performance Dataworks Summit Melbourne February 2019
Hive Performance Dataworks Summit Melbourne February 2019alanfgates Overview of Apache Hive 3 performance, with description of LLAP, caching, materialized views, and integration with Apache Druid.
Driving in the Desert - Running Your HDP Cluster with Helion, Openstack, and ...
Driving in the Desert - Running Your HDP Cluster with Helion, Openstack, and ...DataWorks Summit DataWorks Summit 2017 - Sydney
Alejandro Tesch, Cloud Evangelist, Asia Pacific and Japan, HPE
Big Data is a hot topic today for most organisations today as they race to convert vast amounts of data into useful information that can be leveraged to make critical decisions and recommendations in a very limited time windows. Today, there is a widely accepted talent gap when it comes to creating and managing Hadoop cluster, even for the experts – it can take hours (or days) to get a fully functional hadoop farm up and running. The HDP Ambari plugin for Sahara is looking to address most of this challenges by facilitating the deployment of Hortonworks Hadoop clusters and provide a set of open API to facilitate data analytics tasks in your own cloud. In this presentation we will cover why it makes sense to run your data analytics cluster in your cloud and we will demonstrate basic Sahara / Ambari functionality.
Insights into Real-world Data Management Challenges
Insights into Real-world Data Management ChallengesDataWorks Summit Oracle began with the belief that the foundation of IT was managing information. The Oracle Cloud Platform for Big Data is a natural extension of our belief in the power of data. Oracle’s Integrated Cloud is one cloud for the entire business, meeting everyone’s needs. It’s about Connecting people to information through tools which help you combine and aggregate data from any source.
This session will explore how organizations can transition to the cloud by delivering fully managed and elastic Hadoop and Real-time Streaming cloud services to built robust offerings that provide measurable value to the business. We will explore key data management trends and dive deeper into pain points we are hearing about from our customer base.
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Running Enterprise Workloads in the Cloud
Running Enterprise Workloads in the CloudDataWorks Summit Demand for cloud is through the roof. Cloud is turbo charging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
In this session, we will take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through how the latest from Cloudbreak enables enterprises to easily and securely run big data workloads. This includes deep-dive discussion on autoscaling, Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As a last topic we will discuss how we deployed and operate Cloudbreak as a Service internally which enables rapid cluster deployment for prototyping and testing purposes.
Speakers
Peter Darvasi, Cloudbreak Partner Engineer, Hortonworks
Richard Doktorics, Staff Engineer, Hortonworks
Containers and Big Data
Containers and Big DataDataWorks Summit As containerization continues to gain momentum and become a de facto standard for application deployment, challenges around containerization of big data workloads are coming to light. Great strides have been made within the open source communities towards running big data workloads in containers, but much is left to be done.
Apache Hadoop YARN is the modern distributed operating system for big data applications. It has morphed the Hadoop compute layer into a common resource-management platform that can host a wide variety of applications. At its core, YARN has a very powerful scheduler which enforces global cluster level invariants and helps sites manage user and operator expectations of elastic sharing, resource usage limits, SLAs, and more. YARN recently increased its support for Docker containerization and added a YARN service framework supporting long-running services.
In this session we will explore the emerging patterns and challenges related to containers and big data workloads, including running applications such as Apache Spark, Apache HBase, and Kubernetes in containers on YARN. BILLIE RINALDI, Principal Software Engineer, Hortonworks and SHANE KUMPF, Software Engineer, Hortonworks
Apache Hadoop 3 updates with migration story
Apache Hadoop 3 updates with migration storySunil Govindan The document discusses migrating Hadoop clusters from version 2 to version 3. It provides an overview of new features in HDFS, YARN, and other components in Hadoop 3, including erasure coding in HDFS, global scheduling and new resource types in YARN. It also covers important considerations for upgrading such as recommended source and target versions, upgrade mechanisms, tooling changes, and ensuring Java 8 is used.
Hadoop Operations - Past, Present, and Future
Hadoop Operations - Past, Present, and FutureDataWorks Summit Hadoop operations started on-prem primarily driven by Apache Ambari. However, due to the agility and flexibility of the cloud, it has driven many Hadoop cluster operations to the cloud and to hybrid environments. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
Apache Ambari is used by thousands of Hadoop Operators to manage the deployment, lifecycle, and automation of DevOps for Hadoop ecosystem projects. Starting out, Apache Ambari installed a handful of Apache Hadoop ecosystem projects, on a few operating systems, and helped with the most basic Hadoop operational tasks. Today, the product manages over 20 different services, runs on multiple major operating systems and versions, and automates many of the most challenging Hadoop operational tasks in the most secure customer environments.
In this session, we will also take you through Cloudbreak as a solution to simplify provisioning and managing enterprise workloads while providing an open and common experience for deploying workloads across clouds. We will discuss the challenges (and opportunities) to run enterprise workloads in the cloud and will go through a live demo of how the latest from Cloudbreak enables enterprises to easily and securely run Apache Hadoop. This includes deep-dive discussion on Ambari Blueprints, recipes, custom images, and enabling Kerberos -- which are all key capabilities for Enterprise deployments.
As part of this talk, will walk you through what we've learned, the challenges we've overcome, and how the Apache Ambari and Cloudbreak community has changed the product to handle them. The future is fast approaching, and with it comes new on-premise and cloud deployment architectures. See how Apache Ambari and Cloudbreak are being re-imagined to handle these new challenges.
Saving the elephant—now, not later
Saving the elephant—now, not laterDataWorks Summit Big data security challenges are bit different from traditional client-server applications and are distributed in nature, introducing unique security vulnerabilities. Cloud Security Alliance (CSA) has categorized the different security and privacy challenges into four different aspects of the big data ecosystem. These aspects are infrastructure security, data privacy, data management and, integrity and reactive security. Each of these aspects are further divided into following security challenges:
1. Infrastructure security
a. Secure distributed processing of data
b. Security best practices for non-relational data stores
2. Data privacy
a. Privacy-preserving analytics
b. Cryptographic technologies for big data
c. Granular access control
3. Data management
a. Secure data storage and transaction logs
b. Granular audits
c. Data provenance
4. Integrity and reactive security
a. Endpoint input validation/filtering
b. Real-time security/compliance monitoring
In this talk, we are going to refer above classification and identify existing security controls, best practices, and guidelines. We will also paint a big picture about how collective usage of all discussed security controls (Kerberos, TDE, LDAP, SSO, SSL/TLS, Apache Knox, Apache Ranger, Apache Atlas, Ambari Infra, etc.) can address fundamental security and privacy challenges that encompass the entire Hadoop ecosystem. We will also discuss briefly recent security incidents involving Hadoop systems.
Speakers
Krishna Pandey, Staff Software Engineer, Hortonworks
Kunal Rajguru, Premier Support Engineer, Hortonworks
Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache Knox
Fortifying Multi-Cluster Hybrid Cloud Data Lakes using Apache KnoxDataWorks Summit The document discusses securing multi-cluster hybrid cloud data lakes using Apache Knox. It describes how Knox acts as a reverse proxy and gateway to provide single sign-on, authentication, authorization, and security for clusters spanning on-premises and cloud environments. A demo is presented showing how Knox can authenticate users without moving the on-premises LDAP, restrict unauthorized access, and protect against stolen credentials through features like federation and authorization.
What's new in apache hive 
What's new in apache hive DataWorks Summit Apache Hive is a rapidly evolving project which continues to enjoy great adoption in big data ecosystem. Although, Hive started primarily as batch ingestion and reporting tool, community is hard at work in improving it along many different dimensions and use cases. This talk will provide an overview of latest and greatest features and optimizations which have landed in project over last year. Materialized view, micro managed tables and workload management are some noteworthy features.
I will deep dive into some optimizations which promise to provide major performance gains. Support for ACID tables has also improved considerably. Although some of these features and enhancements are not novel but have existed for years in other DB systems, implementing them on Hive poses some unique challenges and results in lessons which are generally applicable in many other contexts. I will also provide a glimpse of what is expected to come in near future.
Speaker: Ashutosh Chauhan, Engineering Manager, Hortonworks
Getting the Most Out of Your Data in the Cloud with Cloudbreak
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
What is new in Apache Hive 3.0?
What is new in Apache Hive 3.0?DataWorks Summit Apache Hive is a rapidly evolving project which continues to enjoy great adoption in the big data ecosystem. As Hive continues to grow its support for analytics, reporting, and interactive query, the community is hard at work in improving it along with many different dimensions and use cases. This talk will provide an overview of the latest and greatest features and optimizations which have landed in the project over the last year. Materialized views, the extension of ACID semantics to non-ORC data, and workload management are some noteworthy new features.
We will discuss optimizations which provide major performance gains, including significantly improved performance for ACID tables. The talk will also provide a glimpse of what is expected to come in the near future.
Apache Hadoop YARN: state of the union
Apache Hadoop YARN: state of the unionDataWorks Summit The document provides an overview of the state of Apache Hadoop YARN. Key themes discussed include scaling to support very large clusters of 100,000+ nodes, improved global and fast scheduling capabilities, richer placement constraints, and enhanced support for containers, resources like GPUs and FPGAs, and services. The YARN community continues to grow with over 450 contributors.
HDF 3.1 : An Introduction to New Features
HDF 3.1 : An Introduction to New FeaturesTimothy Spann Hortonworks Data Flow 3.1 introduces new features to improve ease of use, stream processing, cross-product integration, and flow management. Key enhancements include NiFi registry for version control of flows, improved Kafka 1.0 support, and new processors for deeper ecosystem integration. HDF 3.1 provides tools for engineers to aggregate, mediate, and gain insights from data across multiple sources when deployed with Hortonworks Data Platform.
Curing the Kafka blindness—Streams Messaging Manager
Curing the Kafka blindness—Streams Messaging ManagerDataWorks Summit Companies who use Kafka today struggle with monitoring and managing Kafka clusters. Kafka is a key backbone of IoT streaming analytics applications. The challenge is understanding what is going on overall in the Kafka cluster including performance, issues and message flows. No open source tool caters to the needs of different users that work with Kafka: DevOps/developers, platform team, and security/governance teams. See how the new Hortonworks Streams Messaging Manager enables users to visualize their entire Kafka environment end-to-end and simplifies Kafka operations.
In this session learn how SMM visualizes the intricate details of how Apache Kafka functions in real time while simultaneously surfacing every nuance of tuning, optimizing, and measuring input and output. SMM will assist users to quickly understand and operate Kafka while providing the much-needed transparency that sophisticated and experienced users need to avoid all the pitfalls of running a Kafka cluster.
Using Spark Streaming and NiFi for the next generation of ETL in the enterprise
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Fast SQL on Hadoop, Really?
Fast SQL on Hadoop, Really?DataWorks Summit The document discusses Apache Hive and Apache Druid for fast SQL on big data. It provides performance benchmarks showing Hive LLAP is faster than Presto and Spark SQL for TPC-DS queries. It describes features of Hive LLAP including in-memory caching, query result caching, and metadata caching. It also discusses new Hive 3 features like materialized views and optimizer improvements. The document then provides an overview of Apache Druid's capabilities for real-time ingestion and querying of streaming data before discussing how Hive and Druid can work together, with Hive able to push down queries to Druid.
Apache Ambari - What's New in 2.1
Apache Ambari - What's New in 2.1Hortonworks Ambari 2.1 includes several new features and improvements, including manual Kerberos configuration, customizable dashboards, guided configurations, rack awareness, and views framework enhancements. It adds support for Storm Nimbus HA, Ranger HA, RHEL/CentOS 7, and new JDKs. The views framework now supports auto-configuration using cluster properties and auto-creation of view instances when cluster requirements are met. Standalone Ambari servers allow running views without managing a cluster.
Hive 3 New Horizons DataWorks Summit Melbourne February 2019
Hive 3 New Horizons DataWorks Summit Melbourne February 2019alanfgates Hive 3 new SQL features including LLAP, workload management, SQL over Kafka and JDBC data sources, integration with Spark via Hive Warehouse Connector, ACID 2, and constraints and default values
One Click Hadoop Clusters - Anywhere (Using Docker)
One Click Hadoop Clusters - Anywhere (Using Docker)DataWorks Summit This document provides an overview of Hortonworks' one-click Hadoop clusters solution that uses Docker and Apache Ambari to provision Hadoop clusters on any infrastructure. It discusses the goals of automating and unifying the Hadoop cluster provisioning process. The technology stack uses Docker for containerization, Swarm for clustering and orchestration, Consul for service discovery and registration, and Ambari for provisioning and management. Cloudbreak is introduced as a cloud-agnostic API that abstracts Hadoop cluster provisioning using these tools, while Periscope provides heuristic scheduling and autoscaling of clusters based on service level agreements.
Managing Enterprise Hadoop Clusters with Apache Ambari
Managing Enterprise Hadoop Clusters with Apache AmbariJayush Luniya The document discusses features of the Apache Ambari platform for managing Hadoop clusters, including:
- Ambari allows provisioning, managing, and monitoring Hadoop clusters at scale through features like stacks, blueprints, views, and smart configurations.
- Stacks define Hadoop services and components and their lifecycles. Blueprints allow automated deployment of clusters. Views extend the Ambari UI.
- Other features discussed include rolling upgrades between stack versions, metrics collection and monitoring, and an alerts framework to notify users of cluster issues.
Managing Enterprise Hadoop Clusters with Apache Ambari
Managing Enterprise Hadoop Clusters with Apache AmbariHortonworks This document discusses Apache Ambari, an open-source platform for managing Hadoop clusters. It provides an overview of Ambari, describing its key features including stacks, blueprints, views and extensibility points. It also demonstrates Ambari's capabilities for cluster deployment, management, monitoring and upgrades through a stack and blueprint demo.
Apache Hadoop YARN: State of the Union
Apache Hadoop YARN: State of the UnionDataWorks Summit Apache Hadoop YARN is the modern distributed operating system for big data applications. It morphed the Hadoop compute layer to be a common resource management platform that can host a wide variety of applications. Many organizations leverage YARN in building their applications on top of Hadoop without themselves repeatedly worrying about resource management, isolation, multi-tenancy issues, etc.
In this talk, we’ll start with the current status of Apache Hadoop YARN—how it is used today in deployments large and small. We'll then move on to the exciting present and future of YARN—features that are further strengthening YARN as the first class resource management platform for data centers running enterprise Hadoop.
We’ll discuss the current status as well as the future promise of features and initiatives like: powerful container placement, global scheduling, support for machine learning and deep learning workloads through GPU and FPGA support, extreme scale with YARN federation, containerized apps on YARN, support for long-running services (alongside applications) natively without any changes, seamless application upgrades, powerful scheduling features like application priorities, intra-queue preemption across applications, and operational enhancements including insights through Timeline Service V2, a new web UI, and better queue management.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
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Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
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This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
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Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
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HBase Tales From the Trenches - Short stories about most common HBase operati...DataWorks Summit Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
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Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Managing the Dewey Decimal System
Managing the Dewey Decimal SystemDataWorks Summit OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
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Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
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Scaling Cloud-Scale Translytics Workloads with Omid and PhoenixDataWorks Summit Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiDataWorks Summit This document discusses using Apache NiFi to build a high-speed cyber security data pipeline. It outlines the challenges of ingesting, transforming, and routing large volumes of security data from various sources to stakeholders like security operations centers, data scientists, and executives. It proposes using NiFi as a centralized data gateway to ingest data from multiple sources using a single entry point, transform the data according to destination needs, and reliably deliver the data while avoiding issues like network traffic and data duplication. The document provides an example NiFi flow and discusses metrics from processing over 20 billion events through 100+ production flows and 1000+ transformations.
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Security Framework for Multitenant ArchitectureDataWorks Summit In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
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Presto: Optimizing Performance of SQL-on-Anything EngineDataWorks Summit Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
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Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
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Securing Data in Hybrid on-premise and Cloud Environments using Apache RangerDataWorks Summit Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
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Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...DataWorks Summit Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Computer Vision: Coming to a Store Near You
Computer Vision: Coming to a Store Near YouDataWorks Summit Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
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Big Data Genomics: Clustering Billions of DNA Sequences with Apache SparkDataWorks Summit Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
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- 1. 1 © Hortonworks Inc. 2011–2018. All rights reserved Data in the Cloud Santhosh B Gowda Engineer, Cloudera DataWorks Summit – Melbourne Feb 2019
- 2. 2 © Hortonworks Inc. 2011–2018. All rights reserved Who Am I ? Santhosh B Gowda Engineer @ Cloudera
- 3. 3 © Hortonworks Inc. 2011–2018. All rights reserved Agenda • Introduction • Primary Use Cases • Cloudbreak Architecture • Cloudbreak Core Concepts • Credentials • Blueprints • Recipes • Images • CLI • Streaming in Cloudbreak • Shared Services Data Lake • Hands-On Lab
- 4. 4 © Hortonworks Inc. 2011–2018. All rights reserved No Upfront HW Costs $0 Unlimited Elastic Scale Ephemeral & Long-Running IT & Business Agility Why Data on Cloud ?
- 5. 5 © Hortonworks Inc. 2011–2018. All rights reserved What Is Cloudbreak ? Cloudbreak is a tool for provisioning Hadoop clusters on any cloud infrastructure Simplified Cluster Provisioning - prescriptive setup, simple automation
- 6. 6 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak: Harness the Agility of Cloud with Ease Cloudbreak • Declarative workload provisioning across multiple cloud providers • Flexible topologies and security configuration options • DevOps friendly, easy setup and simple to automate • Built-in elasticity and auto- scaling • Prescriptive integration with cloud services
- 7. 7 © Hortonworks Inc. 2011–2018. All rights reserved Dev / Test (all HDP services) BI / Analytics (Hive) Data Science (Spark) IoT Apps (Storm, HBase, Hive) Cloudbreak Cloudbreak 1. Pick a Blueprint 2. Choose a Cloud 3. Launch HDP/HDF! Example Ambari Blueprints: IoT Apps, BI / Analytics, Data Science, Dev / Test
- 8. 8 © Hortonworks Inc. 2011–2018. All rights reserved Create Cluster Manage Credentials Manage Templates Manage Network Manage Security Groups Manage Blueprints Advanced Configurati on Configure Credentials for Cloudbreak for connecting to a Cloud Provider Configure size of VM images Manage who can access HDP clusters Define network in which HDP cluster will run Define HDP Services Configure HDP Repository Define Failure Actions Cloud Provider Specific Configurations HDP Specific Configurations
- 9. 10 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak UI
- 10. 11 © Hortonworks Inc. 2011–2018. All rights reserved Manage Cluster
- 11. 12 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak CLI DevOps
- 12. 13 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak CLI
- 13. 14 © Hortonworks Inc. 2011–2018. All rights reserved “SHOW CLI COMMAND”
- 14. 15 © Hortonworks Inc. 2011–2018. All rights reserved Recipes DevOps
- 15. 16 © Hortonworks Inc. 2011–2018. All rights reserved Background: Recipes • Cloudbreak lets you provision cluster using Ambari Blueprint however not all use-cases can be addressed. • Install additional software. • System config changes. • A recipe is a script that runs on all nodes of a selected node group at a specific time. • Support for bash and python scripts. • Available hooks • Pre-ambari-start • Post-ambari-start • Post-cluster-install • Pre-termination
- 16. 17 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak: Add Recipes • Cluster Extensions > Recipes > Create • Add recipe as File, Url or Text
- 17. 18 © Hortonworks Inc. 2011–2018. All rights reserved Cloudbreak: Add Recipes • Clusters > Create Cluster > Cluster Extensions
- 18. 19 © Hortonworks Inc. 2011–2018. All rights reserved Streaming in the Cloud
- 19. 20 © Hortonworks Inc. 2011–2018. All rights reserved
- 20. 21 © Hortonworks Inc. 2011–2018. All rights reserved HDF in CloudBreak 2.7 Apache NiFi and Apache NiFi Registry
- 21. 22 © Hortonworks Inc. 2011–2018. All rights reserved HDF in CloudBreak 2.7 Apache Kafka
- 22. 23 © Hortonworks Inc. 2011–2018. All rights reserved Auto Scaling
- 23. 24 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling • Alerts: Create metric or time-based alerts for cluster scaling • Policies: Scaling policies adjust cluster size based on activity and workload alerts • General Configurations: Boundaries and cooldown period
- 24. 25 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling Time-Based Alert Fire at 10:15 am everyday
- 25. 26 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling Metric-Based Alert Fire after NodeManagers are CRITICAL for 10 minutes
- 26. 27 © Hortonworks Inc. 2011–2018. All rights reserved Auto-Scaling Policies • Define the Scale Adjustment (Node Count, Percentage, Exact) • Select the Host Group (to Scale) • Select Alert (which when fired, executes the Policy)
- 27. 28 © Hortonworks Inc. 2011–2018. All rights reserved Data Lake Shared Services
- 28. 29 © Hortonworks Inc. 2011–2018. All rights reserved Why Data Lake Shared Services • Customers have a need to secure ephemeral workload clusters • Customers need a single metadata repository for Hive schema • Customers want a single pane of glass to define users, groups and authorization policies
- 29. 30 © Hortonworks Inc. 2011–2018. All rights reserved Introducing “Data Lake” in Cloudbreak CLOUD STORAGE WORKLOAD S Durable Ephemeral When data resides in cloud object stores (e.g. S3, ADLS, WASB, GCS), Hadoop optimizes reads/writes and acts as an intermediate cache to increase performance and decrease latency. Metastore SCHEM A Long Running Security access to workload clusters via a Protected Gateway enabled for AuthN and HTTPS. Define your data schema, security policies, and metadata catalog once for your ephemeral and always-on workloads. Atlas CATALOG Ranger POLIC Y SHARED DATA LAKE SERVICES
- 30. 31 © Hortonworks Inc. 2011–2018. All rights reserved Data Lake: Flyover LDAP/AD Hive Database Ranger Database Cloud Storage Data Lake Workload Cluster(s) Ranger Hive Metastore Hive, Spark, Zeppelin Attach
- 31. 32 © Hortonworks Inc. 2011–2018. All rights reserved Learn More • Try Cloudbreak 2.8 (TP) • http://docs.hortonworks.com
- 32. 33 © Hortonworks Inc. 2011–2018. All rights reserved Thank You
- 33. 35 © Hortonworks Inc. 2011–2018. All rights reserved Launch HDP from Cloudbreak UI (Demo#1)
- 34. 36 © Hortonworks Inc. 2011–2018. All rights reserved Launch HDF from Cloudbreak UI (Demo#2)
- 35. 37 © Hortonworks Inc. 2011–2018. All rights reserved Launch HDP from CLI (Demo#3)