How to get started in Big Data without Big Costs - StampedeCon 2016
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Looking to implement Hadoop but haven’t pulled the trigger yet? You are not alone. Many companies have heard the hype about how Hadoop can solve the challenges presented by big data, but few have actually implemented it. What’s preventing them from taking the plunge? Can it be done in small steps to ensure project success? This session will discuss some of the items to consider when getting started with Hadoop and how to go about making the decision to move to the de facto big data platform. Starting small can be a good approach when your company is learning the basics and deciding what direction to take. There is no need to invest large amounts of time and money up front if a proof of concept is all you aim to provide. Using well known data sets on virtual machines can provide a low cost and effort implementation to know if your big data journey will be successful with Hadoop.
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Analyzing Time-Series Data with Apache Spark and Cassandra - StampedeCon 2016
Analyzing Time-Series Data with Apache Spark and Cassandra - StampedeCon 2016StampedeCon Have you ever wanted to analyze sensor data that arrives every second from across the world? Or maybe your want to analyze intra-day trading prices of millions of financial instruments? Or take all the page views from Wikipedia and compare the hourly statistics? To do this or any other similar analysis, you will need to analyze large sequences of measurements over time. And what better way to do this then with Apache Spark? In this session we will dig into how to consume data, and analyze it with Spark, and then store the results in Apache Cassandra.
Creating a Data Driven Organization - StampedeCon 2016
Creating a Data Driven Organization - StampedeCon 2016StampedeCon Companies today are all focused on finding new consumption models to better utilize the data they produce. This presentation will provide insights and best practices for creating the organization and sponsorship necessary to set the foundation for success.
For this session, Dan will provide an overview of the process and methodologies he employs to establish and sustain a Data Driven Culture. Key topics will include:
Data Driven Culture
Executive Sponsorship
Organizational Structure – Collaboration Hubs and Bi-Modal Analytics
Role of Hadoop and Big Data as Part of Data Driven Culture
Interplay of Big Data and IoT - StampedeCon 2016
Interplay of Big Data and IoT - StampedeCon 2016StampedeCon Big Data and IoT are changing the world. The big question is how Big Data and IoT are related? This presentation explores the synergy of Big Data and IoT. We will anatomize Big Data and IoT separately, in terms of what, which, why, where, when, who, how and how much. We then analyze the relationship between IoT and Big Data, specifically the drilldown of how the 4Vs of Big Data (Volume, Variety, Velocity and Value) intersect with the 4Cs of IoT (Connectivity, Collection, Context and Cognition). We will dive deep to the matrix chart of the 1-to-1 mapping of individual aspects. Case studies and best practices will be discussed to further dissect the interlock in real-world business solutions.
Resource Management in Impala - StampedeCon 2016
Resource Management in Impala - StampedeCon 2016StampedeCon Want to run queries in Impala as fast as possible without choking other workloads and services? If you are a Hadoop cluster administrator or a big data application developer, this course will help you understand how Impala Admission Control can help you make good use of available resources, avoid bad performance issues, and provide better user experiences in a multi-tenancy environment.
Batch and Real-time EHR updates into Hadoop - StampedeCon 2015
Batch and Real-time EHR updates into Hadoop - StampedeCon 2015StampedeCon At the StampedeCon 2015 Big Data Conference: Mercy has built a system using batch and streaming technology to allow batch and near real-time updates to flow from its Epic EHR (Electronic Health Records) system into our Hadoop cluster. Mercy is using this system to provide reporting and analytics capabilities to its researchers, business owners, and physicians. The system uses Sqoop, Flume, Pig, Hive, Oozie, and Storm. As part of this system, Mercy developed a generic database replication utility that makes it a one-line configuration change to add new tables and relational data sources.
This session will describe the batch and streaming engines that Mercy uses to integrate the patient data. Adam will talk about how the system came together and where it is headed in the future.
Hadoop Security and Compliance - StampedeCon 2016
Hadoop Security and Compliance - StampedeCon 2016StampedeCon As Hadoop becomes a mainstream data platform across organizations, securing a vast and growing volume of critical information, especially financial and healthcare data, is more essential than ever. In this presentation, Derek will elaborate how to leverage Big Data technologies without sacrificing security and compliance, and will focus specially on how comprehensive security mechanisms should be put in place to secure a production ready Hadoop environment. The presentation will also highlight technologies, such as encryption in-motion and at-rest for Hadoop services, as well as the complicated compliant processes to meet strictest regulatory requirements and standards.
Visualizing Big Data – The Fundamentals
Visualizing Big Data – The FundamentalsStampedeCon This session will touch upon two visual languages, one to describe the context around what is being asked from the data, and the other, to describe what is quantifiable. From these two visual constructs we will go specifically into the following topics: Grids, Balance, Proximity, Contextual Kernels and Hierarchy.
Enabling Diverse Workload Scheduling in YARN
Enabling Diverse Workload Scheduling in YARNDataWorks Summit The document discusses enabling diverse workload scheduling in YARN. It covers several topics including node labeling, resource preemption, reservation systems, pluggable scheduler behavior, and Docker container support in YARN. The presenters are Wangda Tan and Craig Welch from Hortonworks who have experience with big data systems like Hadoop, YARN, and OpenMPI. They aim to discuss how these features can help different types of workloads like batch, interactive, and real-time jobs run together more happily in YARN.
Get most out of Spark on YARN
Get most out of Spark on YARNDataWorks Summit This document provides information about running Spark on YARN including:
- Spark allows processing of large datasets in a distributed manner using Resilient Distributed Datasets (RDDs).
- When running on YARN, Spark is able to leverage existing Hadoop clusters for locality-aware processing, resource management, and other benefits while still using its own execution engine.
- Running Spark on YARN provides advantages like shipping code to where the data is located instead of moving large amounts of data, leveraging existing Hadoop cluster infrastructure, and allowing Spark workloads to run natively within Hadoop.
Using The Internet of Things for Population Health Management - StampedeCon 2016
Using The Internet of Things for Population Health Management - StampedeCon 2016StampedeCon The Internet of (Human) Things is just beginning to take shape. The human body is an inexhaustible source of data about personal health, and the healthcare industry is just beginning to scratch the surface of the potential insights and value that will come from that data. While much of healthcare traditionally focuses on the episodic delivery of services, the Affordable Care Act is pushing healthcare providers, payers, and self-funded employer groups to look at ways to proactively encourage healthy behaviors. Providing personal health devices as a way to promote individual health is one way that healthcare is beginning to take advantage of IoT technologies. This session provides insight into how IoT is being leveraged in population health management through a solution jointly delivered by Amitech Solutions and Big Cloud Analytics. Attendees will learn how Hadoop is being used to gather personal device from various vendors, integrate and analyze that information, differentiate trends across regional and cultural diversity, and provide personal recommendations and insights into health risks. This session presents one important way the healthcare industry is leveraging IoT.
Apache Hadoop YARN – Multi-Tenancy, Capacity Scheduler & Preemption - Stamped...
Apache Hadoop YARN – Multi-Tenancy, Capacity Scheduler & Preemption - Stamped...StampedeCon At the StampedeCon 2015 Big Data Conference: YARN enables Hadoop to move beyond just pure batch processing. With that multiple workloads and tenants now must be able to share a single infrastructure for data processing. Features of the Capacity Scheduler enable resource sharing among multiple tenants in a fair manner with elastic queues to maximize utilization. This talk will focus on the features of the Capacity Scheduler that enable Multi-Tenancy and how resource sharing can be rebalanced using features like Preemption.
Building large scale applications in yarn with apache twill
Building large scale applications in yarn with apache twillHenry Saputra This document summarizes a presentation about Apache Twill, which provides abstractions for building large-scale applications on Apache Hadoop YARN. It discusses why Twill was created to simplify developing on YARN, Twill's architecture and components, key features like real-time logging and elastic scaling, real-world uses at CDAP, and the Twill roadmap.
Harnessing the power of YARN with Apache Twill
Harnessing the power of YARN with Apache TwillTerence Yim This document discusses Apache Twill, which aims to simplify developing distributed applications on YARN. Twill provides a Java thread-like programming model for YARN applications, avoiding the complexity of directly using YARN APIs. Key features of Twill include real-time logging, resource reporting, state recovery, elastic scaling, command messaging between tasks, service discovery, and support for executing bundled JAR applications on YARN. Twill handles communication with YARN and the Application Master while providing an easy-to-use API for application developers.
A Multi Colored YARN
A Multi Colored YARNDataWorks Summit/Hadoop Summit Vinod Kumar Vavilapalli discusses the evolution of Apache Hadoop YARN to support more complex applications and services on a single cluster. YARN is adding capabilities for packaging, simplified APIs, improved scheduling, and management of applications composed of multiple services. These changes will allow users to more easily deploy and manage multi-component "assemblies" on YARN without needing separate infrastructure. Hortonworks is working on enhancements to YARN, frameworks, tools, and user interfaces to simplify running diverse workloads on a unified Hadoop cluster.
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...
Building a Next-gen Data Platform and Leveraging the OSS Ecosystem for Easy W...StampedeCon This session will be a detailed recount of the design, implementation, and launch of the next-generation Shutterstock Data Platform, with strong emphasis on conveying clear, understandable learnings that can be transferred to your own organizations and projects. This platform was architected around the prevailing use of Kafka as a highly-scalable central data hub for shipping data across your organization in batch or streaming fashion. It also relies heavily on Avro as a serialization format and a global schema registry to provide structure that greatly improves quality and usability of our data sets, while also allowing the flexibility to evolve schemas and maintain backwards compatibility.
As a company, Shutterstock has always focused heavily on leveraging open source technologies in developing its products and infrastructure, and open source has been a driving force in big data more so than almost any other software sub-sector. With this plethora of constantly evolving data technologies, it can be a daunting task to select the right tool for your problem. We will discuss our approach for choosing specific existing technologies and when we made decisions to invest time in home-grown components and solutions.
We will cover advantages and the engineering process of developing language-agnostic APIs for publishing to and consuming from the data platform. These APIs can power some very interesting streaming analytics solutions that are easily accessible to teams across our engineering organization.
We will also discuss some of the massive advantages a global schema for your data provides for downstream ETL and data analytics. ETL into Hadoop and creation and maintenance of Hive databases and tables becomes much more reliable and easily automated with historically compatible schemas. To complement this schema-based approach, we will cover results of performance testing various file formats and compression schemes in Hadoop and Hive, the massive performance benefits you can gain in analytical workloads by leveraging highly optimized columnar file formats such as ORC and Parquet, and how you can use good old fashioned Hive as a tool for easily and efficiently converting exiting datasets into these formats.
Finally, we will cover lessons learned in launching this platform across our organization, future improvements and further design, and the need for data engineers to understand and speak the languages of data scientists and web, infrastructure, and network engineers.
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Hitachi Data Systems Hadoop SolutionHitachi Vantara Hitachi Data Systems Hadoop Solution. Customers are seeing exponential growth of unstructured data from their social media websites to operational sources. Their enterprise data warehouses are not designed to handle such high volumes and varieties of data. Hadoop, the latest software platform that scales to process massive volumes of unstructured and semi-structured data by distributing the workload through clusters of servers, is giving customers new option to tackle data growth and deploy big data analysis to help better understand their business. Hitachi Data Systems is launching its latest Hadoop reference architecture, which is pre-tested with Cloudera Hadoop distribution to provide a faster time to market for customers deploying Hadoop applications. HDS, Cloudera and Hitachi Consulting will present together and explain how to get you there. Attend this WebTech and learn how to: Solve big-data problems with Hadoop. Deploy Hadoop in your data warehouse environment to better manage your unstructured and structured data. Implement Hadoop using HDS Hadoop reference architecture. For more information on Hitachi Data Systems Hadoop Solution please read our blog: http://blogs.hds.com/hdsblog/2012/07/a-series-on-hadoop-architecture.html
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50 Shades of SQL
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Hadoop and the Data Warehouse: When to Use Which 
Hadoop and the Data Warehouse: When to Use Which DataWorks Summit In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
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Bridging Oracle Database and Hadoop by Alex Gorbachev, Pythian from Oracle Op...
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5 Things that Make Hadoop a Game Changer
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- 2. My Background • Oracle, MySQL, and PostgreSQL DBA with 15 years of experience • Led database, infrastructure, and business intelligence teams to deliver highly available data systems • Currently responsible for design, implementation, and operational availability of infrastructure and systems at Aisle411
- 3. Aisle411 • Digitizing the indoor world • Indoor maps, positioning, and analytics • Asset and customer tracking within locations • Using augmented reality to make indoor solutions more interactive • Small company - big data
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- 8. Example Datasets • Apache web server logs • Twitter feeds • Stock market prices • Census data • Sports statistics • Song data
- 9. Apache Web Log Data • Many online resources • Potentially large data set • Real business value • Combine with other data sources
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- 11. Quick Wins • Get data into HDFS • Get data into Hive or Impala • Stream live data • Combine with other data sources • Create pretty graphs and charts
- 12. Costs • Start small with a data puddle • Use virtual machines, not the big appliance • Research and experimentation time may be biggest cost
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