An overview of eBay's experience with Hadoop in the Past and Present, as well as directions for the Future. Given by Ryan Hennig at the Big Data Meetup at eBay in Netanya, Israel on Dec 2, 2013
PGConf.ASIA 2019 - PGSpider High Performance Cluster Engine - Shigeo HiroseEqunix Business Solutions
PGSpider is a high-performance SQL cluster engine developed by Toshiba Corporation. It allows distributed querying of heterogeneous data sources using standard SQL. PGSpider improves retrieval performance through parallel queries across nodes and supports multi-tenant querying to retrieve records from the same table across nodes. It utilizes techniques like pushdown of conditional expressions and aggregation functions to nodes to reduce network traffic.
eBay has one of the largest Hadoop clusters in the industry with many petabytes of data. This talk will give an overview of how Hadoop and HBase have been used within eBay, the lessons we have learned from supporting large-scale production clusters, as well as how we plan to use and improve Hadoop and HBase moving forward. Specific use cases, production issues and platform improvement work will be discussed.
This document discusses Hadoop usage at eBay over time from 2007 to 2015. It describes:
- The growth of eBay's Hadoop clusters from 1-10 nodes in 2007 to over 10,000 nodes and 150,000 cores projected for 2015.
- How the amount of data stored in Hadoop has grown from 1PB in 2010 to a projected 150+ PB in 2015.
- The types of clusters eBay uses including dedicated, shared, and HAAS clusters.
- Some key use cases for Hadoop at eBay like building a near real-time search index and processing 1.68 million items in 3 minutes.
- Operational requirements for eBay's large Hadoop ecosystem like high availability, security,
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
"Wire Encryption In HDFS: Protect Your Data From Others, Not Yourself"
ApacheCon 2019, Las Vegas.
SPEAKERS: Chen Liang, Konstantin Shvachko. LinkedIn
Wire data encryption is a key component of the Hadoop Distributed File System (HDFS). HDFS can enforce different levels of data protection, allowing users to specify one based on their own needs. However, such enforcement comes in as an all-or-nothing feature. Namely, wire encryption is enforced either for all accesses or none. Since encryption bears a considerable performance cost, the all-or-nothing condition forces users to choose between 'faster but unencrypted' or 'encrypted but slower' for all clients. In our use case at LinkedIn, we would like to selectively expose fast unencrypted access to fully managed internal clients, which can be trusted, while only expose encrypted access to clients outside of the trusted circle with higher security risks. That way we minimize performance overhead for trusted internal clients while still securing data from potential outside threats. We re-evaluate the RPC encryption mechanism in HDFS. Our design extends HDFS NameNode to run on multiple ports. Depending on the configuration, connecting to different NameNode ports would end up with different levels of encryption protection. This protection then gets enforced for both NameNode RPC and the subsequent data transfers to/from DataNode. System administrators then need to set up a simple firewall rule to allow access to the unencrypted port only for internal clients and expose the encrypted port to the outside clients. This approach comes with minimum operational and performance overhead. The feature has been introduced to Apache Hadoop under HDFS-13541.
Spline: Data Lineage For Spark Structured StreamingVaclav Kosar
Data lineage tracking is one of the significant problems that companies in highly regulated industries face. These companies are forced to have a good understanding of how data flows through their systems to comply with strict regulatory frameworks. Many of these organizations also utilize big and fast data technologies such as Hadoop, Apache Spark and Kafka. Spark has become one of the most popular engines for big data computing. In recent releases, Spark also provides the Structured Streaming component, which allows for real-time analysis and processing of streamed data from many sources. Spline is a data lineage tracking and visualization tool for Apache Spark. Spline captures and stores lineage information from internal Spark execution plans in a lightweight, unobtrusive and easy to use manner.
Additionally, Spline offers a modern user interface that allows non-technical users to understand the logic of Apache Spark applications. In this presentation we cover the support of Spline for Structured Streaming and we demonstrate how data lineage can be captured for streaming applications.
Presented at Spark Summit London 2018
PGConf.ASIA 2019 - PGSpider High Performance Cluster Engine - Shigeo HiroseEqunix Business Solutions
PGSpider is a high-performance SQL cluster engine developed by Toshiba Corporation. It allows distributed querying of heterogeneous data sources using standard SQL. PGSpider improves retrieval performance through parallel queries across nodes and supports multi-tenant querying to retrieve records from the same table across nodes. It utilizes techniques like pushdown of conditional expressions and aggregation functions to nodes to reduce network traffic.
eBay has one of the largest Hadoop clusters in the industry with many petabytes of data. This talk will give an overview of how Hadoop and HBase have been used within eBay, the lessons we have learned from supporting large-scale production clusters, as well as how we plan to use and improve Hadoop and HBase moving forward. Specific use cases, production issues and platform improvement work will be discussed.
This document discusses Hadoop usage at eBay over time from 2007 to 2015. It describes:
- The growth of eBay's Hadoop clusters from 1-10 nodes in 2007 to over 10,000 nodes and 150,000 cores projected for 2015.
- How the amount of data stored in Hadoop has grown from 1PB in 2010 to a projected 150+ PB in 2015.
- The types of clusters eBay uses including dedicated, shared, and HAAS clusters.
- Some key use cases for Hadoop at eBay like building a near real-time search index and processing 1.68 million items in 3 minutes.
- Operational requirements for eBay's large Hadoop ecosystem like high availability, security,
Hadoop Infrastructure @Uber Past, Present and FutureDataWorks Summit
Uber’s mission is to provide transportation as reliable as running water and for fulfilling that mission data plays a critical role. In Uber, Hadoop plays a critical role in Data Infrastructure. We want to talk about the journey of Hadoop @Uber and our future plans in terms of scaling for billions of trips. We will talk about most unique use case Uber have and how Hadoop and eco system which we built, helped us in this journey. We want to talk about how we scaled from 10 -> 2000 and In future to scale up to 10’s X1000 of Nodes. We will talk about our mistakes, learning and wins and how we process billions of events per day. We will talk about the unique challenges and real world use-cases and how we will co-locate the Uber’s service architecture with batch (e.g data pipelines, machine learning and analytical workloads). Uber have done lot of improvements to current Hadoop eco system and uniquely solved some of the problems in a way which is never been solved in the past. This presentation will help audience to use this as an example and even encourage them to enhance the eco system. This will help to increase the community of these project and overall help the whole big data space. Audience is anybody who is working on Big Data and want to understand how to scale Hadoop and eco system for 10s of thousands of node. This talk will help them understand the Hadoop ecosystem and how to efficiently use that. It will also introduce them to some of the awesome technologies which Uber team is building in big data space.
This document provides an overview of Hadoop architecture. It discusses how Hadoop uses MapReduce and HDFS to process and store large datasets reliably across commodity hardware. MapReduce allows distributed processing of data through mapping and reducing functions. HDFS provides a distributed file system that stores data reliably in blocks across nodes. The document outlines components like the NameNode, DataNodes and how Hadoop handles failures transparently at scale.
"Wire Encryption In HDFS: Protect Your Data From Others, Not Yourself"
ApacheCon 2019, Las Vegas.
SPEAKERS: Chen Liang, Konstantin Shvachko. LinkedIn
Wire data encryption is a key component of the Hadoop Distributed File System (HDFS). HDFS can enforce different levels of data protection, allowing users to specify one based on their own needs. However, such enforcement comes in as an all-or-nothing feature. Namely, wire encryption is enforced either for all accesses or none. Since encryption bears a considerable performance cost, the all-or-nothing condition forces users to choose between 'faster but unencrypted' or 'encrypted but slower' for all clients. In our use case at LinkedIn, we would like to selectively expose fast unencrypted access to fully managed internal clients, which can be trusted, while only expose encrypted access to clients outside of the trusted circle with higher security risks. That way we minimize performance overhead for trusted internal clients while still securing data from potential outside threats. We re-evaluate the RPC encryption mechanism in HDFS. Our design extends HDFS NameNode to run on multiple ports. Depending on the configuration, connecting to different NameNode ports would end up with different levels of encryption protection. This protection then gets enforced for both NameNode RPC and the subsequent data transfers to/from DataNode. System administrators then need to set up a simple firewall rule to allow access to the unencrypted port only for internal clients and expose the encrypted port to the outside clients. This approach comes with minimum operational and performance overhead. The feature has been introduced to Apache Hadoop under HDFS-13541.
Spline: Data Lineage For Spark Structured StreamingVaclav Kosar
Data lineage tracking is one of the significant problems that companies in highly regulated industries face. These companies are forced to have a good understanding of how data flows through their systems to comply with strict regulatory frameworks. Many of these organizations also utilize big and fast data technologies such as Hadoop, Apache Spark and Kafka. Spark has become one of the most popular engines for big data computing. In recent releases, Spark also provides the Structured Streaming component, which allows for real-time analysis and processing of streamed data from many sources. Spline is a data lineage tracking and visualization tool for Apache Spark. Spline captures and stores lineage information from internal Spark execution plans in a lightweight, unobtrusive and easy to use manner.
Additionally, Spline offers a modern user interface that allows non-technical users to understand the logic of Apache Spark applications. In this presentation we cover the support of Spline for Structured Streaming and we demonstrate how data lineage can be captured for streaming applications.
Presented at Spark Summit London 2018
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
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 as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
This presentation on Spark Architecture will give an idea of what is Apache Spark, the essential features in Spark, the different Spark components. Here, you will learn about Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Graphx. You will understand how Spark processes an application and runs it on a cluster with the help of its architecture. Finally, you will perform a demo on Apache Spark. So, let's get started with Apache Spark Architecture.
YouTube Video: https://www.youtube.com/watch?v=CF5Ewk0GxiQ
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Apache Spark™ is a fast and general engine for large-scale data processing. Spark is written in Scala and runs on top of JVM, but Python is one of the officially supported languages. But how does it actually work? How can Python communicate with Java / Scala? In this talk, we’ll dive into the PySpark internals and try to understand how to write and test high-performance PySpark applications.
This presentation about HBase will help you understand what is HBase, what are the applications of HBase, how is HBase is different from RDBMS, what is HBase Storage, what are the architectural components of HBase and at the end, we will also look at some of the HBase commands using a demo. HBase is an essential part of the Hadoop ecosystem. It is a column-oriented database management system derived from Google’s NoSQL database Bigtable that runs on top of HDFS. After watching this video, you will know how to store and process large datasets using HBase. Now, let us get started and understand HBase and what it is used for.
Below topics are explained in this HBase presentation:
1. What is HBase?
2. HBase Use Case
3. Applications of HBase
4. HBase vs RDBMS
5. HBase Storage
6. HBase Architectural Components
What is this Big Data Hadoop training course about?
Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document summarizes Hadoop HDFS, which is a distributed file system designed for storing large datasets across clusters of commodity servers. It discusses that HDFS allows distributed processing of big data using a simple programming model. It then explains the key components of HDFS - the NameNode, DataNodes, and HDFS architecture. Finally, it provides some examples of companies using Hadoop and references for further information.
Top 5 Mistakes When Writing Spark Applications by Mark Grover and Ted MalaskaSpark Summit
The document discusses 5 common mistakes people make when writing Spark applications:
1) Not properly sizing executors for memory and cores.
2) Having shuffle blocks larger than 2GB which can cause jobs to fail.
3) Not addressing data skew which can cause joins and shuffles to be very slow.
4) Not properly managing the DAG to minimize shuffles and stages.
5) Classpath conflicts from conflicting dependencies like Guava which can cause errors.
This document provides an overview of Big Data and Hadoop. It defines Big Data as large volumes of structured, semi-structured, and unstructured data that is too large to process using traditional databases and software. It provides examples of the large amounts of data generated daily by organizations. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop including HDFS for distributed storage and fault tolerance, and MapReduce for distributed processing, are described at a high level. Common use cases for Hadoop by large companies are also mentioned.
The document introduces the ELK stack, which consists of Elasticsearch, Logstash, Kibana, and Beats. Beats ship log and operational data to Elasticsearch. Logstash ingests, transforms, and sends data to Elasticsearch. Elasticsearch stores and indexes the data. Kibana allows users to visualize and interact with data stored in Elasticsearch. The document provides descriptions of each component and their roles. It also includes configuration examples and demonstrates how to access Elasticsearch via REST.
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
A quick comparison of Hadoop and Apache Spark with a detailed introduction.
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. They do different things.
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Experimentation plays a vital role in business growth at eBay by providing valuable insights and prediction on how users will reach to changes made to the eBay website and applications. On a given day, eBay has several hundred experiments running at the same time. Our experimentation data processing pipeline handles billions of rows user behavioral and transactional data per day to generate detailed reports covering 100+ metrics over 50 dimensions.
In this session, we will share our journey of how we moved this complex process from Data warehouse to Hadoop. We will give an overview of the experimentation platform and data processing pipeline. We will highlight the challenges and learnings we faced implementing this platform in Hadoop and how this transformation led us to build a scalable, flexible and reliable data processing workflow in Hadoop. We will cover our work done on performance optimizations, methods to establish resilience and configurability, efficient storage formats and choices of different frameworks used in the pipeline.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
Difference with traditional RDBMS
Type system in Hive
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has very large files (over 100 million files) and is optimized for batch processing huge datasets across large clusters (over 10,000 nodes). HDFS stores multiple replicas of data blocks on different nodes to handle failures. It provides high aggregate bandwidth and allows computations to move to where data resides.
This document provides an overview of the partnership between 2degrees Mobile NZ Ltd and WSO2 for developing 2degrees' mobile services using an SOA approach. Key points include:
- 2degrees sought to develop disruptive innovations using complex workflows and a flexible integration platform to rapidly respond to competition. They selected WSO2's middleware due to its lightweight, scalable architecture.
- WSO2 provides an open source middleware platform for both on-premise and cloud deployments with integration, API management, identity management, and other capabilities.
- The document describes some of 2degrees' innovative mobile services developed using the WSO2 platform, including automatic top-ups and Facebook-based top-ups
This document summarizes a webinar on eBay and WSO2. It introduces Abhinav Kumar, a senior manager at eBay who drives the site-on-services vision, using WSO2 products to enable eBay's enterprise SOA strategy. It also introduces Chris Haddad, VP of Technology Evangelism at WSO2, and Paul Fremantle, CTO and co-founder of WSO2. The webinar schedule includes an open discussion led by Chris Haddad and questions from the audience. Resources and information on evaluation, architecture, deployment and support are also provided.
Hadoop is the popular open source like Facebook, Twitter, RFID readers, sensors, and implementation of MapReduce, a powerful tool so on.Your management wants to derive designed for deep analysis and transformation of information from both the relational data and thevery large data sets. Hadoop enables you to unstructuredexplore complex data, using custom analyses data, and wants this information as soon astailored to your information and questions. possible.Hadoop is the system that allows unstructured What should you do? Hadoop may be the answer!data to be distributed across hundreds or Hadoop is an open source project of the Apachethousands of machines forming shared nothing Foundation.clusters, and the execution of Map/Reduce It is a framework written in Java originallyroutines to run on the data in that cluster. Hadoop developed by Doug Cutting who named it after hishas its own filesystem which replicates data to sons toy elephant.multiple nodes to ensure if one node holding data Hadoop uses Google’s MapReduce and Google Filegoes down, there are at least 2 other nodes from System technologies as its foundation.which to retrieve that piece of information. This It is optimized to handle massive quantities of dataprotects the data availability from node failure, which could be structured, unstructured orsomething which is critical when there are many semi-structured, using commodity hardware, thatnodes in a cluster (aka RAID at a server level). is, relatively inexpensive computers. This massive parallel processing is done with greatWhat is Hadoop? performance. However, it is a batch operation handling massive quantities of data, so theThe data are stored in a relational database in your response time is not immediate.desktop computer and this desktop computer As of Hadoop version 0.20.2, updates are nothas no problem handling this load. possible, but appends will be possible starting inThen your company starts growing very quickly, version 0.21.and that data grows to 10GB. Hadoop replicates its data across differentAnd then 100GB. computers, so that if one goes down, the data areAnd you start to reach the limits of your current processed on one of the replicated computers.desktop computer. Hadoop is not suitable for OnLine Transaction So you scale-up by investing in a larger computer, Processing workloads where data are randomly and you are then OK for a few more months. accessed on structured data like a relational When your data grows to 10TB, and then 100TB. database.Hadoop is not suitable for OnLineAnd you are fast approaching the limits of that Analytical Processing or Decision Support Systemcomputer. workloads where data are sequentially accessed onMoreover, you are now asked to feed your structured data like a relational database, to application with unstructured data coming from generate reports that provide business sources intelligence. Hadoop is used for Big Data. It complements OnLine Transaction Processing and OnLine Analytical Pro
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 as well as integration with other big data technologies such as Apache Spark, Druid, and Kafka. The talk will also provide a glimpse of what is expected to come in the near future.
This presentation on Spark Architecture will give an idea of what is Apache Spark, the essential features in Spark, the different Spark components. Here, you will learn about Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Graphx. You will understand how Spark processes an application and runs it on a cluster with the help of its architecture. Finally, you will perform a demo on Apache Spark. So, let's get started with Apache Spark Architecture.
YouTube Video: https://www.youtube.com/watch?v=CF5Ewk0GxiQ
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Apache Spark™ is a fast and general engine for large-scale data processing. Spark is written in Scala and runs on top of JVM, but Python is one of the officially supported languages. But how does it actually work? How can Python communicate with Java / Scala? In this talk, we’ll dive into the PySpark internals and try to understand how to write and test high-performance PySpark applications.
This presentation about HBase will help you understand what is HBase, what are the applications of HBase, how is HBase is different from RDBMS, what is HBase Storage, what are the architectural components of HBase and at the end, we will also look at some of the HBase commands using a demo. HBase is an essential part of the Hadoop ecosystem. It is a column-oriented database management system derived from Google’s NoSQL database Bigtable that runs on top of HDFS. After watching this video, you will know how to store and process large datasets using HBase. Now, let us get started and understand HBase and what it is used for.
Below topics are explained in this HBase presentation:
1. What is HBase?
2. HBase Use Case
3. Applications of HBase
4. HBase vs RDBMS
5. HBase Storage
6. HBase Architectural Components
What is this Big Data Hadoop training course about?
Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. The Big Data Hadoop and Spark developer course have been designed to impart in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document summarizes Hadoop HDFS, which is a distributed file system designed for storing large datasets across clusters of commodity servers. It discusses that HDFS allows distributed processing of big data using a simple programming model. It then explains the key components of HDFS - the NameNode, DataNodes, and HDFS architecture. Finally, it provides some examples of companies using Hadoop and references for further information.
Top 5 Mistakes When Writing Spark Applications by Mark Grover and Ted MalaskaSpark Summit
The document discusses 5 common mistakes people make when writing Spark applications:
1) Not properly sizing executors for memory and cores.
2) Having shuffle blocks larger than 2GB which can cause jobs to fail.
3) Not addressing data skew which can cause joins and shuffles to be very slow.
4) Not properly managing the DAG to minimize shuffles and stages.
5) Classpath conflicts from conflicting dependencies like Guava which can cause errors.
This document provides an overview of Big Data and Hadoop. It defines Big Data as large volumes of structured, semi-structured, and unstructured data that is too large to process using traditional databases and software. It provides examples of the large amounts of data generated daily by organizations. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key components of Hadoop including HDFS for distributed storage and fault tolerance, and MapReduce for distributed processing, are described at a high level. Common use cases for Hadoop by large companies are also mentioned.
The document introduces the ELK stack, which consists of Elasticsearch, Logstash, Kibana, and Beats. Beats ship log and operational data to Elasticsearch. Logstash ingests, transforms, and sends data to Elasticsearch. Elasticsearch stores and indexes the data. Kibana allows users to visualize and interact with data stored in Elasticsearch. The document provides descriptions of each component and their roles. It also includes configuration examples and demonstrates how to access Elasticsearch via REST.
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
A quick comparison of Hadoop and Apache Spark with a detailed introduction.
Hadoop and Apache Spark are both big-data frameworks, but they don't really serve the same purposes. They do different things.
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Experimentation plays a vital role in business growth at eBay by providing valuable insights and prediction on how users will reach to changes made to the eBay website and applications. On a given day, eBay has several hundred experiments running at the same time. Our experimentation data processing pipeline handles billions of rows user behavioral and transactional data per day to generate detailed reports covering 100+ metrics over 50 dimensions.
In this session, we will share our journey of how we moved this complex process from Data warehouse to Hadoop. We will give an overview of the experimentation platform and data processing pipeline. We will highlight the challenges and learnings we faced implementing this platform in Hadoop and how this transformation led us to build a scalable, flexible and reliable data processing workflow in Hadoop. We will cover our work done on performance optimizations, methods to establish resilience and configurability, efficient storage formats and choices of different frameworks used in the pipeline.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Spark-on-YARN: Empower Spark Applications on Hadoop ClusterDataWorks Summit
This document discusses Apache Spark-on-YARN, which allows Spark applications to leverage existing Hadoop clusters. Spark improves efficiency over Hadoop via in-memory computing and supports rich APIs. Spark-on-YARN provides access to HDFS data and resources on Hadoop clusters without extra deployment costs. It supports running Spark jobs in YARN cluster and client modes. The document describes Yahoo's use of Spark-on-YARN for machine learning applications on large datasets.
Technological Geeks Video 13 :-
Video Link :- https://youtu.be/mfLxxD4vjV0
FB page Link :- https://www.facebook.com/bitwsandeep/
Contents :-
Hive Architecture
Hive Components
Limitations of Hive
Hive data model
Difference with traditional RDBMS
Type system in Hive
HDFS has several strengths: horizontally scale its IO bandwidth and scale its storage to petabytes of storage. Further, it provides very low latency metadata operations and scales to over 60K concurrent clients. Hadoop 3.0 recently added Erasure Coding. One of HDFS’s limitations is scaling a number of files and blocks in the system. We describe a radical change to Hadoop’s storage infrastructure with the upcoming Ozone technology. It allows Hadoop to scale to tens of billions of files and blocks and, in the future, to every larger number of smaller objects. Ozone fundamentally separates the namespace layer and the block layer allowing new namespace layers to be added in the future. Further, the use of RAFT protocol has allowed the storage layer to be self-consistent. We show how this technology helps a Hadoop user and also what it means for evolving HDFS in the future. We will also cover the technical details of Ozone.
Speaker: Sanjay Radia, Chief Architect, Founder, Hortonworks
Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has very large files (over 100 million files) and is optimized for batch processing huge datasets across large clusters (over 10,000 nodes). HDFS stores multiple replicas of data blocks on different nodes to handle failures. It provides high aggregate bandwidth and allows computations to move to where data resides.
This document provides an overview of the partnership between 2degrees Mobile NZ Ltd and WSO2 for developing 2degrees' mobile services using an SOA approach. Key points include:
- 2degrees sought to develop disruptive innovations using complex workflows and a flexible integration platform to rapidly respond to competition. They selected WSO2's middleware due to its lightweight, scalable architecture.
- WSO2 provides an open source middleware platform for both on-premise and cloud deployments with integration, API management, identity management, and other capabilities.
- The document describes some of 2degrees' innovative mobile services developed using the WSO2 platform, including automatic top-ups and Facebook-based top-ups
This document summarizes a webinar on eBay and WSO2. It introduces Abhinav Kumar, a senior manager at eBay who drives the site-on-services vision, using WSO2 products to enable eBay's enterprise SOA strategy. It also introduces Chris Haddad, VP of Technology Evangelism at WSO2, and Paul Fremantle, CTO and co-founder of WSO2. The webinar schedule includes an open discussion led by Chris Haddad and questions from the audience. Resources and information on evaluation, architecture, deployment and support are also provided.
The document summarizes eBay's Hadoop analytics platform called Athena:
1. Athena is eBay's large Hadoop cluster with over 500 nodes that was built in 2010 to power advanced analytics on complex data.
2. The platform utilizes Hadoop core components like HDFS, MapReduce, HBase and tools like Hive, Pig and Mobius to access and analyze clickstream, transaction, and other data from eBay.
3. Athena supports a variety of use cases including search relevance, data mining, and analytics to enhance eBay's operations and customer experience.
The document discusses different tenses used to talk about the past in English:
- Past Simple is used for completed past actions at a specific time or habitual past actions.
- Past Progressive describes an action that was in progress at a specific past time or happening simultaneously with another action.
- Past Perfect Simple is used for a past action that was completed before another past action or time.
- Past Perfect Progressive emphasizes the duration of a past action that was ongoing up until another past moment or event.
eBay is an online marketplace founded in 1995 that connects buyers and sellers worldwide. It generates over $9 billion in annual revenue and employs around 17,700 people globally. The company's CEO is John Donahoe, who has expanded eBay's core business and overseen strategic acquisitions. eBay allows the sale of almost any item through online auctions and fixed-price listings, competing against Amazon and Craigslist. It has grown significantly since being founded and remains one of the largest online marketplaces.
Big Data Viz (and much more!) with Apache ZeppelinBruno Bonnin
Slides du talk réalisé à Web2Day 2016 sur Apache Zeppelin (env. dédié à l'exploration des données, avec support de multiples langages, multiples backends)
Explorez vos données avec apache zeppelinBruno Bonnin
Rapide présentation du projet Apache Zeppelin, environnement web facilitant l'exploration et le partage autour de vos données : le support de multiples langages pour le traitement (Spark) et l'accès aux bases de données (PostgreSQL, Cassandra, ...) permet à Zeppelin de s'adapter aux backends les plus divers.
eBay was founded by Pierre Omidyar in 1995 as an online auction website that allows equal access to a global marketplace. It started with the sale of a broken laser pointer for $14.83 and grew to allow the sale of virtually any legal product. eBay's model connects individuals who otherwise would not be connected, and it charges fees on listings and final sales to encourage high volume transactions while deterring low prices and sales. The future of eBay may involve developing mobile apps to enable more personal seller-buyer communication and expanding into developing markets like Africa.
LinkedIn's Logical Data Access Layer for Hadoop -- Strata London 2016Carl Steinbach
Hadoop at LinkedIn has grown significantly over time, from 1 cluster with 20 nodes in 2008 to over 10 clusters with over 10,000 nodes now. The number of users and workflows has also increased dramatically. While hardware scaling is difficult, scaling human infrastructure and managing dependencies between data producers, consumers, and infrastructure providers is even harder. The Dali system aims to abstract away physical data details and make data easier to access and manage through a dataset API, views, and lineage tracking. Views allow decoupling data APIs from the underlying datasets and enable safe evolution of these APIs through versioning. Contracts expressed as logical constraints on views provide clear, understandable, and modifiable agreements between producers and consumers. This approach has helped large projects
This document provides an overview of eBay including its history, services, leadership, finances, and stock performance. It discusses how eBay was founded in 1995 and now has a presence in 27 countries. The conclusion states that eBay is a good way to sell goods as it provides an easy and effective way for people to do business and act as a retailer.
This document summarizes LinkedIn's journey to 450 million members by 2016 through becoming a data-driven company. It discusses how LinkedIn developed data platforms like DALI to standardize reporting, experimentation, and tracking across diverse technologies. This allowed LinkedIn to break down barriers between speed and quality by partnering data scientists and engineers. The document concludes by thanking all those involved in LinkedIn's data-powered journey.
Data Science : Méthodologie, Outillage et Application - MS Cloud Summit Paris...Jean-Pierre Riehl
-- session présentée dans le cadre du MS Cloud Summit Paris 2017 avec Emmanuel Frenod --
L’approche Data Science des données révolutionne l’analyse traditionnelle. La façon d’appréhender les questions, la méthodologie à suivre ainsi que l’outillage à utiliser sont différents de la BI traditionnelle. Nous aborderons dans cette session ces différences et pointeront les bonnes pratiques de la Data Science avec les outils Microsoft au travers d’un cas d’utilisation concret. Ce « retour d’expérience » expliquera, en illustrant le propos à travers des applications réalisées pour des entreprises de transport, des réparateurs et des grossistes en bâtiment, comment la Data Science aide à la mise au point des prix pendant leur négociation
Ebay was founded in 1995 by Pierre Omidyar and was originally called AuctionWeb. It was renamed Ebay in 1997 and received $6.7 million in funding. Ebay connects consumers to buy and sell goods to each other on a global online marketplace. It generates revenue primarily through transaction fees charged to sellers. Ebay has over 100 million active users and competes with Amazon, Google, and Overstock.
Ebay was founded in 1995 and has grown to over 27,000 employees. It is a global e-commerce platform that allows 97 million users worldwide to buy and sell goods and services online or through fixed-price listings. Key services include general services, bidding/buying support, selling support, and customer support. The presentation outlines the registration and shopping/selling processes on Ebay and its payment platform PayPal.
This document provides an overview of the Apache Eagle project, which is an open source platform for monitoring Hadoop ecosystems in real time. It summarizes Eagle's key capabilities as follows:
1. Eagle uses a complex event processing (CEP) engine to evaluate monitoring policies on streamed data and detect access to sensitive data or malicious activities in real time.
2. It integrates with components like Ranger, Sentry, Knox and Splunk to provide a comprehensive solution for securing sensitive data stored in Hadoop.
3. Future releases of Eagle aim to improve the user experience, make the alert engine more scalable and extensible, and allow declarative configuration of data sources and correlation of events from multiple sources.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop that was created by eBay and later open sourced as an Apache Incubator project. It provides security for Hadoop systems by instantly identifying access to sensitive data, recognizing attacks/malicious activity, and blocking access in real time through complex policy definitions and stream processing. Eagle was designed to handle the huge volume of metrics and logs generated by large-scale Hadoop deployments through its distributed architecture and use of technologies like Apache Storm and Kafka.
• Comment installer Apache Flink sur votre PC ou Mac et comment se familiariser avec CLI, Job Client Web interface et Job Manager Web Interface?
• Comment développer une application Big Data en Java / Scala en utilisant un IDE?
• Comment développer avec Apache Flink en mode interactif avec Flink Shell ou Zeppelin Notebook (Scala)?
http://www.meetup.com/fr/Paris-Apache-Flink-Meetup/events/225577395/
Architecting the Future of Big Data and SearchHortonworks
The document discusses the potential for integrating Apache Lucene and Apache Hadoop technologies. It covers their histories and current uses, as well as opportunities and challenges around making them work better together through tighter integration or code sharing. Developers and businesses are interested in ways to improve searching large amounts of data stored using Hadoop technologies.
Hortonworks aims to make Apache Hadoop easier for enterprises to use by addressing gaps in the technology. Its goals are to make Hadoop projects simpler to install, manage, and use through regular releases and compiled code packages. It also wants to make Hadoop more robust with improvements to performance, high availability, administration, and integration. Hortonworks will work with the open source community to develop these enhancements to the Hadoop platform over the next few years.
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of petabytes of data. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and Hadoop MapReduce for processing vast amounts of data in parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. Many large companies use Hadoop for applications such as log analysis, web indexing, and data mining of large datasets.
The document discusses the Apache Hadoop ecosystem and versions. It provides details on Hadoop versioning from 0.1 to the current versions of 0.22, 0.23, and 1.0. It summarizes the key features and testing of Hadoop 0.22, which has been stabilized by eBay for production use. The document recommends Hadoop 0.22 as a reliable version to use until further versions are released.
This document provides an overview and introduction to Hadoop, HDFS, and MapReduce. It covers the basic concepts of HDFS, including how files are stored in blocks across data nodes, and the role of the name node and data nodes. It also explains the MapReduce programming model, including the mapper, reducer, and how jobs are split into parallel tasks. The document discusses using Hadoop from the command line and writing MapReduce jobs in Java. It also mentions some other projects in the Hadoop ecosystem like Pig, Hive, HBase and Zookeeper.
Hadoop - Where did it come from and what's next? (Pasadena Sept 2014)Eric Baldeschwieler
A summary of the History of Hadoop, some observations about the current state of Hadoop for new users and some predictions about its future (Hint, it's gonna be huge).
Presented at:
http://www.meetup.com/Pasadena-Big-Data-Users-Group/events/203961192/
Hadoop - Looking to the Future By Arun Murthyhuguk
Hadoop - Looking to the Future
By Arun Murthy (Founder of Hortonworks, Creator of YARN)
The Apache Hadoop ecosystem began as just HDFS & MapReduce nearly 10 years ago in 2006.
Very much like the Ship of Theseus (http://en.wikipedia.org/wiki/Ship_of_Theseus), Hadoop has undergone incredible amount of transformation from multi-purpose YARN to interactive SQL with Hive/Tez to machine learning with Spark.
Much more lies ahead: whether you want sub-second SQL with Hive or use SSDs/Memory effectively in HDFS or manage Metadata-driven security policies in Ranger, the Hadoop ecosystem in the Apache Software Foundation continues to evolve to meet new challenges and use-cases.
Arun C Murthy has been involved with Apache Hadoop since the beginning of the project - nearly 10 years now. In the beginning he led MapReduce, went on to create YARN and then drove Tez & the Stinger effort to get to interactive & sub-second Hive. Recently he has been very involved in the Metadata and Governance efforts. In between he founded Hortonworks, the first public Hadoop distribution company.
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers. It reliably stores and processes gobs of information across many commodity computers. Key components of Hadoop include the HDFS distributed file system for high-bandwidth storage, and MapReduce for parallel data processing. Hadoop can deliver data and run large-scale jobs reliably in spite of system changes or failures by detecting and compensating for hardware problems in the cluster.
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive DemosLester Martin
A walk-thru of core Hadoop, the ecosystem tools, and Hortonworks Data Platform (HDP) followed by code examples in MapReduce (Java and C#), Pig, and Hive.
Presented at the Atlanta .NET User Group meeting in July 2014.
This 40-hour course provides training to become a Hadoop developer. It covers Hadoop and big data fundamentals, Hadoop file systems, administering Hadoop clusters, importing and exporting data with Sqoop, processing data using Hive, Pig, and MapReduce, the YARN architecture, NoSQL programming with MongoDB, and reporting tools. The course includes hands-on exercises, datasets, installation support, interview preparation, and guidance from instructors with over 8 years of experience working with Hadoop.
This slide deck that Mr. Minh Tran - KMS's Software Architect shared at "Java-Trends and Career Opportunities" seminar of Information Technology Center of HCMC University of Science.
This document discusses big data analytics platforms and techniques. It describes various open-source projects like Hadoop, Spark, and Mahout that can perform analytics on large datasets. It also discusses commercial analytics platforms from vendors like SAS, Alpine, and Revolution Analytics. Spark is highlighted as gaining rapid adoption for its speed and expanding machine learning capabilities. Key questions are raised about which open-source projects and commercial offerings will emerge as leaders in their categories.
Apache Hadoop is a popular open-source framework for storing and processing large datasets across clusters of computers. It includes Apache HDFS for distributed storage, YARN for job scheduling and resource management, and MapReduce for parallel processing. The Hortonworks Data Platform is an enterprise-grade distribution of Apache Hadoop that is fully open source.
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The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins at Google and Yahoo
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Facebook and Amazon use Hadoop to process massive amounts of data
This document provides an overview of big data, Hadoop, and related concepts:
- Big data refers to large datasets that cannot be processed efficiently by traditional systems due to their size. Sources include social media, smartphones, machines, and log files.
- Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements the MapReduce programming model.
- Key Hadoop components include HDFS for storage, MapReduce for distributed processing, and related projects like Pig, Hive, HBase, Flume, Oozie, and Sqoop. Companies use Hadoop for applications involving large datasets, such as log analysis, recommendations, and business intelligence
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It allows for the reliable, scalable, and distributed processing of large data sets across clusters of commodity hardware. The core of Hadoop includes a storage part called HDFS for reliable data storage, and a processing part called MapReduce that processes data in parallel on a large cluster. Hadoop also includes additional projects like Hive, Pig, HBase, Zookeeper, Oozie, and Sqoop that together form a powerful data processing ecosystem.
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How MJ Global Leads the Packaging Industry.pdfMJ Global
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
Understanding User Needs and Satisfying ThemAggregage
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We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
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3. RYAN HENNIG
Born and raised in Seattle, WA
Studied Computer Science at University of Washington in Seattle
Worked on Microsoft SQL Server 2006 – 2012
- Shipped SQL Server 2008, 2008 R2, 2012
Joined eBay Hadoop team in early 2012
- Based in Bellevue, suburb of Seattle
COMPUTE AND DATA INFRASTRUCTURE
3
4. AGENDA
Past: Growth of Hadoop at eBay
Present: Hadoop Use Cases, Operations Tools
Future: Hadoop 2.0
7. ADVENTURES IN FORKING
• 2007-2010: eBay runs shared clusters on Cloudera Distribution of Hadoop
• 2010-2012: eBay runs shared clusters on custom Hadoop versions
– 2010: Wilma (based on 0.20)
– 2011: Argon (based on 0.22)
– 2012: Custom branch abandoned
• Lessons Learned
– Forking a fast-changing open source project is difficult and risky
• Balancing Development and operations needs
• Development team size
– Facebook had 100
– eBay had 15
• Coordination with open source community = lots of overhead
• Divergence from open source: Push changes early and often
HADOOP AT EBAY: PAST
7
9. EBAY AND HORTONWORKS
• 2012: eBay enters partnership with HortonWorks
– Goals
• Focus on eBay-specific development internally
• Leverage HortonWorks expertise for general Hadoop Development
• Avoid source code divergence by making open source contribution a priority
– Benefits to HortonWorks
• Credibility enhanced by having a well-known customer
• Ability to test at large scale
HADOOP AT EBAY: PAST
9
11. SHARED AND DEDICATED CLUSTERS
Shared clusters
–
–
–
–
–
10s of PB and 10s of thousands of slots per cluster
Used primarily for analytics of user behavior and inventory
Mix of production and ad-hoc jobs
Mix of MR, Hive, PIG, Cascading etc.
Hadoop and HBase security enabled
Dedicated clusters
–
–
–
–
Very specific use cases like Index Building
Tight SLAs for jobs (in order of minutes)
Immediate revenue impact
Usually smaller than our shared clusters, but still big (100s of nodes…)
HADOOP AT EBAY: PRESENT
11
13. USE CASE EXAMPLES
•Cassini, eBay’s new search engine:
– Use MR to build full and incremental near-real-time indexes
– Raw Data is stored in HBase for efficient updates and random read
– Strong SLAs: < 10 minutes
– Run on dedicated clusters
•Related and similar Items recommendations:
– Use transactional data, click stream data, search index, etc.
– Production MR jobs on a shared cluster
•Analytics dashboard:
– Run Mobius MR jobs to join click stream data and transactional data
– Store summary data in HBase
– Web application to query HBase
HADOOP AT EBAY: PRESENT
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14. HADOOP OPERATIONS
LDAP Integration
- All users stored in Active Directory, accessed via LDAP
- Access to MapReduce Queues granted via MapReduce queues
- Batch users: shared by a group of users
Security
- Kerberos as implemented by Microsoft Active Directory
- One domain for users, another for service/server principals
- Batch users authenticated via keytabs, not passwords
Misc
- 10’s of slave nodes are broken at any given time
- Often need to add several racks of machines at a time
HADOOP AT EBAY: PRESENT
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15. HADOOP OPERATIONS
Team has Development and Operations Responsibilities
- 2 Huge shared clusters
- 1800+ users, exponential growth
- About 10 Hadoop developers
- Recently: operations work moved to dedicated team
Developed several tools to manage operations
- Hadoop Management Console: user-facing web app
- ldap-admin: swiss-army knife style tool for hadoop admins
- Puppet: for adding machines to the clusters, many racks at a time
- Decom/Recom scripts: automatic detection, repair, decommission, and
recommission of slave nodes
HADOOP AT EBAY: PRESENT
15
16. HADOOP MANAGEMENT CONSOLE
• Custom Web application built on Ruby on Rails
• Self-service tools are continually added to reduce support load
– User Management
• Access Requests
• Group Membership
– Batch User Management
• New Requests
• Sudoer management
– Dataset Management
• Explore Datasets
• Request New dataset transfer between Teradata and Hadoop
– Metadata tools
• Each dataset is stored in custom XML format
• Code Generation: Hive Tables, Java POJOs
HADOOP AT EBAY: PRESENT
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24. ldap-admin
•Command-line tool written in Ruby
•Swiss-army knife tool, features added on demand for support issues
•Often used features:
– Add a user to a group
– View key details for LDAP users and groups
– List all users, batch users, hadoop groups
– Reset batch user passwords and keytabs
– Show/add/remove sudoers for a batch account
– Run user diagnostics: check permissions, keytabs, etc
HADOOP AT EBAY: PRESENT
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26. HDFS HA and Federation
• HDFS High-Availability for Reliability
– NameNode in Hadoop 1.0 is a Single Point of Failure
– Automated failover to hot standby
– Depends on ZooKeeper
• HDFS Federation for Scalability and Isolation
– Hadoop 1.0: Single NameNode service
• “Secondary NameNode” is not for failover
• Storage scales horizontally, but Namespace scales vertically
• No isolation for different tenants or applications
– Hadoop 2.0: HDFS Federation
• Partition the HDFS Namespace
• Many independent NameNodes
• Allows direct access to Block Storage w/o going through HDFS interface
HADOOP AT EBAY: FUTURE
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36. New Scenarios
• Iterative Query
– Stinger (Hive), Impala, etc
– Rapid Data exploration and analysis
• Graph Databases
– TitanDB, Giraph
– Billions of vertices and edges
– Complex Graph Traversals
– Applications: PayPal fraud detection, Social Graph Analysis
• Real-Time Processing
– Storm (Twitter), Apache S4
– Reinforcement Learning, Monitoring
HADOOP AT EBAY: FUTURE
36
37. Efficiency and Reliability
• Storage Efficiency
– HDFS introduces a 3x storage cost for its replicas
– HDFS-RAID: more reliability for 1.5x storage cost
• Reed-Solomon
• Locally Repairable Codes (Project Xorbas)
– Tradeoff: the cost of repairing lost data is much higher
• Operational Efficiency
– More automation
– More self-service tools
– Better Monitoring
HADOOP AT EBAY: FUTURE
37
38. Open Source
• HMC Metadata
– Long term goal: standardize on open source technologies (HCatalog)
– Short term: explore what should be open sourced
• Hadoop Management Console
– Hadoop Access Request Automation
– Batch user creation and management
– Metadata management
– Code generation of dataset to Hive tables and Java POJOs
• ldap_admin tools
– Very useful but tightly coupled to eBay’s LDAP configuration
– Willing to open source if there is interest
HADOOP AT EBAY: FUTURE
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