Sharing of Hadoop cluster deployment experience in production from scratch on real hardware. Brief overview of Hadoop stack, its components, major deployment and configuration challenges, performance tuning and application tuning experience. Some “war stories” about the issues we have faced while operating, the benefits of DevOps approach for running Hadoop apps.
The document discusses data ingestion and storage in Hadoop. It covers topics like ingesting data into Hadoop, using Hadoop as a data warehouse, Pig scripting, using Flume to ingest Twitter and web server logs, Hive as a query layer, HBase as a NoSQL database, and setting up high availability for HBase. It also discusses differences between Hadoop 1.0 and 2.0, how to set up a Hadoop 2.0 cluster including configuration files, and demonstrates upgrading Hadoop.
The document discusses developing a comprehensive monitoring approach for Hadoop clusters. It recommends starting with basic monitoring of nodes using Nagios and Cacti for metrics like CPU usage, disk usage, and network traffic. It then suggests adding Hadoop-specific checks like monitoring DataNodes and graphing NameNode operations using JMX. Finally, it proposes setting alarms based on JMX metrics and regularly reviewing filesystem growth and utilization.
This document provides an overview of five steps to improve PostgreSQL performance: 1) hardware optimization, 2) operating system and filesystem tuning, 3) configuration of postgresql.conf parameters, 4) application design considerations, and 5) query tuning. The document discusses various techniques for each step such as selecting appropriate hardware components, spreading database files across multiple disks or arrays, adjusting memory and disk configuration parameters, designing schemas and queries efficiently, and leveraging caching strategies.
HBase Operations: Best Practices outlines key topics for operating HBase clusters effectively including replication for disaster recovery, backups using snapshots or export, monitoring systems, automation of deployments, hardware recommendations, and useful diagnostic tools. The document provides an overview of HBase internals and discusses solutions for common problems like total cluster failure or accidental data deletion through replication and backup strategies.
Hadoop World 2011: Hadoop Troubleshooting 101 - Kate Ting - ClouderaCloudera, Inc.
Attend this session and walk away armed with solutions to the most common customer problems. Learn proactive configuration tweaks and best practices to keep your cluster free of fetch failures, job tracker hangs, and the like.
The document provides summaries of several workshops and presentations at an HPC conference:
1. The rasdman workshop discussed adding array support to SQL queries, array query operators, and storage techniques for large arrays like tiled storage.
2. The energy efficient HPC talk discussed optimization techniques to improve energy efficiency, with information provided in slides.
3. The data-aware networking workshop included discussions of techniques for improving data transfer performance over networks like pipelining and parallelism in gridftp.
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
Bharath Mundlapudi presented on Disk Fail Inplace in Hadoop. He discussed how a single disk failure currently causes an entire node to be blacklisted. With newer hardware trends of more disks per node, this wastes significant resources. His team developed a Disk Fail Inplace approach where Hadoop can tolerate disk failures until a threshold. This included separating critical and user files, handling failures at startup and runtime in DataNode and TaskTracker, and rigorous testing of the new approach.
The document discusses data ingestion and storage in Hadoop. It covers topics like ingesting data into Hadoop, using Hadoop as a data warehouse, Pig scripting, using Flume to ingest Twitter and web server logs, Hive as a query layer, HBase as a NoSQL database, and setting up high availability for HBase. It also discusses differences between Hadoop 1.0 and 2.0, how to set up a Hadoop 2.0 cluster including configuration files, and demonstrates upgrading Hadoop.
The document discusses developing a comprehensive monitoring approach for Hadoop clusters. It recommends starting with basic monitoring of nodes using Nagios and Cacti for metrics like CPU usage, disk usage, and network traffic. It then suggests adding Hadoop-specific checks like monitoring DataNodes and graphing NameNode operations using JMX. Finally, it proposes setting alarms based on JMX metrics and regularly reviewing filesystem growth and utilization.
This document provides an overview of five steps to improve PostgreSQL performance: 1) hardware optimization, 2) operating system and filesystem tuning, 3) configuration of postgresql.conf parameters, 4) application design considerations, and 5) query tuning. The document discusses various techniques for each step such as selecting appropriate hardware components, spreading database files across multiple disks or arrays, adjusting memory and disk configuration parameters, designing schemas and queries efficiently, and leveraging caching strategies.
HBase Operations: Best Practices outlines key topics for operating HBase clusters effectively including replication for disaster recovery, backups using snapshots or export, monitoring systems, automation of deployments, hardware recommendations, and useful diagnostic tools. The document provides an overview of HBase internals and discusses solutions for common problems like total cluster failure or accidental data deletion through replication and backup strategies.
Hadoop World 2011: Hadoop Troubleshooting 101 - Kate Ting - ClouderaCloudera, Inc.
Attend this session and walk away armed with solutions to the most common customer problems. Learn proactive configuration tweaks and best practices to keep your cluster free of fetch failures, job tracker hangs, and the like.
The document provides summaries of several workshops and presentations at an HPC conference:
1. The rasdman workshop discussed adding array support to SQL queries, array query operators, and storage techniques for large arrays like tiled storage.
2. The energy efficient HPC talk discussed optimization techniques to improve energy efficiency, with information provided in slides.
3. The data-aware networking workshop included discussions of techniques for improving data transfer performance over networks like pipelining and parallelism in gridftp.
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
Bharath Mundlapudi presented on Disk Fail Inplace in Hadoop. He discussed how a single disk failure currently causes an entire node to be blacklisted. With newer hardware trends of more disks per node, this wastes significant resources. His team developed a Disk Fail Inplace approach where Hadoop can tolerate disk failures until a threshold. This included separating critical and user files, handling failures at startup and runtime in DataNode and TaskTracker, and rigorous testing of the new approach.
This document provides an overview of big data and Hadoop. It discusses what big data is, why it has become important recently, and common use cases. It then describes how Hadoop addresses challenges of processing large datasets by distributing data and computation across clusters. The core Hadoop components of HDFS for storage and MapReduce for processing are explained. Example MapReduce jobs like wordcount are shown. Finally, higher-level tools like Hive and Pig that provide SQL-like interfaces are introduced.
Optimizing your Infrastrucure and Operating System for HadoopDataWorks Summit
Apache Hadoop is clearly one of the fastest growing big data platforms to store and analyze arbitrarily structured data in search of business insights. However, applicable commodity infrastructures have advanced greatly in the last number of years and there is not a lot of accurate, current information to assist the community in optimally designing and configuring
Hadoop platforms (Infrastructure and O/S). In this talk we`ll present guidance on Linux and Infrastructure deployment, configuration and optimization from both Red Hat and HP (derived from actual performance data) for clusters optimized for single workloads or balanced clusters that host multiple concurrent workloads.
This presentation surveys different ways one can geographically distribute PostgreSQL, including master-slave and multi-master solutions. It discusses pitfalls and emphasizes understanding requirements. The presentation covers some of the existing tools that are available in the community. It also touches upon upcoming PostgreSQL solutions.
Impetus provides expert consulting services around Hadoop implementations, including R&D, assessment, deployment (on private and public clouds), optimizations for enhanced static shared data implementations.
This presentation speaks about Advanced Hadoop Tuning and Optimisation.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
This document provides an overview of Apache Hadoop and its two main components - HDFS and MapReduce. It describes the fundamental ideas behind Hadoop such as storing data reliably across commodity hardware and moving computation to data. It then discusses HDFS in more detail, explaining how it stores very large files reliably through data replication and partitioning files into blocks. It also covers the roles of the NameNode and DataNodes and common HDFS commands. Finally, it discusses some challenges encountered when using HDFS in practice and potential solutions.
The document discusses using Hadoop for scientific workloads and summarizes early results from benchmarking Hadoop. It explores using Hadoop and MapReduce for data-intensive scientific applications like BLAST sequence analysis. Performance results show that Hadoop can provide comparable performance to existing parallel file systems. Challenges include lack of turn-key solutions, managing data formats, and performance tuning. The research aims to understand the unique needs of science clouds and how to effectively support data-intensive scientific applications on cloud platforms.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Architectural Overview of MapR's Apache Hadoop Distributionmcsrivas
Describes the thinking behind MapR's architecture. MapR"s Hadoop achieves better reliability on commodity hardware compared to anything on the planet, including custom, proprietary hardware from other vendors. Apache HDFS and Cassandra replication is also discussed, as are SAN and NAS storage systems like Netapp and EMC.
Improving Hadoop Cluster Performance via Linux ConfigurationAlex Moundalexis
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses:
- The background and architecture of Hadoop, including its core components HDFS and MapReduce.
- How Hadoop is used to process diverse large datasets across commodity hardware clusters in a scalable and fault-tolerant manner.
- Examples of use cases for Hadoop including ETL, log processing, and recommendation engines.
- The Hadoop ecosystem including related projects like Hive, HBase, Pig and Zookeeper.
- Basic installation, security considerations, and monitoring of Hadoop clusters.
The document discusses backup and disaster recovery strategies for Hadoop. It focuses on protecting data sets stored in HDFS. HDFS uses data replication and checksums to protect against disk and node failures. Snapshots can protect against data corruption and accidental deletes. The document recommends copying data from the primary to secondary site for disaster recovery rather than teeing, and discusses considerations for large data movement like bandwidth needs and security. It also notes the importance of backing up metadata like Hive configurations along with core data.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
Know how to setup a Hadoop Cluster With HDFS High Availability here : www.edureka.co/blog/how-to-set-up-hadoop-cluster-with-hdfs-high-availability/
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.
Improving Hadoop Cluster Performance via Linux ConfigurationDataWorks Summit
1. The document provides 7 simple Linux configuration tips to improve Hadoop cluster performance. The tips include disabling swapping, mounting data disks with noatime, disabling root reserved space, enabling nscd, increasing file handle limits, using a dedicated OS disk, and ensuring proper name resolution.
2. It also discusses additional optional tips like checking disk I/O, disabling transparent huge pages, enabling jumbo frames, and monitoring systems.
3. The document recommends reading the Hadoop Operations book and taking questions.
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)Adam Kawa
Adam Kawa shares his experiences working with a large, rapidly growing Hadoop cluster at Spotify. He details five "adventures" where various problems broke the cluster or made it unstable. These included issues with user permissions causing NameNode instability, DataNodes becoming blocked in deadlocks, Hive jobs being killed by the Fair Scheduler, and the JobTracker becoming slow due to overly large jobs. Each time, the problems were troubleshot and lessons were learned about proper cluster management, testing changes, and making data-driven decisions.
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)VMware Tanzu
Recorded at SpringOne2GX 2013 in Santa Clara, CA
Speaker: Adam Shook
This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more.
Learn More about Spring XD at: http://projects.spring.io/spring-xd
Learn More about Gemfire XD at:
http://www.gopivotal.com/big-data/pivotal-hd
Hadoop and Genomics - What You Need to Know - London - Viadex RCC - 2015.03.17Allen Day, PhD
This document discusses how genomics and DNA sequencing data can be analyzed using big data technologies like Hadoop. It describes how early DNA sequencing efforts faced bottlenecks but scaling out storage and computing using Hadoop helped overcome these issues. Large-scale analysis of sequencing data allows for variant calling and genome-wide association studies to identify disease causes at a large scale. Recent efforts like India's biometric identity system Aadhaar show how similar techniques can be applied to very large biological data sets to gain health insights.
DataEngConf: Uri Laserson (Data Scientist, Cloudera) Scaling up Genomics with...Hakka Labs
New DNA sequencing technologies are revolutionizing the life sciences by generating extremely large data sets. Traditional tools for processing this data will have difficulty scaling to the coming deluge of genomics data. We discuss how the innovations of Hadoop and Spark are solving core problems that enable scientists to address questions that were previously out of reach.
This document provides an overview of big data and Hadoop. It discusses what big data is, why it has become important recently, and common use cases. It then describes how Hadoop addresses challenges of processing large datasets by distributing data and computation across clusters. The core Hadoop components of HDFS for storage and MapReduce for processing are explained. Example MapReduce jobs like wordcount are shown. Finally, higher-level tools like Hive and Pig that provide SQL-like interfaces are introduced.
Optimizing your Infrastrucure and Operating System for HadoopDataWorks Summit
Apache Hadoop is clearly one of the fastest growing big data platforms to store and analyze arbitrarily structured data in search of business insights. However, applicable commodity infrastructures have advanced greatly in the last number of years and there is not a lot of accurate, current information to assist the community in optimally designing and configuring
Hadoop platforms (Infrastructure and O/S). In this talk we`ll present guidance on Linux and Infrastructure deployment, configuration and optimization from both Red Hat and HP (derived from actual performance data) for clusters optimized for single workloads or balanced clusters that host multiple concurrent workloads.
This presentation surveys different ways one can geographically distribute PostgreSQL, including master-slave and multi-master solutions. It discusses pitfalls and emphasizes understanding requirements. The presentation covers some of the existing tools that are available in the community. It also touches upon upcoming PostgreSQL solutions.
Impetus provides expert consulting services around Hadoop implementations, including R&D, assessment, deployment (on private and public clouds), optimizations for enhanced static shared data implementations.
This presentation speaks about Advanced Hadoop Tuning and Optimisation.
Hadoop is an open source framework for distributed storage and processing of large datasets across commodity hardware. It has two main components - the Hadoop Distributed File System (HDFS) for storage, and MapReduce for processing. HDFS stores data across clusters in a redundant and fault-tolerant manner. MapReduce allows distributed processing of large datasets in parallel using map and reduce functions. The architecture aims to provide reliable, scalable computing using commodity hardware.
This document provides an overview of Apache Hadoop and its two main components - HDFS and MapReduce. It describes the fundamental ideas behind Hadoop such as storing data reliably across commodity hardware and moving computation to data. It then discusses HDFS in more detail, explaining how it stores very large files reliably through data replication and partitioning files into blocks. It also covers the roles of the NameNode and DataNodes and common HDFS commands. Finally, it discusses some challenges encountered when using HDFS in practice and potential solutions.
The document discusses using Hadoop for scientific workloads and summarizes early results from benchmarking Hadoop. It explores using Hadoop and MapReduce for data-intensive scientific applications like BLAST sequence analysis. Performance results show that Hadoop can provide comparable performance to existing parallel file systems. Challenges include lack of turn-key solutions, managing data formats, and performance tuning. The research aims to understand the unique needs of science clouds and how to effectively support data-intensive scientific applications on cloud platforms.
Hadoop Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
Architectural Overview of MapR's Apache Hadoop Distributionmcsrivas
Describes the thinking behind MapR's architecture. MapR"s Hadoop achieves better reliability on commodity hardware compared to anything on the planet, including custom, proprietary hardware from other vendors. Apache HDFS and Cassandra replication is also discussed, as are SAN and NAS storage systems like Netapp and EMC.
Improving Hadoop Cluster Performance via Linux ConfigurationAlex Moundalexis
Administering a Hadoop cluster isn't easy. Many Hadoop clusters suffer from Linux configuration problems that can negatively impact performance. With vast and sometimes confusing config/tuning options, it can can tempting (and scary) for a cluster administrator to make changes to Hadoop when cluster performance isn't as expected. Learn how to improve Hadoop cluster performance and eliminate common problem areas, applicable across use cases, using a handful of simple Linux configuration changes.
This document provides an overview of Hadoop, an open source framework for distributed storage and processing of large datasets. It discusses:
- The background and architecture of Hadoop, including its core components HDFS and MapReduce.
- How Hadoop is used to process diverse large datasets across commodity hardware clusters in a scalable and fault-tolerant manner.
- Examples of use cases for Hadoop including ETL, log processing, and recommendation engines.
- The Hadoop ecosystem including related projects like Hive, HBase, Pig and Zookeeper.
- Basic installation, security considerations, and monitoring of Hadoop clusters.
The document discusses backup and disaster recovery strategies for Hadoop. It focuses on protecting data sets stored in HDFS. HDFS uses data replication and checksums to protect against disk and node failures. Snapshots can protect against data corruption and accidental deletes. The document recommends copying data from the primary to secondary site for disaster recovery rather than teeing, and discusses considerations for large data movement like bandwidth needs and security. It also notes the importance of backing up metadata like Hive configurations along with core data.
Distributed Computing with Apache Hadoop is a technology overview that discusses:
1) Hadoop is an open source software framework for distributed storage and processing of large datasets across clusters of commodity hardware.
2) Hadoop addresses limitations of traditional distributed computing with an architecture that scales linearly by adding more nodes, moves computation to data instead of moving data, and provides reliability even when hardware failures occur.
3) Core Hadoop components include the Hadoop Distributed File System for storage, and MapReduce for distributed processing of large datasets in parallel on multiple machines.
With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
Know how to setup a Hadoop Cluster With HDFS High Availability here : www.edureka.co/blog/how-to-set-up-hadoop-cluster-with-hdfs-high-availability/
This document discusses the integration of Apache Pig with Apache Tez. Pig provides a procedural scripting language for data processing workflows, while Tez is a framework for executing directed acyclic graphs (DAGs) of tasks. Migrating Pig to use Tez as its execution engine provides benefits like reduced resource usage, improved performance, and container reuse compared to Pig's default MapReduce execution. The document outlines the design changes needed to compile Pig scripts to Tez DAGs and provides examples and performance results. It also discusses ongoing work to achieve full feature parity with MapReduce and further optimize performance.
Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.
Improving Hadoop Cluster Performance via Linux ConfigurationDataWorks Summit
1. The document provides 7 simple Linux configuration tips to improve Hadoop cluster performance. The tips include disabling swapping, mounting data disks with noatime, disabling root reserved space, enabling nscd, increasing file handle limits, using a dedicated OS disk, and ensuring proper name resolution.
2. It also discusses additional optional tips like checking disk I/O, disabling transparent huge pages, enabling jumbo frames, and monitoring systems.
3. The document recommends reading the Hadoop Operations book and taking questions.
Hadoop Adventures At Spotify (Strata Conference + Hadoop World 2013)Adam Kawa
Adam Kawa shares his experiences working with a large, rapidly growing Hadoop cluster at Spotify. He details five "adventures" where various problems broke the cluster or made it unstable. These included issues with user permissions causing NameNode instability, DataNodes becoming blocked in deadlocks, Hive jobs being killed by the Fair Scheduler, and the JobTracker becoming slow due to overly large jobs. Each time, the problems were troubleshot and lessons were learned about proper cluster management, testing changes, and making data-driven decisions.
Hadoop - Just the Basics for Big Data Rookies (SpringOne2GX 2013)VMware Tanzu
Recorded at SpringOne2GX 2013 in Santa Clara, CA
Speaker: Adam Shook
This session assumes absolutely no knowledge of Apache Hadoop and will provide a complete introduction to all the major aspects of the Hadoop ecosystem of projects and tools. If you are looking to get up to speed on Hadoop, trying to work out what all the Big Data fuss is about, or just interested in brushing up your understanding of MapReduce, then this is the session for you. We will cover all the basics with detailed discussion about HDFS, MapReduce, YARN (MRv2), and a broad overview of the Hadoop ecosystem including Hive, Pig, HBase, ZooKeeper and more.
Learn More about Spring XD at: http://projects.spring.io/spring-xd
Learn More about Gemfire XD at:
http://www.gopivotal.com/big-data/pivotal-hd
Hadoop and Genomics - What You Need to Know - London - Viadex RCC - 2015.03.17Allen Day, PhD
This document discusses how genomics and DNA sequencing data can be analyzed using big data technologies like Hadoop. It describes how early DNA sequencing efforts faced bottlenecks but scaling out storage and computing using Hadoop helped overcome these issues. Large-scale analysis of sequencing data allows for variant calling and genome-wide association studies to identify disease causes at a large scale. Recent efforts like India's biometric identity system Aadhaar show how similar techniques can be applied to very large biological data sets to gain health insights.
DataEngConf: Uri Laserson (Data Scientist, Cloudera) Scaling up Genomics with...Hakka Labs
New DNA sequencing technologies are revolutionizing the life sciences by generating extremely large data sets. Traditional tools for processing this data will have difficulty scaling to the coming deluge of genomics data. We discuss how the innovations of Hadoop and Spark are solving core problems that enable scientists to address questions that were previously out of reach.
The SKA Project - The World's Largest Streaming Data Processorinside-BigData.com
In this presentation from the 2014 HPC Advisory Council Europe Conference, Paul Calleja from University of Cambridge presents: The SKA Project - The World's Largest Streaming Data Processor.
"The Square Kilometre Array Design Studies is an international effort to investigate and develop technologies which will enable us to build an enormous radio astronomy telescope with a million square meters of collecting area."
Watch the video presentation: http://wp.me/p3RLHQ-cot
The document discusses the Square Kilometre Array (SKA) radio telescope project and its significance for Africa. It makes three key points:
1) Building the SKA in Africa would be a major breakthrough that could change perceptions of the continent and attract young Africans into STEM fields. It would enable African scientists to conduct "big science" and fundamental research.
2) The SKA presents major economic opportunities for Africa in areas like infrastructure development, scientific skills training, and spin-off industries in fields like computing and engineering. International partnerships on the project could also benefit African universities and industries.
3) South Africa is well-positioned to host the SKA through the progress made on precursors
Big Data, a space adventure - Mario Cartia - Codemotion Rome 2015Codemotion
Codemotion Rome 2015 - I Big Data sono indubbiamente tra i temi più "caldi" del panorama tecnologico attuale. Ad oggi nel mondo sono stati prodotti circa 5 Exabytes di dati che costituiscono una potenziale fonte di "intelligenza" che è possibile sfruttare, grazie alle tecnologie più recenti, in svariati ambiti che spaziano dalla medicina alla sociologia passando per il marketing. Il talk si propone, tramite una gita virtuale nello spazio, di introdurre i concetti, le tecniche e gli strumenti che consentono di iniziare a sfruttare il potenziale dei Big Data nel lavoro quotidiano.
Making Hadoop Realtime by Dr. William Bain of Scaleout SoftwareData Con LA
Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours or minutes. This works well for mining large volumes of historical data stored on disk, but it is not suitable for gaining real-time insights from live operational data. Still, the idea of using Hadoop for real-time data analytics on live data is appealing because it leverages existing programming skills and infrastructure – and the parallel architecture of Hadoop itself. This presentation will describe how real-time analytics using Hadoop can be performed by combining an in-memory data grid (IMDG) with an integrated, stand-alone Hadoop MapReduce execution engine. This new technology delivers fast results for live data and also accelerates the analysis of large, static data sets.
Casterbridge Tours offers customized educational tours to destinations around the world. They design itineraries based on student and teacher interests, and include admissions to cultural sites. Tour guides accompany each group 24/7 to provide expertise and ensure a safe, rewarding experience. Since 1979 they have expanded globally and now serve over 200 schools and organizations annually.
High-Performance Networking Use Cases in Life SciencesAri Berman
Big data has arrived in the life science research domain and has driven the need for optimized high-performance networks in these research environments. Many petabytes of data transfer, storage and analytics are now a reality due to the fact that data is being produced cheaply and rapidly at unprecedented rates in academic, commercial and clinical laboratories. These data flows are complicated by the combination of high-frequency mouse flows as well as high-volume elephant flows, sometimes from the same application operating in parallel environments. Additional complicating factors include collaborative research efforts on large data stores that utilize both common and disparate compute resources, the need for high-performance data encryption in-flight to cover the transmission and handling of clinical data, and the relatively poor state of algorithm development from an IO standpoint throughout the industry. This presentation will cover representative advanced networking use cases from life sciences research, the challenges that they present in networking environments, some solutions that are being deployed with in both small and large institutions, and an overview of a few of the unresolved problems to date.
Turning Petabytes of Data into Profit with Hadoop for the World’s Biggest Ret...Cloudera, Inc.
PRGX is the world's leading provider of accounts payable audit services and works with leading global retailers. As new forms of data started to flow into their organizations, standard RDBMS systems were not allowing them to scale. Now, by using Talend with Cloudera Enterprise, they are able to acheive a 9-10x performance benefit in processing data, reduce errors, and now provide more innovative products and services to end customers.
Watch this webinar to learn how PRGX worked with Cloudera and Talend to create a high-performance computing platform for data analytics and discovery that rapidly allows them to process, model, and serve massive amount of structured and unstructured data.
Hadoop as a Platform for Genomics - Strata 2015, San JoseAllen Day, PhD
Personalized medicine holds much promise to improve the quality of human life.
However, personalizing medicine depends on genome analysis software that does not scale well. Given the potential impact on society, genomics takes first place among fields of science that can benefit from Hadoop.
A single human genome contains about 3 billion base pairs. This is less than 1 gigabyte of data but the intermediate data produced by a DNA sequencer, required to produce a sequenced human genome, is many hundreds of times larger. Beyond the huge storage requirement, deep genomic analysis across large populations of humans requires enormous computational capacity as well.
Interestingly enough, while genome scientists have adopted the concept of MapReduce for parallelizing I/O, they have not embraced the Hadoop ecosystem. For example, the popular Genome Analysis Toolkit (GATK) uses a proprietary MapReduce implementation that can scale vertically but not horizontally.
How to Crunch Petabytes with Hadoop and Big Data using InfoSphere BigInsights...Vladimir Bacvanski, PhD
This document discusses how to analyze large datasets using Hadoop and BigInsights. It describes how IBM's Watson uses Hadoop to distribute its workload and load information into memory from sources like 200 million pages of text, CRM data, POS data, and social media to provide distilled insights. The document provides two use case examples of how energy companies and global media firms could use big data analytics to analyze weather data and identify unauthorized streaming content.
Jasper Horrell - SKA and Big Data: Up in Space and on the GroundSaratoga
Jasper Horrell manages the Science Computing and Innovation sector at SKA SA, focussing on the science and engineering of the telescope, Jasper has a vision for Africa as a leader in knowledge-based activity.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
Big Data Analytic with Hadoop: Customer StoriesYellowfin
Why watch?
Looking to analyze your growing data assets to unlock real business benefits today? But, are you sick of all the Big Data hype and whoopla?
Watch this on-demand Webinar from Actian and Yellowfin – Big Data Analytics with Hadoop – to discover how we’re making Big Data Analytics fast and easy:
Learn how a telecommunications provider has already transformed its business using Big Data Analytics with Hadoop.
Hold on as we go from data in Hadoop to predictive analytics in just 40-minutes.
Learn how to combine Hadoop with the most advanced Big Data technologies, and world’s easiest BI solution, to quickly generate real business value from Big Data Analytics.
What will you learn?
Discover how Actian’s market-leading Big Data Analytics technologies, combined with Yellowfin’s consumer-oriented platform for reporting and analytics, makes generating value from Big Data Analytics faster and easier than you thought possible.
Join us as we demonstrate how to:
• Connect to, prepare and optimize Big Data in Hadoop for reporting and analytics.
• Perform predictive analytics on streaming Big Data: Learn how to empower all your analytics stakeholders to move from historical reports to predictive analytics and gain a sustainable competitive advantage.
• Communicate insights attained from Big Data: Optimize the value of your Big Data insights by learning how to effectively communicate analytical information to defined user groups and types.
This Webinar is ideal if…
• You want to act on more data and data types in shorter timeframes
• You want to understand the steps involved in achieving Big Data success – both front and back end
• You want to see how market leaders are leveraging Big Data to become data-driven organizations today
Looking to analyze and exploit Big Data assets stored in Hadoop? Then this Webinar is a must.
Architecting for Massive Scalability - St. Louis Day of .NET 2011 - Aug 6, 2011Eric D. Boyd
The Cloud now makes seemingly infinite amounts of computing power accessible to everyone. However, to maximize this power, your applications need to scale. In this session, we will explore patterns that enable massive scalability. We will examine Brewer’s CAP Theorem and contrast it to the ACID principles that guide traditional LOB applications. And finally, we will explore how to apply these patterns when building applications for the Cloud using Windows Azure.
The document discusses how big data and Hadoop technologies are rapidly growing in popularity and adoption across organizations. While increased data volumes and cheaper storage costs have enabled this trend, these factors alone do not fully explain why adoption is happening so quickly and widely among both large and small companies. The key reasons are that analytics scaling laws have changed to allow for much better returns through linear scaling on commodity hardware, and data practices have evolved to embrace flexible structures and denormalized data. These developments have created a tipping point where big data approaches provide radically improved value compared to traditional methods.
Hadoop ecosystem framework n hadoop in live environmentDelhi/NCR HUG
The document provides an overview of the Hadoop ecosystem and how several large companies such as Google, Yahoo, Facebook, and others use Hadoop in production. It discusses the key components of Hadoop including HDFS, MapReduce, HBase, Pig, Hive, Zookeeper and others. It also summarizes some of the large-scale usage of Hadoop at these companies for applications such as web indexing, analytics, search, recommendations, and processing massive amounts of data.
Scaling up Business Intelligence from the scratch and to 15 countries worldwi...Sergii Khomenko
In the talk described our experience of setting up data reporting and Business Intelligence processes for an international company. Starting with an Excel file and bunch SQL queries, switching from in-house reporting solution to centralised hosted reports for building a flexible system for monitoring KPI of the company.
Attendees will learn from our experience how to integrate Tableau into the processes of a company, how to build independent ETL subsystems that scale to petabyte size and other useful learnings.
We will cover our early days with cloud solutions, that do not provide a DWH platform, so you can not expect any kind of production requirements. In the talk, we will go through the process of automatically duplicating our Tableau datasources to Amazon Redshift. That will enable us to be more flexible with scaling data, be sure about backup strategies and many-many more points. We will introduce our python toolchain that helps us in a daily management of our BI.
Petabyte scale on commodity infrastructureelliando dias
This document discusses Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. It describes how Hadoop addresses the need to reliably process huge datasets using a distributed file system and MapReduce processing on commodity hardware. It also provides details on how Hadoop has been implemented and used at Yahoo to process petabytes of data and support thousands of jobs weekly on large clusters.
Big Data and Hadoop in Cloud - Leveraging Amazon EMRVijay Rayapati
This document discusses big data, Hadoop, and using Hadoop in the cloud via Amazon EMR. It provides an overview of big data and what Hadoop is, explains how Hadoop works and how it can help store and process large datasets. It then discusses how Amazon EMR can be used to deploy Hadoop clusters in the cloud without having to manage the underlying infrastructure, and provides instructions on setting up and using EMR. Finally, it discusses debugging, profiling, and performance tuning Hadoop jobs and EMR clusters.
The document provides an agenda for a Hadoop/Big Data introductory session. The agenda covers introductions to big data concepts and Hadoop components like HDFS, MapReduce, Hive, HBase and Sqoop. It discusses working with HDFS and MapReduce, including file reads/writes in HDFS and MapReduce architecture, jobs and execution. Hands-on demos and code samples are proposed to supplement the theoretical content. The goal is to develop an understanding of big data theory and practice.
Andrew Ryan describes how Facebook operates Hadoop to provide access as a shared resource between groups.
More information and video at:
http://developer.yahoo.com/blogs/hadoop/posts/2011/02/hug-feb-2011-recap/
Architecting and productionising data science applications at scalesamthemonad
This document discusses architecting and productionizing data science applications at scale. It covers topics like parallel processing with Spark, streaming platforms like Kafka, and scalable machine learning approaches. It also discusses architectures for data pipelines and productionizing models, with a focus on automation, avoiding SQL databases, and using Kafka streams and Spark for batch and streaming workloads.
The document discusses Hadoop infrastructure at TripAdvisor including:
1) TripAdvisor uses Hadoop across multiple clusters to analyze large amounts of data and power analytics jobs that were previously too large for a single machine.
2) They implement high availability for the Hadoop infrastructure including automatic failover of the NameNode using DRBD, Corosync and Pacemaker to replicate the NameNode across two servers.
3) Monitoring of the Hadoop clusters is done through Ganglia and Nagios to track hardware, jobs and identify issues. Regular backups of HDFS and Hive metadata are also performed for disaster recovery.
Redis Developers Day 2014 - Redis Labs TalksRedis Labs
These are the slides that the Redis Labs team had used to accompany the session that we gave during the first ever Redis Developers Day on October 2nd, 2014, London. It includes some of the ideas we've come up with to tackle operational challenges in the hyper-dense, multi-tenants Redis deployments that our service - Redis Cloud - consists of.
Big data refers to large, complex datasets that are difficult to process using traditional methods. This document discusses three examples of real-world big data challenges and their solutions. The challenges included storage, analysis, and processing capabilities given hardware and time constraints. Solutions involved switching databases, using Hadoop/MapReduce, and representing complex data structures to enable analysis of terabytes of ad serving data. Flexibility and understanding domain needs were key to feasible versus theoretical solutions.
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
This document discusses challenges in large scale machine learning. It begins by discussing why distributed machine learning is necessary when data is too large for one computer to store or when models have too many parameters. It then discusses various challenges that arise in distributed machine learning including scalability issues, class imbalance, the curse of dimensionality, overfitting, and algorithm complexities related to data loading times. Specific examples are provided of distributing k-means clustering and spectral clustering algorithms. Distributed implementations of support vector machines are also discussed. Throughout, it emphasizes the importance of understanding when and where distributed approaches are suitable compared to single machine learning.
(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin
(Berkeley CS186 guest lecture)
Big Data Analytics Systems: What Goes Around Comes Around
Introduction to MapReduce, GFS, HDFS, Spark, and differences between "Big Data" and database systems.
This document summarizes the roles of servers in a Hadoop cluster, including manager, name nodes, edge nodes, and data nodes. It discusses hardware considerations for Hadoop cluster design like CPU to memory to disk ratios for different use cases. It also provides an overview of Dell's Hadoop solutions that integrate PowerEdge servers, Dell Networking switches, and support from Etu for analytic software and Dell Professional Services for implementation. It briefly discusses futures around in-memory processing and virtualized Hadoop deployments.
This document provides an introduction to Hadoop, including:
- An overview of big data and the challenges it poses for data storage and processing.
- How Hadoop addresses these challenges through its distributed, scalable architecture based on MapReduce and HDFS.
- Descriptions of key Hadoop components like MapReduce, HDFS, Hive, and Sqoop.
- Examples of how to perform common data processing tasks like word counting and friend recommendations using MapReduce.
- Some best practices, limitations, and other tools in the Hadoop ecosystem.
The document discusses big data and distributed computing. It provides examples of the large amounts of data generated daily by organizations like the New York Stock Exchange and Facebook. It explains how distributed computing frameworks like Hadoop use multiple computers connected via a network to process large datasets in parallel. Hadoop's MapReduce programming model and HDFS distributed file system allow users to write distributed applications that process petabytes of data across commodity hardware clusters.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It addresses problems posed by large and complex datasets that cannot be processed by traditional systems. Hadoop uses HDFS for storage and MapReduce for distributed processing of data in parallel. Hadoop clusters can scale to thousands of nodes and petabytes of data, providing low-cost and fault-tolerant solutions for big data problems faced by internet companies and other large organizations.
The document discusses big data and distributed computing. It explains that big data refers to large, unstructured datasets that are too large for traditional databases. Distributed computing uses multiple computers connected via a network to process large datasets in parallel. Hadoop is an open-source framework for distributed computing that uses MapReduce and HDFS for parallel processing and storage across clusters. HDFS stores data redundantly across nodes for fault tolerance.
Web-scale data processing: practical approaches for low-latency and batchEdward Capriolo
The document is a slide deck presentation about batch processing, stream processing, and relational and NoSQL databases. It introduces the speaker and their experience with Hadoop, Cassandra, and Hive. It then covers batch processing using Hadoop, describing common architectures and use cases like processing web server logs. It discusses limitations of batch processing and then introduces stream processing concepts like Kafka and Storm. It provides an example of using Storm to perform word counting on streams of text data and discusses storing streaming results. Finally, it covers temporal databases and storing streaming results incrementally in Cassandra.
Chicago Data Summit: Keynote - Data Processing with Hadoop: Scalable and Cost...Cloudera, Inc.
Hadoop is a new paradigm for data processing that scales near linearly to petabytes of data. Commodity hardware running open source software provides unprecedented cost effectiveness. It is affordable to save large, raw datasets, unfiltered, in Hadoop's file system. Together with Hadoop's computational power, this facilitates operations such as ad hoc analysis and retroactive schema changes. An extensive open source tool-set is being built around these capabilities, making it easy to integrate Hadoop into many new application areas.
Yahoo is the largest corporate contributor, tester, and user of Hadoop. They have 4000+ node clusters and contribute all their Hadoop development work back to Apache as open source. They use Hadoop for large-scale data processing and analytics across petabytes of data to power services like search and ads optimization. Some challenges of using Hadoop at Yahoo's scale include unpredictable user behavior, distributed systems issues, and the difficulties of collaboration in open source projects.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
Similar to BDM37: Hadoop in production – the war stories by Nikolaï Grigoriev, Principal Software Engineer, SociableLabs (20)
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Takashi Kobayashi and Hironori Washizaki, "SWEBOK Guide and Future of SE Education," First International Symposium on the Future of Software Engineering (FUSE), June 3-6, 2024, Okinawa, Japan
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Unveiling the Advantages of Agile Software Development.pdfbrainerhub1
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
What is Augmented Reality Image Trackingpavan998932
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
8 Best Automated Android App Testing Tool and Framework in 2024.pdfkalichargn70th171
Regarding mobile operating systems, two major players dominate our thoughts: Android and iPhone. With Android leading the market, software development companies are focused on delivering apps compatible with this OS. Ensuring an app's functionality across various Android devices, OS versions, and hardware specifications is critical, making Android app testing essential.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
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✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
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✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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What is Master Data Management by PiLog Groupaymanquadri279
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
BDM37: Hadoop in production – the war stories by Nikolaï Grigoriev, Principal Software Engineer, SociableLabs
1. Hadoop – The War Stories
Running Hadoop in large enterprise environment
Nikolai Grigoriev (ngrigoriev@gmail.com, @nikgrig)
Principal Software Engineer, http://sociablelabs.com
2. Agenda
● Why Hadoop?
● Planning Hadoop deployment
● Hadoop and read hardware
● Understanding the software stack
● Tuning HDFS, MapReduce and HBase
● Troubleshooting examples
● Testing your applications
Disclaimer: this presentation is based on the combined work experience from more than
one company and represents the author's personal point of view on the problems discussed in it.
3. Why Hadoop (and why have we decided to
use it)?
● Need to store hundreds of Tb of info
● Need to process it in parallel
● Desire to have both storage and processing
horizontally scalable
● Having and open-source platform with
commercial support
5. Our application in numbers
● Thousands of user sessions per second
● Average session log size: ~30Kb, 3-7 events
per log
● Target retention period – at least ~90 days
● Redundancy and HA everywhere
● Pluggable “ETL” modules for additional data
processing
6. Main problem
Team had no practical knowledge
of Hadoop, HDFS and HBase…
...and there was nobody at the
company to help
7. But we did not realize...
It was not THE ONLY problem we
were about to face!
8. First fight – capacity planning
● Tons of articles are written about Hadoop
capacity planning
● Architects may be spending months making
educated guesses
● Capacity planning is really about finding the
amount of $$$ to be spent on your cluster for
target workload
– If we had infinite amount of $$$ why would we
bother at all? ;)
10. It is all about the balance
● Your Hadoop cluster and your apps use all
these resources at different time
● Over-provisioning of one of the resources
usually lead to the shortage of another one -
wasted $$$
11. What can we say about an app?
● It is going to store X Tb of data
– Amount of storage (not to forget the RF!)
– Accommodate for growth and failures
● It is going to ingest the data at Y Mb/s
– Your network speed and number of nodes
● Latency
– More HDDs and faster HDDs
– More RAM
– More nodes
12. We are big enterprise...
Geeky Hadoop developer
Old School Senior IT Guy
- many “commodity+” hosts
- good but inexpensive
networking
- more regular HDDs
- lots of RAM
- I also love cloud…
- my recent OS
- my software configuration
- simple network
SANs, RAIDs, SCSI, racks,
Blades, redundancy,
Cisco, HP, fiber optics,
4-year-old
rock-solid RHEL, SNMP
monitoring…
what? I am the Boss...
13. Hadoop cluster vs. old school
application servers
● Mostly identical “commodity+” machines
– Probably with the exception of NN, JT
● Better to have more simpler machines than fewer
monster ones
● No RAID, just JBOD!
● Ethernet depending on the storage density, bonded
1Gbit may be enough
● Hadoop achieves with software what used to be
achievable with [expensive!] hardware
14. But still, your application is the
driver, not the IT guy!
From Cloudera website – Hadoop machine configuration according to workload
15. Your job is:
● Educate your IT, get them on your side or at
least earn their trust
● Try to build a capacity planning spreadsheet
based on what you do know
● Apply common sense to guess what you do not
know
● ...and plan a decent buffer
● Set reasonable performance targets for your
application
16. Fight #2 – OMG, our application is
slow!!!
● Main part of our application was the MR job merging the
logs
● We have committed to deliver X logs/sec on a target test
cluster with sample workload
● We were delivering like ~30% of that
● ...weeks before release :)
● ...and we have ran out of other excuses :(
● It was clearly our software and/or
configuration
17. Wait a second – we have support
contract from Hadoop vendor!
● I mean no disrespect to the vendors!
● But they do not know your application
● And they do not know your hardware
● And they do not know exactly your OS
● And they do not know your network equipment
● They can help you with some tuning, they can
help you with bugs and crashes – but they
won't be able (or sometimes simply qualified) to
do your job!
18. We are on our own :(
● We have realized that our testing methods were
not adequate to Hadoop-based ETL process
● Testing the product end-to-end was too difficult,
tracking changes was impossible
● Turn-around was too long, we could not try
something quickly and revert back
● Observing and monitoring the live system with
dummy incoming data was not productive
enough
19. Key to successful testing
● Representative data set
● Ability to repeat the same operation as many
times as needed with quick turnaround
● Each engineer had to be able to run the tests
and try something
● Establishing the key metrics you monitor and try
to improve
● Methodological approach – analyze, change,
test, be ready to roll back
20. Our “reference runner”
Large sample
dataset
“Reset” tool Runner tool Statistics
Recreates HBase tables
(predefined regions),
cleans HDFS etc
Injects the test data,
prepares the environment,
launches the MR job like real
application, allows to quickly
rebuild and redeploy the part
of the application
Any improvements since
last run?
Manager
21. Tuning results
● In two weeks we had the job that worked about
3 times faster
● Tuning was done everywhere – from OS to
Hadoop/HBase and our code
● We were confident that the software was ready
to go to production
● During following 2 years later we realized how
bad was our design and how it should have
been done ;)
22. Hadoop MapReduce DOs
● Think processes, not threads
● Reusable objects, lower GC overhead
● Snappy data compression is generally good
● Reasonable use of counters provides important
information
● For frequently running jobs, distributed cache helps a
lot
● Minimize disk I/O (spills etc), RAM is cheap
● Avoid unnecessary serialization/deserialization
23. Hadoop MapReduce DONTs
● Small files in HDFS
● Multithreaded programming inside
mapper/reducer
● Fat tasks using too much heap
● Any I/O in M-R other than HDFS, ZK or HBase
● Over-complicated code (simple things work
better)
24. Fight #3 – Going Production!
● Remember the slide about engineer vs. IT God
preferences ;)
● Production hardware was slightly different from
the test cluster
● Cluster has been deployed by the people who
did not know Hadoop
● First attempt to run the software resulted in
major failure and the cluster was finally handed
over to the developers for fixing ;)
25. Production hardware
● HP blade servers, 32 core, 128GB of RAM
● Emulex dual-port 10G Ethernet NICs
● 14 HDDs per machine
● OEL 6.3
● 10G switch modules
● Company hosting center with dedicated
networking and operations staff
26. Hardware
BIOS/Firmware(s)
BIOS/Firmware settings
OS (Linux)
Java (JVM)
Hadoop services
Your application(s)
Step back – 10,000 ft look at
Hadoop stack
Hardware
BIOS/Firmware(s)
BIOS/Firmware settings
OS (Linux)
Java (JVM)
Hadoop services
Your application(s)
Network
- Hadoop is not just a bunch
of Java apps
- It is a data and application
platform
- It can run well, just run,
barely run and cause
constant headache –
depending on how much
love does it receive :)
27. Hadoop stack (continued)
● In Hadoop a small problem, even sometimes on
a single node can be a major pain
● Isolating and finding that small problem may be
difficult
● Symptoms are often obvious only at high level
(e.g. application)
● Complex hardware (like HP) adds more
potential problems
28. Example of one of the problems we
had initially
● Jobs were failing because of timeouts
● Numerous I/O errors observed in job and HDFS logs
● This simple test was failing:
$ dd if=/dev/zero of=test8Gb.bin bs=1M count=8192
$ time hdfs dfs -copyFromLocal test8Gb.bin /
Zzz..zzz...zzz...5min...zzz…
real 4m10.002s
user 0m15.130s
sys 0m4.094s
● IT was clueless but did not really bother
● In fact, 8192Mb / (4 * 60 + 10) = 32Mb/s (!?!?!)
● 10Gb network transfers to HDFS at ~160Mb/s
29. Role of HDFS in Hadoop
● In Hadoop HDFS is the key layer that provides
the distributed filesystem services for other
components
● Health of HDFS directly (and drastically) affects
the health of other components
HDFS
Map-Reduce
Data
HBase
30. So, clearly HDFS was the problem
● But what was the problem with HDFS??
● How exactly HDFS writing works?
31. Chasing it down
● Due to node-to-node streaming it was difficult to
understand who was responsible
● Theory of “one bad node in pipeline” was ruled
out as results were consistently bad with the
cluster of 14 nodes
● Idea (isolating the problem is good):
$ time hdfs -Ddfs.replication=1 dfs -copyFromLocal test8Gb.bin /
real 0m42.002s
$ time hdfs -Ddfs.replication=2 dfs -copyFromLocal test8Gb.bin /
real 2m53.184s
$ time hdfs -Ddfs.replication=3 dfs -copyFromLocal test8Gb.bin /
real 3m41.072s
● 8192/42=195 Mb/s – hmmm….
32. Discoveries
● To make even longer story short...
– Bug in “cubic” TCP congestion protocol in Linux kernel
– NIC firmware was too old
– Kernel driver for Emulex 10G NICs was too old
– Only one out of 8 NIC RX queues was enabled on some
hosts
– A number of network settings were not appropriate for 10G
network
– “irqbalance” process (due to kernel bug) was locking NIC
RX queues by “losing” NIC IRQ handlers
– ...
33. More discoveries
– Nodes were set up multi-homed, even HDFS at that
time did not support that
– Misconfigured DNS and reverse DNS
● On disk I/O side
– Bad filesystem parameters
– Read-ahead settings were wrong
– Disk controller firmware was old
34. HDFS “litmus” test - TestDFSIO
13/03/13 16:30:02 INFO fs.TestDFSIO: ----- TestDFSIO ----- : write
13/03/13 16:30:02 INFO fs.TestDFSIO: Date & time: Wed Mar 13 16:30:02 UTC 2013
13/03/13 16:30:02 INFO fs.TestDFSIO: Number of files: 16
13/03/13 16:30:02 INFO fs.TestDFSIO: Total MBytes processed: 160000.0
13/03/13 16:30:02 INFO fs.TestDFSIO: Throughput mb/sec: 103.42190773343779
13/03/13 16:30:02 INFO fs.TestDFSIO: Average IO rate mb/sec: 103.61066436767578
13/03/13 16:30:02 INFO fs.TestDFSIO: IO rate std deviation: 4.513343367320971
13/03/13 16:30:02 INFO fs.TestDFSIO: Test exec time sec: 114.876
13/03/13 16:31:31 INFO fs.TestDFSIO: ----- TestDFSIO ----- : read
13/03/13 16:31:31 INFO fs.TestDFSIO: Date & time: Wed Mar 13 16:31:31 UTC 2013
13/03/13 16:31:31 INFO fs.TestDFSIO: Number of files: 16
13/03/13 16:31:31 INFO fs.TestDFSIO: Total MBytes processed: 160000.0
13/03/13 16:31:31 INFO fs.TestDFSIO: Throughput mb/sec: 586.8243268024676
13/03/13 16:31:31 INFO fs.TestDFSIO: Average IO rate mb/sec: 648.8555908203125
13/03/13 16:31:31 INFO fs.TestDFSIO: IO rate std deviation: 267.0954600161208
13/03/13 16:31:31 INFO fs.TestDFSIO: Test exec time sec: 33.683
13/03/13 16:31:31 INFO fs.TestDFSIO:
35. Fight #4 – tuning Hadoop
● Why do people tune things
(IT was not interested ;) )?
● With your own expensive
hardware you want the
maximum IOPS and CPU
power for $$$ you have
paid
● Not to mention that you simply want your apps to
run faster
● Tuning is an endless process but 80/20 rule
works perfectly
36. Even before you have something to
tune….
● Pick reasonably good hardware but do not go
high-end
● Same for network equipment
● Hadoop scales well and the redundancy is
achieved by software
● More nodes is almost always better than going
for extra node power and/or storage space
● Simpler systems are easier to tune, maintain
and troubleshoot
● Different machines for master nodes
37. Tuning the hardware and BIOS
● Updating BIOS and firmwares to recent versions
● Disabling dynamic CPU frequency scaling
● Tuning memory speed, power profile
● Disk controller, tune disk cache
38. OS Tuning
● Pick the filesystem (ext3, ext4, XFS...), parameters (reserve
blocks 0%) and mount options (noatime,nodiratime, barriers
etc)
● I/O scheduler depending on your disks and tasks
● Read-ahead settings
● Disable swap!
● irqbalance for big machines
● Tune other parameters (number of FDs, sockets)
● Install major troubleshooting tools (iostat, iotop, tcpdump,
strace…) on every one
39. Network tuning
● Test your TCP performance with iperf, ttcp or any other
tools you like
● Know your NICs well, install right firmware and kernel
modules
● Tune your TCP and IP parameters (work harder if you
have expensive 10G network)
● If your NIC supports TCP offload and it works – use it
● txqueuelen, MTU 9000 (if appropriate), HDFS is chatty
● Learn ethtool and see what it can do for you
● Basic IP networking set-up (DNS etc) has to be 100%
perfect
40. JVM tuning
● Hadoop allows you to set JVM options for all
processes
● Your Data Node, Name Node and HBase
Region Servers are going to work hard and you
need to help them to deal with your workload
● If your MR code is well designed you will most
likely NOT need to tune JVM for MR tasks
● Your main enemy will be GC – until you
become at lease allies, if not friends :)
41. Tuning Hadoop services
● NameNode deals with many connections and
needs ~150 bytes per HDFS block
● NameNode and DataNode are highly concurrent,
latter needs many threads
● Use HDFS short-circuit reads if appropriate
● ZooKeeper needs to handle enough connections
● HBase uses LOTS of heap
● Reuse JVMs for MR jobs if appropriate
42. Tuning MapReduce tasks (that means
tuning for your code and data)
● If you run different MR jobs, consider tuning
parameters for each of them, not once and for
all of them
● Configure job scheduler to enforce the SLAs
● Estimate the resource needed for each job
● Plan how are you going to run your jobs
43. Tuning your own code
● Test and profile your complex MR code outside of
Hadoop (your savings will scale too!)
● Check for GC overhead
● Use reusable objects
● Avoid using expensive formats like JSON and XML
● Anything you waste is multiplied by the number of
rows and the number of tasks!
● Evaluate the need for intermediate data compression
44. Tuning HBase
● That requires separate presentation
● You will need to fight hard for reducing GC
pauses and overhead
● Pre-splitting regions may be a good idea to
better balance the load
● Understand HBase compactions and deal with
major compactions your way
45. Set up your monitoring (and
alarming)
● You cannot improve what you cannot see!
● Monitor OS, Hadoop and your app metrics
● Ganglia, Graphite, LogStash, even Cloudera
Manager are your friends
● Set the baseline, track your changes, observe
the outcome
46. Fight #5 - Operations
● Real hand-over to the Operations people
actually never happened
● In case of any problems either it was ignored or
escalation to engineers was taking about 1
minute
● Neither NOC nor Operations staff wanted to
acquire enough knowledge of Hadoop and the
apps
● Monitoring was nearly non-existing
● Same for appropriate alarms
47. If you are serious...
● Send your Ops for Hadoop training (or buy
them books and have them read those!)
● Have them automate everything
● Ops have to understand your applications, not
just the platform they are running on
● Your Ops need to be decent Linux admins
● ...and it would be great if they are also OK
programmers (scripting, Java…)
● Of course, the motivation is the key
48. Plan and train for disaster
● Train your Ops how to
help your system to
survive till Monday
morning
● Decide what sort of
loss you will tolerate
(BigData is not always
so precious)
● Design your system for resilience, async
processing, queuing etc
49. Fight #6 - evolution
● Sooner or later you will need to increase your
capacity
– Unless your business is stagnating
● Technically, you will either
– Run out of storage space
– Start hitting the wall on IOPS or CPU and fail to
respect your SLAs (even if only internal ones)
– Won't be able to deploy new applications
50. Understand your application - again
● Even if your apps runs fine you need to monitor the
performance factors
● Build spreadsheets reflecting your current numbers
● Plan for the business growth
● Translate this into the number of additional nodes
and networking equipment
● Especially important if your hardware purchase
cycle takes months
51. Conclusions
● Not all companies are ready for BigData – often
because of conservative people in key positions
● Traditional IT/Ops/NOC organizations are often
unable to support these platforms
● Engineers have to be given more power to
control how the things they build are ran
(DevOps)
● Hadoop is a complex platform and has to be
taken seriously for serious applications
● If you really depend on Hadoop you do need to
build in-house expertise