This presentation speaks about -
1) How to perform big data testing
2) Tools that can be used for testing
3) Different validation stages involved
4) Performance testing
Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analysed with traditional computing techniques.
Testing Big Data: Automated ETL Testing of HadoopBill Hayduk
Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
The document discusses big data testing and provides examples of big data projects. It defines big data as large volumes of data that are analyzed to make better decisions. Big data has three characteristics - volume, velocity, and variety. Traditional testing approaches are not suitable for big data, which requires new testing strategies and tools to handle the scale and complexity. Automating testing and understanding the data and processes are important for big data testing. The document outlines challenges and provides examples of batch and real-time systems as well as automation tools like Talend Open Studio.
Testistanbul 2016 - Keynote: "Performance Testing of Big Data" by Roland LeusdenTurkish Testing Board
Agile, Continous Intergration, DevOps, Big data are not longer buzzwords but part of the day today process of everyone working in software development and delivery. To cope with applications that need to be deployed in production almost the same moment they were created, software development has changed, impacting the way of working for everyone in the team. In this talk, Roland will discuss the challenges performance testers face with Big Data applications and how Architecture, Agile, Continous Intergration and DevOps come together to create solutions.
Iasi code camp 20 april 2013 testing big data-anca sfecla - embarcaderoCodecamp Romania
This document discusses testing of big data systems. It defines big data and its key characteristics of volume, variety, velocity and value. It provides examples of big data success stories and compares enterprise data warehouses to big data. The document outlines the typical architecture of a big data system including pre-processing, MapReduce, data extraction and loading. It identifies potential problems at each stage and for non-functional testing. Finally, it covers new challenges for testers in validating big data systems.
Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer
This presentation is an explanation of the research work done in the topic of 'hadoop integration into data warehouse architectures'. It explains where Hadoop fits into data warehouse architecture. Furthermore, it purposes a BI assessment model to determine the capability of current BI program and how to define roadmap for its maturity.
Testistanbul 2016 - Keynote: "Enterprise Challenges of Test Data" by Rex BlackTurkish Testing Board
If you are testing a simple mobile app, you may find it relatively easy to find representative test data. However, what if you are testing enterprise scale applications? In the enterprise data center, one hundred or more applications of various sizes, complexity, and criticality co-exist, operating on various data repositories, in some cases shared data repositories. In some cases, disparate data repositories hold related data, and the ability to test integration across applications that access these data sets is critical. In this keynote speech, Rex Black will talk about the challenges facing his clients as they deal with these testing problems. You’ll go away with a better understanding of the nature of the challenges, as well as ideas on how to handle them, grounded in lessons Rex has learned in over 30 years of software engineering and testing.
Hotel inspection data set analysis copySharon Moses
The document provides an analysis of a hotel inspection dataset using Apache Hadoop. It discusses storing large datasets using Hadoop Distributed File System (HDFS) and processing the data using MapReduce. The project involves installing Hadoop, moving the hotel inspection data to HDFS, creating tables in Hive to analyze the data, executing queries in Hive to generate reports on code violations by hotels. This allows analyzing big data to help hotels improve and comply with regulations.
Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analysed with traditional computing techniques.
Testing Big Data: Automated ETL Testing of HadoopBill Hayduk
Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
The document discusses big data testing and provides examples of big data projects. It defines big data as large volumes of data that are analyzed to make better decisions. Big data has three characteristics - volume, velocity, and variety. Traditional testing approaches are not suitable for big data, which requires new testing strategies and tools to handle the scale and complexity. Automating testing and understanding the data and processes are important for big data testing. The document outlines challenges and provides examples of batch and real-time systems as well as automation tools like Talend Open Studio.
Testistanbul 2016 - Keynote: "Performance Testing of Big Data" by Roland LeusdenTurkish Testing Board
Agile, Continous Intergration, DevOps, Big data are not longer buzzwords but part of the day today process of everyone working in software development and delivery. To cope with applications that need to be deployed in production almost the same moment they were created, software development has changed, impacting the way of working for everyone in the team. In this talk, Roland will discuss the challenges performance testers face with Big Data applications and how Architecture, Agile, Continous Intergration and DevOps come together to create solutions.
Iasi code camp 20 april 2013 testing big data-anca sfecla - embarcaderoCodecamp Romania
This document discusses testing of big data systems. It defines big data and its key characteristics of volume, variety, velocity and value. It provides examples of big data success stories and compares enterprise data warehouses to big data. The document outlines the typical architecture of a big data system including pre-processing, MapReduce, data extraction and loading. It identifies potential problems at each stage and for non-functional testing. Finally, it covers new challenges for testers in validating big data systems.
Hadoop Integration into Data Warehousing ArchitecturesHumza Naseer
This presentation is an explanation of the research work done in the topic of 'hadoop integration into data warehouse architectures'. It explains where Hadoop fits into data warehouse architecture. Furthermore, it purposes a BI assessment model to determine the capability of current BI program and how to define roadmap for its maturity.
Testistanbul 2016 - Keynote: "Enterprise Challenges of Test Data" by Rex BlackTurkish Testing Board
If you are testing a simple mobile app, you may find it relatively easy to find representative test data. However, what if you are testing enterprise scale applications? In the enterprise data center, one hundred or more applications of various sizes, complexity, and criticality co-exist, operating on various data repositories, in some cases shared data repositories. In some cases, disparate data repositories hold related data, and the ability to test integration across applications that access these data sets is critical. In this keynote speech, Rex Black will talk about the challenges facing his clients as they deal with these testing problems. You’ll go away with a better understanding of the nature of the challenges, as well as ideas on how to handle them, grounded in lessons Rex has learned in over 30 years of software engineering and testing.
Hotel inspection data set analysis copySharon Moses
The document provides an analysis of a hotel inspection dataset using Apache Hadoop. It discusses storing large datasets using Hadoop Distributed File System (HDFS) and processing the data using MapReduce. The project involves installing Hadoop, moving the hotel inspection data to HDFS, creating tables in Hive to analyze the data, executing queries in Hive to generate reports on code violations by hotels. This allows analyzing big data to help hotels improve and comply with regulations.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
This document discusses how Hadoop can be used in data warehousing and analytics. It begins with an overview of data warehousing and analytical databases. It then describes how organizations traditionally separate transactional and analytical systems and use extract, transform, load processes to move data between them. The document proposes using Hadoop as an alternative to traditional data warehousing architectures by using it for extraction, transformation, loading, and even serving analytical queries.
Testing the Data Warehouse―Big Data, Big ProblemsTechWell
Data warehouses have become a popular mechanism for collecting, organizing, and making information readily available for strategic decision making. The ability to review historical trends and monitor near real-time operational data has become a key competitive advantage for many organizations. Yet the methods for assuring the quality of these valuable assets are quite different from those of transactional systems. Ensuring that the appropriate testing is performed is a major challenge for many enterprises. Geoff Horne has led a number of data warehouse testing projects in both the telecommunications and ERP sectors. Join Geoff as he shares his approaches and experiences, focusing on the key “uniques” of data warehouse testing including methods for assuring data completeness, monitoring data transformations, and measuring quality. He also explores the opportunities for test automation as part of the data warehouse process, describing how it can be harnessed to streamline and minimize overhead.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
This presentation covers "Introduction to Big Data" for enterprises. It includes challenges and benefits of Big Data including transition plan based on few case studies.
Interactive SQL POC on Hadoop (Hive, Presto and Hive-on-Tez)Sudhir Mallem
Interactive SQL POC on Hadoop (Hive, Presto and Hive-on-Tez)
using storage: parquet, ORC, RCFile and Avro
Compression: snappy, zlib and default compression (gzip)
Presto is an open source distributed SQL query engine that allows interactive analysis of data across multiple data stores. At Facebook, Presto is used for ad-hoc queries of their Hadoop data warehouse, which processes trillions of rows and scans petabytes of data daily. Presto's low latency also makes it suitable for powering analytics in user-facing products. New features of Presto include improved SQL support, performance optimizations, and connectors to additional data sources like Redis and MongoDB.
The document discusses the evolution of big data architectures from Hadoop and MapReduce to Lambda architecture and stream processing frameworks. It notes the limitations of early frameworks in terms of latency, scalability, and fault tolerance. Modern architectures aim to unify batch and stream processing for low latency queries over both historical and new data.
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsCloudera, Inc.
The enormous legacy of EDW experience and best practices can be adapted to the unique capabilities of the Hadoop environment. In this webinar, in a point-counterpoint format, Dr. Kimball will describe standard data warehouse best practices including the identification of dimensions and facts, managing primary keys, and handling slowly changing dimensions (SCDs) and conformed dimensions. Eli Collins, Chief Technologist at Cloudera, will describe how each of these practices actually can be implemented in Hadoop.
The document discusses big data and Hadoop. It describes the three V's of big data - variety, volume, and velocity. It also discusses Hadoop components like HDFS, MapReduce, Pig, Hive, and YARN. Hadoop is a framework for storing and processing large datasets in a distributed computing environment. It allows for the ability to store and use all types of data at scale using commodity hardware.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
The document provides an agenda and slides for a presentation on architectural considerations for data warehousing with Hadoop. The presentation discusses typical data warehouse architectures and challenges, how Hadoop can complement existing architectures, and provides an example use case of implementing a data warehouse with Hadoop using the Movielens dataset. Key aspects covered include ingestion of data from various sources using tools like Flume and Sqoop, data modeling and storage formats in Hadoop, processing the data using tools like Hive and Spark, and exporting results to a data warehouse.
About Streaming Data Solutions for HadoopLynn Langit
This document discusses selecting the best approach for fast big data and streaming analytics projects. It describes key considerations for the architectural design phases such as scalable ingestion, real-time ETL, analytics, alerts and actions, and visualization. Component selection factors include the overall architecture, enterprise-grade streaming engine, ease of use and development, and management/DevOps. The document provides definitions of relevant technologies and compares representative solutions to help identify the best fit based on an organization's needs and skills.
Data Driving Yahoo Mail Growth and Evolution with a 50 PB Hadoop WarehouseDataWorks Summit
Yahoo Mail has 200+ million users a month and generates hundreds of terabytes of data per day, which continues to grow steadily. The nature of email messages has also evolved: for example, today the majority of them are generated by machines, consisting of newsletters, social media notifications, purchase invoices, travel bookings, and the like, which drove innovations in product development to help users organize their inboxes.
Since 2014, the Yahoo Mail Data Engineering team took on the task of revamping the Mail data warehouse and analytics infrastructure in order to drive the continued growth and evolution of Yahoo Mail. Along the way we have built a 50 PB Hadoop warehouse, and surrounding analytics and machine learning programs that have transformed the way data plays in Yahoo Mail.
In this session we will share our experience from this 3 year journey, from the system architecture, analytics systems built, to the learnings from development and drive for adoption.
This document introduces Amazon Aurora, a MySQL-compatible relational database developed by Amazon Web Services. It provides high performance and availability through a new architecture that leverages distributed storage across three Availability Zones with synchronous replication and automatic failover. Aurora is designed to be simple and cost-effective like open source databases while delivering the performance and availability of commercial databases through its unique storage technology and integration with other AWS services.
This presentation given by Flip Kromer and Huston Hoburg on March 24, 2014 at the MongoDB Meetup in Austin.
Vayacondios is a system we're building at Infochimps to gather metrics on highly complex systems and help humans make sense of their operation. You can think of it as a "data goes in, the right thing happens" machine: send in facts from anywhere about anything, and Vayacondios will promptly process and syndicate them to all consumers. Producers don't have to (or get to) worry about the needs of those who will use the data, or the details of transport, storage, filtering or anything else: the data will go where it needs to go. Each consumer, meanwhile, finds that everything they need to know is available to them, on the fly or on demand, without crufty adapters or extraneous dependencies. They don't have to (or get to) worry about the distribution of their sources, the tempo of update, or how the data came to be.
Vayacondios was built for our technical ops team to monitor all the databases and systems they superintend, but it suggests a better way to build database driven applications of any kind. The quiet tyranny of developing against a traditional database has left us with many bad habits: not duplicating data, using models that serve the query engine not the user, assembling application objects from raw parts on every page refresh. Combining streaming data processing systems with distributed datastores like MongoDB let you do your query on the way _in_ to the database -- any number of queries, decoupled, of any complexity or tempo. The resulting approach is simpler, fault-tolerant, and scales in terms of machines and developers. Most importantly, your data models are purely faithful to the needs of your application, uncontaminated by differing opinions of other consumers or by incidentals of the robots that gather and process and store the data.
The document describes Big Data Ready Enterprise (BDRE), an open source product that addresses common challenges in implementing and operating big data solutions at large scale. It provides out-of-the-box features to accelerate implementations using pluggable architecture, community support, and distribution compatibility. The document outlines BDRE's key benefits and capabilities for data ingestion, workflow automation, operational metadata management, and more. It also provides examples of BDRE implementations and screenshots of the product's interface.
This document discusses the challenges of big data and potential solutions. It addresses the volume, variety, and velocity of big data. Hadoop is presented as a solution for distributed storage and processing. The document also discusses data storage options, flexible resources like cloud computing, and achieving scalability and multi-platform support. Real-world examples of big data applications are provided.
Member privacy is of paramount importance to LinkedIn. The company must protect the sensitive data users provide. On the other hand, our members join LinkedIn to find each other, necessitating the sharing of certain data. This privacy paradox can only be addressed by giving users control over where and how their data is used. While this approach is extremely important, it also presents scaling challenges.
In this talk, we will discuss the challenges behind enforcing compliance at scale as well as LinkedIn's solution. Our comprehensive record-level offline compliance framework includes schema metadata tracking, alternate read-time views of the same dataset, physical purging of data on HDFS, and features for users to define custom filtering rules using SQL, assigning such customizations to specific datasets, groups of datasets, or use cases. We achieve this using many open-source projects like Hadoop, Hive, Gobblin, and Wherehows, as well as a homegrown data access layer called Dali. We also show how the same Hadoop-powered framework can be used for enforcing compliance on other stores like Pinot, Salesforce, and Espresso.
While there is no one-size fits all solution to guaranteeing user data privacy, this talk will provide a blueprint and concrete example of how to enforce compliance at scale, which we hope proves useful to organizations working to improve their privacy commitments. ISSAC BUENROSTRO, Staff Software Engineer, LinkedIn and ANTHONY HSU, Staff Software Engineer, LinkedIn
Tools and approaches for migrating big datasets to the cloudDataWorks Summit
This presentation describes the journey taken by the Hotels.com big data platform team when tasked with migrating big data sets and pipelines from on-premises clusters to cloud based platforms. We present two open source tools that we built to overcome the unexpected challenges we faced.
The first of these is Circus Train—a dataset replication tool that copies Hive tables between clusters and clouds. We will also discuss various other options for dataset replication and what unique features Circus train has. The second tool is Waggle Dance—a federated Hive query service that enables querying of data stored across multiple Hive metastores. We will demonstrate the differences between Waggle Dance and existing federated SQL query engine tools and what use cases it enables. Giving real world examples, we will describe how we've used these tools to successfully build a petabyte scale platform that is now also being used by other brands within the Expedia organisation. We focus on actual problems and solutions that have arisen in a huge, organically grown corporation, rather than idealised architectures.
Speakers
Adrian Woodhead, Principal Engineer, Hotels.com
Elliot West, Senior Engineer, Hotels.com
How to Test Big Data Systems | QualiTest GroupQualitest
Big Data is perceived as a huge amount of data and information but it is a lot more than this. Big Data may be said to be a whole set of approach, tools and methods of processing large volumes of unstructured as well as structured data. The three parameters on which Big Data is defined i.e. Volume, Variety and Velocity describes how you have to process an enormous amount of data in different formats at different rates.
QualiTest is the world’s second largest pure play software testing and QA company. Testing and QA is all that we do! visit us at: www.QualiTestGroup.com
Performance Testing of Big Data Applications - Impetus WebcastImpetus Technologies
Impetus webcast "Performance Testing of Big Data Applications" available at http://lf1.me/cqb/
This Impetus webcast talks about:
• A solution approach to measure performance and throughput of Big Data applications
• Insights into areas to focus for increasing the effectiveness of Big Data performance testing
• Tools available to address Big Data specific performance related challenges
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
This document discusses how Hadoop can be used in data warehousing and analytics. It begins with an overview of data warehousing and analytical databases. It then describes how organizations traditionally separate transactional and analytical systems and use extract, transform, load processes to move data between them. The document proposes using Hadoop as an alternative to traditional data warehousing architectures by using it for extraction, transformation, loading, and even serving analytical queries.
Testing the Data Warehouse―Big Data, Big ProblemsTechWell
Data warehouses have become a popular mechanism for collecting, organizing, and making information readily available for strategic decision making. The ability to review historical trends and monitor near real-time operational data has become a key competitive advantage for many organizations. Yet the methods for assuring the quality of these valuable assets are quite different from those of transactional systems. Ensuring that the appropriate testing is performed is a major challenge for many enterprises. Geoff Horne has led a number of data warehouse testing projects in both the telecommunications and ERP sectors. Join Geoff as he shares his approaches and experiences, focusing on the key “uniques” of data warehouse testing including methods for assuring data completeness, monitoring data transformations, and measuring quality. He also explores the opportunities for test automation as part of the data warehouse process, describing how it can be harnessed to streamline and minimize overhead.
Big Data Architecture Workshop - Vahid Amiridatastack
Big Data Architecture Workshop
This slide is about big data tools, thecnologies and layers that can be used in enterprise solutions.
TopHPC Conference
2019
This presentation covers "Introduction to Big Data" for enterprises. It includes challenges and benefits of Big Data including transition plan based on few case studies.
Interactive SQL POC on Hadoop (Hive, Presto and Hive-on-Tez)Sudhir Mallem
Interactive SQL POC on Hadoop (Hive, Presto and Hive-on-Tez)
using storage: parquet, ORC, RCFile and Avro
Compression: snappy, zlib and default compression (gzip)
Presto is an open source distributed SQL query engine that allows interactive analysis of data across multiple data stores. At Facebook, Presto is used for ad-hoc queries of their Hadoop data warehouse, which processes trillions of rows and scans petabytes of data daily. Presto's low latency also makes it suitable for powering analytics in user-facing products. New features of Presto include improved SQL support, performance optimizations, and connectors to additional data sources like Redis and MongoDB.
The document discusses the evolution of big data architectures from Hadoop and MapReduce to Lambda architecture and stream processing frameworks. It notes the limitations of early frameworks in terms of latency, scalability, and fault tolerance. Modern architectures aim to unify batch and stream processing for low latency queries over both historical and new data.
Best Practices for the Hadoop Data Warehouse: EDW 101 for Hadoop ProfessionalsCloudera, Inc.
The enormous legacy of EDW experience and best practices can be adapted to the unique capabilities of the Hadoop environment. In this webinar, in a point-counterpoint format, Dr. Kimball will describe standard data warehouse best practices including the identification of dimensions and facts, managing primary keys, and handling slowly changing dimensions (SCDs) and conformed dimensions. Eli Collins, Chief Technologist at Cloudera, will describe how each of these practices actually can be implemented in Hadoop.
The document discusses big data and Hadoop. It describes the three V's of big data - variety, volume, and velocity. It also discusses Hadoop components like HDFS, MapReduce, Pig, Hive, and YARN. Hadoop is a framework for storing and processing large datasets in a distributed computing environment. It allows for the ability to store and use all types of data at scale using commodity hardware.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
The document provides an agenda and slides for a presentation on architectural considerations for data warehousing with Hadoop. The presentation discusses typical data warehouse architectures and challenges, how Hadoop can complement existing architectures, and provides an example use case of implementing a data warehouse with Hadoop using the Movielens dataset. Key aspects covered include ingestion of data from various sources using tools like Flume and Sqoop, data modeling and storage formats in Hadoop, processing the data using tools like Hive and Spark, and exporting results to a data warehouse.
About Streaming Data Solutions for HadoopLynn Langit
This document discusses selecting the best approach for fast big data and streaming analytics projects. It describes key considerations for the architectural design phases such as scalable ingestion, real-time ETL, analytics, alerts and actions, and visualization. Component selection factors include the overall architecture, enterprise-grade streaming engine, ease of use and development, and management/DevOps. The document provides definitions of relevant technologies and compares representative solutions to help identify the best fit based on an organization's needs and skills.
Data Driving Yahoo Mail Growth and Evolution with a 50 PB Hadoop WarehouseDataWorks Summit
Yahoo Mail has 200+ million users a month and generates hundreds of terabytes of data per day, which continues to grow steadily. The nature of email messages has also evolved: for example, today the majority of them are generated by machines, consisting of newsletters, social media notifications, purchase invoices, travel bookings, and the like, which drove innovations in product development to help users organize their inboxes.
Since 2014, the Yahoo Mail Data Engineering team took on the task of revamping the Mail data warehouse and analytics infrastructure in order to drive the continued growth and evolution of Yahoo Mail. Along the way we have built a 50 PB Hadoop warehouse, and surrounding analytics and machine learning programs that have transformed the way data plays in Yahoo Mail.
In this session we will share our experience from this 3 year journey, from the system architecture, analytics systems built, to the learnings from development and drive for adoption.
This document introduces Amazon Aurora, a MySQL-compatible relational database developed by Amazon Web Services. It provides high performance and availability through a new architecture that leverages distributed storage across three Availability Zones with synchronous replication and automatic failover. Aurora is designed to be simple and cost-effective like open source databases while delivering the performance and availability of commercial databases through its unique storage technology and integration with other AWS services.
This presentation given by Flip Kromer and Huston Hoburg on March 24, 2014 at the MongoDB Meetup in Austin.
Vayacondios is a system we're building at Infochimps to gather metrics on highly complex systems and help humans make sense of their operation. You can think of it as a "data goes in, the right thing happens" machine: send in facts from anywhere about anything, and Vayacondios will promptly process and syndicate them to all consumers. Producers don't have to (or get to) worry about the needs of those who will use the data, or the details of transport, storage, filtering or anything else: the data will go where it needs to go. Each consumer, meanwhile, finds that everything they need to know is available to them, on the fly or on demand, without crufty adapters or extraneous dependencies. They don't have to (or get to) worry about the distribution of their sources, the tempo of update, or how the data came to be.
Vayacondios was built for our technical ops team to monitor all the databases and systems they superintend, but it suggests a better way to build database driven applications of any kind. The quiet tyranny of developing against a traditional database has left us with many bad habits: not duplicating data, using models that serve the query engine not the user, assembling application objects from raw parts on every page refresh. Combining streaming data processing systems with distributed datastores like MongoDB let you do your query on the way _in_ to the database -- any number of queries, decoupled, of any complexity or tempo. The resulting approach is simpler, fault-tolerant, and scales in terms of machines and developers. Most importantly, your data models are purely faithful to the needs of your application, uncontaminated by differing opinions of other consumers or by incidentals of the robots that gather and process and store the data.
The document describes Big Data Ready Enterprise (BDRE), an open source product that addresses common challenges in implementing and operating big data solutions at large scale. It provides out-of-the-box features to accelerate implementations using pluggable architecture, community support, and distribution compatibility. The document outlines BDRE's key benefits and capabilities for data ingestion, workflow automation, operational metadata management, and more. It also provides examples of BDRE implementations and screenshots of the product's interface.
This document discusses the challenges of big data and potential solutions. It addresses the volume, variety, and velocity of big data. Hadoop is presented as a solution for distributed storage and processing. The document also discusses data storage options, flexible resources like cloud computing, and achieving scalability and multi-platform support. Real-world examples of big data applications are provided.
Member privacy is of paramount importance to LinkedIn. The company must protect the sensitive data users provide. On the other hand, our members join LinkedIn to find each other, necessitating the sharing of certain data. This privacy paradox can only be addressed by giving users control over where and how their data is used. While this approach is extremely important, it also presents scaling challenges.
In this talk, we will discuss the challenges behind enforcing compliance at scale as well as LinkedIn's solution. Our comprehensive record-level offline compliance framework includes schema metadata tracking, alternate read-time views of the same dataset, physical purging of data on HDFS, and features for users to define custom filtering rules using SQL, assigning such customizations to specific datasets, groups of datasets, or use cases. We achieve this using many open-source projects like Hadoop, Hive, Gobblin, and Wherehows, as well as a homegrown data access layer called Dali. We also show how the same Hadoop-powered framework can be used for enforcing compliance on other stores like Pinot, Salesforce, and Espresso.
While there is no one-size fits all solution to guaranteeing user data privacy, this talk will provide a blueprint and concrete example of how to enforce compliance at scale, which we hope proves useful to organizations working to improve their privacy commitments. ISSAC BUENROSTRO, Staff Software Engineer, LinkedIn and ANTHONY HSU, Staff Software Engineer, LinkedIn
Tools and approaches for migrating big datasets to the cloudDataWorks Summit
This presentation describes the journey taken by the Hotels.com big data platform team when tasked with migrating big data sets and pipelines from on-premises clusters to cloud based platforms. We present two open source tools that we built to overcome the unexpected challenges we faced.
The first of these is Circus Train—a dataset replication tool that copies Hive tables between clusters and clouds. We will also discuss various other options for dataset replication and what unique features Circus train has. The second tool is Waggle Dance—a federated Hive query service that enables querying of data stored across multiple Hive metastores. We will demonstrate the differences between Waggle Dance and existing federated SQL query engine tools and what use cases it enables. Giving real world examples, we will describe how we've used these tools to successfully build a petabyte scale platform that is now also being used by other brands within the Expedia organisation. We focus on actual problems and solutions that have arisen in a huge, organically grown corporation, rather than idealised architectures.
Speakers
Adrian Woodhead, Principal Engineer, Hotels.com
Elliot West, Senior Engineer, Hotels.com
How to Test Big Data Systems | QualiTest GroupQualitest
Big Data is perceived as a huge amount of data and information but it is a lot more than this. Big Data may be said to be a whole set of approach, tools and methods of processing large volumes of unstructured as well as structured data. The three parameters on which Big Data is defined i.e. Volume, Variety and Velocity describes how you have to process an enormous amount of data in different formats at different rates.
QualiTest is the world’s second largest pure play software testing and QA company. Testing and QA is all that we do! visit us at: www.QualiTestGroup.com
Performance Testing of Big Data Applications - Impetus WebcastImpetus Technologies
Impetus webcast "Performance Testing of Big Data Applications" available at http://lf1.me/cqb/
This Impetus webcast talks about:
• A solution approach to measure performance and throughput of Big Data applications
• Insights into areas to focus for increasing the effectiveness of Big Data performance testing
• Tools available to address Big Data specific performance related challenges
Testing Big Data: Automated Testing of Hadoop with QuerySurgeRTTS
Are You Ready? Stepping Up To The Big Data Challenge In 2016 - Learn why Testing is pivotal to the success of your Big Data Strategy.
According to a new report by analyst firm IDG, 70% of enterprises have either deployed or are planning to deploy big data projects and programs this year due to the increase in the amount of data they need to manage.
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data and Hadoop. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data - all with one data testing tool.
The Age of Exabytes: Tools & Approaches for Managing Big DataReadWrite
This document discusses the rise of big data and innovations needed to manage exabytes of information. It covers developments in data storage from the chip level to large data centers. New databases like NoSQL are designed to handle non-relational and distributed data at web-scale. Real-time processing of big data requires distributed computing across many servers. Overall the document explores challenges posed by the growth of digital information and technological solutions emerging to process and analyze exabytes of data.
The document provides tips for effectively using LinkedIn for professional networking. It recommends establishing a full profile with a photo, skills, and work experience. It also suggests connecting with people from past jobs and groups, commenting on updates, and providing value to connections through recommendations and content sharing. The goal is to develop relationships and a "Return on Engagement" through an active online presence on LinkedIn.
Ravikanth Marpuri has over 9 years of experience in IT testing with expertise in ETL/BI/data migration testing. He has extensive experience testing tools such as IBM Datastage, Oracle Warehouse Builder, Cognos Data Manager, Informatica, SSIS, and SSAS. He has worked on projects in banking, finance, telecom, and energy domains. Ravikanth is skilled in test automation, performance testing, and leading teams of up to 20 members. He created a test automation framework that reduced regression testing time.
The document discusses testing challenges with big data and proposes the test pyramid approach. It provides an overview of big data concepts like Hadoop, HDFS, MapReduce and HBase. Testing big data is challenging due to long feedback cycles and data dependencies. The test pyramid advocates automating basic tests like unit tests while using techniques like sampling and test automation to counter long test cycles. It also provides a high level example of using a Hive query validator as part of the testing approach.
Data Driven Design - Web Analytics & Testing for Designers (Web Directions So...Luke Stevens
This document summarizes a presentation by Luke Stevens on data-driven web design. [1] It discusses how performance metrics can be used to test different design variations and identify the best performing one using A/B testing. A case study is presented of redesigning PhotographyBLOG.com where testing found user performance was identical between two designs. The presentation advocates for using tools like Google Website Optimizer to serve different CSS files and measure results in Google Analytics to objectively test web designs.
How to perform Analytics testing on your website and toolsMayank Solanki
Using Metrics to Investigate, Evaluate and Decide is a presentation about using web analytics and metrics to measure the performance of online marketing campaigns and websites. It discusses tools like Google Analytics, Facebook Insights, Twitter analytics and others that can track metrics like website traffic, social media engagement, search traffic and more. The goal is to set objectives for campaigns and sites, then use analytics to see what strategies and content are most effective at meeting those goals and driving actions like donations, purchases or signups. The presenters provide examples of how nonprofits can use various analytics to improve their online strategies.
The document summarizes an international conference on Islamic microfinance in Mauritius organized by the Center of Islamic Banking & Economics. It discusses technology for Islamic finance and whether it is time to invest. The conference will discuss Oracle and Islamic finance, the need to re-architect Islamic banking technology, and the benefits of a re-architected technology platform. It concludes by outlining how a re-architected platform can provide benefits such as being designed for universal banking, end-to-end integration, and regulatory and Shariah compliance.
Tackling non-determinism in Hadoop - Testing and debugging distributed system...Akihiro Suda
Earthquake is a tool for controlling non-determinism in distributed systems testing. It can schedule disk access, network packet, and function call events in a programmable way. Earthquake has found bugs in systems like ZooKeeper, YARN, and HDFS by reproducing rare non-deterministic execution paths. It achieves higher test coverage and bug reproduction rates compared to traditional testing approaches. Earthquake aims to be non-invasive, incrementally adoptable as understanding improves, and language independent.
Big Data: Working with Big SQL data from Spark Cynthia Saracco
Follow this hands-on lab to discover how Spark programmers can work with data managed by Big SQL, IBM's SQL interface for Hadoop. Examples use Scala and the Spark shell in a BigInsights 4.3 technical preview 2 environment.
This document discusses the challenges of user acceptance testing (UAT) for large-scale card portfolio migrations. It identifies three main challenge phases: scoping, test planning, and test execution. For each phase, it provides examples of common challenges like changing requirements, insufficient documentation, coordinating multiple test teams and environments, issues with migrated data quality, and tracking defects across releases. The document aims to increase awareness of typical UAT challenges to help create more comprehensive test strategies for migration projects.
This document discusses data warehousing over Hadoop and strategies for low-latency querying. It covers columnar formats like ORC and Parquet that provide optimizations for queries. It also discusses different query engines like Hive, Impala, Presto, and Spark SQL and their capabilities. The document notes that converting data to optimized formats during or after collection can help minimize latency conflicts between processing and query performance. Technologies like Sqoop, Hive ACID transactions, streaming ingest into Hive, and Flume can help convert or ingest data in optimized formats with lower latency.
This document provides an overview of Hive and HBase. It discusses how Hive allows SQL-like queries over data stored in Hadoop files, and how data can be loaded into and manipulated within Hive tables. It also describes HBase as a column-oriented NoSQL database built on Hadoop that allows for fast random reads and updates of large datasets. Key concepts covered include HiveQL, user defined functions, dynamic partitioning, and loading data. For HBase, it discusses tables, rows, columns, and cells as well as its architecture, client APIs, and integration with MapReduce.
This document provides user acceptance test scripts for cash management. It outlines the pre-requisite steps needed to set up cash management including:
1. Setting up cash management system parameters
2. Defining banks, bank branches, and bank accounts
3. Mapping bank statements to the required format
The test scripts then provide steps to test the key cash management processes like managing bank statements, bank reconciliation, inter-bank transfers, and petty cash funds. Each process is broken down into specific scenarios with expected results.
Learning Objectives - In this module, you will learn what is Pig, in which type of use case we can use Pig, how Pig is tightly coupled with MapReduce, and Pig Latin scripting.
From Relational Database Management to Big Data: Solutions for Data Migration...Cognizant
Big data migration testing for transferring relational database management files is a very time-consuming, high-compute task; we offer a hands-on, detailed framework for data validation in an open source (Hadoop) environment incorporating Amazon Web Services (AWS) for cloud capacity, S3 (Simple Storage Service) and EMR (Elastic MapReduce), Hive tables, Sqoop tools, PIG scripting and Jenkins Slave Machines.
Google Data Engineering Cheatsheet provides an overview of key concepts in data engineering including data collection, transformation, visualization, and machine learning. It discusses Google Cloud Platform services for data engineering like Compute, Storage, Big Data, and Machine Learning. The document also summarizes concepts like Hadoop, HDFS, MapReduce, Spark, data warehouses, streaming data, and the Google Cloud monitoring and access management tools.
This document provides an overview of Google Cloud Platform (GCP) data engineering concepts and services. It discusses key data engineering roles and responsibilities, as well as GCP services for compute, storage, databases, analytics, and monitoring. Specific services covered include Compute Engine, Kubernetes Engine, App Engine, Cloud Storage, Cloud SQL, Cloud Spanner, BigTable, and BigQuery. The document also provides primers on Hadoop, Spark, data modeling best practices, and security and access controls.
Strengthening the Quality of Big Data ImplementationsCognizant
The increasing volume of big data has also increased the need to assure the quality of these critical assets. It is now essential for organizations to deploy customizable testing platforms and frameworks. An open, robust validation framework like Hadoop can significantly improve high-volume big data testing.
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.
The document discusses Project Voldemort, a distributed key-value storage system developed at LinkedIn. It provides an overview of Voldemort's motivation and features, including high availability, horizontal scalability, and consistency guarantees. It also describes LinkedIn's use of Voldemort and Hadoop for applications like event logging, online lookups, and batch processing of large datasets.
This document discusses performance analysis of Hadoop applications on heterogeneous systems. It analyzes the throughput and processing rate of Hadoop jobs with variable sized input files on Intel Core 2 Duo and Intel Core i3 systems. The experiments show that throughput increases with larger single input files compared to multiple smaller files of the same total size. Processing records was also faster on the Intel Core i3 system compared to the Intel Core 2 Duo system. Ensuring proper data quality testing and tuning Hadoop parameters can help optimize performance.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
Hadoop is an open source implementation of the MapReduce Framework in the realm of distributed processing.
A Hadoop cluster is a unique type of computational cluster designed for storing and analyzing large datasets
across cluster of workstations. To handle massive scale data, Hadoop exploits the Hadoop Distributed File
System termed as HDFS. The HDFS similar to most distributed file systems share a familiar problem on data
sharing and availability among compute nodes, often which leads to decrease in performance. This paper is an
experimental evaluation of Hadoop's computing performance which is made by designing a rack aware cluster
that utilizes the Hadoop’s default block placement policy to improve data availability. Additionally, an adaptive
data replication scheme that relies on access count prediction using Langrange’s interpolation is adapted to fit
the scenario. To prove, experiments were conducted on a rack aware cluster setup which significantly reduced
the task completion time, but once the volume of the data being processed increases there is a considerable
cutback in computational speeds due to update cost. Further the threshold level for balance between the update
cost and replication factor is identified and presented graphically.
Harnessing Hadoop: Understanding the Big Data Processing Options for Optimizi...Cognizant
This document discusses big data processing options for optimizing analytical workloads using Hadoop. It provides an overview of Hadoop and its core components HDFS and MapReduce. It also discusses the Hadoop ecosystem including tools like Pig, Hive, HBase, and ecosystem projects. The document compares building Hadoop clusters to using appliances or Hadoop-as-a-Service offerings. It also briefly mentions some Hadoop competitors for real-time processing use cases.
Finding URL pattern with MapReduce and Apache HadoopNushrat
The document discusses analyzing web log data from the 1998 FIFA World Cup website using Apache Hadoop's MapReduce framework on a cluster of 6 machines. The goal is to find the most frequently accessed IP address and specific URL patterns. The experiment counted the number of days each IP address most frequently accessed a URL. It found which IP addresses accessed the site most often and what types of content (e.g. images, text) were most frequently requested over time.
With data flowing from different mediums (RDBMS, SocialMedium, Legacy files),one of the efficacious mean for processing huge data is through Big Data, where Data Lake plays a critical role for storing Structured/Semi Structured and Unstructured Data. I have tried to give you a glimpse of , how testing of Data Lake is generally performed, what are the different types and approaches we follow.
Load balancing is an approach to distributing work units across multiple servers in a distributed system. The load balancer acts as a reverse proxy to distribute network or application traffic evenly among servers. It allocates the first task to the first server, second task to the second server, and so on, to balance loads. Load balancing provides security, protects applications from threats using a web application firewall, authenticates user access, protects against DDoS attacks, and improves performance by reducing load on servers and optimizing traffic.
The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. It was developed to support distributed processing of large datasets. The document provides an overview of Hadoop architecture including HDFS, MapReduce and key components like NameNode, DataNode, JobTracker and TaskTracker. It also discusses Hadoop history, features, use cases and configuration.
1. The document describes building an analytical platform for a retailer by using open source tools R and RStudio along with SAP Sybase IQ database.
2. Key aspects included setting up SAP Sybase IQ as a column-store database for storage and querying of data, implementing R and RStudio for statistical analysis, and automating running of statistical models on new data.
3. The solution provided a low-cost platform capable of rapid prototyping of analytical models and production use for predictive analytics.
1. The customer asked the author to build an analytical platform to store data in a database and perform statistical analysis from a front-end interface.
2. The author chose an SAP Sybase IQ column-store database to store data, the open-source R programming language to perform statistical analysis, and RStudio as the front-end interface.
3. The solution provided a simple way to load and query large amounts of data, automated running of statistical models, and could be deployed in the cloud.
Similar to Big Data Testing Approach - Rohit Kharabe (20)
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
Flutter vs. React Native: A Detailed Comparison for App Development in 2024dhavalvaghelanectarb
Choosing the right framework for your cross-platform mobile app can be a tough decision. Both Flutter and React Native offer compelling features and have earned their place in the development world. Here is a detailed comparison to help you weigh their strengths and weaknesses. Here are the pros and cons of developing mobile apps in React Native vs Flutter.
What is Continuous Testing in DevOps - A Definitive Guide.pdfkalichargn70th171
Once an overlooked aspect, continuous testing has become indispensable for enterprises striving to accelerate application delivery and reduce business impacts. According to a Statista report, 31.3% of global enterprises have embraced continuous integration and deployment within their DevOps, signaling a pervasive trend toward hastening release cycles.
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...kalichargn70th171
Visual testing plays a vital role in ensuring that software products meet the aesthetic requirements specified by clients in functional and non-functional specifications. In today's highly competitive digital landscape, users expect a seamless and visually appealing online experience. Visual testing, also known as automated UI testing or visual regression testing, verifies the accuracy of the visual elements that users interact with.
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
DECODING JAVA THREAD DUMPS: MASTER THE ART OF ANALYSISTier1 app
Are you ready to unlock the secrets hidden within Java thread dumps? Join us for a hands-on session where we'll delve into effective troubleshooting patterns to swiftly identify the root causes of production problems. Discover the right tools, techniques, and best practices while exploring *real-world case studies of major outages* in Fortune 500 enterprises. Engage in interactive lab exercises where you'll have the opportunity to troubleshoot thread dumps and uncover performance issues firsthand. Join us and become a master of Java thread dump analysis!
The Role of DevOps in Digital Transformation.pdfmohitd6
DevOps plays a crucial role in driving digital transformation by fostering a collaborative culture between development and operations teams. This approach enhances the speed and efficiency of software delivery, ensuring quicker deployment of new features and updates. DevOps practices like continuous integration and continuous delivery (CI/CD) streamline workflows, reduce manual errors, and increase the overall reliability of software systems. By leveraging automation and monitoring tools, organizations can improve system stability, enhance customer experiences, and maintain a competitive edge. Ultimately, DevOps is pivotal in enabling businesses to innovate rapidly, respond to market changes, and achieve their digital transformation goals.
Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio, Inc.
Alluxio Webinar
June. 18, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jianjian Xie (Staff Software Engineer, Alluxio)
As Trino users increasingly rely on cloud object storage for retrieving data, speed and cloud cost have become major challenges. The separation of compute and storage creates latency challenges when querying datasets; scanning data between storage and compute tiers becomes I/O bound. On the other hand, cloud API costs related to GET/LIST operations and cross-region data transfer add up quickly.
The newly introduced Trino file system cache by Alluxio aims to overcome the above challenges. In this session, Jianjian will dive into Trino data caching strategies, the latest test results, and discuss the multi-level caching architecture. This architecture makes Trino 10x faster for data lakes of any scale, from GB to EB.
What you will learn:
- Challenges relating to the speed and costs of running Trino in the cloud
- The new Trino file system cache feature overview, including the latest development status and test results
- A multi-level cache framework for maximized speed, including Trino file system cache and Alluxio distributed cache
- Real-world cases, including a large online payment firm and a top ridesharing company
- The future roadmap of Trino file system cache and Trino-Alluxio integration
Superpower Your Apache Kafka Applications Development with Complementary Open...Paul Brebner
Kafka Summit talk (Bangalore, India, May 2, 2024, https://events.bizzabo.com/573863/agenda/session/1300469 )
Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
Photoshop Tutorial for Beginners (2024 Edition)alowpalsadig
Photoshop Tutorial for Beginners (2024 Edition)
Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."
Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
Photoshop Tutorial for Beginners (2024 Edition)Explore the evolution of programming and software development and design in 2024. Discover emerging trends shaping the future of coding in our insightful analysis."Here's an overview:Introduction: The Evolution of Programming and Software DevelopmentThe Rise of Artificial Intelligence and Machine Learning in CodingAdopting Low-Code and No-Code PlatformsQuantum Computing: Entering the Software Development MainstreamIntegration of DevOps with Machine Learning: MLOpsAdvancements in Cybersecurity PracticesThe Growth of Edge ComputingEmerging Programming Languages and FrameworksSoftware Development Ethics and AI RegulationSustainability in Software EngineeringThe Future Workforce: Remote and Distributed TeamsConclusion: Adapting to the Changing Software Development LandscapeIntroduction: The Evolution of Programming and Software Development
The importance of developing and designing programming in 2024
Programming design and development represents a vital step in keeping pace with technological advancements and meeting ever-changing market needs. This course is intended for anyone who wants to understand the fundamental importance of software development and design, whether you are a beginner or a professional seeking to update your knowledge.
Course objectives:
1. **Learn about the basics of software development:
- Understanding software development processes and tools.
- Identify the role of programmers and designers in software projects.
2. Understanding the software design process:
- Learn about the principles of good software design.
- Discussing common design patterns such as Object-Oriented Design.
3. The importance of user experience (UX) in modern software:
- Explore how user experience can improve software acceptance and usability.
- Tools and techniques to analyze and improve user experience.
4. Increase efficiency and productivity through modern development tools:
- Access to the latest programming tools and languages used in the industry.
- Study live examples of applications
How GenAI Can Improve Supplier Performance Management.pdfZycus
Data Collection and Analysis with GenAI enables organizations to gather, analyze, and visualize vast amounts of supplier data, identifying key performance indicators and trends. Predictive analytics forecast future supplier performance, mitigating risks and seizing opportunities. Supplier segmentation allows for tailored management strategies, optimizing resource allocation. Automated scorecards and reporting provide real-time insights, enhancing transparency and tracking progress. Collaboration is fostered through GenAI-powered platforms, driving continuous improvement. NLP analyzes unstructured feedback, uncovering deeper insights into supplier relationships. Simulation and scenario planning tools anticipate supply chain disruptions, supporting informed decision-making. Integration with existing systems enhances data accuracy and consistency. McKinsey estimates GenAI could deliver $2.6 trillion to $4.4 trillion in economic benefits annually across industries, revolutionizing procurement processes and delivering significant ROI.
Orca: Nocode Graphical Editor for Container OrchestrationPedro J. Molina
Tool demo on CEDI/SISTEDES/JISBD2024 at A Coruña, Spain. 2024.06.18
"Orca: Nocode Graphical Editor for Container Orchestration"
by Pedro J. Molina PhD. from Metadev
Hands-on with Apache Druid: Installation & Data Ingestion StepsservicesNitor
Supercharge your analytics workflow with https://bityl.co/Qcuk Apache Druid's real-time capabilities and seamless Kafka integration. Learn about it in just 14 steps.
2. Data Source
(RDBMS,
web logs,
social
media, etc.)
Data Lake
(HDFS
Cluster)
Refined Data
(HDFS
Cluster)
Enterprise
Data
WareHouse
(Data
Factory for
Query and
Analysis)
BI
Data Stage
Validation
1
“MapReduce” Process Validation(Clustering,
Data aggregation or segregation rules,
KeyValue pairs Validation)
2
Algorithm / Output
Validation
3
Report / Business Requirements
Validation4
Python
Data
Integration
and
Refinement
Data
Synthesis
Structured
Data
ETL
Data
Preparation
2
ETL Process
Validation
3. Data Staging Validation
First stage involves process validation :
1) Data from various source like RDBMS, weblogs, social media, etc. should be validated to make
sure that correct data is pulled into system
2) Comparing source data with the data pushed into the Hadoop system to make sure they match
3) Verify the right data is extracted and loaded into the correct HDFS location
4) Tools like Talend, Datameer can be used for data staging validation
3
4. "MapReduce" Validation
The second step is a validation of "MapReduce". In this stage, the tester verifies the business logic
validation on every node and then validating them after running against multiple nodes, ensuring that
the :
1) Map Reduce process works correctly
2) Data aggregation or segregation rules are implemented on the data
3) Key value pairs are generated
4) Validating the data after Map Reduce process
Big data tools used for MapReduce are Hadoop, Spark, Hive, Pig, Cascading, Oozie, Kafka, S4, MapR, and
Flume
4
5. 1) MRUnit - Unit testing for MR jobs :
MRUnit lets users define key-value pairs to be given to map and reduce functions, and it tests that the correct
key-value pairs are emitted from each of these functions
2) Local job runner testing - Running MR jobs on a single machine in a single JVM :
The local job runner lets you run Hadoop on a local machine, in one JVM, making MR jobs a little easier to debug
in the case of a job failing
3) Pseudo-distributed testing - Running MR jobs on a single machine using Hadoop :
A pseudo-distributed cluster is composed of a single machine running all Hadoop daemons. This cluster is still
relatively easy to manage (though harder than local job runner) and tests integration with Hadoop better than
the local job runner does
4) Full integration testing - Running MR jobs on a QA cluster
Used to test your MR jobs by running them on a QA cluster composed of at least a few machines. By running
your MR jobs on a QA cluster, you will be testing all aspects of both your job and its integration with Hadoop.
Testing Methods for Hadoop MapReduce Processes
5
6. Output Validation Phase
The third stage of Big Data testing is the output validation process. The output data files are generated
and ready to be moved to an EDW (Enterprise Data Warehouse) or any other system based on the
requirement.
Activities in third stage includes :
1) To check the transformation rules are correctly applied
2) To check the data integrity and successful data load into the target system
3) To check that there is no data corruption by comparing the target data with the HDFS file system data
6
7. Report Validation
In this final phase of testing, reports are checked against target data warehouse. Report data should match
with target data warehouse.
7
9. 1) Apache Flume Enterprises -
Used to ingest log files from application servers or other systems
2) Apache Sqoop -
Used to import data from a MySQL or Oracle database into HDFS
3) Hive -
Hive is a tool that structures data in Hadoop into the form of relational-like tables and allows queries
using a subset of SQL. An Infrastructure which provides us with various tools for easy extraction,
transformation and loading of data. Hive allows its users to embed customized mappers and reducers.
4) Pig Apache -
Pig provides an alternative language to SQL,called Pig Latin, for querying data stored in HDFS
5) NoSQL -
This enables them to store and retrieve data with all the features of the NoSQL database. Some NoSQL
databases available are CouchDB, MongoDB, Cassandra, Redis, ZooKeeper and Hbase.
Tools for validating pre-Hadoop processing
9
10. 10
6) MapR-FS is a POSIX file system that provides distributed, reliable, high performance, scalable, and
full read/write data storage for the MapR Converged Data Platform. MapR-FS supports the HDFS API,
fast NFS access, access controls (MapR ACEs), and transparent data compression.
7) Apache Spark is an open source big data processing framework built around speed, ease of use, and
sophisticated analytics. Spark gives us a comprehensive, unified framework to manage big data
processing requirements with a variety of data sets that are diverse in nature (text data, graph data etc)
as well as the source of data (batch v. real-time streaming data.
8) HBase is a column-oriented database management system that runs on top of HDFS. It is well suited
for sparse data sets, which are common in many big data use cases.
9) Lucene/Solr -
The most popular open source tool for indexing large blocks of unstructured text is Lucene
11. Performance Testing
Performance Testing for Big Data includes :
Data ingestion and Throughout: In this stage, the tester verifies how the fast system can
consume data from various data source. Testing involves identifying different message that the
queue can process in a given time frame. It also includes how quickly data can be inserted into
underlying data store for example insertion rate into a Mongo and Cassandra database.
Data Processing: It involves verifying the speed with which the queries or map reduce jobs are
executed. It also includes testing the data processing in isolation when the underlying data store
is populated within the data sets. For example running Map Reduce jobs on the underlying
HDFS
Sub-Component Performance: These systems are made up of multiple components, and it is
essential to test each of these components in isolation. For example, how quickly message is
indexed and consumed, mapreduce jobs, query performance, search, etc.
11
12. Parameters for Performance Testing
Various parameters to be verified for performance testing are :
Data Storage: How data is stored in different nodes
Commit logs: How large the commit log is allowed to grow
Concurrency: How many threads can perform write and read operation
Caching: Tune the cache setting "row cache" and "key cache."
Timeouts: Values for connection timeout, query timeout, etc.
JVM Parameters: Heap size, GC collection algorithms, etc.
Map reduce performance: Sorts, merge, etc.
Message queue: Message rate, size, etc.
12
13. Installation Testing - Installation testing is a kind of quality assurance work that focuses on
what customers will need to do to install and set up the new big data application successfully.
The testing process may involve full, partial or upgrades install/uninstall processes.
End-to-End Test Environment Operational Testing - To provide complete end to end testing for
application . To verify the process right from the first phase ,i.e., when data gets fetched into
data lake till the last phase ,i.e., output validation of machine learning algorithms
Backup and Restore Testing – To make ensure that back up nodes are working correctly or not.
Cluster is properly configured or not for backup nodes. All the nodes in cluster are interacting
with each other properly as expected
Fail over Testing - To make sure that in the case of failure, back up nodes come into action and
the system will resume its work as before with the help of backup node without any much
degradation in performance
Recovery Testing – To make sure that information can be recovered easily from backup node in
case of failure of data node
13
Editor's Notes
A data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data. Each data element in a lake is assigned a unique identifier and tagged with a set of extended metadata tags. When a business question arises, the data lake can be queried for relevant data, and that smaller set of data can then be analyzed to help answer the question.
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data into the Hadoop Distributed File System (HDFS).