Originally Published on Oct 27, 2014
An overview of IBM's audited Hadoop-DS comparing IBM Big SQL, Cloudera Impala and Hortonworks Hive for performance and SQL compatibility. For more information, visit: http://www-01.ibm.com/software/data/infosphere/hadoop/
Big SQL provides an SQL interface for querying data stored in Hadoop. It uses a new query engine derived from IBM's database technology to optimize queries. Big SQL allows SQL users easy access to Hadoop data through familiar SQL tools and syntax. It supports creating and loading tables, standard SQL queries including joins and subqueries, and integrating Hadoop data with external databases in a single query.
SQL on Hadoop: Defining the New Generation of Analytic SQL DatabasesOReillyStrata
The document summarizes Carl Steinbach's presentation on SQL on Hadoop. It discusses how earlier systems like Hive had limitations for analytics workloads due to using MapReduce. A new architecture runs PostgreSQL on worker nodes co-located with HDFS data to enable push-down query processing for better performance. Citus Data's CitusDB product was presented as an example of this architecture, allowing SQL queries to efficiently analyze petabytes of data stored in HDFS.
The document summarizes several popular options for SQL on Hadoop including Hive, SparkSQL, Drill, HAWQ, Phoenix, Trafodion, and Splice Machine. Each option is reviewed in terms of key features, architecture, usage patterns, and strengths/limitations. While all aim to enable SQL querying of Hadoop data, they differ in support for transactions, latency, data types, and whether they are native to Hadoop or require separate processes. Hive and SparkSQL are best for batch jobs while Drill, HAWQ and Splice Machine provide lower latency but with different integration models and capabilities.
Big SQL Competitive Summary - Vendor LandscapeNicolas Morales
IBM's Big SQL is their SQL for Hadoop product that allows users to run SQL queries on Hadoop data. It uses the Hive metastore to catalog table definitions and shares data logic with Hive. Big SQL is architected for high performance with a massively parallel processing (MPP) runtime and runs directly on the Hadoop cluster with no proprietary storage formats required. The document compares Big SQL to other SQL on Hadoop solutions and outlines its performance and architectural advantages.
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
This document discusses Big SQL 3.0, a SQL query engine for analyzing large datasets in Hadoop. Big SQL 3.0 leverages an advanced SQL compiler and native runtime to provide high performance SQL queries without requiring data to be copied. It supports features like stored procedures, functions, and comprehensive security including row and column level access controls. The document provides an overview of Big SQL 3.0's architecture and how it integrates with and utilizes existing Hadoop components to analyze data stored in HDFS and Hive.
Conduct data discovery or rapid BI prototyping without becoming a Hadoop expert by analyzing big data with standard BI tools, including Cognos. View the webinar video recording and download this deck: http://www.senturus.com/resources/running-cognos-on-hadoop/.
See a cost effective, scalable solution that does not have the barriers to entry common with big data applications. The webinar explains: 1) use cases for Hadoop, 2) pros and cons of different visualization tools and their integration with Hadoop and 3) a demonstration of BigInsights, IBM’s solution.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
This document discusses the challenges of implementing SQL on Hadoop. It begins by explaining why SQL is useful for Hadoop, as it provides a familiar syntax and separates querying logic from implementation. However, Hadoop's architecture presents challenges for matching the functionality of a traditional data warehouse. Key challenges discussed include random data placement in HDFS, limitations on indexing due to this random placement, difficulties performing joins without data colocation, and limitations of existing "indexing" approaches in systems like Hive. The document explores approaches some systems are taking to address these issues.
Big SQL provides an SQL interface for querying data stored in Hadoop. It uses a new query engine derived from IBM's database technology to optimize queries. Big SQL allows SQL users easy access to Hadoop data through familiar SQL tools and syntax. It supports creating and loading tables, standard SQL queries including joins and subqueries, and integrating Hadoop data with external databases in a single query.
SQL on Hadoop: Defining the New Generation of Analytic SQL DatabasesOReillyStrata
The document summarizes Carl Steinbach's presentation on SQL on Hadoop. It discusses how earlier systems like Hive had limitations for analytics workloads due to using MapReduce. A new architecture runs PostgreSQL on worker nodes co-located with HDFS data to enable push-down query processing for better performance. Citus Data's CitusDB product was presented as an example of this architecture, allowing SQL queries to efficiently analyze petabytes of data stored in HDFS.
The document summarizes several popular options for SQL on Hadoop including Hive, SparkSQL, Drill, HAWQ, Phoenix, Trafodion, and Splice Machine. Each option is reviewed in terms of key features, architecture, usage patterns, and strengths/limitations. While all aim to enable SQL querying of Hadoop data, they differ in support for transactions, latency, data types, and whether they are native to Hadoop or require separate processes. Hive and SparkSQL are best for batch jobs while Drill, HAWQ and Splice Machine provide lower latency but with different integration models and capabilities.
Big SQL Competitive Summary - Vendor LandscapeNicolas Morales
IBM's Big SQL is their SQL for Hadoop product that allows users to run SQL queries on Hadoop data. It uses the Hive metastore to catalog table definitions and shares data logic with Hive. Big SQL is architected for high performance with a massively parallel processing (MPP) runtime and runs directly on the Hadoop cluster with no proprietary storage formats required. The document compares Big SQL to other SQL on Hadoop solutions and outlines its performance and architectural advantages.
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
This document discusses Big SQL 3.0, a SQL query engine for analyzing large datasets in Hadoop. Big SQL 3.0 leverages an advanced SQL compiler and native runtime to provide high performance SQL queries without requiring data to be copied. It supports features like stored procedures, functions, and comprehensive security including row and column level access controls. The document provides an overview of Big SQL 3.0's architecture and how it integrates with and utilizes existing Hadoop components to analyze data stored in HDFS and Hive.
Conduct data discovery or rapid BI prototyping without becoming a Hadoop expert by analyzing big data with standard BI tools, including Cognos. View the webinar video recording and download this deck: http://www.senturus.com/resources/running-cognos-on-hadoop/.
See a cost effective, scalable solution that does not have the barriers to entry common with big data applications. The webinar explains: 1) use cases for Hadoop, 2) pros and cons of different visualization tools and their integration with Hadoop and 3) a demonstration of BigInsights, IBM’s solution.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
This document discusses the challenges of implementing SQL on Hadoop. It begins by explaining why SQL is useful for Hadoop, as it provides a familiar syntax and separates querying logic from implementation. However, Hadoop's architecture presents challenges for matching the functionality of a traditional data warehouse. Key challenges discussed include random data placement in HDFS, limitations on indexing due to this random placement, difficulties performing joins without data colocation, and limitations of existing "indexing" approaches in systems like Hive. The document explores approaches some systems are taking to address these issues.
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...Cynthia Saracco
Got Big Data? Then check out what Big SQL can do for you . . . . Learn how IBM's industry-standard SQL interface enables you to leverage your existing SQL skills to query, analyze, and manipulate data managed in an Apache Hadoop environment on cloud or on premise. This quick technical tour is filled with practical examples designed to get you started working with Big SQL in no time. Specifically, you'll learn how to create Big SQL tables over Hadoop data in HDFS, Hive, or HBase; populate Big SQL tables with data from HDFS, a remote file system, or a remote RDBMS; execute simple and complex Big SQL queries; work with non-traditional data formats and more. These charts are for session ALB-3663 at the IBM World of Watson 2016 conference.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
Big SQL 3.0: Datawarehouse-grade Performance on Hadoop - At last!Nicolas Morales
This document provides an overview of IBM's Big SQL product for running SQL queries on Hadoop data. It discusses how Big SQL uses a massively parallel processing (MPP) architecture to replace MapReduce for improved performance. Big SQL nodes run directly on the Hadoop cluster to process data locally. The document highlights Big SQL's full SQL query capabilities and support for analytic functions. It also notes how Big SQL leverages the existing Hive metadata and is designed to integrate with the broader Hadoop ecosystem.
The Cloudera Impala project is pioneering the next generation of Hadoop capabilities: the convergence of interactive SQL queries with the capacity, scalability, and flexibility of a Hadoop cluster. In this webinar, join Cloudera and MicroStrategy to learn how Impala works, how it is uniquely architected to provide an interactive SQL experience native to Hadoop, and how you can leverage the power of MicroStrategy 9.3.1 to easily tap into more data and make new discoveries.
Big Data: SQL query federation for Hadoop and RDBMS dataCynthia Saracco
Explore query federation capabilities in IBM Big SQL, which enables programmers to transparently join Hadoop data with relational database management (RDBMS) data.
The document discusses moving from traditional ETL processes to "analytics with no ETL" using Hadoop. It describes how Hadoop currently supports some ETL functions by storing raw and transformed data together. However, this still requires periodic loading of new data. The vision is to support complex schemas, perform background format conversion incrementally, and enable schema inference and evolution to allow analyzing data as it arrives without explicit ETL steps. This would provide an up-to-date, performant single view of all data.
This document provides an overview of a SQL-on-Hadoop tutorial. It introduces the presenters and discusses why SQL is important for Hadoop, as MapReduce is not optimal for all use cases. It also notes that while the database community knows how to efficiently process data, SQL-on-Hadoop systems face challenges due to the limitations of running on top of HDFS and Hadoop ecosystems. The tutorial outline covers SQL-on-Hadoop technologies like storage formats, runtime engines, and query optimization.
This document discusses Tableau's role in big data architectures and its integration with Hadoop. It outlines different workload categories for business intelligence and their considerations for Tableau. Three integration models are described: isolated exploration, live interactive query, and integrated advanced analytics. Capability models are presented for each integration approach regarding suitability for Hadoop. Finally, architecture patterns are shown for isolated exploration, live interactive querying, and an integrated advanced analytics platform using Tableau and Hadoop.
Big SQL 3.0 is a SQL-on-Hadoop solution that provides SQL access to data stored in Hadoop. It uses the same table definitions and metadata as Hive, accessing data already stored in Hadoop without requiring a proprietary format. Big SQL extends Hive's syntax with features like primary keys and foreign keys. Tables in Big SQL and Hive represent views of data stored in Hadoop rather than separate storage structures.
A powerful feature in Postgres called Foreign Data Wrappers lets end users integrate data from MongoDB, Hadoop and other solutions with their Postgres database and leverage it as single, seamless database using SQL.
Use of these features has skyrocketed since EDB released to the open source community new FDWs for MongoDB, Hadoop and MySQL that support both read and write capabilities. Now greatly enhanced, FDWs enable integrating data across disparate deployments to support new workloads, expanded development goals and harvesting greater value from data.
Learn more about Foreign Data Wrappers (FDWs) and Postgres with Sameer Kumar, Database Consultant from Ashnik.
Target Audience: This presentation is intended for IT Professionals seeking to do more with Postgres in his every day projects and build new applications.
Hadoop Architecture Options for Existing Enterprise DataWarehouseAsis Mohanty
The document discusses various options for integrating Hadoop with an existing enterprise data warehouse (EDW). It describes 7 options: 1) Teradata Unified Data Architecture, 2) using an existing EDW with a new Apache Hadoop cluster, 3) using an existing EDW with a new Cloudera Hadoop cluster, 4) using an existing EDW with a new Hortonworks Hadoop cluster, 5) IBM PureData, 6) Oracle Big Data Appliance, and 7) SAP HANA for Hadoop integration. Each option involves using the existing EDW for structured data and Hadoop for unstructured/semi-structured data, with analytics capabilities available across both platforms.
YARN: the Key to overcoming the challenges of broad-based Hadoop AdoptionDataWorks Summit
The document discusses how YARN (Yet Another Resource Negotiator) in Hadoop 2.0 overcomes challenges to broad adoption of Hadoop by allowing applications to directly operate on Hadoop without needing to generate MapReduce code. It introduces RedPoint as a YARN-compliant data management tool that brings together big and traditional data for data integration, quality, and governance tasks in a graphical user interface without coding. RedPoint executes directly on Hadoop using YARN to make data management easier, faster and lower cost compared to previous MapReduce-based options.
The proliferation of different database systems has led to data silos and inconsistencies. In the past, there was a single data warehouse but now there are many types of databases optimized for different purposes like transactions, analytics, streaming, etc. This can be addressed by having a common platform like Hadoop that supports different database types to reduce silos and enable data integration. However, more integration tools are still needed to fully realize this vision.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Benchmarking Hadoop - Which hadoop sql engine leads the herdGord Sissons
Stewart Tate (tates@us.ibm.com), a key architect behind the industry's first ever Hadoop-DS benchmark at 30TB scale, describes the benchmark and comparative testing between IBM, Cloudera Impala and Hortonworks Hive
The Most Trusted In-Memory database in the world- AltibaseAltibase
This document provides an overview of an in-memory database company and its product capabilities. It discusses the company's history and growth, the changing data landscape driving demand for real-time analytics, and how the company's in-memory and hybrid database technologies provide extremely fast transaction processing, high availability, scalability, and flexibility for deploying on-premise or in the cloud. Example customer use cases and implementations are described to demonstrate how the database has helped organizations tackle challenges of high volume data processing and analytics.
Which Change Data Capture Strategy is Right for You?Precisely
Change Data Capture or CDC is the practice of moving the changes made in an important transactional system to other systems, so that data is kept current and consistent across the enterprise. CDC keeps reporting and analytic systems working on the latest, most accurate data.
Many different CDC strategies exist. Each strategy has advantages and disadvantages. Some put an undue burden on the source database. They can cause queries or applications to become slow or even fail. Some bog down network bandwidth, or have big delays between change and replication.
Each business process has different requirements, as well. For some business needs, a replication delay of more than a second is too long. For others, a delay of less than 24 hours is excellent.
Which CDC strategy will match your business needs? How do you choose?
View this webcast on-demand to learn:
• Advantages and disadvantages of different CDC methods
• The replication latency your project requires
• How to keep data current in Big Data technologies like Hadoop
Hadoop and SQL: Delivery Analytics Across the OrganizationSeeling Cheung
This document summarizes a presentation given by Nicholas Berg of Seagate and Adriana Zubiri of IBM on delivering analytics across organizations using Hadoop and SQL. Some key points discussed include Seagate's plans to use Hadoop to enable deeper analysis of factory and field data, the evolving Hadoop landscape and rise of SQL, and a performance comparison showing IBM's Big SQL outperforming Spark SQL, especially at scale. The document provides an overview of Seagate and IBM's strategies and experiences with Hadoop.
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...Cynthia Saracco
Got Big Data? Then check out what Big SQL can do for you . . . . Learn how IBM's industry-standard SQL interface enables you to leverage your existing SQL skills to query, analyze, and manipulate data managed in an Apache Hadoop environment on cloud or on premise. This quick technical tour is filled with practical examples designed to get you started working with Big SQL in no time. Specifically, you'll learn how to create Big SQL tables over Hadoop data in HDFS, Hive, or HBase; populate Big SQL tables with data from HDFS, a remote file system, or a remote RDBMS; execute simple and complex Big SQL queries; work with non-traditional data formats and more. These charts are for session ALB-3663 at the IBM World of Watson 2016 conference.
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
Big SQL 3.0: Datawarehouse-grade Performance on Hadoop - At last!Nicolas Morales
This document provides an overview of IBM's Big SQL product for running SQL queries on Hadoop data. It discusses how Big SQL uses a massively parallel processing (MPP) architecture to replace MapReduce for improved performance. Big SQL nodes run directly on the Hadoop cluster to process data locally. The document highlights Big SQL's full SQL query capabilities and support for analytic functions. It also notes how Big SQL leverages the existing Hive metadata and is designed to integrate with the broader Hadoop ecosystem.
The Cloudera Impala project is pioneering the next generation of Hadoop capabilities: the convergence of interactive SQL queries with the capacity, scalability, and flexibility of a Hadoop cluster. In this webinar, join Cloudera and MicroStrategy to learn how Impala works, how it is uniquely architected to provide an interactive SQL experience native to Hadoop, and how you can leverage the power of MicroStrategy 9.3.1 to easily tap into more data and make new discoveries.
Big Data: SQL query federation for Hadoop and RDBMS dataCynthia Saracco
Explore query federation capabilities in IBM Big SQL, which enables programmers to transparently join Hadoop data with relational database management (RDBMS) data.
The document discusses moving from traditional ETL processes to "analytics with no ETL" using Hadoop. It describes how Hadoop currently supports some ETL functions by storing raw and transformed data together. However, this still requires periodic loading of new data. The vision is to support complex schemas, perform background format conversion incrementally, and enable schema inference and evolution to allow analyzing data as it arrives without explicit ETL steps. This would provide an up-to-date, performant single view of all data.
This document provides an overview of a SQL-on-Hadoop tutorial. It introduces the presenters and discusses why SQL is important for Hadoop, as MapReduce is not optimal for all use cases. It also notes that while the database community knows how to efficiently process data, SQL-on-Hadoop systems face challenges due to the limitations of running on top of HDFS and Hadoop ecosystems. The tutorial outline covers SQL-on-Hadoop technologies like storage formats, runtime engines, and query optimization.
This document discusses Tableau's role in big data architectures and its integration with Hadoop. It outlines different workload categories for business intelligence and their considerations for Tableau. Three integration models are described: isolated exploration, live interactive query, and integrated advanced analytics. Capability models are presented for each integration approach regarding suitability for Hadoop. Finally, architecture patterns are shown for isolated exploration, live interactive querying, and an integrated advanced analytics platform using Tableau and Hadoop.
Big SQL 3.0 is a SQL-on-Hadoop solution that provides SQL access to data stored in Hadoop. It uses the same table definitions and metadata as Hive, accessing data already stored in Hadoop without requiring a proprietary format. Big SQL extends Hive's syntax with features like primary keys and foreign keys. Tables in Big SQL and Hive represent views of data stored in Hadoop rather than separate storage structures.
A powerful feature in Postgres called Foreign Data Wrappers lets end users integrate data from MongoDB, Hadoop and other solutions with their Postgres database and leverage it as single, seamless database using SQL.
Use of these features has skyrocketed since EDB released to the open source community new FDWs for MongoDB, Hadoop and MySQL that support both read and write capabilities. Now greatly enhanced, FDWs enable integrating data across disparate deployments to support new workloads, expanded development goals and harvesting greater value from data.
Learn more about Foreign Data Wrappers (FDWs) and Postgres with Sameer Kumar, Database Consultant from Ashnik.
Target Audience: This presentation is intended for IT Professionals seeking to do more with Postgres in his every day projects and build new applications.
Hadoop Architecture Options for Existing Enterprise DataWarehouseAsis Mohanty
The document discusses various options for integrating Hadoop with an existing enterprise data warehouse (EDW). It describes 7 options: 1) Teradata Unified Data Architecture, 2) using an existing EDW with a new Apache Hadoop cluster, 3) using an existing EDW with a new Cloudera Hadoop cluster, 4) using an existing EDW with a new Hortonworks Hadoop cluster, 5) IBM PureData, 6) Oracle Big Data Appliance, and 7) SAP HANA for Hadoop integration. Each option involves using the existing EDW for structured data and Hadoop for unstructured/semi-structured data, with analytics capabilities available across both platforms.
YARN: the Key to overcoming the challenges of broad-based Hadoop AdoptionDataWorks Summit
The document discusses how YARN (Yet Another Resource Negotiator) in Hadoop 2.0 overcomes challenges to broad adoption of Hadoop by allowing applications to directly operate on Hadoop without needing to generate MapReduce code. It introduces RedPoint as a YARN-compliant data management tool that brings together big and traditional data for data integration, quality, and governance tasks in a graphical user interface without coding. RedPoint executes directly on Hadoop using YARN to make data management easier, faster and lower cost compared to previous MapReduce-based options.
The proliferation of different database systems has led to data silos and inconsistencies. In the past, there was a single data warehouse but now there are many types of databases optimized for different purposes like transactions, analytics, streaming, etc. This can be addressed by having a common platform like Hadoop that supports different database types to reduce silos and enable data integration. However, more integration tools are still needed to fully realize this vision.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
Benchmarking Hadoop - Which hadoop sql engine leads the herdGord Sissons
Stewart Tate (tates@us.ibm.com), a key architect behind the industry's first ever Hadoop-DS benchmark at 30TB scale, describes the benchmark and comparative testing between IBM, Cloudera Impala and Hortonworks Hive
The Most Trusted In-Memory database in the world- AltibaseAltibase
This document provides an overview of an in-memory database company and its product capabilities. It discusses the company's history and growth, the changing data landscape driving demand for real-time analytics, and how the company's in-memory and hybrid database technologies provide extremely fast transaction processing, high availability, scalability, and flexibility for deploying on-premise or in the cloud. Example customer use cases and implementations are described to demonstrate how the database has helped organizations tackle challenges of high volume data processing and analytics.
Which Change Data Capture Strategy is Right for You?Precisely
Change Data Capture or CDC is the practice of moving the changes made in an important transactional system to other systems, so that data is kept current and consistent across the enterprise. CDC keeps reporting and analytic systems working on the latest, most accurate data.
Many different CDC strategies exist. Each strategy has advantages and disadvantages. Some put an undue burden on the source database. They can cause queries or applications to become slow or even fail. Some bog down network bandwidth, or have big delays between change and replication.
Each business process has different requirements, as well. For some business needs, a replication delay of more than a second is too long. For others, a delay of less than 24 hours is excellent.
Which CDC strategy will match your business needs? How do you choose?
View this webcast on-demand to learn:
• Advantages and disadvantages of different CDC methods
• The replication latency your project requires
• How to keep data current in Big Data technologies like Hadoop
Hadoop and SQL: Delivery Analytics Across the OrganizationSeeling Cheung
This document summarizes a presentation given by Nicholas Berg of Seagate and Adriana Zubiri of IBM on delivering analytics across organizations using Hadoop and SQL. Some key points discussed include Seagate's plans to use Hadoop to enable deeper analysis of factory and field data, the evolving Hadoop landscape and rise of SQL, and a performance comparison showing IBM's Big SQL outperforming Spark SQL, especially at scale. The document provides an overview of Seagate and IBM's strategies and experiences with Hadoop.
Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin MotgiFelicia Haggarty
The document discusses challenges with building operational data applications on Hadoop and introduces the Cask Data Application Platform (CDAP) as a solution. It provides an agenda that covers data applications, challenges, CDAP motivation and goals, use cases, and an introduction and architecture overview of CDAP. The document aims to demonstrate how CDAP provides a unified platform that simplifies application development and lifecycle while supporting reusable data and processing patterns.
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
This document discusses how organizations can leverage data and analytics to power their business models. It provides examples of Fortune 100 companies that are using Attunity products to build data lakes and ingest data from SAP and other sources into Hadoop, Apache Kafka, and the cloud in order to perform real-time analytics. The document outlines the benefits of Attunity's data replication tools for extracting, transforming, and loading SAP and other enterprise data into data lakes and data warehouses.
Move to Hadoop, Go Faster and Save Millions - Mainframe Legacy ModernizationDataWorks Summit
In spite of recent advances in computing, many core business processes are batch-oriented running on Mainframes. Annual Mainframe costs are counted in 6+ figure Dollars per year, potentially growing with capacity needs. In order to tackle the cost challenge, many organizations have considered or attempted multi-year mainframe migration/re-hosting strategies. Traditional approaches to Mainframe elimination call for large initial investments and carry significant risks – It is hard to match Mainframe performance and reliability. Using Hadoop, Sears/MetaScale developed an innovative alternative that enables batch processing migration to Hadoop, without the risks, time and costs of other methods. This solution has been adopted in multiple businesses with excellent results and associated cost savings, as Mainframes are physically eliminated or downsized: Millions of dollars in savings based on MIP reductions have been seen – A reduction of 200 MIPS can yield $1 million in annual savings. MetaScale eliminated over 900 MIPs and an entire Mainframe system for one fortune 500 client. This presentation illustrates reference architecture and approach successfully used by MetaScale to move mainframe processing to the Hadoop platform without altering user-facing business applications.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the future of data management through the use of an enterprise data hub (EDH). It notes that an EDH provides a centralized platform for ingesting, storing, exploring, processing, analyzing and serving diverse data from across an organization on a large scale in a cost effective manner. This approach overcomes limitations of traditional data silos and enables new analytic capabilities.
Hadoop and the Data Warehouse: Point/Counter PointInside Analysis
Robin Bloor and Teradata
Live Webcast on April 22, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=2e69345c0a6a4e5a8de6fc72652e3bc6
Can you replace the data warehouse with Hadoop? Is Hadoop an ideal ETL subsystem? And what is the real magic of Hadoop? Everyone is looking to capitalize on the insights that lie in the vast pools of big data. Generating the value of that data relies heavily on several factors, especially choosing the right solution for the right context. With so many options out there, how do organizations best integrate these new big data solutions with the existing data warehouse environment?
Register for this episode of The Briefing Room to hear veteran analyst Dr. Robin Bloor as he explains where Hadoop fits into the information ecosystem. He’ll be briefed by Dan Graham of Teradata, who will offer perspective on how Hadoop can play a critical role in the analytic architecture. Bloor and Graham will interactively discuss big data in the big picture of the data center and will also seek to dispel several common misconceptions about Hadoop.
Visit InsideAnlaysis.com for more information.
Better Total Value of Ownership (TVO) for Complex Analytic Workflows with the...ModusOptimum
Customers are looking for ways to streamline analytic decisioning, looking for quicker deployments, faster time to value, lower risks of failure and higher revenues/profits. The IBM & Hortonworks solution delivers on these customer needs.
https://event.on24.com/eventRegistration/EventLobbyServlet?target=reg20.jsp&eventid=1789452&sessionid=1&eventid=1789452&sessionid=1&mode=preview&key=E0F94DE1191C59223B6522A075023215
Pivotal deep dive_on_pivotal_hd_world_class_hdfs_platformEMC
The document discusses Pivotal HD, a Hadoop distribution from Pivotal. It provides an overview of key features of Pivotal HD 2.0 including improved support for real-time analytics using Gemfire XD, enhanced machine learning and SQL capabilities, and integration with the Isilon storage platform. The presentation highlights how Pivotal HD can help customers build a "data lake" to store all of their data and gain insights to create new data-driven services and applications.
IBM's Big Data platform provides tools for managing and analyzing large volumes of structured, unstructured, and streaming data. It includes Hadoop for storage and processing, InfoSphere Streams for real-time streaming analytics, InfoSphere BigInsights for analytics on data at rest, and PureData System for Analytics (formerly Netezza) for high performance data warehousing. The platform enables businesses to gain insights from all available data to capitalize on information resources and make data-driven decisions.
IBM's Big Data platform provides tools for managing and analyzing large volumes of data from various sources. It allows users to cost effectively store and process structured, unstructured, and streaming data. The platform includes products like Hadoop for storage, MapReduce for processing large datasets, and InfoSphere Streams for analyzing real-time streaming data. Business users can start with critical needs and expand their use of big data over time by leveraging different products within the IBM Big Data platform.
Hp Converged Systems and Hortonworks - Webinar SlidesHortonworks
Our experts will walk you through some key design considerations when deploying a Hadoop cluster in production. We'll also share practical best practices around HP and Hortonworks Data Platform to get you started on building your modern data architecture.
Learn how to:
- Leverage best practices for deployment
- Choose a deployment model
- Design your Hadoop cluster
- Build a Modern Data Architecture and vision for the Data Lake
ITsubbotnik Spring 2017: Dmitriy Yatsyuk "Готовое комплексное инфраструктурно...epamspb
This document discusses a confidential proposal from EPAM to provide big data solutions and services for a client. It outlines EPAM's experience with Hadoop, AWS, data engineering, ETL, analytics dashboards, and security implementations. The proposal describes setting up production and staging environments with Hadoop, Zabbix, Jenkins, Chef, Tableau, and integrating them with the client's existing infrastructure. It highlights EPAM's big data competency center and capabilities in data strategy, architecture, analytics, and platform support.
The document discusses Apache Hive and Apache Druid for fast SQL on big data. It provides performance benchmarks showing Hive LLAP is faster than Presto and Spark SQL for TPC-DS queries. It describes features of Hive LLAP including in-memory caching, query result caching, and metadata caching. It also discusses new Hive 3 features like materialized views and optimizer improvements. The document then provides an overview of Apache Druid's capabilities for real-time ingestion and querying of streaming data before discussing how Hive and Druid can work together, with Hive able to push down queries to Druid.
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Similar to Hadoop-DS: Which SQL-on-Hadoop Rules the Herd (20)
An introduction to IBM Data Lake by Mandy Chessell CBE FREng CEng FBCS, Distinguished Engineer & Master Inventor.
Learn more about IBM Data Lake: https://ibm.biz/Bdswi9
10 WealthTech podcasts every wealth advisor should listen toIBM Analytics
Listen to this “Finance in Focus” podcast series to hear a cast of interesting experts discuss how the wealth management industry is adapting to new and emerging technologies that include robo-advisors, blockchain, analytics, and cognitive. Over the course of 10 episodes, hosts Rob Stanich and Alex Baghdjian are joined by wealth management experts to discuss behavior financing, DOL fiduciary rule, social media marketing, account aggregation, millennials, surveillance, and regulations.
Advantages of an integrated governance, risk and compliance environmentIBM Analytics
Risk management is increasingly becoming a strategic, executive-sponsored solution that many organizations view as providing a competitive advantage. When companies have an aggregated view of all the different kinds of risk and compliance data, they can start to generate insights about how to run the business better. In this presentation, learn why and how to empower business leaders to make more risk-aware decisions with visibility across controls and associated issues and actions throughout the organization.
The banking industry will benefit from adopting the latest technology advancements that include artificial intelligence and cognitive computing. These technologies provide the opportunity to mine the massive amounts of transactional data that banks have collected over the past decades to better serve their customers, automate time-consuming mundane tasks and to integrate the act of banking into the lives of consumers.
Learn how: http://ibm.co/bankinganalytics
Sales performance management and C-level goalsIBM Analytics
The document outlines the C-level goals for sales performance management in 2017 that were discussed with the CEO and heads of sales at a multibillion-dollar company. The goals were to double sales productivity in three years, break complacency in the sales culture, execute a strategy of offering full solutions based on customer needs, build an organization with one face to the customer, and align best sales resources with high-value customers further up in the organization. These goals would define the major priorities for the sales compensation plan.
The science of client insight: Increase revenue through improved engagementIBM Analytics
This document discusses how banks and wealth management firms are using customer data and analytics to gain insights and improve engagement. It notes that banks hold significant customer data but have struggled to derive meaningful insights. However, in 2016 banks are expected to increase investments in analytics to better understand customers and personalize services. Wealth management firms also face challenges from digital competitors and are looking to analytics to provide more holistic advice based on client needs and life events. The document explores how insights can be used across banking and wealth management to strengthen relationships and increase loyalty and revenue.
Expert opinion on managing data breachesIBM Analytics
For the first time, cybersecurity strategy is an integral part of the platforms for the US presidential candidates. Today, data breaches and cyber attacks are no longer just one offs but seemingly happen daily and impact both the private sector and government. What are we doing to minimize these data breaches and counter cyber attacks? What’s the role of government in fighting cyber crime? Where does the public sector fit in the cyber-crime puzzle? Cybersecurity experts Dan Lohrmann, Scott Schober, Shahid Shah, Eric Vanderburg and Morgan Wright address these questions.
Top industry use cases for streaming analyticsIBM Analytics
Organizations need to get high value from streaming data to gain new clients and capitalize on market opportunities. Discover how IBM Streams is best suited for use cases that has the need for high speed and low latency.
The key to the cognitive business is putting data to work. What is needed is a platform, an ecosystem, and a method.
Learn more about http://ibm.co/dataworks
IBM CDO Fall Summit 2016 Keynote: Driving innovation in the cognitive eraIBM Analytics
What does it take to drive Innovation in the Cognitive Era? Bob Picciano, Senior Vice President IBM Analytics and Inderpal Bhandari, Global Chief Data Officer, IBM gave this presentation to the CDOs and data professionals in attendance at the IBM Chief Data Officer Strategy Summit in Fall of 2016.
Learn more about the role of CDO: http://ibm.co/2cXasXy
4 common headaches with sales compensation managementIBM Analytics
Gain insights and solutions to four highly common headaches that companies face in their sales performance management processes. Learn more: http://ibm.com/spm
IBM Virtual Finance Forum 2016: Top 10 reasons to attendIBM Analytics
Explore the top 10 reasons to attend IBM's Virtual Finance Forum 2016 for insights and best practices on performance management in the cognitive era. Attend your choice of three broadcasts of IBM's Virtual Finance Forum 2016: http://bit.ly/oct5am, http://bit.ly/Oct512Noon or http://bit.ly/oct5eve.
In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. Organizations can then use these insights to strengthen customer relationships, improve service delivery and drive new opportunities. To help guide the processes and activities within a given domain, data scientists and engineers need a foundational methodology that provides a framework for how to proceed with whichever methods or tools they will use to obtain answers and deliver results. In this presentation, we will share data science tips for data engineers.
Join the Data Science Experience: http://ibm.co/data-science
How secure is your enterprise from threats? IBM Analytics
The document discusses cybersecurity threats to organizations based on survey findings, including that 89% of organizations believe they are susceptible to insider threats, 54% of breaches are caused by internal sources, and the average breach goes undetected for 8 months, costing organizations $1.6 million on average. It suggests that cyber threat intelligence platforms can provide greater visibility and faster detection and response to help organizations protect against evolving threats.
Collaboration is crucial to today’s workforce. Whether you are in a traditional office setting, work from home or travel extensively, there are tools needed to achieve successful content collaboration.
Whether your mission is to improve external collaboration, increase scalability or focus on security and compliance, find out how content collaboration with Box can improve your ROI.
To find out more on how to improve your content journey, visit IBM ECM and Box: http://ibm.co/ibm-box-partnership
The digital transformation of the French OpenIBM Analytics
For decades, IBM and the Fédération Francaise de Tennis have been tennis partners, using real-time analytics as their winning serve to create a cross-platform fan experience. Learn about IBM’s game-changing site redesign for The French Open and platform innovations to enhance the tournament experience for both attendees and virtual fans.
Bridging to a hybrid cloud data services architectureIBM Analytics
Enterprises are increasingly evolving their data infrastructures into entire cloud-facing environments. Interfacing private and public cloud data assets is a hallmark of initiatives such as logical data warehouses, data lakes and online transactional data hubs. These projects may involve deploying two or more of the following cloud-based data platforms into a hybrid architecture: Apache Hadoop, data warehouses, graph databases, NoSQL databases, multiworkload SQL databases, open source databases, data refineries and predictive analytics.
Data application developers, data scientists and analytics professionals are driving their organizations’ efforts to bridge their data to the cloud. Several questions are of keen interest to those who are driving an organization’s evolution of its data and analytics initiatives into more holistic cloud-facing environments:
• What is a hybrid cloud data services architecture?
• What are the chief applications and benefits of a hybrid cloud data services architecture?
• What are the best practices for bridging a logical data warehouse to the cloud?
• What are the best practices for bridging advanced analytics and data lakes to the cloud?
• What are the best practices for bridging an enterprise database hub to the cloud?
• What are the first steps to take for bridging private data assets to the cloud?
• How can you measure ROI from bridging private data to public cloud data services?
• Which case studies illustrate the value of bridging private data to the cloud?
Sign up now for a free 3-month trial of IBM Analytics for Apache Spark and IBM Cloudant, IBM dashDB or IBM DB2 on Cloud.
http://ibm.co/ibm-cloudant-trial
http://ibm.co/ibm-dashdb-trial
http://ibm.co/ibm-db2-trial
http://ibm.co/ibm-spark-trial
What does data tell you about the customer journey?IBM Analytics
In this omnichannel world, consumers leave clues about their purchasing decisions at every touch point. What data analytics can you leverage to optimize your marketing message and merchandising? Well, it turns out, a lot.
What CEOs want from CDOs and how to deliver on itIBM Analytics
Cortnie Abercrombie, Emerging Roles Leader, IBM gave this presentation on what Chief Executive Officers want from Chief Data Officers and how to deliver on it at CDOvision 2016.
Learn more about the various roles of CDOs: http://ibm.co/cdolookbook
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, 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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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!