The Other Way of Doing Big Data: Declarative, Decoupled, Federated, Simple, and Resilient.
Also known as: How to Win at Scale and its Influence of People. Originally presented by Flip Kromer to the Research Board, http://www.researchboard.com/ June 2012
The document provides an introduction to Apache Drill, an open source SQL query engine for analysis of large-scale datasets across Hadoop, NoSQL and cloud storage systems. It discusses Tomer Shiran's role in Apache Drill, provides an agenda for the talk, describes the need for interactive analysis of big data and how existing solutions are limited. It then outlines Apache Drill's architecture, key features like full SQL support, optional schemas and support for nested data formats.
Description of the work done for the Semantic Markup activity of the Semantic Sensor Networks Incubator activity (at W3C).
Presentation made at the Australian Ontology Workshop, Melbourne, December 2009. The full title of the paper is: "Review of semantic enablement techniques used in geospatial and semantic standards for legacy and opportunistic mashups" (and it is available via crpit.com)
Apache Drill [1] is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is a design goal to scale to 10,000 servers or more and to be able to process Petabytes of data and trillions of records in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community. In this talk we focus on how Apache Drill enables interactive analysis and query at scale. First we walk through typical use cases and then delve into Drill's architecture, the data flow and query languages as well as data sources supported.
[1] http://incubator.apache.org/drill/
Summary of recent progress on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
Understanding the Value and Architecture of Apache DrillDataWorks Summit
This document summarizes Apache Drill, an open source SQL query engine for interactive analysis of large-scale datasets. It was inspired by Google's Dremel and allows for interactive, ad-hoc queries across data sources using standard SQL. The key features highlighted are its support for nested data, optional schemas, extensibility points, and full ANSI SQL 2003 compatibility. An overview of Drill's architecture is provided, including its use of distributed Drillbit processes and a coordinator node.
Video: http://www.youtube.com/watch?v=BT8WvQMMaV0
Hadoop is the technology of choice for processing large data sets. At salesforce.com, we service internal and product big data use cases using a combination of Hadoop, Java MapReduce, Pig, Force.com, and machine learning algorithms. In this webinar, we will discuss an internal use case and a product use case:
Product Metrics: Internally, we measure feature usage using a combination of Hadoop, Pig, and the Force.com platform (Custom Objects and Analytics).
Community-Based Recommendations: In Chatter, our most successful people and file recommendations are built on a collaborative filtering algorithm that is implemented on Hadoop using Java MapReduce.
This document discusses integrating Apache Hive and HBase. It provides an overview of Hive and HBase, describes use cases for querying HBase data using Hive SQL, and outlines features and improvements for Hive and HBase integration. Key points include mapping Hive schemas and data types to HBase tables and columns, pushing filters and other operations down to HBase, and using a storage handler to interface between Hive and HBase. The integration allows analysts to query both structured Hive and unstructured HBase data using a single SQL interface.
Couchbase is a distributed database that provides simple, fast, and elastic scalability. A social game company called Tribal Crossing was facing challenges with scaling their MySQL database for their game Animal Party. They deployed Couchbase on Amazon EC2 to take advantage of Couchbase's speed, simplicity, and ability to scale out elastically. Tribal Crossing represented their game data as JSON documents in Couchbase and was able to easily access and modify player and game object data.
The document provides an introduction to Apache Drill, an open source SQL query engine for analysis of large-scale datasets across Hadoop, NoSQL and cloud storage systems. It discusses Tomer Shiran's role in Apache Drill, provides an agenda for the talk, describes the need for interactive analysis of big data and how existing solutions are limited. It then outlines Apache Drill's architecture, key features like full SQL support, optional schemas and support for nested data formats.
Description of the work done for the Semantic Markup activity of the Semantic Sensor Networks Incubator activity (at W3C).
Presentation made at the Australian Ontology Workshop, Melbourne, December 2009. The full title of the paper is: "Review of semantic enablement techniques used in geospatial and semantic standards for legacy and opportunistic mashups" (and it is available via crpit.com)
Apache Drill [1] is a distributed system for interactive analysis of large-scale datasets, inspired by Google’s Dremel technology. It is a design goal to scale to 10,000 servers or more and to be able to process Petabytes of data and trillions of records in seconds. Since its inception in mid 2012, Apache Drill has gained widespread interest in the community. In this talk we focus on how Apache Drill enables interactive analysis and query at scale. First we walk through typical use cases and then delve into Drill's architecture, the data flow and query languages as well as data sources supported.
[1] http://incubator.apache.org/drill/
Summary of recent progress on Apache Drill, an open-source community-driven project to provide easy, dependable, fast and flexible ad hoc query capabilities.
Understanding the Value and Architecture of Apache DrillDataWorks Summit
This document summarizes Apache Drill, an open source SQL query engine for interactive analysis of large-scale datasets. It was inspired by Google's Dremel and allows for interactive, ad-hoc queries across data sources using standard SQL. The key features highlighted are its support for nested data, optional schemas, extensibility points, and full ANSI SQL 2003 compatibility. An overview of Drill's architecture is provided, including its use of distributed Drillbit processes and a coordinator node.
Video: http://www.youtube.com/watch?v=BT8WvQMMaV0
Hadoop is the technology of choice for processing large data sets. At salesforce.com, we service internal and product big data use cases using a combination of Hadoop, Java MapReduce, Pig, Force.com, and machine learning algorithms. In this webinar, we will discuss an internal use case and a product use case:
Product Metrics: Internally, we measure feature usage using a combination of Hadoop, Pig, and the Force.com platform (Custom Objects and Analytics).
Community-Based Recommendations: In Chatter, our most successful people and file recommendations are built on a collaborative filtering algorithm that is implemented on Hadoop using Java MapReduce.
This document discusses integrating Apache Hive and HBase. It provides an overview of Hive and HBase, describes use cases for querying HBase data using Hive SQL, and outlines features and improvements for Hive and HBase integration. Key points include mapping Hive schemas and data types to HBase tables and columns, pushing filters and other operations down to HBase, and using a storage handler to interface between Hive and HBase. The integration allows analysts to query both structured Hive and unstructured HBase data using a single SQL interface.
Couchbase is a distributed database that provides simple, fast, and elastic scalability. A social game company called Tribal Crossing was facing challenges with scaling their MySQL database for their game Animal Party. They deployed Couchbase on Amazon EC2 to take advantage of Couchbase's speed, simplicity, and ability to scale out elastically. Tribal Crossing represented their game data as JSON documents in Couchbase and was able to easily access and modify player and game object data.
The document discusses MyCassandra, a modular distributed data store that allows selecting different storage engines like MySQL, Bigtable, Redis, and MongoDB. It can be deployed in a heterogeneous cluster with different nodes using various storage engines. This allows queries to be routed to the node that can process it most efficiently. The data model remains the same as Cassandra but with additional features like secondary indexes and pluggable storage engines. Performance tests showed MyCassandra cluster had up to 6.53 times higher throughput than Cassandra in write-heavy and read-heavy workloads.
1) HBase satisfied Facebook's requirements for a real-time data store by providing excellent write performance, horizontal scalability, and features like atomic operations.
2) At Facebook, HBase is used for messaging and user activity tracking applications that involve massive write-throughput and petabytes of data.
3) HBase's integration with HDFS provides fault tolerance and scalability, while its column orientation enables complex queries on user activity data.
Hadoop Summit 2012 - Hadoop and Vertica: The Data Analytics Platform at TwitterBill Graham
The document discusses Twitter's data analytics platform, including Hadoop and Vertica. It outlines Twitter's data flow, which ingests 400 million tweets daily into HDFS, then uses various tools like Crane, Oink, and Rasvelg to run jobs on the main Hadoop cluster before loading analytics into Vertica and MySQL for web tools and analysts. It also describes Twitter's heterogeneous technology stack and the various teams that use the analytics platform.
Offline processing with Hadoop allows for scalable, simplified batch processing of large datasets across distributed systems. It enables increased innovation by supporting complex analytics over large data sets without strict schemas. Hadoop adoption is moving beyond legacy roles to focus on data processing and value creation through scalable and customizable systems like Cascading.
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.
Analyzing Real-World Data with Apache Drilltshiran
This document provides an overview of Apache Drill, an open source SQL query engine for analysis of both structured and unstructured data. It discusses how Drill allows for schema-free querying of data stored in Hadoop, NoSQL databases and other data sources using SQL. The document outlines some key features of Drill, such as its flexible data model, ability to discover schemas on the fly, and distributed execution architecture. It also presents examples of using Drill to analyze real-world data from sources like HDFS, MongoDB and more.
Oct 2012 HUG: Project Panthera: Better Analytics with SQL, MapReduce, and HBaseYahoo Developer Network
Project Panthera is an open source effort that showcases better data analytics capabilities on Hadoop/HBase (e.g., better integration with existing infrastructure using SQL, better query processing on HBase, and efficiently utilizing new HW platform technologies). In this talk, we will discusses two new capabilities that we are currently working on under Project Panthera: (1) a SQL Engine for MapReduce (built on top of Hive) that supports common SQL constructs used in analytic queries, including some important features (e.g., sub-query in WHERE clauses, multiple-table SELECT statement, etc.) that are not supported in Hive today; (2) a Document-Oriented Store on HBase for better Hive/SQL query processing, which brings up-to 3x reduction in table storage and up-to 1.8x speedup in query processing.
Presenter: Jason Dai, Principal Engineer, Intel Software and Services Group
Building a Modern Data Warehouse: Deep Dive on Amazon Redshift - SRV337 - Chi...Amazon Web Services
In this chalk talk, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Amazon Redshift Spectrum, a feature of Amazon Redshift that enables you to analyze data across Amazon Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
Apache Drill is a scalable SQL query engine for analysis of large-scale datasets across various data sources like HDFS, HBase, Hive and others. It allows for ad-hoc analysis of datasets without requiring knowledge of the schema beforehand. Drill uses a distributed architecture with query coordinators and workers to process queries in parallel. It supports various interfaces like JDBC, ODBC and a web console for running SQL queries on different data sources.
R is an in-memory based scripting language and is capable of handling big data, tens of gigabytes and hundreds of millions of rows. And when combined with SAP HANA, R offers the potential to take the in-memory analytics to a whole new level. Imagine performing advanced statistical analysis such as decision tree, game-theory, linear and multiple regressions and much more inside SAP HANA on millions of rows and turning around with critical business insights at the speed of thought.
This document provides an overview of Apache Drill and how it enables ad-hoc querying and analysis of structured and unstructured data stored in Hadoop. Some key points:
1) Apache Drill allows for schema-free SQL queries against data in HDFS, HBase, Hive and other data sources, empowering self-service data exploration and "zero-day" analytics.
2) Drill's queries can handle complex, nested data through features like automatic schema discovery, repeated value support, and SQL extensions.
3) Examples show how Drill provides a familiar SQL interface and tooling to analyze JSON, text and other file formats to gain insights from large volumes of real-time data.
Scaling HDFS to Manage Billions of Files with Key-Value StoresDataWorks Summit
The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.
This document introduces COSBench, a benchmark tool developed by Intel to measure the performance of cloud object storage services. It describes key components of COSBench including the workload configuration, performance metrics collected, and a web console for managing tests. The document also provides a case study using COSBench to evaluate the performance of OpenStack Swift, an open source cloud storage system, by describing the entities and architecture of OpenStack Swift and the test configuration used.
The document discusses the "Madison" SQL Server parallel data warehouse architecture from Microsoft. It covers concepts of symmetric multiprocessing (SMP) vs massively parallel processing (MPP) architectures. It then presents Microsoft's "FastTrack" reference architectures which provide validated hardware configurations for data warehousing. These balanced configurations are designed to optimize performance and scalability by avoiding bottlenecks. They also reduce costs and risks compared to custom-built systems.
Want to discover how you can get self-service data exploration capabilities on data stored in multiple formats in files or NoSQL databases? Watch this session of Free Code Fridays to get a basic understanding of Apache Drill.
Drill is an open source, low-latency query engine for Hadoop that delivers secure, interactive SQL analytics at petabyte scale. With the ability to discover schemas on-the-fly, you can get faster time-to-value without waiting for IT to prepare the data for analysis. By adhering to ANSI SQL standards, Drill does not require a learning curve and integrates seamlessly with visualization tools.
The document discusses MyCassandra, a modular distributed data store that allows selecting different storage engines like MySQL, Bigtable, Redis, and MongoDB. It can be deployed in a heterogeneous cluster with different nodes using various storage engines. This allows queries to be routed to the node that can process it most efficiently. The data model remains the same as Cassandra but with additional features like secondary indexes and pluggable storage engines. Performance tests showed MyCassandra cluster had up to 6.53 times higher throughput than Cassandra in write-heavy and read-heavy workloads.
1) HBase satisfied Facebook's requirements for a real-time data store by providing excellent write performance, horizontal scalability, and features like atomic operations.
2) At Facebook, HBase is used for messaging and user activity tracking applications that involve massive write-throughput and petabytes of data.
3) HBase's integration with HDFS provides fault tolerance and scalability, while its column orientation enables complex queries on user activity data.
Hadoop Summit 2012 - Hadoop and Vertica: The Data Analytics Platform at TwitterBill Graham
The document discusses Twitter's data analytics platform, including Hadoop and Vertica. It outlines Twitter's data flow, which ingests 400 million tweets daily into HDFS, then uses various tools like Crane, Oink, and Rasvelg to run jobs on the main Hadoop cluster before loading analytics into Vertica and MySQL for web tools and analysts. It also describes Twitter's heterogeneous technology stack and the various teams that use the analytics platform.
Offline processing with Hadoop allows for scalable, simplified batch processing of large datasets across distributed systems. It enables increased innovation by supporting complex analytics over large data sets without strict schemas. Hadoop adoption is moving beyond legacy roles to focus on data processing and value creation through scalable and customizable systems like Cascading.
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.
Analyzing Real-World Data with Apache Drilltshiran
This document provides an overview of Apache Drill, an open source SQL query engine for analysis of both structured and unstructured data. It discusses how Drill allows for schema-free querying of data stored in Hadoop, NoSQL databases and other data sources using SQL. The document outlines some key features of Drill, such as its flexible data model, ability to discover schemas on the fly, and distributed execution architecture. It also presents examples of using Drill to analyze real-world data from sources like HDFS, MongoDB and more.
Oct 2012 HUG: Project Panthera: Better Analytics with SQL, MapReduce, and HBaseYahoo Developer Network
Project Panthera is an open source effort that showcases better data analytics capabilities on Hadoop/HBase (e.g., better integration with existing infrastructure using SQL, better query processing on HBase, and efficiently utilizing new HW platform technologies). In this talk, we will discusses two new capabilities that we are currently working on under Project Panthera: (1) a SQL Engine for MapReduce (built on top of Hive) that supports common SQL constructs used in analytic queries, including some important features (e.g., sub-query in WHERE clauses, multiple-table SELECT statement, etc.) that are not supported in Hive today; (2) a Document-Oriented Store on HBase for better Hive/SQL query processing, which brings up-to 3x reduction in table storage and up-to 1.8x speedup in query processing.
Presenter: Jason Dai, Principal Engineer, Intel Software and Services Group
Building a Modern Data Warehouse: Deep Dive on Amazon Redshift - SRV337 - Chi...Amazon Web Services
In this chalk talk, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Amazon Redshift Spectrum, a feature of Amazon Redshift that enables you to analyze data across Amazon Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
Apache Drill is a scalable SQL query engine for analysis of large-scale datasets across various data sources like HDFS, HBase, Hive and others. It allows for ad-hoc analysis of datasets without requiring knowledge of the schema beforehand. Drill uses a distributed architecture with query coordinators and workers to process queries in parallel. It supports various interfaces like JDBC, ODBC and a web console for running SQL queries on different data sources.
R is an in-memory based scripting language and is capable of handling big data, tens of gigabytes and hundreds of millions of rows. And when combined with SAP HANA, R offers the potential to take the in-memory analytics to a whole new level. Imagine performing advanced statistical analysis such as decision tree, game-theory, linear and multiple regressions and much more inside SAP HANA on millions of rows and turning around with critical business insights at the speed of thought.
This document provides an overview of Apache Drill and how it enables ad-hoc querying and analysis of structured and unstructured data stored in Hadoop. Some key points:
1) Apache Drill allows for schema-free SQL queries against data in HDFS, HBase, Hive and other data sources, empowering self-service data exploration and "zero-day" analytics.
2) Drill's queries can handle complex, nested data through features like automatic schema discovery, repeated value support, and SQL extensions.
3) Examples show how Drill provides a familiar SQL interface and tooling to analyze JSON, text and other file formats to gain insights from large volumes of real-time data.
Scaling HDFS to Manage Billions of Files with Key-Value StoresDataWorks Summit
The document discusses scaling HDFS to manage billions of files. It describes how HDFS usage has grown from millions of files in 2007 to potentially billions of files in the future. To address this, the speakers propose storing HDFS metadata in a key-value store like LevelDB instead of solely in memory. They evaluate this approach and find comparable performance to HDFS for most operations. Future work includes improving operations like compaction and failure recovery in the new architecture.
This document introduces COSBench, a benchmark tool developed by Intel to measure the performance of cloud object storage services. It describes key components of COSBench including the workload configuration, performance metrics collected, and a web console for managing tests. The document also provides a case study using COSBench to evaluate the performance of OpenStack Swift, an open source cloud storage system, by describing the entities and architecture of OpenStack Swift and the test configuration used.
The document discusses the "Madison" SQL Server parallel data warehouse architecture from Microsoft. It covers concepts of symmetric multiprocessing (SMP) vs massively parallel processing (MPP) architectures. It then presents Microsoft's "FastTrack" reference architectures which provide validated hardware configurations for data warehousing. These balanced configurations are designed to optimize performance and scalability by avoiding bottlenecks. They also reduce costs and risks compared to custom-built systems.
Want to discover how you can get self-service data exploration capabilities on data stored in multiple formats in files or NoSQL databases? Watch this session of Free Code Fridays to get a basic understanding of Apache Drill.
Drill is an open source, low-latency query engine for Hadoop that delivers secure, interactive SQL analytics at petabyte scale. With the ability to discover schemas on-the-fly, you can get faster time-to-value without waiting for IT to prepare the data for analysis. By adhering to ANSI SQL standards, Drill does not require a learning curve and integrates seamlessly with visualization tools.
Understanding Enterprise Quality Management Systems (EQMS)Sparta Systems
The quality software landscape has progressed to become enterprise-level solutions, whose integrated systems enable organizations to implement automated quality processes tailored to align with each of their specific products and business practices. This presentation from Sparta Systems explains the concept of Enterprise Quality Management Systems (EQMS).
The document provides an overview of big data technologies including Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, MongoDB, and Cassandra. It discusses how these technologies enable processing and analyzing very large datasets across commodity hardware. It also outlines the growth and market potential of the big data sector, which is expected to reach $48 billion by 2018.
This document discusses integrating Apache Hive with Apache HBase. It provides an overview of Hive and HBase, the motivation for integrating the two systems, and how the integration works. Specifically, it covers how the schema and data types are mapped between Hive and HBase, how filters can be pushed down from Hive to HBase to optimize queries, bulk loading data from Hive into HBase, and security aspects of the integrated system. The document is intended to provide background and technical details on using Hive and HBase together.
This document summarizes Tagomori Satoshi's presentation on handling "not so big data" at the YAPC::Asia 2014 conference. It discusses different types of data processing frameworks for various data sizes, from sub-gigabytes up to petabytes. It provides overviews of MapReduce, Spark, Tez, and stream processing frameworks. It also discusses what Hadoop is and how the Hadoop ecosystem has evolved to include these additional frameworks.
This document discusses real-time big data applications and provides a reference architecture for search, discovery, and analytics. It describes combining analytical and operational workloads using a unified data model and operational database. Examples are given of organizations using this approach for real-time search, analytics and continuous adaptation of large and diverse datasets.
Dataflow in 104corp - AWS UserGroup TW 2018Gavin Lin
This document discusses migrating data processing workflows from on-premises to cloud-based serverless architectures. It outlines reasons for upgrading systems like HDFS and Pig to cloud services including AWS EMR, Kinesis, and S3 for improved resource utilization, high availability, and performance. The document then details considerations for how to migrate components like streaming, storage, computing, exploration and serving to various AWS services, and concludes with recommendations to leverage AWS services where possible for ease of use while balancing controllability and cost versus performance.
This document discusses using Hadoop and HBase on Amazon Web Services (AWS) for distributed data storage and analytics. It introduces Hadoop and describes how AWS Elastic MapReduce simplifies implementing and managing Hadoop clusters. It also discusses using HBase, an open-source NoSQL database, on AWS for scalable access to large, unstructured datasets. Finally, it covers strategies for optimizing costs when running big data workloads on AWS infrastructure.
Cloudera Sessions - Clinic 1 - Getting Started With HadoopCloudera, Inc.
If you are interested in Hadoop and its capabilities, but you are not sure where to begin, this is the session for you. Learn the basics of Hadoop, see how to spin up a development cluster in the cloud or on-premise, and start exploring ETL processing with SQL and other familiar tools
Cloud computing, big data, and mobile technologies are driving major changes in the IT world. Cloud computing provides scalable computing resources over the internet. Big data involves extremely large data sets that are analyzed to reveal business insights. Hadoop is an open-source software framework that allows distributed processing of big data across commodity hardware. It includes tools like HDFS for storage and MapReduce for distributed computing. The Hadoop ecosystem also includes additional tools for tasks like data integration, analytics, workflow management, and more. These emerging technologies are changing how businesses use and analyze data.
hbaseconasia2019 BigData NoSQL System: ApsaraDB, HBase and SparkMichael Stack
Wei Li of Alibaba
Track 2: Ecology and Solutions
https://open.mi.com/conference/hbasecon-asia-2019
THE COMMUNITY EVENT FOR APACHE HBASE™
July 20th, 2019 - Sheraton Hotel, Beijing, China
https://hbase.apache.org/hbaseconasia-2019/
Building a Business on Hadoop, HBase, and Open Source Distributed ComputingBradford Stephens
This is a talk on a fundamental approach to thinking about scalability, and how Hadoop, HBase, and Lucene are enabling companies to process amazing amounts of data. It's also about how Social Media is making the traditional RDBMS irrelevant.
Big data refers to the massive amounts of unstructured data that are growing exponentially. Hadoop is an open-source framework that allows processing and storing large data sets across clusters of commodity hardware. It provides reliability and scalability through its distributed file system HDFS and MapReduce programming model. The Hadoop ecosystem includes components like Hive, Pig, HBase, Flume, Oozie, and Mahout that provide SQL-like queries, data flows, NoSQL capabilities, data ingestion, workflows, and machine learning. Microsoft integrates Hadoop with its BI and analytics tools to enable insights from diverse data sources.
Building Data Warehouses and Data Lakes in the Cloud - DevDay Austin 2017 Day 2Amazon Web Services
This document discusses building data warehouses and data lakes in the cloud using AWS services. It provides an overview of AWS databases, analytics, and machine learning services that can be used to store and analyze data at scale. These services allow customers to migrate existing data warehouses to the cloud, build new data warehouses and data lakes more cost effectively, and gain insights from their data more easily.
Apachecon Euro 2012: Elastic, Multi-tenant Hadoop on DemandRichard McDougall
Elastic, Multi-tenant Hadoop on Demand! Richard McDougall, Chief Architect, Application Infrastructure and Big Data, VMware, Inc @richardmcdougll ApacheCon Europe, 2012. Broadens the application of Hadoop technology with horizontal and vertical use cases. Hadoop enables parallel processing through a programming framework for highly parallel data processing using MapReduce and the Hadoop Distributed File System (HDFS) for distributed data storage. Serengeti automates deployment of Hadoop on virtual platforms in under 30 minutes for multi-tenant elastic Hadoop as a service.
The document summarizes a data Tuesday presentation by Laurence Hubert, CEO/CTO of Hurence SAS, a company that builds and manages Hadoop-based clusters and provides end-to-end big data solutions including ETL and OLAP cube products that leverage Hadoop and scale to big data. Key products highlighted include a pure Hadoop job engine, the first NoSQL OLAP cube implemented on HBase, and Hurence mappings for ETL and an OLAP cube that scales for big data using HBase columns.
Hw09 Making Hadoop Easy On Amazon Web ServicesCloudera, Inc.
Amazon Elastic MapReduce enables customers to easily and cost-effectively process vast amounts of data utilizing a hosted Hadoop framework running on Amazon's web-scale infrastructure. It was launched in 2009 and allows customers to spin up large or small Hadoop job flows in minutes without having to manage compute clusters or tune Hadoop themselves. The service provides a reliable, fault tolerant and cost effective way for customers to solve problems like data mining, genome analysis, financial simulation and web indexing using their existing Hadoop applications.
Apache Hive provides SQL-like access to your stored data in Apache Hadoop. Apache HBase stores tabular data in Hadoop and supports update operations. The combination of these two capabilities is often desired, however, the current integration show limitations such as performance issues. In this talk, Enis Soztutar will present an overview of Hive and HBase and discuss new updates/improvements from the community on the integration of these two projects. Various techniques used to reduce data exchange and improve efficiency will also be provided.
Cloud computing, big data, and mobile are three major trends that will change the world. Cloud computing provides scalable and elastic IT resources as services over the internet. Big data involves large amounts of both structured and unstructured data that can generate business insights when analyzed. The hadoop ecosystem, including components like HDFS, mapreduce, pig, and hive, provides an architecture for distributed storage and processing of big data across commodity hardware.
Chicago Data Summit: Apache HBase: An IntroductionCloudera, Inc.
Apache HBase is an open source distributed data-store capable of managing billions of rows of semi-structured data across large clusters of commodity hardware. HBase provides real-time random read-write access as well as integration with Hadoop MapReduce, Hive, and Pig for batch analysis. In this talk, Todd will provide an introduction to the capabilities and characteristics of HBase, comparing and contrasting it with traditional database systems. He will also introduce its architecture and data model, and present some example use cases.
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.
In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.
This document summarizes a presentation about big data analytics solutions from Think Big Analytics and Infochimps. It discusses using their platforms together to power applications with next-generation big data stacks. It highlights case studies, architecture diagrams, and polls to demonstrate how their services can accelerate time to value through a combination of data science, engineering, strategy, and hands-on training and education.
Infochimps Survey: What IT Teams Want CIOs to Know About Big Data - Learn the top items that IT team members would like their CIOs to understand concerning their Big Data projects.
The report - CIOs & Big Data: What Your IT Team Wants You to Know - is based on a survey of more than 300 IT department employees, 58% of whom are currently engaged in Big Data projects, and aims to identify pitfalls that implementation teams encounter, and could avoid, if top management had a more complete view.
The document discusses challenges faced by IT teams in big data projects. It finds that 55% of big data projects are not completed due to inaccurate scoping, technical roadblocks, and siloed data. Only 6% of enterprises list big data as a top 10 IT priority. When it comes to analytics projects, the top reasons for failure are lack of expertise to connect insights and lack of business context. Real-time and batch analytics are both important. The most significant challenges for big data projects are finding the right tools and allocating time. IT teams believe data scientists, application developers, and business users can most impact revenue through increased access to enterprise data.
Jump start into 2013 by exploring how Big Data can transform your business. Listen to Infochimps Director of Product, Tim Gasper, cover the leading use cases for 2013, sharing where the data comes from, how the systems are architected and most importantly, how they drive business insights for data-driven decisions.
Learn why 451 Research believes Infochimps is well-positioned with an easy-to-consume managed service for those without Hadoop expertise, as well as a stack of technologically interesting projects for the 'devops' crowd.
Opening with a market positioning statement and ending with a competitive and SWOT analysis, Matt Aslett provides a comprehensive impact report.
Big Data expert and Infochimps Product Manager Tim Gasper shares insights and explains how to effectively execute your Big Data project while avoiding the most common pitfalls.
Learn how retailers can leverage their own Big Data. Go from data sources to increasing profits, margins and market share at a fraction of the time and cost.
Big data expert and Infochimps CEO, Jim Kaskade presents the Infinite Monkey Theorem at CloudCon Expo. He provides an energetic, inspiring, and practical perspective on why Big Data is disrupting. It’s more than historic data analyzed on Hadoop. It’s also more than real-time streaming data stored and queried using NoSQL. Learn more at www.Infochimps.com
Intel Developer Forum: Taming the Big Data Tsunami
using Intel® Architecture by Clive D’Souza, Solutions Architect, Intel Corporation and
Dhruv Bansal, Chief Science Officer, Infochimps
Dhruv Bansal, Infochimps CSO and Co-founder, discusses how Big Data tools are helping agencies build out repeatable revenue platforms, and what factors are important when setting down the path of Big Data.
Data comes from Twitter, Facebook, Omniture, Google, CRM systems, data warehouses, and much more. Tap into that data to enhance your analytics products and services.
Ironfan is the foundation for your Big Data stack, making provisioning and configuring your Big Data infrastructure simple. Spin up clusters when you need them, kill them when you don't, so you can spend your time, money, and engineering focus on finding insights, not getting your machines ready.
Learn more at http://infochimps.com
A horizontally-scalable, distributed database built on Apache’s Lucene that delivers a full-featured search experience across terabytes of data with a simple yet powerful API.
Learn more at http://infochimps.com
Today’s top contenders in the digital marketing and advertising market have a new set of challenges in creating and managing brands. Now, with an abundance of data at their fingertips, agencies are learning how to leverage their customers’ internal data to message and communicate brands and products more effectively. To further strengthen their efforts, they are tapping into technologies that allow them to leverage massive data streams from social and other web sources.
Learn more at http://infochimps.com
This document outlines Infochimps' big data solutions. It discusses common big data problems around scaling, time, reliability, efficiency, staffing and data sourcing. It then describes Infochimps' platform which uses technologies like Ironfan, Wukong and partners to provide data infrastructure, analytics and a marketplace. Services include implementation, hosting, support and consulting. Infochimps differentiates itself by offering a complete solution while leveraging data augmentation and expertise to address clients' big data challenges.
More from Infochimps, a CSC Big Data Business (17)
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
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GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
11. • Manage 100s of machines: architecture as code
• Contain system complexity: relentlessly decouple
• Maintain coherency: federated truth
• Manage true costs: optimize for people not machines
• Manage failure & change:resiliency engineering
12. The Other Way
Declarative, not Homogenous
Decoupled, not Standardized
Federated, not Centralized
Simple, not Performant
Resilient, not Reliable
38. Data Stores in Production
• HBase • MySQL
• ElasticSearch • Redis
• Cassandra • sqlite
• TokyoTyrant • whisper (graphite)
• SimpleDB • file system
• MongoDB • S3
39. Programs Used for This Talk
• Emacs • Skitch
• Keynote • finder
• Preview • flickr.com
• Chrome • google image search
• ruby (pry) • ssh
40. How’s my Batch Job Going?
• 1 x Job Status
• 1 x Counters & App Metrics
• N x Task Status
• M x Machine System Stats
• 1 x Cloud Status
• 1 x Chef Server
52. n^2 law of coupling
100 things 5 + 3 + 2 things
+ 2 (tax)
53. n^2 law of coupling
2500
+
900
+
400
+
400
=
10,000 things 4200 things
to go wrong to go wrong
54.
55. Infochimps.com 2011
text search
Planet of the
API acct'g
APIs
infochimps.com models
A/B testing
cloud
services
56. Infochimps.com 2012
datasets catalog API
API docs
text search
content
dashboards Planet of the
API acct'g
APIs
auth & payment
layout
console
models
A/B testing
blog
press cloud
services
collateral
57. Infochimps.com 2012
(infochimps)
icsexpl catalog API
(saas)
capuchin
elasticsrch
kanzi
beergoggls Planet of the
MongoDB
APIs
george george
alphamale
MySQL
redis
WPEngine
totem cloud
services
hubspot
58. this drawing fits in my head
datasets catalog API
this app fits in my head,
and my laptop
59. Infochimps.com 2012
(infochimps)
icsexpl catalog API
(saas)
capuchin
elasticsrch
kanzi
beergoggls Planet of the
MongoDB
APIs
george george
alphamale
MySQL
redis
WPEngine
totem cloud
services
hubspot
This is on a 15-person organization\nFederated, meaning the data is semantically disparate\n
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people are walking around as if we used to have one kind of database and now we have two\nThe important fact isn’t that one of them is sharded \nThe important fact is that they’re proliferating -- and that’s a good thing.\n
Google, Facebook, Amazon had to solve the scalability problem\n
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Now I know this sounds like the lunacy of a ritalin-addled architecture astronaut spending too much time on StackOverflow. \n
Now I know this sounds like the lunacy of a ritalin-addled architecture astronaut spending too much time on StackOverflow. \n