The document discusses the evolving on-demand infrastructure for big data. It describes how infrastructure has evolved from a structured data warehouse approach to a more flexible approach incorporating Hadoop, NoSQL, and in-memory databases to handle multi-structured and streaming data sources. This new approach allows for bidirectional integration between data sources and supports various analytics applications and visualizations.
Enterprise architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
1. The document discusses Trifacta, a company focused on data wrangling and preparation. It provides an overview of the company, its key differentiators including being interoperable, interactive and visual, and predictive.
2. Trifacta's workflow in Hadoop is described, utilizing YARN and Spark to execute transformations across clusters in a scalable way.
3. An example is given of Trifacta being selected as an OEM partner for Google Cloud Dataprep, integrating its interface and engine within Google Cloud.
Enterprise Architecture in the Era of Big Data and Quantum ComputingKnowledgent
Deck from April 2014 Big Data Palooza Meetup sponsored by Knowledgent. Enterprise Architect James Luisi spoke
Summary: Several characteristics identify the presence of big data. Invariably as new use cases emerge, new products emerge to address them. At this point, there are so many use cases, and so many products, that frameworks to organize and manage them are necessary. A couple of examples of useful frameworks to manage and organize include families of use cases and architectural disciplines.
Výběr Big Data platformy - Jan Sovka - IBMProfinit
This document discusses selecting the right big data platform for your needs. It covers key considerations like data processing concepts, analytic appliances, security, data movement and integration with existing systems. The document also provides an overview of the IBM BigInsights platform for Apache Hadoop, including its open source components, management capabilities and tools for data scientists and analysts. Integration with other systems and choosing the right vendor while avoiding oversizing are emphasized as important factors for a big data platform.
Pentaho provides open source business analytics tools including Kettle for extraction, transformation and loading (ETL) of data, and Weka for machine learning and data mining. Kettle allows users to run ETL jobs directly on Hadoop clusters and its JDBC layer enables SQL queries to be pushed down to databases for better performance. While bringing Weka analytics to Hadoop data provides gains, challenges include ensuring true parallel machine learning algorithms and keeping clients notified of database updates.
Graph-based Network & IT Management.
Linkurious is a graph visualization and analysis startup founded in 2013 in Paris that helps customers unlock insights from graph data. Their software helps visualize interconnected IT infrastructure components and detect issues by analyzing relationships and patterns in real-time. Linkurious supports graph databases like Neo4j, DataStax, Titan and AllegroGraph and is used by organizations for tasks like cybersecurity monitoring, IT operations management, and enterprise architecture planning.
- The solution proposes a cloud-based e-commerce application using a microservices architecture hosted on Azure. Key services include Azure WAF, VPN, subnets, API Management, Azure AD/OAuth 2.0, Azure Cosmos DB, and Azure Media Services.
- The application would be broken into bounded contexts and microservices for functions like search, browse, cart, orders, recommendations, and administration. Services like Elasticsearch, Redis, Cassandra, and SQL would be used for data storage.
- High risks include cost optimization on the cloud, testing environments, infrastructure as code, microservices communication complexity, training on cloud technologies, and implementing continuous integration/deployment pipelines.
Enterprise architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
1. The document discusses Trifacta, a company focused on data wrangling and preparation. It provides an overview of the company, its key differentiators including being interoperable, interactive and visual, and predictive.
2. Trifacta's workflow in Hadoop is described, utilizing YARN and Spark to execute transformations across clusters in a scalable way.
3. An example is given of Trifacta being selected as an OEM partner for Google Cloud Dataprep, integrating its interface and engine within Google Cloud.
Enterprise Architecture in the Era of Big Data and Quantum ComputingKnowledgent
Deck from April 2014 Big Data Palooza Meetup sponsored by Knowledgent. Enterprise Architect James Luisi spoke
Summary: Several characteristics identify the presence of big data. Invariably as new use cases emerge, new products emerge to address them. At this point, there are so many use cases, and so many products, that frameworks to organize and manage them are necessary. A couple of examples of useful frameworks to manage and organize include families of use cases and architectural disciplines.
Výběr Big Data platformy - Jan Sovka - IBMProfinit
This document discusses selecting the right big data platform for your needs. It covers key considerations like data processing concepts, analytic appliances, security, data movement and integration with existing systems. The document also provides an overview of the IBM BigInsights platform for Apache Hadoop, including its open source components, management capabilities and tools for data scientists and analysts. Integration with other systems and choosing the right vendor while avoiding oversizing are emphasized as important factors for a big data platform.
Pentaho provides open source business analytics tools including Kettle for extraction, transformation and loading (ETL) of data, and Weka for machine learning and data mining. Kettle allows users to run ETL jobs directly on Hadoop clusters and its JDBC layer enables SQL queries to be pushed down to databases for better performance. While bringing Weka analytics to Hadoop data provides gains, challenges include ensuring true parallel machine learning algorithms and keeping clients notified of database updates.
Graph-based Network & IT Management.
Linkurious is a graph visualization and analysis startup founded in 2013 in Paris that helps customers unlock insights from graph data. Their software helps visualize interconnected IT infrastructure components and detect issues by analyzing relationships and patterns in real-time. Linkurious supports graph databases like Neo4j, DataStax, Titan and AllegroGraph and is used by organizations for tasks like cybersecurity monitoring, IT operations management, and enterprise architecture planning.
- The solution proposes a cloud-based e-commerce application using a microservices architecture hosted on Azure. Key services include Azure WAF, VPN, subnets, API Management, Azure AD/OAuth 2.0, Azure Cosmos DB, and Azure Media Services.
- The application would be broken into bounded contexts and microservices for functions like search, browse, cart, orders, recommendations, and administration. Services like Elasticsearch, Redis, Cassandra, and SQL would be used for data storage.
- High risks include cost optimization on the cloud, testing environments, infrastructure as code, microservices communication complexity, training on cloud technologies, and implementing continuous integration/deployment pipelines.
The document discusses the importance of a hybrid data model for Hadoop-driven analytics. It notes that traditional data warehousing is not suitable for large, unstructured data in Hadoop environments due to limitations in handling data volume, variety, and velocity. The hybrid model combines a data lake in Hadoop for raw, large-scale data with data marts and warehouses. It argues that Pentaho's suite provides tools to lower technical barriers for extracting, transforming, and loading (ETL) data between the data lake and marts/warehouses, enabling analytics on Hadoop data.
Getting started with Cosmos DB + Linkurious EnterpriseLinkurious
Nowadays, many real-world applications generate data that is naturally connected, but traditional systems fail to capture the value it represents. Thanks to its graph API, the multi-model database Cosmos DB lets you model and store graph-like data. On top of Cosmos DB, Linkurious Enterprise is turnkey solution to detect and investigate insights through an interface for graph data visualization and analysis.
In this presentation, we will explain the value of graphs and show how to get started with Cosmos DB and Linkurious Enterprise to accelerate the discovery of new insights in your connected data.
The document discusses the confusing landscape of big data tools and applications. It provides an overview of the different types of structured and unstructured data as well as databases, analytics platforms, and visualization tools that can be used to manage and analyze both structured and unstructured data at massive scale. The document also includes various diagrams and infographics from different sources that depict the big data ecosystem and the many interrelated tools and technologies involved.
1. The document discusses Pentaho's approach to big data analytics using a component-based data integration and visualization platform.
2. The platform allows business analysts and data scientists to prepare and analyze big data without advanced technical skills.
3. It provides a visual interface for building reusable data pipelines that can be run locally or deployed to Hadoop for analytics on large datasets.
This document discusses SAP's data services for processing unstructured data. It notes that most business information exists outside standard databases as unstructured data like documents, emails and sensor data. SAP BO Data Services provides a single solution for both structured and unstructured data with text analytics capabilities. It allows extraction of entities from unstructured text sources like emails through linguistic processing and stores binary files like images as binary large objects for querying, reporting and analytics. A proof of concept demonstrates processing an email message file and image file as unstructured text and binary sources respectively.
MongoDB IoT City Tour LONDON: Hadoop and the future of data management. By, M...MongoDB
Mark Lewis, Senior MArketing Director EMEA, Cloudera.
Hadoop and the Future of Data Management. As Hadoop takes the data management market by storm, organisations are evolving the role it plays in the modern data centre. Explore how this disruptive technology is quickly transforming an industry and how you can leverage it today, in combination with MongoDB, to drive meaningful change in your business.
Prescient leverages decades of experience in threat analysis and complex systems design to keep international travelers safe. It aggregates data from many sources and uses advanced analytic systems to evaluate, distribute, and visualize threat and safety information in real-time. This helps provide situational awareness for travelers through mobile apps and other tools that alert users to emerging threats and monitor their locations.
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQLDATAVERSITY
Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection.
Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges.
In this webinar we’ll explore:
Where Kafka fits into the big data ecosystem
How companies are using Kafka for both real-time processing and as a bus for data exchange
An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems
Several real-world use case architectures
Linkurious Enterprise is compatible with Azure Cosmos DB and offers investigation teams a turnkey solution to detect and investigate threats hidden in graph data. In this post, we explain how Linkurious Enterprise connects to Cosmos DB graph database.
This document provides an overview of various architecture domains and components for building applications on AWS. It discusses business architecture, information architecture, infrastructure architecture, data architecture, integration architecture and more. It also covers key AWS concepts like scalability, elasticity, pay per use model, availability across regions, multi-tenant architecture, NoSQL databases, risk frameworks, security architectures, use cases, reference models, gap analysis and roadmaps. Finally, it lists several AWS services for compute, storage, databases, analytics, networking, developer tools, and security.
2.5 billion gigabytes of data are generated daily, which organizations use to gain customer insights, improve offerings, and optimize operations. Working with large volumes and varieties of data generated too quickly presents challenges. Traditional methods of collecting, preparing, and analyzing data using coding tools and Excel are difficult. New AI-based tools now empower users to more intuitively work with data by automating data collection, cleaning, and analysis.
The document discusses different technologies used in big data architectures including flat files, XML files, relational databases, multiple input data sources, Sqoop, Flume, Kafka, Spark, MapReduce and Python for processing large datasets, and various reporting and visualization tools like Tableau, QlikView, and SAP WebI for analyzing and viewing results from big data systems supporting many applications.
Bloor Research & DataStax: How graph databases solve previously unsolvable bu...DataStax
This webinar covered graph databases and how they can solve problems that were previously difficult for traditional databases. It included presentations on why graph databases are useful, common use cases like recommendations and network analysis, different types of graph databases, and a demonstration of the DataStax Enterprise graph database. There was also a question and answer session where attendees could ask about graph databases and DataStax Enterprise graph.
This document provides an overview and introduction to Cambridge Semantics Inc. and their Anzo Smart Data Platform for building smart data lakes using semantics. Key points include:
- Cambridge Semantics was founded in 2007 and their Anzo software suite uses open semantic web standards to create data analytics and management solutions from diverse data sources.
- While data lakes make it easy to assemble large volumes of data, identifying and linking data across sources remains challenging without harmonization of meanings. Semantic models and tools can help address these issues.
- The Anzo Analytics and Data Integration Suite uses business understandable semantic models to describe, search, query and analyze data from various structured and unstructured sources to build a smart data lake.
The document provides an introduction to the Semantic Web by defining it in multiple ways: a) as a family of Web standards to make data easier to use and reuse, b) as an upgrade to the current Web enabling more intelligent applications, and c) as a collection of metadata technologies to improve business software adaptability and responsiveness. It notes what the Semantic Web is not (e.g. not a better search engine or tagged HTML) and provides examples of how the Semantic Web could benefit individuals by making their lives simpler and businesses by empowering new capabilities and reducing IT costs through standardized metadata linking. Finally, it discusses some early examples and implementations as well as next steps for exploring and prototyping with Semantic
The document outlines the various architectures that make up a solution architecture for Sunpower, including business architecture, information architecture, infrastructure architecture, data architecture, integration architecture, and service architecture. Business architecture defines the business objectives, strategy, capabilities, processes, and structure. Information architecture shows how data will be captured from various social media and legacy systems and stored in a data lake using column families and denormalized tables. Infrastructure architecture and data architecture are also included as key components of the overall solution architecture.
The document discusses Oracle Enterprise Metadata Management (OEMM) which allows users to manage metadata, data lineage, and business glossaries. It harvests metadata from popular platforms including BI tools, ETL tools, databases, and big data tools. OEMM provides vertical lineage that shows traceability from business terms to IT artifacts, and horizontal lineage that traces columns and fields across multiple systems. It allows interactive exploration of metadata relationships through zooming and filtering capabilities.
Big Data Integration Webinar: Reducing Implementation Efforts of Hadoop, NoSQ...Pentaho
This document discusses approaches to implementing Hadoop, NoSQL, and analytical databases. It describes:
1) The current landscape of big data databases including Hadoop, NoSQL, and analytical databases that are often used together but come from different vendors with different interfaces.
2) Common uses of transactional databases, Hadoop, NoSQL databases, and analytical databases.
3) The complexity of current implementation approaches that involve multiple coding steps across various tools.
4) How Pentaho provides a unified platform and visual tools to reduce the time and effort needed for implementation by eliminating disjointed steps and enabling non-coders to develop workflows and analytics for big data.
Disruptive Impact of Big Data Analytics on Insurance- Capgemini Australia Poi...dipak sahoo
The document discusses how big data and analytics are disrupting the insurance industry. It outlines that:
1) Insurers are now able to access vast new sources of data like social media, wearables, connected devices and more to better understand risks and strengthen customer relationships.
2) Technologies like telematics allow insurers to access real-time driver behavior data to more accurately price and manage risk.
3) Insurers must adopt a proactive, data-driven approach to predict events rather than just react, in order to remain competitive in this new environment of abundant data and advanced analytics.
The document discusses the importance of a hybrid data model for Hadoop-driven analytics. It notes that traditional data warehousing is not suitable for large, unstructured data in Hadoop environments due to limitations in handling data volume, variety, and velocity. The hybrid model combines a data lake in Hadoop for raw, large-scale data with data marts and warehouses. It argues that Pentaho's suite provides tools to lower technical barriers for extracting, transforming, and loading (ETL) data between the data lake and marts/warehouses, enabling analytics on Hadoop data.
Getting started with Cosmos DB + Linkurious EnterpriseLinkurious
Nowadays, many real-world applications generate data that is naturally connected, but traditional systems fail to capture the value it represents. Thanks to its graph API, the multi-model database Cosmos DB lets you model and store graph-like data. On top of Cosmos DB, Linkurious Enterprise is turnkey solution to detect and investigate insights through an interface for graph data visualization and analysis.
In this presentation, we will explain the value of graphs and show how to get started with Cosmos DB and Linkurious Enterprise to accelerate the discovery of new insights in your connected data.
The document discusses the confusing landscape of big data tools and applications. It provides an overview of the different types of structured and unstructured data as well as databases, analytics platforms, and visualization tools that can be used to manage and analyze both structured and unstructured data at massive scale. The document also includes various diagrams and infographics from different sources that depict the big data ecosystem and the many interrelated tools and technologies involved.
1. The document discusses Pentaho's approach to big data analytics using a component-based data integration and visualization platform.
2. The platform allows business analysts and data scientists to prepare and analyze big data without advanced technical skills.
3. It provides a visual interface for building reusable data pipelines that can be run locally or deployed to Hadoop for analytics on large datasets.
This document discusses SAP's data services for processing unstructured data. It notes that most business information exists outside standard databases as unstructured data like documents, emails and sensor data. SAP BO Data Services provides a single solution for both structured and unstructured data with text analytics capabilities. It allows extraction of entities from unstructured text sources like emails through linguistic processing and stores binary files like images as binary large objects for querying, reporting and analytics. A proof of concept demonstrates processing an email message file and image file as unstructured text and binary sources respectively.
MongoDB IoT City Tour LONDON: Hadoop and the future of data management. By, M...MongoDB
Mark Lewis, Senior MArketing Director EMEA, Cloudera.
Hadoop and the Future of Data Management. As Hadoop takes the data management market by storm, organisations are evolving the role it plays in the modern data centre. Explore how this disruptive technology is quickly transforming an industry and how you can leverage it today, in combination with MongoDB, to drive meaningful change in your business.
Prescient leverages decades of experience in threat analysis and complex systems design to keep international travelers safe. It aggregates data from many sources and uses advanced analytic systems to evaluate, distribute, and visualize threat and safety information in real-time. This helps provide situational awareness for travelers through mobile apps and other tools that alert users to emerging threats and monitor their locations.
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQLDATAVERSITY
Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection.
Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges.
In this webinar we’ll explore:
Where Kafka fits into the big data ecosystem
How companies are using Kafka for both real-time processing and as a bus for data exchange
An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems
Several real-world use case architectures
Linkurious Enterprise is compatible with Azure Cosmos DB and offers investigation teams a turnkey solution to detect and investigate threats hidden in graph data. In this post, we explain how Linkurious Enterprise connects to Cosmos DB graph database.
This document provides an overview of various architecture domains and components for building applications on AWS. It discusses business architecture, information architecture, infrastructure architecture, data architecture, integration architecture and more. It also covers key AWS concepts like scalability, elasticity, pay per use model, availability across regions, multi-tenant architecture, NoSQL databases, risk frameworks, security architectures, use cases, reference models, gap analysis and roadmaps. Finally, it lists several AWS services for compute, storage, databases, analytics, networking, developer tools, and security.
2.5 billion gigabytes of data are generated daily, which organizations use to gain customer insights, improve offerings, and optimize operations. Working with large volumes and varieties of data generated too quickly presents challenges. Traditional methods of collecting, preparing, and analyzing data using coding tools and Excel are difficult. New AI-based tools now empower users to more intuitively work with data by automating data collection, cleaning, and analysis.
The document discusses different technologies used in big data architectures including flat files, XML files, relational databases, multiple input data sources, Sqoop, Flume, Kafka, Spark, MapReduce and Python for processing large datasets, and various reporting and visualization tools like Tableau, QlikView, and SAP WebI for analyzing and viewing results from big data systems supporting many applications.
Bloor Research & DataStax: How graph databases solve previously unsolvable bu...DataStax
This webinar covered graph databases and how they can solve problems that were previously difficult for traditional databases. It included presentations on why graph databases are useful, common use cases like recommendations and network analysis, different types of graph databases, and a demonstration of the DataStax Enterprise graph database. There was also a question and answer session where attendees could ask about graph databases and DataStax Enterprise graph.
This document provides an overview and introduction to Cambridge Semantics Inc. and their Anzo Smart Data Platform for building smart data lakes using semantics. Key points include:
- Cambridge Semantics was founded in 2007 and their Anzo software suite uses open semantic web standards to create data analytics and management solutions from diverse data sources.
- While data lakes make it easy to assemble large volumes of data, identifying and linking data across sources remains challenging without harmonization of meanings. Semantic models and tools can help address these issues.
- The Anzo Analytics and Data Integration Suite uses business understandable semantic models to describe, search, query and analyze data from various structured and unstructured sources to build a smart data lake.
The document provides an introduction to the Semantic Web by defining it in multiple ways: a) as a family of Web standards to make data easier to use and reuse, b) as an upgrade to the current Web enabling more intelligent applications, and c) as a collection of metadata technologies to improve business software adaptability and responsiveness. It notes what the Semantic Web is not (e.g. not a better search engine or tagged HTML) and provides examples of how the Semantic Web could benefit individuals by making their lives simpler and businesses by empowering new capabilities and reducing IT costs through standardized metadata linking. Finally, it discusses some early examples and implementations as well as next steps for exploring and prototyping with Semantic
The document outlines the various architectures that make up a solution architecture for Sunpower, including business architecture, information architecture, infrastructure architecture, data architecture, integration architecture, and service architecture. Business architecture defines the business objectives, strategy, capabilities, processes, and structure. Information architecture shows how data will be captured from various social media and legacy systems and stored in a data lake using column families and denormalized tables. Infrastructure architecture and data architecture are also included as key components of the overall solution architecture.
The document discusses Oracle Enterprise Metadata Management (OEMM) which allows users to manage metadata, data lineage, and business glossaries. It harvests metadata from popular platforms including BI tools, ETL tools, databases, and big data tools. OEMM provides vertical lineage that shows traceability from business terms to IT artifacts, and horizontal lineage that traces columns and fields across multiple systems. It allows interactive exploration of metadata relationships through zooming and filtering capabilities.
Big Data Integration Webinar: Reducing Implementation Efforts of Hadoop, NoSQ...Pentaho
This document discusses approaches to implementing Hadoop, NoSQL, and analytical databases. It describes:
1) The current landscape of big data databases including Hadoop, NoSQL, and analytical databases that are often used together but come from different vendors with different interfaces.
2) Common uses of transactional databases, Hadoop, NoSQL databases, and analytical databases.
3) The complexity of current implementation approaches that involve multiple coding steps across various tools.
4) How Pentaho provides a unified platform and visual tools to reduce the time and effort needed for implementation by eliminating disjointed steps and enabling non-coders to develop workflows and analytics for big data.
Disruptive Impact of Big Data Analytics on Insurance- Capgemini Australia Poi...dipak sahoo
The document discusses how big data and analytics are disrupting the insurance industry. It outlines that:
1) Insurers are now able to access vast new sources of data like social media, wearables, connected devices and more to better understand risks and strengthen customer relationships.
2) Technologies like telematics allow insurers to access real-time driver behavior data to more accurately price and manage risk.
3) Insurers must adopt a proactive, data-driven approach to predict events rather than just react, in order to remain competitive in this new environment of abundant data and advanced analytics.
Montreal info session - Market Data an Market Data Company (MDC) Point of ViewRobert Benedetto
Market Data Company Challenges of Managing Market Data - Montreal Info Session
Hosted by the Market Data Company October 21st, 2015 Market Data Company Montreal Market Data Info Session Agenda Market Data Management Challenges Market Data Scope Market Ownership & Accountability Market Data Value proposition
An MDC Point of View
The document discusses the Toronto Stock Exchange (TSX) as a leading market for clean technology companies. It notes that the TSX ranks #1 globally for listed clean technology companies with 116 such firms representing $20 billion in market capitalization. These companies cover a diverse range of clean technology sectors such as energy efficiency, low impact materials, waste reduction, and renewable energy. The TSX has also been an important source of capital for clean technology companies, facilitating the raising of over $6 billion since 2008 with more than $1 billion raised each year from 2009 to 2012.
A recent survey indicated significant growth of big data adoption among enterprise companies. The survey also indicated growing interest in Hadoop in the cloud.
Introduce the Big-Data data characteristic, big-data process flow/architecture, and take out an example about EKG solution to explain why we are run into big data issue, and try to build up a big-data server farm architecture. From there, you can have more concrete point of view, what the big-data is.
Think Big: A New Social Point Of View for Marketing.Andy Hunter
Presentation to the UT McCombs Business School-Marketing Fellows group.
(for more thinking see experiencefreak.com)
Think Big: A New Social Point of View, discusses the impact of technology on marketing and the need to change the management context, organizational structures and philosophy behind executing marketing campaigns.
Everything's increasing social.. and social media as a tactic is the wrong context. Storytelling campaigns that have inherently social elements and a social currency will be the future of brand marketing.
Inspiration and fodder for the presentation includes past thinking from thought leaders Henry Jenkins, Jane McGonigal, Mark Earls, Russell Davies and others.
Exclusive Verizon Employee Webinar: Getting More From Your CDR DataPentaho
This document discusses a project between Pentaho and Verizon to leverage big data analytics. Verizon generates vast amounts of call detail record (CDR) data from mobile networks that is currently stored in a data warehouse for 2 years and then archived to tape. Pentaho's platform will help optimize the data warehouse by using Hadoop to store all CDR data history. This will free up data warehouse capacity for high value data and allow analysis of the full 10 years of CDR data. Pentaho tools will ingest raw CDR data into Hadoop, execute MapReduce jobs to enrich the data, load results into Hive, and enable analyzing the data to understand calling patterns by geography over time.
This document discusses organizing data in a data lake or "data reservoir". It describes the changing data landscape with multiple platforms for different analytical workloads. It outlines issues with the current siloed approach to data integration and management. The document introduces the concept of a data reservoir - a collaborative, governed environment for rapidly producing information. Key capabilities of a data reservoir include data collection, classification, governance, refinery, consumption, and virtualization. It describes how a data reservoir uses zones to organize data at different stages and uses workflows and an information catalog to manage the information production process across the reservoir.
As users gain more experience with Hadoop, they are building on their early success and expanding the size and scope of Hadoop projects. Syncsort’s third annual Hadoop Market Adoption Survey reflects the fact that Hadoop is no longer considered a technology for the future as it was when we first started conducting this research.
Get an in-depth look at the survey results and five trends to watch for in 2017. You’ll also learn:
• The best uses for Hadoop in 2017 – real-word examples of how Enterprises are realizing the value of Big Data
• Solutions to help you address the challenges enterprises still face in employing Hadoop
• What the future of Hadoop means for your business
Up Your Analytics Game with Pentaho and Vertica Pentaho
Big Data is a game-changer.
In the face of exploding volumes and varieties of data, traditional data management and ETL systems just aren’t cutting it anymore. A new way of sifting through vast volumes of data to find the most relevant info, combining this data with other data sources to extract faster insights is desperately needed. Enter HP|Vertica and Pentaho with a proven solution for lightning fast queries and blended data and analytics capabilities for your business users.
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
Join Cloudian, Hortonworks and 451 Research for a panel-style Q&A discussion about the latest trends and technology innovations in Big Data and Analytics. Matt Aslett, Data Platforms and Analytics Research Director at 451 Research, John Kreisa, Vice President of Strategic Marketing at Hortonworks, and Paul Turner, Chief Marketing Officer at Cloudian, will answer your toughest questions about data storage, data analytics, log data, sensor data and the Internet of Things. Bring your questions or just come and listen!
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Just a copy from http://www.isim.ac.in/Infovision%202012/presentations/sunilshirguppilinkedin.pdf for saving purpose. All right reserved by the above link.
How Experian increased insights with HadoopPrecisely
This document provides an overview of MapR Technologies and their products. It discusses how MapR helps companies harness big data by providing an enterprise-grade distribution of Apache Hadoop that includes data protection, security, and high performance capabilities. It also highlights MapR partnerships with companies like Syncsort to provide data integration, migration, and analytics solutions that help customers derive more value from their data.
Big Data in Action – Real-World Solution ShowcaseInside Analysis
The Briefing Room with Radiant Advisors and IBM
Live Webcast on February 25, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=53c9b7fa2000f98f5b236747e3602511
The power of Big Data depends heavily upon the context in which it's used, and most organizations are just beginning to figure out where, how and when to leverage it. One key to success is integration with existing information systems, many of which still rely on relational database technologies. Finding ways to blend these two worlds can help companies generate measurable business value in fairly short order.
Register for this episode of The Briefing Room to hear Analysts Lindy Ryan and John O'Brien as they explain how the combination of traditional Business Intelligence with Big Data Analytics can provide game-changing results in today's information economy. They'll be briefed by Eric Poulin and Paul Flach of Stream Integration who will share best practices for designing and implementing Big Data solutions. They'll discuss the components of IBM BigInsights, and explain how BigSheets can empower non-technical users who need to explore self-structured data.
Visit InsideAnlaysis.com for more information.
This document discusses big data and Cloudera's Enterprise Data Hub solution. It begins by noting that big data is growing exponentially and now includes structured, complex, and diverse data types from various sources. Traditional data architectures using relational databases cannot effectively handle this scale and variety of big data. The document then introduces Cloudera's Hadoop-based Enterprise Data Hub as an open, scalable, and cost-effective platform that can ingest and process all data types and bring compute capabilities to the data. It provides an overview of Cloudera's history and product offerings that make up its full big data platform.
Presentation given at OpexCon in Prague this October, titled Incorporating Cloud Computing for Enhanced Communication. In it I'm discussing how cloud computing and technology can help enterprises build operational excellence
This document discusses Oracle's data integration and governance solutions for big data. It describes how Oracle uses data integration to load and transform data from various sources into a data reservoir. It also emphasizes the importance of data governance when managing big data and describes Oracle's metadata management, data profiling, and data cleansing tools to help govern data in the reservoir.
INTRODUCTION TO BIG DATA AND HADOOP
9
Introduction to Big Data, Types of Digital Data, Challenges of conventional systems - Web data, Evolution of analytic processes and tools, Analysis Vs reporting - Big Data Analytics, Introduction to Hadoop - Distributed Computing
Challenges - History of Hadoop, Hadoop Eco System - Use case of Hadoop – Hadoop Distributors – HDFS – Processing Data with Hadoop – Map Reduce.
Self Service Analytics and a Modern Data Architecture with Data Virtualizatio...Denodo
Watch full webinar here: https://bit.ly/32TT2Uu
Data virtualization is not just for self-service, it’s also a first-class citizen when it comes to modern data platform architectures. Technology has forced many businesses to rethink their delivery models. Startups emerged, leveraging the internet and mobile technology to better meet customer needs (like Amazon and Lyft), disrupting entire categories of business, and grew to dominate their categories.
Schedule a complimentary Data Virtualization Discovery Session with g2o.
Traditional companies are still struggling to meet rising customer expectations. During this webinar with the experts from g2o and Denodo we covered the following:
- How modern data platforms enable businesses to address these new customer expectation
- How you can drive value from your investment in a data platform now
- How you can use data virtualization to enable multi-cloud strategies
Leveraging the strategy insights of g2o and the power of the Denodo platform, companies do not need to undergo the costly removal and replacement of legacy systems to modernize their systems. g2o and Denodo can provide a strategy to create a modern data architecture within a company’s existing infrastructure.
The Maturity Model: Taking the Growing Pains Out of HadoopInside Analysis
The Briefing Room with Rick van der Lans and Think Big, a Teradata Company
Live Webcast on June 16, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=197f8106531874cc5c14081ca214eaff
Hadoop is arguably one of the most disruptive technologies of the last decade. Once lauded solely for its ability to transform the speed of batch processing, it has marched steadily forward and promulgated an array of performance-enhancing accessories, notably Spark and YARN. Hadoop has evolved into much more than a file system and batch processor, and it now promises to stand as the data management and analytics backbone for enterprises.
Register for this episode of The Briefing Room to learn from veteran Analyst Rick van der Lans, as he discusses the emerging roles of Hadoop within the analytics ecosystem. He’ll be briefed by Ron Bodkin of Think Big, a Teradata Company, who will explore Hadoop’s maturity spectrum, from typical entry use cases all the way up the value chain. He’ll show how enterprises that already use Hadoop in production are finding new ways to exploit its power and build creative, dynamic analytics environments.
Visit InsideAnalysis.com for more information.
Limitless Data, Rapid Discovery, Powerful Insight: How to Connect Cloudera to...Cloudera, Inc.
What if…
…your data stores were limitless and accessible?
…data discovery was fast… really fast?
…connectivity was so seamless you could almost take it for granted?
And what if you could do all this with your preferred BI tool?
Learn how to integrate Cloudera Enterprise with SAP Lumira via embedded connectivity from Simba Technologies.
In this interactive webinar, experts from Cloudera, SAP, and Simba Technologies will introduce strategies for overcoming current data-discovery challenges, show you how to achieve powerful analytical insight, and demonstrate how to integrate Cloudera Enterprise with SAP Lumira.
This document summarizes a presentation about using Hadoop as an analytic platform. It discusses how Actian has added seven key ingredients to Hadoop to unlock its full potential for analytics. These include high-speed data integration, a visual framework for data science and modeling, open-source analytic operators, high-performance data processing engines, vector-based SQL processing natively on HDFS, an extremely fast parallel analytics engine, and a next-generation big data analytics platform. The goal is to transform Hadoop from merely a data reservoir to a fully-featured analytics platform.
This document provides an overview and strategy for big and fast data initiatives in 2017. It discusses the data landscape including volume, velocity, variety and validity. It evaluates different data platform technologies and outlines requirements. The vision is described as "Business Insights at the Speed of Light". The strategy focuses on speed and leveraging key technologies like Spark. A roadmap with initiatives around insights, infrastructure, ingestion and big BI is presented. High level architectures for streaming and data flow are shown. Finally, data preparation vendors are compared.
Similar to POV on Evolving On-Demand Infrastructure for Big Data (20)
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
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
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
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.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Hi my name is…Prime Dimensions is…Our host today is…Housekeeping…
Store and Analyze ApproachPerceived high value per byte – rigorous cleansing and transformationThe store and analyze approach integrates source data into a consolidated data storebefore it is analyzed. This approach is used by a traditional data warehousing systemto create data analytics. In a data warehousing system, the consolidated data store isusually an enterprise data warehouse or data mart managed by a relational ormultidimensional DBMS. The advantages of this approach are improved dataintegration and data quality management, plus the ability to maintain historicalinformation. The disadvantages are additional data storage requirements and thelatency introduced by the data integration task.What is a data warehouse?In the 1990s, Bill Inmon defined a design known as a data warehouse. In 2005, Gartner clarified and updated those definitions. From these we summarize that a data warehouse is:1. Subject oriented: The data is modeled after business concepts, organizing them into subjects areas like sales, finance, and inventory. Each subject area contains detailed data.2. Integrated: The logical model is integrated and consistent. Data formats and values are standardized. Thus, dates are in the same format, male/female codes are consistent, etc. More important, all subject areas use the same customer record, not copies.3. Nonvolatile: Data is stored in the data warehouse unmodified, and retained for long periods of time.4. Time variant: When changes to a record are needed, new versions of the record are captured using effective dates or temporal functions.5. Not virtual: The data warehouse is a physical, persistent repository.http://searchdatamanagement.techtarget.com/definition/OLAPOLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in amultidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the Eastern region above a certain price during a certain time period) and display them. Attributes such as time periods can be broken down into subattributes.OLAP can be used for data mining or the discovery of previously undiscerned relationships between data items. An OLAP database does not need to be as large as a data warehouse, since not all transactional data is needed for trend analysis. Using Open Database Connectivity (ODBC), data can be imported from existing relational databases to create a multidimensional database for OLAP.Two leading OLAP products are Hyperion Solution's Essbase and Oracle's Express Server. OLAP products are typically designed for multiple-user environments, with the cost of thesoftware based on the number of users.Data Warehouse DifferentiatorsAfter nearly 30 years of investment, refinement and growth, the list of features available in a data warehouse is quite staggering. Built upon relational database technology using schemas and integrating Business Intelligence (BI) tools, the major differences in this architecture are:> Data warehouse performance> Integrated data that provides business value> Interactive BI tools for end usersData Warehouse PerformanceBasic indexing, found in open source databases, such as MySQL or Postgres, is a standard feature used to improve query response times or enforce constraints on data. More advanced forms such as materialized views, aggregate join indexes, cube indexes and sparse join indexes enable numerous performance gains in data warehouses. However, the most important performance enhancement to date is the cost-based optimizer. The optimizer examines incoming SQL and considers multiple plans for executing each query as fast as possible. It achieves this by comparing the SQL request to the database design and extensive data statistics that help identify the best combination of execution steps. In essence, the optimizer is like having a genius programmer examine every query and tune it for the best performance. Lacking an optimizer or data demographic statistics, a query that could run in minutes may take hours, even with many indexes. For this reason, database vendors are constantly adding new index types, partitioning, statistics, and optimizer features. For the past 30 years, every software release has been a performance release.Integrating Data: the Raison d’ÊtreAt the heart of any data warehouse is the promise to answer essential business questions. Integrated data is the unique foundation required to achieve this goal. Pulling data from multiple subject areas and numerous applications into one repository is the raison d’être for data warehouses. Data model designers and ETL architects armed with metadata, data cleansing tools, and patience must rationalize data formats, source systems, and semantic meaning of the data to make it understandable and trustworthy. This creates a common vocabulary within the corporation so that critical concepts such as “customer,” “end of month,”or “price elasticity,” are uniformly measured and understood. Nowhere else in the entire IT data center is data collected, cleaned, and integrated as it is in the data warehouse.Interactive BI ToolsBI tools such as MicroStrategy, Tableau, IBM Cognos, and others provide business users with direct access to data warehouse insights. First, the business user can create reports and complex analysis quickly and easily using these tools. As a result, there is a trend in many data warehouse sites towards end-user self service. Business users can easily demand more reports than IT has staffing to provide. More important than self service however, is that the users become intimately familiar with the data. They can run a report, discover they missed a metric or filter, make an adjustment, and run their report again all within minutes. This process results in significant changes in business users’ understanding the business and their decision-making process. First, users stop asking trivial questions and start asking more complex strategic questions. Generally, the more complex and strategic the report, the more revenue and cost savings the user captures. This leads to some users becoming “power users” in a company. These individuals become wizards at teasing business value from the data and supplying valuable strategic information to the executive staff. Every data warehouse has anywhere from two to 20 power users.Query performance with BI tools lowers the analytic pain threshold. If it takes 24 hours to ask and get an answer, users only ask once. If it takes minutes, they will ask dozens of questions. For example, a major retailer was comparing stock-on-hand to planned newspaper coupon advertising. Initially they ran an eight-hour report that analyzed hundreds of stores. One power user saw they could make more money if the advertising was customized for stores by geographic region. By adding filters and constraints and selecting small groups of regional stores, the by-region query ran in two minutes. They added more constraints and filters and ran it again. They discovered that inventory and regional preferences would sell more and increase profits. Where an eight-hour query was discouraging, two-minute queries were an enabler. The power user was then willing to spend a few hours analyzing each region for the best sales, inventory, and profit mix. The lower pain threshold to analytics was enabled by data warehouse performance and the interactivity of the BI tools.
Distributed DW architecture. The issue in a multi-workload environment is whether a single-platform data warehouse can be designed and optimized such that all workloads run optimally, even when concurrent. More DW teams are concluding that a multi-platform data warehouse environment is more cost-effective and flexible. Plus, some workloads receive better optimization when moved to a platform beside the data warehouse. In reaction, many organizations now maintain a core DW platform for traditional workloads but offload other workloads to other platforms. For example, data and processing for SQL-based analytics are regularly offloaded to DW appliances and columnar DBMSs. A few teams offload workloads for big data and advanced analytics to HDFS, discovery platforms, MapReduce, and similar platforms. The result is a strong trend toward distributed DW architectures, where many areas of the logical DW architecture are physically deployed on standalone platforms instead of the core DW platform. Big Data requires a new generation of scalable technologies designed to extract meaning from very large volumes of disparate, multi-structured data by enabling high velocity capture, discovery, and analysisSource of second graphic: http://www.saama.com/blog/bid/78289/Why-large-enterprises-and-EDW-owners-suddenly-care-about-BigDatahttp://www.cloudera.com/content/dam/cloudera/Resources/PDF/Hadoop_and_the_Data_Warehouse_Whitepaper.pdfComplex Hadoop jobs can use the data warehouse as a data source, simultaneously leveraging the massively parallel capabilities of two systems. Any MapReduce program can issue SQL statements to the data warehouse. In one context, a MapReduce program is “just another program,” and the data warehouse is “just another database.” Now imagine 100 MapReduce programs concurrently accessing 100 data warehouse nodes in parallel. Both raw processing and the data warehouse scale to meet any big data challenge. Inevitably, visionary companies will take this step to achieve competitive advantages.Promising Uses of Hadoop that Impact DW Architectures I see a handful of areas in data warehouse architectures where HDFS and other Hadoop products have the potential to play positive roles: Data staging. A lot of data processing occurs in a DW’s staging area, to prepare source data for specific uses (reporting, analytics, OLAP) and for loading into specific databases (DWs, marts, appliances). Much of this processing is done by homegrown or tool-based solutions for extract, transform, and load (ETL). Imagine staging and processing a wide variety of data on HDFS. For users who prefer to hand-code most of their solutions for extract, transform, and load (ETL), they will most likely feel at home in code-intense environments like Apache MapReduce. And they may be able to refactor existing code to run there. For users who prefer to build their ETL solutions atop a vendor tool, the community of vendors for ETL and other data management tools is rolling out new interfaces and functions for the entire Hadoop product family. Note that I’m assuming that (whether you use Hadoop or not), you should physically locate your data staging area(s) on standalone systems outside the core data warehouse, if you haven’t already. That way, you preserve the core DW’s capacity for what it does best: squeaky clean, well modeled data (with an audit trail via metadata and master data) for standard reports, dashboards, performance management, and OLAP. In this scenario, the standalone data staging area(s) offload most of the management of big data, archiving source data, and much of the data processing for ETL, data quality, and so on. Data archiving. When organizations embrace forms of advanced analytics that require detail source data, they amass large volumes of source data, which taxes areas of the DW architecture where source data is stored. Imagine managing detailed source data as an archive on HDFS. You probably already do archiving with your data staging area, though you probably don’t call it archiving. If you think of it as an archive, maybe you’ll adopt the best practices of archiving, especially information lifecycle management (ILM), which I feel is valuable but woefully vacant from most DWs today. Archiving is yet another thing the staging area in a modern DW architecture must do, thus another reason to offload the staging area from the core DW platform. Traditionally, enterprises had three options when it came to archiving data: leave it within a relational database, move it to tape or optical disk, or delete it. Hadoop’s scalability and low cost enable organizations to keep far more data in a readily accessible online environment. An online archive can greatly expand applications in business intelligence, advanced analytics, data exploration, auditing, security, and risk management. Multi-structured data. Relatively few organizations are getting BI value from semi- and unstructured data, despite years of wishing for it. Imagine HDFS as a special place within your DW environment for managing and processing semi-structured and unstructured data. Another way to put it is: imagine not stretching your RDBMS-based DW platform to handle data types that it’s not all that good with. One of Hadoop’s strongest complements to a DW is its handling of semi- and unstructured data. But don’t go thinking that Hadoop is only for unstructured data: HDFS handles the full range of data, including structured forms, too. In fact, Hadoop can manage just about any data you can store in a file and copy into HDFS. Processing flexibility. Given its ability to manage diverse multi-structured data, as I just described, Hadoop’sNoSQL approach is a natural framework for manipulating non-traditional data types. Note that these data types are often free of schema or metadata, which makes them challenging for SQL-based relational DBMSs. Hadoop supports a variety of programming languages (Java, R, C), thus providing more capabilities than SQL alone can offer. In addition, Hadoop enables the growing practice of “late binding”. Instead of transforming data as it’s ingested by Hadoop (the way you often do with ETL for data warehousing), which imposes an a priori model on data, structure is applied at runtime. This, in turn, enables the open-ended data exploration and discovery analytics that many users are looking for today. Advanced analytics. Imagine HDFS as a data stage, archive, or twenty-first-century operational data store that manages and processes big data for advanced forms of analytics, especially those based on MapReduce, data mining, statistical analysis, and natural language processing (NLP). There’s much to say about this; in a future blog I’ll drill into how advanced analytics is one of the strongest influences on data warehouse architectures today, whether Hadoop is in use or not.Analyze and Store Approach (ELT?)The analyze and store approach analyzes data as it flows through businessprocesses, across networks, and between systems. The analytical results can then bepublished to interactive dashboards and/or published into a data store (such as a datawarehouse) for user access, historical reporting and additional analysis. Thisapproach can also be used to filter and aggregate big data before it is brought into adata warehouse.There are two main ways of implementing the analyze and store approach:• Embedding the analytical processing in business processes. This techniqueworks well when implementing business process management and serviceorientedtechnologies because the analytical processing can be called as a servicefrom the process workflow. This technique is particularly useful for monitoring andanalyzing business processes and activities in close to real-time – action times ofa few seconds or minutes are possible here. The process analytics created canalso be published to an operational dashboard or stored in a data warehouse forsubsequent use.• Analyzing streaming data as it flows across networks and between systems.This technique is used to analyze data from a variety of different (possiblyunrelated) data sources where the volumes are too high for the store and analyzeapproach, sub-second action times are required, and/or where there is a need toanalyze the data streams for patterns and relationships. To date, many vendorshave focused on analyzing event streams (from trading systems, for example)using the services of a complex event processing (CEP) engine, but this style ofprocessing is evolving to support a wider variety of streaming technologies anddata. Creates stream analytics from many types of streamingdata such as event, video and GPS data.The benefits of the analyze and store approach are fast action times and lower datastorage overheads because the raw data does not have to be gathered andconsolidated before it can be analyzed.using HiveQL to create a load-ready file for a relational database.