This document discusses the changing landscape of data management as the volume of data grows exponentially. It introduces the concept of "Total Data" which advocates a flexible approach to data management that processes all applicable data across operational databases, data warehouses, Hadoop, and archives. The trends driving more data include greater understanding of data's value, improved processing capabilities, and the rise of machine-generated data. New approaches are needed to virtually access and analyze large datasets at lower costs. RainStor provides a specialized database that can reduce, retain, and retrieve large volumes of historical structured data at 10x lower costs than alternatives.
Extended Data Warehouse - A New Data Architecture for Modern BI with Claudia ...Denodo
This presentation has been extracted from a full webinar organized by Denodo. To learn more click here: http://bit.ly/1FOMD90
Big Data, Internet of Things, Data Lakes, Streaming Analytics, Machine Learning… these are just a few of the buzzwords being thrown around in the world of data management today. They provide us with new sources of data, new forms of analytics, and new ways of storing, managing and utilizing our data. The reality however, is that traditional Data Warehouse architectures are no longer able to handle many of these new technologies and a new data architecture is required.
So what does the new architecture look like? Does the enterprise data warehouse still have a role? Where do these new technologies fit in? How can business users easily and quickly access the various sources of data and analytic results at the right time to make the right decisions in this new world order?
Dr. Claudia Imhoff addresses these questions and presents the Extended Data Warehouse architecture (XDW), demonstrating the need for each component and how an enterprise combines these into appropriate workflows for proper decision support.
Presentation at Data Summit 2015 in NYC.
Elliott Cordo shared real-world insights across a range of topics, including the evolving best practices for building a data warehouse on Hadoop that also coexists with multiple processing frameworks and additional non-Hadoop storage platforms, the place for massively parallel-processing and relational databases in analytic architectures, and the ways in which the cloud offers the ability to quickly and cost-effectively establish a scalable platform for your Big Data warehouse.
For more information, visit www.casertaconcepts.com
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Making the Case for Hadoop in a Large Enterprise-British AirwaysDataWorks Summit
Making the Case for Hadoop in a Large Enterprise
British Airways
Alan Spanos
Data Exploitation Manager
British Airways
Jay Aubby
Architect
British Airways
Built on Oracle Analytics Cloud and powered by Oracle Autonomous Data Warehouse Cloud, Fusion Analytics Warehouse
(FAW) provides Oracle ERP and HCM Cloud Application customers with best-practice key performance indicators (KPIs)
and actionable insights driven by advanced analytics
The Pivotal Business Data Lake provides a flexible blueprint to meet your business's future information and analytics needs while avoiding the pitfalls of typical EDW implementations. Pivotal’s products will help you overcome challenges like reconciling corporate and local needs, providing real-time access to all types of data, integrating data from multiple sources and in multiple formats, and supporting ad hoc analysis.
Extended Data Warehouse - A New Data Architecture for Modern BI with Claudia ...Denodo
This presentation has been extracted from a full webinar organized by Denodo. To learn more click here: http://bit.ly/1FOMD90
Big Data, Internet of Things, Data Lakes, Streaming Analytics, Machine Learning… these are just a few of the buzzwords being thrown around in the world of data management today. They provide us with new sources of data, new forms of analytics, and new ways of storing, managing and utilizing our data. The reality however, is that traditional Data Warehouse architectures are no longer able to handle many of these new technologies and a new data architecture is required.
So what does the new architecture look like? Does the enterprise data warehouse still have a role? Where do these new technologies fit in? How can business users easily and quickly access the various sources of data and analytic results at the right time to make the right decisions in this new world order?
Dr. Claudia Imhoff addresses these questions and presents the Extended Data Warehouse architecture (XDW), demonstrating the need for each component and how an enterprise combines these into appropriate workflows for proper decision support.
Presentation at Data Summit 2015 in NYC.
Elliott Cordo shared real-world insights across a range of topics, including the evolving best practices for building a data warehouse on Hadoop that also coexists with multiple processing frameworks and additional non-Hadoop storage platforms, the place for massively parallel-processing and relational databases in analytic architectures, and the ways in which the cloud offers the ability to quickly and cost-effectively establish a scalable platform for your Big Data warehouse.
For more information, visit www.casertaconcepts.com
The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
Making the Case for Hadoop in a Large Enterprise-British AirwaysDataWorks Summit
Making the Case for Hadoop in a Large Enterprise
British Airways
Alan Spanos
Data Exploitation Manager
British Airways
Jay Aubby
Architect
British Airways
Built on Oracle Analytics Cloud and powered by Oracle Autonomous Data Warehouse Cloud, Fusion Analytics Warehouse
(FAW) provides Oracle ERP and HCM Cloud Application customers with best-practice key performance indicators (KPIs)
and actionable insights driven by advanced analytics
The Pivotal Business Data Lake provides a flexible blueprint to meet your business's future information and analytics needs while avoiding the pitfalls of typical EDW implementations. Pivotal’s products will help you overcome challenges like reconciling corporate and local needs, providing real-time access to all types of data, integrating data from multiple sources and in multiple formats, and supporting ad hoc analysis.
Watch full webinar here: https://bit.ly/3FcgiyK
Denodo recently released the Denodo Cloud Survey 2021. Learn about some of the insights we have from the survey as well as some of the use cases Denodo comes across in the cloud. We will also conduct a brief product demonstration highlighting how easy it is to migrate to the cloud and support access to data in hybrid cloud architectures.
In this session not only will we look at what you, the customers are saying in the Denodo Cloud Survey but also:
- We will explore how, in reality, many organizations are already operating in a hybrid or multi-cloud environment and how their needs are being met through the use of a logical data fabric and data virtualization
- We will discuss how easy it is to reduce the risk and minimize disruption when migrating to the cloud
- We will educate you on why a uniform security layer removes regulatory risk in data governance.
- Finally we will demonstrate some of the key capabilities of the Denodo Platform to support the above.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial:
1. What Is The Need For BI?
2. What Is Data Warehousing?
3. Key Terminologies Related To Data Warehouse Architecture:
a. OLTP Vs OLAP
b. ETL
c. Data Mart
d. Metadata
4. Data Warehouse Architecture
5. Demo: Creating A Data Warehouse
From Traditional Data Warehouse To Real Time Data WarehouseOsama Hussein
Summarising the 'From Traditional Data Warehouse To Real Time Data Warehouse' paper.
1. S. Bouaziz, A. Nabli and F. Gargouri, "From Traditional Data Warehouse To Real Time Data Warehouse", 2017.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/39AhUB7
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Data Ninja Webinar Series: Realizing the Promise of Data LakesDenodo
Watch the full webinar: Data Ninja Webinar Series by Denodo: https://goo.gl/QDVCjV
The expanding volume and variety of data originating from sources that are both internal and external to the enterprise are challenging businesses in harnessing their big data for actionable insights. In their attempts to overcome big data challenges, organizations are exploring data lakes as consolidated repositories of massive volumes of raw, detailed data of various types and formats. But creating a physical data lake presents its own hurdles.
Attend this session to learn how to effectively manage data lakes for improved agility in data access and enhanced governance.
This is session 5 of the Data Ninja Webinar Series organized by Denodo. If you want to learn more about some of the solutions enabled by data virtualization, click here to watch the entire series: https://goo.gl/8XFd1O
Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
Fast and Furious: From POC to an Enterprise Big Data Stack in 2014MapR Technologies
View this webinar presentation as CenturyLink Technology Solutions (Formerly Savvis) and MapR as we deconstruct and demystify “the enterprise big data stack.” We provide you with a more holistic view of the landscape, explore use cases to show how you can derive business value from it, and share best practices for navigating through the fragmented big data environment.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
Exploring the Wider World of Big Data- Vasalis KapsalisNetAppUK
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
Watch full webinar here: https://bit.ly/3FcgiyK
Denodo recently released the Denodo Cloud Survey 2021. Learn about some of the insights we have from the survey as well as some of the use cases Denodo comes across in the cloud. We will also conduct a brief product demonstration highlighting how easy it is to migrate to the cloud and support access to data in hybrid cloud architectures.
In this session not only will we look at what you, the customers are saying in the Denodo Cloud Survey but also:
- We will explore how, in reality, many organizations are already operating in a hybrid or multi-cloud environment and how their needs are being met through the use of a logical data fabric and data virtualization
- We will discuss how easy it is to reduce the risk and minimize disruption when migrating to the cloud
- We will educate you on why a uniform security layer removes regulatory risk in data governance.
- Finally we will demonstrate some of the key capabilities of the Denodo Platform to support the above.
Meaning making – separating signal from noise. How do we transform the customer's next input into an action that creates a positive customer experience? We make the data more intelligent, so that it is able to guide our actions. The Data Lake builds on Big Data strengths by automating many of the manual development tasks, providing several self-service features to end-users, and an intelligent management layer to organize it all. This results in lower cost to create solutions, "smart" analytics, and faster time to business value.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Data Warehouse Tutorial For Beginners | Data Warehouse Concepts | Data Wareho...Edureka!
This Data Warehouse Tutorial For Beginners will give you an introduction to data warehousing and business intelligence. You will be able to understand basic data warehouse concepts with examples. The following topics have been covered in this tutorial:
1. What Is The Need For BI?
2. What Is Data Warehousing?
3. Key Terminologies Related To Data Warehouse Architecture:
a. OLTP Vs OLAP
b. ETL
c. Data Mart
d. Metadata
4. Data Warehouse Architecture
5. Demo: Creating A Data Warehouse
From Traditional Data Warehouse To Real Time Data WarehouseOsama Hussein
Summarising the 'From Traditional Data Warehouse To Real Time Data Warehouse' paper.
1. S. Bouaziz, A. Nabli and F. Gargouri, "From Traditional Data Warehouse To Real Time Data Warehouse", 2017.
Accelerate Self-Service Analytics with Data Virtualization and VisualizationDenodo
Watch full webinar here: https://bit.ly/39AhUB7
Enterprise organizations are shifting to self-service analytics as business users need real-time access to holistic and consistent views of data regardless of its location, source or type for arriving at critical decisions.
Data Virtualization and Data Visualization work together through a universal semantic layer. Learn how they enable self-service data discovery and improve performance of your reports and dashboards.
In this session, you will learn:
- Challenges faced by business users
- How data virtualization enables self-service analytics
- Use case and lessons from customer success
- Overview of the highlight features in Tableau
The Data Lake - Balancing Data Governance and Innovation Caserta
Joe Caserta gave the presentation "The Data Lake - Balancing Data Governance and Innovation" at DAMA NY's one day mini-conference on May 19th. Speakers covered emerging trends in Data Governance, especially around Big Data.
For more information on Caserta Concepts, visit our website at http://casertaconcepts.com/.
Data Ninja Webinar Series: Realizing the Promise of Data LakesDenodo
Watch the full webinar: Data Ninja Webinar Series by Denodo: https://goo.gl/QDVCjV
The expanding volume and variety of data originating from sources that are both internal and external to the enterprise are challenging businesses in harnessing their big data for actionable insights. In their attempts to overcome big data challenges, organizations are exploring data lakes as consolidated repositories of massive volumes of raw, detailed data of various types and formats. But creating a physical data lake presents its own hurdles.
Attend this session to learn how to effectively manage data lakes for improved agility in data access and enhanced governance.
This is session 5 of the Data Ninja Webinar Series organized by Denodo. If you want to learn more about some of the solutions enabled by data virtualization, click here to watch the entire series: https://goo.gl/8XFd1O
Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
Fast and Furious: From POC to an Enterprise Big Data Stack in 2014MapR Technologies
View this webinar presentation as CenturyLink Technology Solutions (Formerly Savvis) and MapR as we deconstruct and demystify “the enterprise big data stack.” We provide you with a more holistic view of the landscape, explore use cases to show how you can derive business value from it, and share best practices for navigating through the fragmented big data environment.
ADV Slides: The Evolution of the Data Platform and What It Means to Enterpris...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here?
In this webinar, we look at this foundational technology for modern Data Management and show how it evolved to meet the workloads of today, as well as when other platforms make sense for enterprise data.
Exploring the Wider World of Big Data- Vasalis KapsalisNetAppUK
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
Got data?… now what? An introduction to modern data platformsJamesAnderson599331
What are Data Analytics Platforms? What decision points are necessary in creating a modern, unified analytics data platform? What benefits are there to building your analytics data platform on Google Cloud Platform? Susan Pierce walks us through it all.
Data lakes are central repositories that store large volumes of structured, unstructured, and semi-structured data. They are ideal for machine learning use cases and support SQL-based access and programmatic distributed data processing frameworks. Data lakes can store data in the same format as its source systems or transform it before storing it. They support native streaming and are best suited for storing raw data without an intended use case. Data quality and governance practices are crucial to avoid a data swamp. Data lakes enable end-users to leverage insights for improved business performance and enable advanced analytics.
Every second of every day you hear about Electronic systems creating ever increasing quantities of data. Systems in markets such as finance, media, healthcare, government and scientific research feature strongly in the Big Data processing conversation. While extracting business value from Big Data is forecast to bring customer and competitive advantage and benefits. In this session hear Vas Kapsalis, NetApp Big Data Business Development Manager, discuss his views and experience on the wider world of Big Data.
Enterprise Archiving with Apache Hadoop Featuring the 2015 Gartner Magic Quad...LindaWatson19
Read how Solix leverages the Apache Hadoop big data platform to provide low cost, bulk data storage for Enterprise Archiving. The Solix Big Data Suite provides a unified archive for both structured and unstructured data and provides an Information Lifecycle Management (ILM) continuum to reduce costs, ensure enterprise applications are operating at peak performance and manage governance, risk and compliance.
How to Optimize Sales Analytics Using 10x the Data at 1/10th the CostAtScale
Being able to analyze sales at the most granular level with up-to-date data, provides a competitive advantage for unlocking additional revenue -- especially for e-commerce and retail companies heading into the holiday season.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Data Ninja Webinar Series: Accelerating Business Value with Data Virtualizati...Denodo
Watch the full webinar - Session one: Data Ninja Webinar Series by Denodo: https://goo.gl/yAdMpL
The following presentation was used during the webinar entitled: "Accelerating Business Value with Data Virtualization Solutions". It discusses the role of data virtualization in delivering real business value from your new and existing data assets.
This is session 1 of the Data Ninja Webinar Series organized by Denodo. If you want to learn more about some of the solutions enabled by data virtualization, click here to watch the entire series: https://goo.gl/8XFd1O
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Smarter Management for Your Data Growth
1. Smarter Management for Your Data Growth Retain Critical Data Online At A Fraction of The Cost April 2011
2. Introductions Changing Data Management Landscape & Trends From Operational to Analytical Cloud and Hadoop Where do They Fit? RainStor and How it Works Analytics Data Retention Use-case Economics Q&A Matt Aslett, The 451 Group Deirdre Mahon, VP Marketing – RainStor Ramon Chen, VP Product Management - RainStor Agenda
4. 451 Research is focused on the business of enterprise IT innovation. The company’s analysts provide critical and timely insight into the competitive dynamics of innovation in emerging technology segments. The 451 Group Tier1 Research is a single-source research and advisory firm covering the multi-tenant datacenter, hosting, IT and cloud-computing sectors, blending the best of industry and financial research. The Uptime Institute is ‘The Global Data Center Authority’ and a pioneer in the creation and facilitation of end-user knowledge communities to improve reliability and uninterruptible availability in datacenter facilities. TheInfoPro is a leading IT advisory and research firm that provides real-world perspectives on the customer and market dynamics of the enterprise information technology landscape, harnessing the collective knowledge and insight of leading IT organizations worldwide. ChangeWave Research is a research firm that identifies and quantifies ‘change’ in consumer spending behavior, corporate purchasing, and industry, company and technology trends.
5. Overview The changing data management landscape One overarching trend: Total Data Impacting four technology areas: Operational database Analytic database Data archiving Machine-generated data The trends driving data management 5
6. Trends driving data management The volume, variety and velocity of data has never been greater and is growing The value of data has never been better understood The capabilities for processing data have never been better Higher processor performance and density are enabling advanced processing on commodity hardware Software enhancements designed to make best use of processing performance and scalable architecture Advanced and in-database analytics bring processing to the data, reducing latency and improving efficiency The data deluge problem is also a big data opportunity 6
7. Introducing Total Data A concept define by The 451 Group to describe new approaches to data management – beyond restrictive silos Reflects the changing data management landscape as pragmatic choices are being made about data storage and analysis techniques Processing any data that might be applicable to analytics in the operational database, data warehouse, or Hadoop, or archive Structured, semi-structured or unstructured Relational or non-relational, on-premise or in the cloud Inspired by ‘Total Football’ 7
8. Total Football meets Total Data “You make space, you come into space. And if the ball doesn’t come, you leave this space and another player will come into it.” BernadusHulshoff, Ajax 1966-77 Abandonment of restrictive (self-imposed) rules about individual roles and responsibility Enabled and relied on fluidity and flexibility to respond to changing requirements Reliant on, and exploited, improved performance levels 8
13. Infrastructure primarily exists to support the data/application layerEnterprise app Operationaldatabase Data cleansing/sampling/MDM EDW Data archive Infrastructure
17. Polyglot persistence – use the most appropriate data storage for the applicationEnterprise app Reporting/BI Reporting/BI Distributed data Data cleansing/sampling/MDM Operational database Operational database Operational database Operational database EDW Data archive Infrastructure
20. Data warehouse administrators are fighting a losing battle for controlEnterprise app Reporting/BI Reporting/BI Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data archive Infrastructure
21.
22. Advanced in-database analytics bring processing to the data, reducing latency and improving efficiencyEnterprise app Reporting/BI Reporting/BI Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data archive Infrastructure
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24. Taking further advantage of hardware economicsEnterprise app Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data archive Infrastructure
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26. Greater acceptance that the EDW is part of a broader data analytics architectureEnterprise app Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data archive Infrastructure
27. Data location, data location, data location Not the end of the EDW, but the EDW is one of many sources of BI, rather than the only source of BI The issue of data location becomes paramount Choose the right storage technology – software and hardware EDW, Hadoop or archive On-premise or on the cloud Memory, disk or SSD Understand the requirements: Value and temperature of the data Ensure data can be queried using existing tools/skills Cost 15
28. EDW requirements/characteristics High performance query/analysis response Ability to support multiple users concurrently Capacity for multi-terabyte storage and scale Fast data load and staging for data transformation Ability to operate with BI/analytics tools Security and governance Cost - $20k-$50k per TB Alternatives Do nothing and suffer the consequences Deploy appliances and/or Hadoop for specific use-cases Offload to an online repository 16
31. Previously little need for querying/analyticsEnterprise app Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data archive Infrastructure
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33. Focus shifts on to how to enable querying easily and cost effectively
34. Becomes an online repository for historical dataEnterprise app Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data repository Infrastructure
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36. “Machine generated data” an untapped source of dataEnterprise app Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data repository Infrastructure
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38. Likely to transform into data-generating and data-processing infrastructure as analytics capabilities are applied directly to the data sourceEnterprise app Reporting/BI Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase EDW Data repository Datastructure
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41. Greater opportunities for business intelligenceEnterprise app Hadoop/DW Data archive Analytic DB Reporting/BI Reporting/BI Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Reporting Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase Analytic database Analytic database Analyticdatabase EDW Cloud Infrastructure Data repository Datastructure
42. Data location, data location, data location Avoid data movement and duplication – retain governance Virtual data marts and data clouds Data virtualization to provide access to multiple data sources 23
43. Data virtualization 24 Enterprise app Hadoop/DW Data archive Analytic DB Reporting/BI Reporting/BI Reporting/BI Reporting/BI Reporting/BI Reporting Reporting Reporting Reporting Reporting Reporting Reporting Distributed data Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Analytic database Analytic database Analyticdatabase Analytic database Analytic database Analyticdatabase EDW Cloud Infrastructure Data repository Datastructure
44. Data virtualization 25 Enterprise app Analytic DB Hadoop/DW Data archive Reporting/BI Reporting/BI Reporting Reporting Reporting Reporting Reporting Reporting Reporting Reporting Distributed data Datavirtualization Data cleansing/sampling/MDM Hadoop Operational database Operational database Operational database Operational database Virtualdata mart Virtualdata mart Virtualdata mart Virtualdata mart Virtualdata mart Virtualdata mart EDW Cloud Infrastructure Data repository Datastructure
45. Who is RainStor? Specialized database for cost effective reduction, retention & on-demand retrieval of historical structured data At 10x Less Cost OEM Partner Model Cloud or On-premise
59. ISSBig Data Volumes - Needs to be online & Query-able Found the needle – where’s the haystack? Volumes are rising- Regulated - Infrastructure needs - Reaching Telco-scale Multi- billions of records Strict Compliance RDBMS’s Break Analytics Required 10’s of Petabytes Retained
60. How Does RainStor Do It? Reduce SIZE: Massive de-dupe ~97% savings in storage HARDWARE: On commodity server/disk infrastructure RESOURCES: Without specialist DBA support Retain PRESERVED: Massive record volumes in original form IMMUTABLE: Tamper proofed with audit trail CONFIGURABLE: With retention & expiry policies Retrieve STANDARDS: SQL & BI tools via ODBC/JDBC PERFORMANT: Fast queries for large complex data sets FLEXIBLE: With schema evolution & point-in-time access
64. Fast Queries in stored format without re-inflation.Smith Pharma Peter $40,000 Pharma Smith $40,000 Peter Finance Paul $35,000 Pharma Smith $40,000 Peter Finance Paul Brown $35,000 John
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66. Run query on RainStor and import results to data warehouse
70. Add more data sources for broader analysis50 Quarters Source DB e.g. Oracle Analytics/DW 5 Quarters
71. RainStor Cloud 2. Encrypted data stored in private containers ensuring security and easy management. 1. Compressed de-duplicated data sent to the cloud resulting in quicker and cheaper uploads. VM Software Appliance Amazon Send S3 Search EC2 ODBC/JDBC Store 3. Data accessed on demand using standard SQL tools leveraging elasticity of the cloud
73. Quick summary The growing volume, variety and velocity of data is a problem, but it is also an opportunity Requires a broader approach to data management Deploy appliances and Hadoop for specific use-cases, and online repository for historical data ‘Datastructure’ will become increasingly valuable, not only as a source of data but also as a source of intelligence Data location, and the role of data virtualization will come into greater focus 36
De-dupe & ReductionAny storage / PlatformCloud EnabledLimitless Data VolumesFast load – Ingestion RatesSQL Query – High PerformanceImmutable Compliant Store
So if we take a look at Matt’s earlier high level architecture diagram, I think its worth pointing out the key areas RainStor technology can be applied – at the top, we have a RS repository which can be deployed alongside the RDBMS … and can be archived / retired saving by compressing the data to a much smaller footprint. Our INFA partnership focuses on this area predominantly and retires a large number of applications such as Oracle ebusiness suite… On the lower part of the screen – RS can be deployed as the leading repository to store long term historical data for EDW’s and additionally the same data sets can be stored on the cloud…
Security Industry:The combination of the increase in cybercrime, changing regulations, and public exposures is increasing the attention and resources dedicated to data security. Over the next three years it's expected that data security issues (and the related application security) will account for over 60% of new enterprise security spending- this includes spending on new technologies, and excludes maintenance of existing technologies such as firewalls and antivirus, which account for most current security costs.Data and business application security will drive most of the new growth of the security market over the next 3-5 years.Business network traffic for 2010 > 3,800 Pb / month> 2,500 Pb internet traffic > 1,200 Pb WAN traffic > 58 Pb mobile trafficCisco forecasts 20% CAGRData breaches are common - 95% of records stolen externally - 90% involved malware - 70% were uncovered by outsiders - 50% went unnoticed for monthsCSPs: Global mobile data traffic will increase 26-fold between 2010 and 2015. Mobile data traffic will grow at acompound annual growth rate (CAGR) of 92 percent from 2010 to 2015, reaching 6.3 exabytes per month by 2015.Last year’s mobile data traffic was three times the size of the entire global Internet in 2000.