Pivotal Big Data Suite is a comprehensive platform that allows companies to modernize their data infrastructure, gain insights through advanced analytics, and build analytic applications at scale. It includes components for data processing, storage, analytics, in-memory processing, and application development. The suite is based on open source software, supports multiple deployment options, and provides an agile approach to help companies transform into data-driven enterprises.
Hitachi Data Systems Hadoop Solution. Customers are seeing exponential growth of unstructured data from their social media websites to operational sources. Their enterprise data warehouses are not designed to handle such high volumes and varieties of data. Hadoop, the latest software platform that scales to process massive volumes of unstructured and semi-structured data by distributing the workload through clusters of servers, is giving customers new option to tackle data growth and deploy big data analysis to help better understand their business. Hitachi Data Systems is launching its latest Hadoop reference architecture, which is pre-tested with Cloudera Hadoop distribution to provide a faster time to market for customers deploying Hadoop applications. HDS, Cloudera and Hitachi Consulting will present together and explain how to get you there. Attend this WebTech and learn how to: Solve big-data problems with Hadoop. Deploy Hadoop in your data warehouse environment to better manage your unstructured and structured data. Implement Hadoop using HDS Hadoop reference architecture. For more information on Hitachi Data Systems Hadoop Solution please read our blog: http://blogs.hds.com/hdsblog/2012/07/a-series-on-hadoop-architecture.html
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Denodo
Watch full webinar here: https://bit.ly/34iCruM
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
Watch full webinar here: https://bit.ly/2xc6IO0
To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture.
In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn:
- What data virtualization really is
- How it differs from other enterprise data integration technologies
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
Hitachi Data Systems Hadoop Solution. Customers are seeing exponential growth of unstructured data from their social media websites to operational sources. Their enterprise data warehouses are not designed to handle such high volumes and varieties of data. Hadoop, the latest software platform that scales to process massive volumes of unstructured and semi-structured data by distributing the workload through clusters of servers, is giving customers new option to tackle data growth and deploy big data analysis to help better understand their business. Hitachi Data Systems is launching its latest Hadoop reference architecture, which is pre-tested with Cloudera Hadoop distribution to provide a faster time to market for customers deploying Hadoop applications. HDS, Cloudera and Hitachi Consulting will present together and explain how to get you there. Attend this WebTech and learn how to: Solve big-data problems with Hadoop. Deploy Hadoop in your data warehouse environment to better manage your unstructured and structured data. Implement Hadoop using HDS Hadoop reference architecture. For more information on Hitachi Data Systems Hadoop Solution please read our blog: http://blogs.hds.com/hdsblog/2012/07/a-series-on-hadoop-architecture.html
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Denodo
Watch full webinar here: https://bit.ly/34iCruM
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
Watch full webinar here: https://bit.ly/2xc6IO0
To solve these challenges, according to Gartner "through 2022, 60% of all organizations will implement data virtualization as one key delivery style in their data integration architecture". It is clear that data virtualization has become a driving force for companies to implement agile, real-time and flexible enterprise data architecture.
In this session we will look at the data integration challenges solved by data virtualization, the main use cases and examine why this technology is growing so fastly. You will learn:
- What data virtualization really is
- How it differs from other enterprise data integration technologies
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations
A-B-C Strategies for File and Content BrochureHitachi Vantara
Explains each strategy, including archive 1st, back up less, consolidate more, distributed IT efficiency, enable e-discovery and compliance, and facilitate cloud. For more information on Unstructured Data Management Solutions by HDS please visit: http://www.hds.com/solutions/it-strategies/unstructured-data-management.html?WT.ac=us_mg_sol_udm
The Future of Data Warehousing: ETL Will Never be the SameCloudera, Inc.
Traditional data warehouse ETL has become too slow, too complicated, and too expensive to address the torrent of new data sources and new analytic approaches needed for decision making. The new ETL environment is already looking drastically different.
In this webinar, Ralph Kimball, founder of the Kimball Group, and Manish Vipani, Vice President and Chief Architect of Enterprise Architecture at Kaiser Permanente will describe how this new ETL environment is actually implemented at Kaiser Permanente. They will describe the successes, the unsolved challenges, and their visions of the future for data warehouse ETL.
Designing Fast Data Architecture for Big Data using Logical Data Warehouse a...Denodo
Companies such as Autodesk are fast replacing the once-true- and-tried physical data warehouses with logical data warehouses/ data lakes. Why? Because they are able to accomplish the same results in 1/6 th of the time and with 1/4 th of the resources.
In this webinar, Autodesk’s Platform Lead, Kurt Jackson,, will describe how they designed a modern fast data architecture as a single unified logical data warehouse/ data lake using data virtualization and contemporary big data analytics like Spark.
Logical data warehouse / data lake is a virtual abstraction layer over the physical data warehouse, big data repositories, cloud, and other enterprise applications. It unifies both structured and unstructured data in real-time to power analytical and operational use cases.
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Fast Data Strategy Houston Roadshow PresentationDenodo
Fast Data Strategy Houston Roadshow focused on the next industrial revolution on the horizon, driven by the application of big data, IoT and Cloud technologies.
• Denodo’s innovative customer, Anadarko, elaborated on how data virtualization serves as the key component in their prescriptive and predictive analytics initiatives, driven by multi-structured data ranging from customer data to equipment data.
• Denodo’s session, Unleashing the Power of Data, described the complexity of the modern data ecosystem and how to overcome challenges and successfully harness insights.
• Our Partner Noah Consulting, an expert analytics solutions provider in the energy industry, explained how your peers are innovating using new business models and reducing cost in areas such as Asset Management and Operations by leveraging Data Virtualization and Prescriptive and Predictive Analytics.
For more information on upcoming roadshows near you, follow this link: https://goo.gl/WBDHiE
Consumption based analytics enabled by Data VirtualizationDenodo
Watch full webinar here: https://buff.ly/2NM5Jtf
An eclectic mix of old and new data drives every decision and every interaction, but too many organisations are attempting unsuccessfully to consolidate this data into a single repository which is time-consuming, resource-intensive, expensive, and risky.
Join this Denodo and HCL Webinar to discover how data virtualization provides an effective modern day architecture and an alternative to data consolidation and the challenges of fragmented data ecosystems and traditional integration approaches. We will share stories and provide multiple perspectives on best practices and solutions.
Content will include:
- Business use cases that highlight challenges and solutions that result in faster time-to-market and greater ROI.
- Suggested approaches to achieve extreme agility for competitive advantage.
Hitachi Unified Storage 100 family systems consolidate and manage block, file and object data on a central platform. For more information on our unified storage please visit: http://www.hds.com/products/storage-systems/hitachi-unified-storage-100-family.html?WT.ac=us_mg_pro_hus100
Data Services and the Modern Data Ecosystem (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2YdstdU
Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management.
Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo with Data Virtualization.
We will discuss how a business can easily support and manage a Data Service platform, providing a more flexible approach for information sharing supporting an ever-diverse community of consumers.
Watch this on-demand webinar as we cover:
- Why Data Services are a critical part of a modern data ecosystem
- How IT teams can manage Data Services and the increasing demand by businesses
- How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary
- How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)
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
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
Over the last decade, cloud computing has transformed the market for IT services. But the journey to cloud adoption has not been without its share of twists and turns. This report looks at lessons that can be derived from companies' experiences implementing cloud computing technology.
Pervasive analytics through data & analytic centricityCloudera, Inc.
Cloudera and Teradata discuss the best-in-class solution enabling companies to put data and analytics at the center of their strategy, achieve the highest forms of agility, while reducing the costs and complexity of their current environment.
Delivering Self-Service Analytics using Big Data and Data Virtualization on t...Denodo
Watch full webinar here: [https://buff.ly/2FHWnMD]
Headquartered in New York City, Guardian Life is one of the largest mutual life insurance companies in the United States. Guardian offerings range from life insurance, disability income insurance, annuities, and investments to dental and vision insurance and employee benefits. The Enterprise Data Program was initiated to modernize Guardian’s technology capabilities and transform how Guardian leverages data – the Enterprise Data Lake was implemented to democratize data and drive self-service analytics throughout the organization. Data virtualization has played a key role for delivering data services through Guardian’s Enterprise Data Marketplace, a centralized portal for analytics and reporting.
Attend this session to learn:
Who is Guardian and what were the key drivers for building a data lake?
What are the data architectural patterns on the cloud?
How data virtualization is powering analytics and reporting?
Data integration in the big data world can be very complex. When using the MapR Distribution including Apache Hadoop as an integral component of your enterprise architecture, there is still the need to work with existing systems such as MPP data warehouses and traditional data repositories. Moving data from these sources to MapR needs to be efficient, and leveraging the processing capabilities of Hadoop to transform data is critical.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
A-B-C Strategies for File and Content BrochureHitachi Vantara
Explains each strategy, including archive 1st, back up less, consolidate more, distributed IT efficiency, enable e-discovery and compliance, and facilitate cloud. For more information on Unstructured Data Management Solutions by HDS please visit: http://www.hds.com/solutions/it-strategies/unstructured-data-management.html?WT.ac=us_mg_sol_udm
The Future of Data Warehousing: ETL Will Never be the SameCloudera, Inc.
Traditional data warehouse ETL has become too slow, too complicated, and too expensive to address the torrent of new data sources and new analytic approaches needed for decision making. The new ETL environment is already looking drastically different.
In this webinar, Ralph Kimball, founder of the Kimball Group, and Manish Vipani, Vice President and Chief Architect of Enterprise Architecture at Kaiser Permanente will describe how this new ETL environment is actually implemented at Kaiser Permanente. They will describe the successes, the unsolved challenges, and their visions of the future for data warehouse ETL.
Designing Fast Data Architecture for Big Data using Logical Data Warehouse a...Denodo
Companies such as Autodesk are fast replacing the once-true- and-tried physical data warehouses with logical data warehouses/ data lakes. Why? Because they are able to accomplish the same results in 1/6 th of the time and with 1/4 th of the resources.
In this webinar, Autodesk’s Platform Lead, Kurt Jackson,, will describe how they designed a modern fast data architecture as a single unified logical data warehouse/ data lake using data virtualization and contemporary big data analytics like Spark.
Logical data warehouse / data lake is a virtual abstraction layer over the physical data warehouse, big data repositories, cloud, and other enterprise applications. It unifies both structured and unstructured data in real-time to power analytical and operational use cases.
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Fast Data Strategy Houston Roadshow PresentationDenodo
Fast Data Strategy Houston Roadshow focused on the next industrial revolution on the horizon, driven by the application of big data, IoT and Cloud technologies.
• Denodo’s innovative customer, Anadarko, elaborated on how data virtualization serves as the key component in their prescriptive and predictive analytics initiatives, driven by multi-structured data ranging from customer data to equipment data.
• Denodo’s session, Unleashing the Power of Data, described the complexity of the modern data ecosystem and how to overcome challenges and successfully harness insights.
• Our Partner Noah Consulting, an expert analytics solutions provider in the energy industry, explained how your peers are innovating using new business models and reducing cost in areas such as Asset Management and Operations by leveraging Data Virtualization and Prescriptive and Predictive Analytics.
For more information on upcoming roadshows near you, follow this link: https://goo.gl/WBDHiE
Consumption based analytics enabled by Data VirtualizationDenodo
Watch full webinar here: https://buff.ly/2NM5Jtf
An eclectic mix of old and new data drives every decision and every interaction, but too many organisations are attempting unsuccessfully to consolidate this data into a single repository which is time-consuming, resource-intensive, expensive, and risky.
Join this Denodo and HCL Webinar to discover how data virtualization provides an effective modern day architecture and an alternative to data consolidation and the challenges of fragmented data ecosystems and traditional integration approaches. We will share stories and provide multiple perspectives on best practices and solutions.
Content will include:
- Business use cases that highlight challenges and solutions that result in faster time-to-market and greater ROI.
- Suggested approaches to achieve extreme agility for competitive advantage.
Hitachi Unified Storage 100 family systems consolidate and manage block, file and object data on a central platform. For more information on our unified storage please visit: http://www.hds.com/products/storage-systems/hitachi-unified-storage-100-family.html?WT.ac=us_mg_pro_hus100
Data Services and the Modern Data Ecosystem (ASEAN)Denodo
Watch full webinar here: https://bit.ly/2YdstdU
Digital Transformation has changed IT the way information services are delivered. The pace of business engagement, the rise of Digital IT (formerly known as “Shadow IT), has also increased demands on IT, especially in the area of Data Management.
Data Services exploits widely adopted interoperability standards, providing a strong framework for information exchange but also has enabled growth of robust systems of engagement that can now exploit information that was normally locked away in some internal silo with Data Virtualization.
We will discuss how a business can easily support and manage a Data Service platform, providing a more flexible approach for information sharing supporting an ever-diverse community of consumers.
Watch this on-demand webinar as we cover:
- Why Data Services are a critical part of a modern data ecosystem
- How IT teams can manage Data Services and the increasing demand by businesses
- How Digital IT can benefit from Data Services and how this can support the need for rapid prototyping allowing businesses to experiment with data and fail fast where necessary
- How a good Data Virtualization platform can encourage a culture of Data amongst business consumers (internally and externally)
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
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
Over the last decade, cloud computing has transformed the market for IT services. But the journey to cloud adoption has not been without its share of twists and turns. This report looks at lessons that can be derived from companies' experiences implementing cloud computing technology.
Pervasive analytics through data & analytic centricityCloudera, Inc.
Cloudera and Teradata discuss the best-in-class solution enabling companies to put data and analytics at the center of their strategy, achieve the highest forms of agility, while reducing the costs and complexity of their current environment.
Delivering Self-Service Analytics using Big Data and Data Virtualization on t...Denodo
Watch full webinar here: [https://buff.ly/2FHWnMD]
Headquartered in New York City, Guardian Life is one of the largest mutual life insurance companies in the United States. Guardian offerings range from life insurance, disability income insurance, annuities, and investments to dental and vision insurance and employee benefits. The Enterprise Data Program was initiated to modernize Guardian’s technology capabilities and transform how Guardian leverages data – the Enterprise Data Lake was implemented to democratize data and drive self-service analytics throughout the organization. Data virtualization has played a key role for delivering data services through Guardian’s Enterprise Data Marketplace, a centralized portal for analytics and reporting.
Attend this session to learn:
Who is Guardian and what were the key drivers for building a data lake?
What are the data architectural patterns on the cloud?
How data virtualization is powering analytics and reporting?
Data integration in the big data world can be very complex. When using the MapR Distribution including Apache Hadoop as an integral component of your enterprise architecture, there is still the need to work with existing systems such as MPP data warehouses and traditional data repositories. Moving data from these sources to MapR needs to be efficient, and leveraging the processing capabilities of Hadoop to transform data is critical.
Data Science Operationalization: The Journey of Enterprise AIDenodo
Watch full webinar here: https://bit.ly/3kVmYJl
As we move into a world driven by AI initiatives, we find ourselves facing new and diverse challenges when it comes to operationalization. Creating a solution and putting it into practice, is certainly not the same. The challenges span various organizational and data facades. In many instances, the data scientists may be working in silos and connecting to the live data may not always be possible. But how does one guarantee their developed model in a silo is still relevant to live data? How can we manage the data flow and data access across the entire AI operationalization cycle?
Watch on-demand to explore:
- The journey and challenges of the Data Scientist
- How Denodo data virtualization with data movement streamlines operationalization
- The best practices and techniques when dealing with siloed data
- How customers have used data virtualization in their data science initiatives
This new solution from Capgemini, implemented in
partnership with Informatica, Cloudera and Appfluent,
optimizes the ratio between the value of data and storage
costs, making it easy to take advantage of new big data
technologies.
Lyftrondata enables enterprises to load data from 300+ connectors to Google Bigquery in minutes without any engineering requirements. Simply connect, organize, centralize and share your data on Bigquery with zero code data pipeline, ETL & ELT tool.
Watch full webinar here: https://bit.ly/2vN59VK
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
- What data virtualization really is.
- How it differs from other enterprise data integration technologies.
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations.
Learn about IBM's Hadoop offering called BigInsights. We will look at the new features in version 4 (including a discussion on the Open Data Platform), review a couple of customer examples, talk about the overall offering and differentiators, and then provide a brief demonstration on how to get started quickly by creating a new cloud instance, uploading data, and generating a visualization using the built-in spreadsheet tooling called BigSheets.
Originally Published on Sep 23, 2014
IBM InfoSphere BigInsights, an enterprise-ready distribution of Hadoop, is designed to address the challenges of big data and modern IT by analyzing larger volumes of data more cost-effectively. Deployed on the cloud, it enables rapid deployment of clusters and real-time analytics.
FYI: The value of Hadoop and many more questions will be pondered at this year’s Strata/Hadoop World event in NYC (October 15-17, 2014) and certainly at IBM Insight (October 26-30, 2014).
For Impetus’ White Papers archive, visit- http://www.impetus.com/whitepaper
In this paper, Impetus focuses at why organizations need to design an Enterprise Data Warehouse (EDW) to support the business analytics derived from the Big Data.
Data Fabric - Why Should Organizations Implement a Logical and Not a Physical...Denodo
Watch full webinar here: https://bit.ly/3fBpO2M
Data Fabric has been a hot topic in town and Gartner has termed it as one of the top strategic technology trends for 2022. Noticeably, many mid-to-large organizations are also starting to adopt this logical data fabric architecture while others are still curious about how it works.
With a better understanding of data fabric, you will be able to architect a logical data fabric to enable agile data solutions that honor enterprise governance and security, support operations with automated recommendations, and ultimately, reduce the cost of maintaining hybrid environments.
In this on-demand session, you will learn:
- What is a data fabric?
- How is a physical data fabric different from a logical data fabric?
- Which one should you use and when?
- What’s the underlying technology that makes up the data fabric?
- Which companies are successfully using it and for what use case?
- How can I get started and what are the best practices to avoid pitfalls?
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization Denodo
Watch here: https://bit.ly/2NGQD7R
In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But that scenario is rapidly changing with the increasing adoption of real-time data virtualization - a paradigm shift in the approach that organizations take towards accessing, integrating, and provisioning data required to meet business goals.
As data analytics and data-driven intelligence takes centre stage in today’s digital economy, logical data integration across the widest variety of data sources, with proper security and governance structure in place has become mission-critical.
Attend this session to learn:
- Learn how you can meet cloud and data science challenges with data virtualization.
- Why data virtualization is increasingly finding enterprise-wide adoption
- Discover how customers are reducing costs and improving ROI with data virtualization
Bridging the Last Mile: Getting Data to the People Who Need ItDenodo
Watch full webinar here: https://bit.ly/3cUA0Qi
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
Cisco Big Data Warehouse Expansion Featuring MapR DistributionAppfluent Technology
Learn more about the Cisco Big Data Warehouse Expansion Solution featuring MapR Distribution including Apache Hadoop.
The BDWE solution begins with the collection of data usage statistics by Appfluent. Then the BDWE solution optimizes Cisco UCS hardware for running the MapR Distribution including Hadoop, software for federating multiple data sources, and a comprehensive services methodology for assessing, migrating, virtualizing, and operating a logically expanded warehouse.
1. pivotal.io
PIVOTAL HANDOUT
Pivotal Big Data Suite
Product Suite
COMPLETE PLATFORM FOR DATA-DRIVEN ENTERPRISES
Many industry stalwarts have found their traditional business models under threat by a
new generation of fast growing competitors that leverage big data and analytics. These
new companies are transforming and redefining markets by creating innovative customer
experiences with intelligent, customer-centered applications.
Powering these applications are significant advances made in data processing and analytics
with technologies such as scale-out processing, machine learning, and in-memory
computation. These advances leverage hardware trends such as cloud computing,
convergence of storage and compute resources, and rapidly increasing RAM per system.
Collectively known as big data and advanced analytics, these technologies are developed
within open source communities.
Pivotal Software is a leading contributor to many big data and analytics open source
software projects, and is dedicated to driving innovation in the open source ecosystem.
To help companies adopt big data and analytics and create data-driven business models,
Pivotal has rolled these open source technologies into a comprehensive platform called
Pivotal Big Data Suite, as depicted in Figure 1. Big Data Suite allows companies to
modernize their data infrastructure, discover more insights with advanced analytics, and
build analytic applications at scale.
KEY ADVANTAGES
• Quickly deploy and manage an
analytics-optimized business data lake
based on Hadoop
• Discover more insights using advanced
analytics with SQL on Hadoop or an
analytics data warehouse
• Innovate at scale with smart,
predictive applications backed by
distributed in-memory data stores
FEATURES OF BIG DATA SUITE
• Comprehensive offering covering
data processing & storage, advanced
analytics, in-memory data processing
& messaging
• Works with Pivotal Cloud Foundry –
deploy with Ops Manager, consume
as services within Pivotal Cloud
Foundry apps
• Compatible with Open Data
Platform (ODP) core based
distributions of Hadoop
• Based on open source
• Processing core-based subscription
license for 1 to 3 years
• Flexible licensing – reallocate licensed
core capacity between components
depending on need
• Multiple deployment options:
commodity hardware, appliance,
virtualized, cloud and hybrid cloud
Overview
2. pivotal.io
PIVOTAL HANDOUT
MODERNIZE DATA INFRASTRUCTURE
Store and Process Any Size and Type of Data
A first step for many companies in becoming a data-driven enterprise is to deploy a
modern data infrastructure for storage and data processing based on Hadoop. Pivotal
Big Data Suite helps companies with this transformation at the data processing layer by
including Spring XD, Pivotal HD, and Cloud Foundry Operations Manager.
In an agile infrastructure, data scientists and architects need a rapid, scalable way to
develop specific data flows for ingestion and processing. Spring XD helps customers
quickly create data pipelines to orchestrate the flow of data from any source, between
processing steps, and into any final repository.
Massive data volumes and enterprise IT transformation will require that future data
storage will be based on HDFS. Pivotal HD is a distribution of Hadoop based on Open
Data Platform (ODP) core that is targeted for analytical use cases. Pivotal HD provides a
scale-out flexible data management framework that can handle any data type. Pivotal HD
can work with any big data ecosystem applications or tools that support ODP-based
Hadoop distributions.
PIVOTAL BIG DATA SUITE
COMPONENTS OF BIG DATA SUITE
• Pivotal HD - ODP core-based Hadoop
distribution targeting SQL and
advanced analytics
• Pivotal Greenplum Database®
-
Leading analytical massively-parallel
processing data warehouse
• Pivotal HAWQ®
- Highly scalable ANSI-
compliant SQL on Hadoop analytic
query engine
• Pivotal GemFire®
- High-performing
distributed in-memory NoSQL
database
• Spring XD - Distributed data pipeline
data ingestion, stream processing
and orchestration
• Redis - Leading scalable key-value
store and data structure server
• RabbitMQ™
- Leading scalable open
source reliable message queue for
applications
• Pivotal Big Data Suite on Pivotal
Cloud Foundry - Big Data Suite
components exposed as data
services in Pivotal Cloud Foundry
• Pivotal Cloud Foundry Ops Manager -
deployment and management of
Cloud Foundry PaaS
Figure 1. Pivotal Big Data Suite is the advanced analytics and in-memory processing stack for
data-driven enterprises.
3. pivotal.io
PIVOTAL HANDOUT
In-memory computing, where entire data sets reside in memory, are future state of
the art for analytics and processing. Pivotal HD includes the powerful Spark stack for
in-memory distributed data processing.
IT infrastructures are migrating to open cloud platforms. To help customers make this
transition, an instance of Pivotal Cloud Foundry Ops Manager is provided to automate
deployment of Big Data Suite components and help Cloud Foundry applications leverage
Big Data Suite capabilities as services. This delivers a complete agile data stack, in a single
subscription offering.
Modernizing data infrastructure allows customers to implement a business data lake.
Data from any source can be ingested in any format, whether as batch files or at
real-time streaming velocity. Now customers have additional flexibility for performing
large scale ETL such as processing a data stream before storage. It becomes practical to
run SQL queries on very large data sets at interactive speed.
DISCOVER MORE INSIGHTS WITH ADVANCED ANALYTICS
Massively Parallel Processing on Large Data Sets
A key capability of data-driven enterprises is their ability to leverage data science and
advanced analytics. For advanced analytics, Pivotal Big Data Suite includes two massively
scalable SQL engines: HAWQ and Pivotal Greenplum Database. HAWQ is the most
advanced SQL on Hadoop engine in the industry. It provides interactive and complex query
processing on very large data leveraging compute resources directly in Hadoop nodes.
Pivotal Greenplum Database is the leading analytical data warehouse with a shared-nothing
scale-out architecture, fast data loading, and enterprise-grade reliability, administration,
and advanced security capabilities. Both HAWQ and Greenplum Database share the cost-
based Pivotal Query Optimizer technology which dramatically speeds up execution of
complex joins. Both engines provide massively parallel execution of powerful open source
data science libraries such as MADlib.
HAWQ will run in any ODP-based distribution of Hadoop and tightly integrates with
management tools within Hadoop such as Ambari, YARN, and HCatalog. Pivotal Greenplum
database provides import and export integration with most leading Hadoop distributions.
By deploying an advanced analytics platform, customers can apply data science to discover
new insights for solving business problems. Data scientists can run complex queries at
breakthrough speed on petabyte-scale data sets, and access powerful predictive analytics
and machine learning capabilities based on SQL.
PIVOTAL BIG DATA SUITE
4. pivotal.io
PIVOTAL HANDOUT
BUILD ANALYTIC APPLICATIONS AT SCALE
Scale-Out Apps with Elastic, Distributed In-memory Data Stores
Data-driven enterprises are able to take insights they glean from their data and
operationalize them through massively scaled analytic-driven applications.
Pivotal Big Data Suite provides key building blocks for rapid development and deployment
of high scale data-centric applications. These include Big Data Suite on Pivotal Cloud
Foundry, Pivotal GemFire, Redis, and RabbitMQ.
Big data application development teams can radically speed time to market by leveraging
Pivotal Cloud Foundry as their development and deployment environment. All components
of Big Data Suite can be accessed as services within Pivotal Cloud Foundry, and Big
Data Suite includes an instance of Pivotal Cloud Foundry Ops Manager to automate
this deployment.
Pivotal GemFire is a distributed, in-memory NoSQL database. This enables enterprises
to build scaled-out, highly available transactional systems with sub-second latency
requirements. GemFire-powered applications can process many simultaneous operations
and maintain sub-second response time at linear scale. Examples of such applications
include large scale ticketing or financial trading applications.
The large volumes of historical data typically generated by these kinds of applications
can be archived into traditional RDBMS or pipelined to the analytical components
within Big Data Suite using Spring XD.
Big Data Suite also provides support for Redis and RabbitMQ either as services within
Pivotal Cloud Foundry, or as part of a stand alone application stack.
With Pivotal Big Data Suite, data-driven companies can rapidly turn their insights into
action and deploy high scale analytic applications.. Such applications can support
mobile customer experiences, mass market transactions, and global Internet of
Things networks leading to new revenue opportunities and competitive advantages.
PIVOTAL BIG DATA SUITE