The use of an emerging data fabric, offers enterprises a number of benefits and advantages including the ability to break through the gravitational pull of legacy data architectures and capture the full potential of all your data.
This webinar will detail how the deployment of a data fabric can enable enterprises to more quickly and easily scale across data volumes, data types and locations. The session will also provide an overview on how a data fabric reduces storage costs and increases application agility and reliability – with the underpinning to support the successful pursuit of:
* IoT through a data fabric’s capability of handling data flows from the edge to the cloud, centralizing learning, and distributing intelligence back to the edge for real-time responsiveness.
* Machine Learning/AI with the fabric able to handle the complex data flows and logistics to support the rapid deployment and coordination across machine learning models, algorithms and analytic tools
* Microservices and containers with the underlying data fabric able to support intelligent streams and support the mobility and flexibility for elastic stateful applications and analytic processes relying on shared data.
For more than 25 years IT organizations have spent many cycles building enterprise data warehouses, but both speed to market and high cost has left people continually searching for a better way. Over the last 10 years, many found an answer with Hadoop, but the inability to recruit skilled resources, combined with common enterprise necessities such as ANSI compliant SQL, security and the overall complexity has Hadoop relegated to an inexpensive, but scalable data repository.
Join Noel Yuhanna from Forrester and Pivotal’s Jacque Istok for an interactive discussion about the most recent data architecture evolution; the Big Data Fabric. During this webinar you will learn:
What a Big Data Fabric is
- How does it leverage your existing investments in enterprise data warehouses, data marts, cloud analytics, and Hadoop clusters?
How to leverage your team’s expertise to build a Big Data Fabric
- What skills should you be investing in to continue evolving with the market?
When is it appropriate for an organization to move to a Big Data Fabric
- Can you afford to divert from your existing path? Can you afford not to?
The skills and technologies that will ease the move to this new architecture
- What bets can you place that will keep you moving forward?
If data is not already the lifeblood of your business, it will soon be a critical competitive imperative. Several major impediments keep most organizations from taking full advantage of their data, but new technology is now making possible the creation of a modern global data fabric that can radically modernize an organization’s data management strategy while also enabling unlocking the business value to directly drive transformation of the business in a more compelling way.
Join us to learn:
•New Challenges around managing distributed data
•Current and Emerging technologies
•Crafting a modern data strategy
Big data fabric combines essential big data capabilities in a single platform to automate the many facets of data discovery, preparation, curation, orchestration, and integration across a multitude of data sources.
Attend this session to learn how Big Data Fabric enabled by data virtualization constitutes a recipe for:
* Enabling new actionable insights with minimal effort
* Securing big data end-to-end
* Addressing big data skillset scarcity
* Providing easy access to data without having to decipher various data formats
* Big Data with Data Virtualization
* Product Demonstration
* Summary & Next Steps
Businesses large and small are increasingly turning to comprehensive in-memory data processing solutions, such as the GridGain In-Memory Data Fabric, to address their Fast Data challenges and create a competitive advantage by operating as a real-time business. When deploying an In-Memory Data Fabric into a production environment, typical challenges that need to be addressed are around availability and resilience, security and manageability, among other things.
Join GridGain Solution Architect Mac Moore, as he explains how to harden the deployment of the GridGain In-Memory Data Fabric by taking advantage of a number of enterprise-grade features in the commercial version of the product designed for always up, always on real-time data processing.
This webinar is a must-see for technology leaders in the transition to high-speed, low-latency Fast Data systems.
Although most of today’s enterprises are data-aware, this may not be sufficient to drive tangible business outcomes.
- Are your data-driven applications providing contextual and actionable insight?
- Are you deriving insights from all the enterprise data?
Embrace Forrester’s latest analytical framework for insights-driven businesses: Systems of Insight (SOI).
Join this session to discover the key principles that differentiate data-aware or data-driven businesses from their insights-driven peers and competitors. The session will explore the roles that data virtualization (aka Data Fabric) plays in modern SOI architectures, such as:
- A single virtual catalog / view on all enterprise data sources including data lakes.
- A more agile and flexible virtual enterprise data warehouse.
- A common semantic layer for business intelligence (BI) and analytical applications (aka BI Fabric).
Is Your Data Ready for GDPR?
As the deadline for GDPR approaches, it is time to get practical about protecting personal data.
We break down the steps for turning a data lake into a data hub with appropriate data management and governance activities: from capturing and reconciling personal data to providing for consent management, data anomyzation, and the rights of the data subject.
A smart approach to GDPR compliance lays a foundation for personalized and profitable customer and employee relations.
Watch, as experts from MAPR and Talend show you how to:
Diagnose the maturity of your GDPR compliance;
Set up milestones and priorities to reach compliance;
Create a foundation to manage personal data through a data lake;
Master compliance operations - from data inventory to data transfers to individual rights management.
Data Scientists are rare and highly valued individuals, and for good reason: making sense of data, and using the machine learning libraries requires an unusual blend of advanced skills. Why is it then that Data Scientists spend the majority of their time getting data ready for models, and a fraction actually doing the high value work?
In this talk we introduce the concept of Data Fabric, a new way to provide a self-service model for data, where data scientists can easily discover, curate, share, and accelerate data analysis using Python, R, and visualization tools, no matter where the data is managed, no matter the structure, and no matter the size.
We will talk through the role of Apache Arrow, the in-memory columnar data standard that is accelerating analytics for GPU-based processing, as well as the role of Pandas and Arrow in providing unprecedented speed in accessing datasets from Python.
With growing adoption of technologies like Hadoop, new sources of data are being ingested into the organization with hopes of developing more compelling reports, predictions, and actions. But data analysts are held back from creating useful insights because they are forced to manually find and reconcile siloed and inconsistent data across the organization. Analytics end up filled with inaccuracies stemming from data that is incomplete, inconsistent, and insecure. Organizations are turning to big data fabric reference architectures to automatically and intelligently bring together disparate big data sources, processing them in a big data platform technology, such as Hadoop, and finally deliver a unified, trusted, and comprehensive view of the business.
Join Noel Yuhanna, Principal Analyst of Forrester Research, on this webinar to learn about new opportunities for your organization, including:
•How to deliver trusted insights more quickly for your business
•How to make data management a more repeatable process
•How to incorporate self-service and collaboration into data management
When data moves freely between clouds and on-premises environments, good things happen. Especially for those in the service provider business.
Hear how Data Fabric delivers the ability to enable more flexibility and agility for your customers—and grow your business as a result.
We’ll share recent customer case studies and expert opinions on how our approach to cloud storage can help your organization better meet customer needs and stand out from your competition.
Denodo Platform offers one of the most sought after data fabric capabilities through data discovery, preparation, curation and integration across the broadest range of data sources. As data volume and variety grows exponentially, Denodo Platform 7.0 will offer in-memory massive parallel processing (MPP) capability for the most advanced query optimization in the market.
Attend this session to learn:
* How Denodo Platform 7.0’s native built-in integration with MPP systems will provide query acceleration and MPP caching
* How to successfully approach highly complex big data scenarios, leveraging inexpensive MPP solutions
* With the MPP capability in place, how data driven insights can be generated in real-time with Denodo Platform
* Challenges with traditional architectures
Denodo Platform MPP capabilities and applications
* Product demonstration
Majority of enterprises today are data-aware. Being data-aware, or even data-driven, however, is not enough. Are your data-driven applications providing contextual and actionable insight? Are your analytics applications driving tangible business outcomes? Are you deriving insights from all the enterprise data? Enter Systems Of Insight (SOI), Forrester's latest analytical framework for insights-driven businesses. In this webinar you will learn about the key principles that differentiate data-aware or data-driven businesses from their insights-driven peers and competitors. Specifically the webinar will explore roles data virtualization (aka Data Fabric) plays in modern SOI architectures such as
* A single virtual catalog / view on all enterprise data sources including data lakes
* A more agile and flexible virtual enterprise data warehouse
* A common semantic layer for business intelligence (BI) and analytical applications (aka BI Fabric)
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
In this webinar, John L. Myers of Enterprise Management Associates (EMA) and Jack Norris of MapR will discuss how the new business advancements require data-rich applications that enable global, real-time data integration, microservices support, and in-place and continuous machine learning/AI and SQL capabilities.
Watch this video to learn:
Examples of disruptive business models
Drivers of changes to the management landscape
Best practices associated with meeting requirements for data-driven applications
Join GridGain CTO NIkita Ivanov as he takes a deep dive into the strategy and architecture of the GridGain's In-Memory Data Fabric as well as exploring various other technologies on the market, and how they address big data challenges like distributed data grids and clusters, streaming data and accelerating Hadoop.
This technically-oriented webinar was designed for people on the front lines of the transition to high-speed, low-latency big data systems.