RESEARCH & RESOURCES

Featured Webinars

  • What’s Ahead in Data Management in 2019?

    Data is evolving into larger volumes from new sources in a wide array of structures, containers, and interfaces. An expert panel discusses data management trends, including new data platforms (Hadoop, cloud computing, and columnar databases), cloud data management, advancements in data catalogs and metadata, and data governance and stewardship. December 18, 2018 Register

  • Augmenting BI and Analytics in the Age of AI and Big Data

    Learn about key trends in BI and analytics based on research findings in a just-published TDWI Best Practices Report: "BI and Analytics in the Age of AI and Big Data." We'll cover priorities for augmenting BI, analytics, and data preparation with new AI capabilities, in particular to capture value from big data and cloud data systems. January 16, 2019 Register

  • Cloud Data Lakes: Enabling New Business Analytics at High Scale and Low Cost

    This webinar will discuss the business benefits of data lakes, the benefits of deploying a data lake in the cloud (and how use cases and technology requirements affect your platform choice), and why substantial data integration is necessary. February 20, 2019 Register

Upcoming Webinars

  • What’s Ahead in Data Management in 2019?

    Data is evolving into larger volumes from new sources in a wide array of structures, containers, and interfaces. An expert panel discusses data management trends, including new data platforms (Hadoop, cloud computing, and columnar databases), cloud data management, advancements in data catalogs and metadata, and data governance and stewardship. December 18, 2018 Register

  • Augmenting BI and Analytics in the Age of AI and Big Data

    Learn about key trends in BI and analytics based on research findings in a just-published TDWI Best Practices Report: "BI and Analytics in the Age of AI and Big Data." We'll cover priorities for augmenting BI, analytics, and data preparation with new AI capabilities, in particular to capture value from big data and cloud data systems. January 16, 2019 Register

  • Cloud Data Lakes: Enabling New Business Analytics at High Scale and Low Cost

    This webinar will discuss the business benefits of data lakes, the benefits of deploying a data lake in the cloud (and how use cases and technology requirements affect your platform choice), and why substantial data integration is necessary. February 20, 2019 Register

TDWI Webinars on Big Data, Business Intelligence, Data Warehousing & Analytics

TDWI Webinars deliver unbiased information on pertinent issues in the big data, business intelligence, data warehousing, and analytics industry. Each live Webinar is roughly one hour in length and includes an interactive question-and-answer session following the presentation.


On Demand

Emerging Design Patterns for Data Management

Organizations that seek to be data-driven are experiencing considerable change of late, because data itself, the management of data, and the ways businesses leverage data are all evolving at accelerated rates. These changes sound like problems, but they are actually opportunities for organizations that can embrace new big data, implement new design patterns and platforms for data, scale to greater volumes and processing loads, and react accordingly via analytics for organizational advantage.

Philip Russom, Ph.D.


SQL for Hadoop: When to Use Which Approach

In a 2015 survey by TDWI, 69% of respondents identified SQL on Hadoop as a must-have for making Hadoop ready for enterprise use. This is not surprising because both technical and business users know and love SQL, plus have portfolios of tools that rely on it. The catch is that early versions of Hadoop were devoid of ANSI-standard SQL.

Philip Russom, Ph.D.

Content Provided IBM, Looker, Teradata


The Modern Data Warehouse: What Enterprises Must Have Today and What They’ll Need in the Future

Many organizations need a more modern data warehouse platform to address a number of new and future business and technology requirements. Most of the new requirements relate to big data and advanced analytics, so the data warehouse of the future must support these in multiple ways, while still supporting older data types, technologies, and business practices. Hence, a leading goal of the modern data warehouse is to enable more and bigger data management solutions and analytic applications, which in turn help the organization automate more business processes, operate closer to real time, and through analytics learn valuable new facts about business operations, customers, products, and so on.

Philip Russom, Ph.D.


Data-centric Security- Seven Best Practices for Protecting Your Most Sensitive Data

As organizations incorporate newer data strategies, they also need to consider data-centric security. Data-centric security focuses security controls on the data, rather than perimeter servers or other infrastructure or the network. The goal is to protect sensitive data where it is stored and where it moves. This is becoming increasingly important as organizations start to deal with big data and newer data management platforms and hybrid architectures that include Hadoop and the cloud. Yet, TDWI research suggests that organizations still seem to focus on perimeter security and on application centric security for sensitive data. They think they are focused on protecting their data, but the reality is that many organizations don’t classify their data or know where their sensitive data lives, much less how to protect it.

Fern Halper, Ph.D.


Peering Under the Hood: Fine-Tuning Solutions for Hard Operational Data Governance

Many organizations are responding to their raised awareness of the need for data governance by introducing data governance programs, hiring Chief Data Officers, and forming a data governance council. And while there are numerous guidelines and methods for the operating models for a data governance practice, recommendations regarding its day-to-day operationalization are much harder to come by. Specifically, how does an organization design an operational environment for instituting business data policies for usability and enforcing those policies consistently across the enterprise? Answering this question is necessary for achieving the data governance discipline without getting in the way of the business.

David Loshin


Dynamic Metadata: Enabling Modern BI Architecture

In a highly competitive market, today’s forward-looking organizations are seeking to optimize and modernize their IT investments, specifically in enterprise business intelligence (BI). There’s a strong push to capitalize on newer features such as self-service BI, advanced analytics, and customized visualizations—all of which relinquish the centralized data governance necessary for corporate and regulatory compliance.

David Stodder


Big Data Management Best Practices for Data Lakes

Organizations are pursuing data lakes in a fury. Organizations in many industries are attempting to deploydata lakes for a variety of purposes, including the persistence of raw detailed source data, data landing and staging, continuous ingestion, archiving analytic data, broad exploration of data, data prep, the capture of big data, and the augmentation of data warehouse environments. These general design patterns are being applied to industry and departmental domain specific solutions, namely marketing data lakes, sales performance data lakes, healthcare data lakes, and financial fraud data lakes.

Philip Russom, Ph.D.


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