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RESEARCH & RESOURCES

Featured Webinars

Upcoming Webinars

International Broadcasts

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

Panel Discussion: Data Lake Principles and Economics

Without design principles, swimming in circles in a big data lake can make your arms tired. Fortunately, the data lake concept has evolved sufficiently that best practices have emerged. In an open discussion, these big data experts will shed light on how a data lake changes the data storage, data processing, and analytic workflows in data management architectures.

Wayne Eckerson


Emerging Technologies: Innovations and Evolutions in BI, Analytics, and Data Warehousing

Some emerging technologies (ETs) are so new that they are truly just emerging—for example, technologies for agile BI and analytics, data visualizations, BI on clouds or SaaS, event processing, Hadoop, Apache Spark and Shark, mashups, mobile BI, NoSQL, social media, the Internet of things, solid-state drives, and streaming data. Other ETs have been around for a few years, but are just now seeing appreciable user adoption—for example, appliances, competency centers, collaborative BI, columnar databases, data virtualization, open source, in-database analytics, in-memory databases, MDM, real-time operation, predictive analytics, and unstructured data.

Philip Russom, Ph.D., Fern Halper, Ph.D., David Stodder


Cloud: Your Adaptive Integration Platform?

When a new technology or platform enters IT, we often see it applied first with operational applications and their servers. Then BI platforms and data warehouses adopt the new technology, followed by data management tools. We’ve seen this with various technologies, including Java and services. We’re now seeing the same sequence with clouds (whether public, private, or hybrid).

Philip Russom, Ph.D.


New Approaches for Fast Decision Making with Analytics: 5 Tips You Should Know

Changes are occurring in how businesses make decisions. Successful companies are not willing to wait a week or even a day for insight from IT. They want it on-demand, close to real time, and more frequently—and embedded into business processes. Organizations want easy-to-use analytics software for both traditional BI and even more advanced analytics. The need for speed, flexibility, and agility in decision making is becoming a business imperative.

Fern Halper, Ph.D.


Igniting Analytics: Apache Spark’s Promise and Potential Perils

Apache Spark is a parallel processing engine for big data that achieves high speed and low latency by leveraging in-memory computing and cyclic data flows. Benchmarks show Spark to be up to 100 times faster than Hadoop MapReduce with in-memory operations and 10 times fast with disk-bound ones. High performance aside, interest in Spark is rising rapidly because it offers a number of other advantages over Hadoop MapReduce, while also aligning with the needs of enterprise users and IT organizations.

Philip Russom, Ph.D.


Using New Database Technologies to Drive Competitive Advantage

For the past three decades, relational database management systems have formed the bedrock on which most operational and analytics applications have been built. Over this time frame, these systems evolved and matured to the level where database technology became a commodity. Recently, however, many database products have added significant improvements that make possible what was previously impossible. These improvements not only enhance performance and reduce costs, but also enable the handling of new types of data and applications.

Colin White


Taking Data Integration to the Next Level

Few organizations design an a priori “enterprise architecture.” Rather, systems environments evolve organically as technology decisions are made to address particular business challenges. In essence, this engineers complexity into the environment and establishes data integration as a necessity for interoperation.

David Loshin


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