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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

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


Practical Predictive Analytics for the Line-of-Business Analyst

Can business analysts effectively use predictive analytics? Adoption of predictive analytics and other advanced analytics has increased for a number of reasons, including a better understanding of the value of the technology and the availability of computing power. Economic factors are also a driving force in utilizing predictive analytics for business as companies strive to remain competitive. Companies want to better understand customer behavior. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing.

Fern Halper, Ph.D.


Stream Processing: Streaming Data in Real Time, in Memory

As big data continues to grow bigger, become more diverse, and more real-time, forward looking organizations are looking to manage and analyze this data using advanced analytics in an environment which might include multiple approaches and technologies. For real time streaming data this could include utilizing technologies that support in-memory processing, where data and mathematical computations are performed in RAM rather than on disk; enabling processing thousands of times faster than data access from disk.

Fern Halper, Ph.D.


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