TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Evolution of the Data Lake—Implementing Real-Time Change Data in Hadoop

A ten-fold increase in worldwide data by 2025 is one of many predictions about big data. With such growth rates in data, the “data lake” is a very popular concept today. Everybody touts their platform capabilities for the data lake, and it is all about Apache Hadoop. With its proven cost-effective, highly scalable, and reliable means of storing vast data sets on cost-effective commodity hardware regardless of format, it seems to be the ideal analytics repository. However, the power of discovery that comes with the lack of a schema also creates a barrier for integrating well-understood transaction data that is more comfortably stored in a relational database. Rapidly changing data can quickly turn a data lake into a data swamp.

Krish Krishnan

Sponsored by IBM

Up to the Minute: The Need for Rapid Adoption of Streaming Data

As Internet of Things (IoT) technologies become more common and web data grows in volume, there is growing evidence that the ability to analyze continuous data is not only valuable but necessary. In fact, those with the ability to capture and analyze massive numbers of independent continuous data streams will have a powerful capability that will help them to power operational intelligence and predictive analytics. A growing number of applications increasingly rely on fast analysis, but tomorrow’s world will be even more dependent on up-to-the-minute consumption of data streams.

David Loshin

Sponsored by Snowflake

Ask the Expert on the Roles and Construct of a Thriving Analytic Team

TDWI Members Only

Most organizations believe they will achieve better analytic results if they populate a deeper bench of experienced data scientists and machine learning practitioners. But this is akin to building a home exclusively with highly skilled framers, brick layers and cabinet makers. You’ll end up with a solid structure and great workmanship, but not a true functional home.

Keith McCormick

Building a Successful Data Lake in the Cloud

Data lakes on Hadoop have come on strong in recent years because they help many types of user organizations – from Internet firms to mainstream industries – capture big data at scale and analyze or otherwise process it for business value.

Philip Russom, Ph.D.

Sponsored by Oracle

Location, Location, Location: How Geoenrichment Can Improve Business Intelligence and Analytics

Geospatial data is growing in importance for business intelligence as users seek to make sense of diverse data. One element that many types of data have in common is location. Critical attributes of human, machine, and application-generated data become clearer when the source’s location—or movement from one location to another—is known and incorporated into reporting and analysis. Business users can spot trends, patterns, gaps, and other data relationships more clearly if they are able to visually integrate different types of data with maps. If organizations can enrich demographic, behavioral, operational, and other data with location information, they will be on a faster path to generate breakthrough insights and make smarter decisions.

David Stodder

Sponsored by Pitney Bowes Software Solutions

Using Design Thinking to Unleash Creativity in BI and Analytics Development

Design Thinking methods can help organizations overcome the limitations of traditional BI and analytics development. Design Thinking has enabled retail, banking, and other types of firms to revolutionize how they develop products and services to deliver exceptional customer experiences. These methods offer similar potential for unleashing your organization’s creativity in developing applications and services that delight internal users. With organizations under pressure to deliver higher ROI from data—and frustrated by BI and analytics applications and services that don’t meet users’ requirements or realize value from all the data that they have—now is the time to consider new approaches such as Design Thinking.

David Stodder

Sponsored by Teradata

Location Analytics for Your Data Lake: Driving New Business Insights and Outcomes

Location information has been a growth area in recent years in data management, as user organizations of many sizes and industries have realized how location information can inspire new business insights, practices, and outcomes. In response, many users have reworked older enterprise data environments to enrich the data with more location information. At the same time they have begun capturing data from new sources that include location information, especially from sensors, machines, devices, vehicles, and the Internet of Things (IoT). Much of this new data is being managed in data lakes, which in turn are usually deployed atop Hadoop.

Philip Russom, Ph.D.

Sponsored by Pitney Bowes Software Solutions