The concept of the data lake has been gaining traction as a big data solution to fast-track data ingestion and analysis for users across the enterprise. But what is driving data lake implementation and how is the data in the lake managed, secured, and maintained?
“How can we use the technologies we may already have to help our users find information?”
This is a question we’ve been hearing from several of our clients, many of whom are assessing big data and advanced analytic tools and technologies in the hope of using them to enable data-driven decision making. In many cases, they have moved beyond the stage of educating themselves about the strategic benefits of big data to implementing foundational data lakes and analytic sandboxes. Continue reading How MDM Enables Data-Driven Decision Making in a Big Data World
What goes into designing and configuring a Data Lake? How does Hadoop figure in? Is security a concern? Our own Mike Vogel, Big Data Architect, answers these questions and more in this video Q&A. Continue reading A Plunge Into the Data Lake: Q&A with Mike Vogel
Our client was experiencing problems with managing their data assets. Data ingestion and processing were not managed in an organized way, causing users to waste time and effort when trying to find data. The large volume and variety of structured and unstructured data were only expected to increase as the organization grew. IT was struggling to balance the requirements of users with building a scalable, solid data foundation.
To meet their growing data needs, our client wanted fast, searchable, on-demand access to data and a way to conduct analytics and visualization on the data that users selected.
Proponents of the Data Lake are quick to point out the many potential benefits. For example, data will be readily available for end-user consumption, or users will have access to the “full historical view” of data and will be able to make innovative connections between different types of data (such as relational data, documents, or images). Continue reading The Data Lake: Is It All About the Metadata?