Every company has its own set of problems that it attempts to solve. In our case, we needed a more efficient and accurate way to identify the relationships between businesses on which we maintain data.
With THINK 2019 just around the corner, 12 through 15 February, there’s no better time to discover the variety of hybrid data management solutions and strategies, along with how each can help uncover actionable insights.
If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all.
Imagine a searchable data management system that would enable you to review crowdsourced, categorized and classified data. Consider that this system would apply to all types of data — structured and unstructured — and become more robust as more users analyze it.
So what happens now when we go beyond the frontiers of the data warehouse and into the world of the data lake? – the world of Hadoop, of NoSQL, the world of schema on read, of discovering the data as is? For many organizations, the holy grail is to reap the benefits of the data lake while retaining
Making your data lake a “governed data lake” is the game changer. Without governance, organizations risk securing the data and as well as protecting it. When data is cataloged and governed, an organization can effectively discover, classify, track history and lineage, quality of data and thereby
The data lake may be all about Apache Hadoop, but integrating operational data can be a challenge. Learn how to deliver real-time feeds of transactional data from mainframes and distributed environments directly into Hadoop clusters and make constantly changing data more available.
Upon reading his own obituary in the newspaper, famed author Mark Twain is said to have remarked that reports of his death were greatly exaggerated. I can only imagine that if the data warehouse appliance were a 19th century American novelist, it might say the same thing. For a while now,
Perhaps one the single most significant changes to the analytics landscape in recent years had been the emergence of the data scientist. This role is continuing to evolve, with many organizations still in the process of establishing how best to incorporate this relatively new discipline into their
It’s easy to be blinded (and impressed) with the rapid innovation and evolution in the arena of big data. Today’s most technically sophisticated companies have the opportunity to exploit big data tools to address mind-numbingly cool use cases and produce very enticing results. However, so many