Читать книгу Data Lakes For Dummies - Alan R. Simon - Страница 35

IN THIS CHAPTER

Taking advantage of big data

Broadening your data type horizons

Implementing a built-to-last analytical data environment

Reeling in existing stand-alone data marts

Blockading new stand-alone data marts

Deciding what to do about your data warehouses

Aligning your data lake plans with your organization’s analytical needs

Setting your data velocity speed limits

Getting a handle on your analytical costs

Suppose that you and about 15 other family members or friends all head to your favorite lake for a weeklong summer vacation.

You love going to the lake because you jump into your sailboat every day and spend hours out on the water. Others in your group, though, have their own favorite pastimes. Some prefer a boat with a little more “oomph” and spend their days in speedboats, zooming up and down the length of the lake. Others prefer leisurely canoeing. Some are into waterskiing, so they take turns latching onto one of those speedboats and zipping along the water. Others in your group are into fishing, and that’s how they spend most of their time at the lake. Still others aren’t all that interested in even going out on the water at all — they plop down on the beach to read, soak up some rays, and even grab a snooze every afternoon.

A data lake is very much like that weeklong trip to your favorite lake. Because a data lake is an enterprise-scale effort, spanning numerous organizations and departments, as well as many different business functions, you and your coworkers will likely seek a variety of varying benefits and outcomes from all that hard work.

The best data lakes are those that satisfy the needs of a broad range of constituencies — basically, something for everyone to make the results well worth the effort.