Graph Advantage: Building a Smarter Data Lake

Organizations today are amassing data at faster rate than ever before into their data lakes and often that data lake is where that data remains. Enterprises are looking for effective ways to utilize the huge volumes and varying data they’ve been collecting in their data lakes in order to respond to competitive pressures, regulations and provide empirical business guidance. It’s time to build a smarter data lake and let your data drive your organization forward.

What is a Data Lake?

For those that may not know, a data lake is a storage medium that houses large volumes of raw data in its native format until it’s needed by the organization. Common implementations today utilize Hadoop, which is effective at storing massive amounts of data. When a business-related question is being brought up, the data lake can be queried for pertinent data, and a smaller dataset can be reviewed to address the question. Most operations require long-running map-reduce jobs where large amounts of data are operated on to make a determination or drive updates.

While data lakes have become a powerful means to addressing challenges of data aggregation and integration as enterprises are increasingly collecting data from all their cloud, mobile and Internet of Things (IoT) data sources. The major downside to this approach is that none of the data lake interaction is real-time by default. Layers must be added on top of the data lake to make this interaction real-time.

There is a transition happening within the enterprise, driven by the desire to get more from their data. The question being asked is, now that we have all this data, how do we utilize it to further our business objectives?

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