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Graph Advantage: Building a Smarter Data Lake

Build a Smarter Data Lake with Neo4jOrganizations 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?

Graph Brings Your Data Lake to Life

The most effective NoSQL technology pairing to help enterprises avoid building big data graveyards is the introduction of a graph database like Neo4j. The Neo4j graph database provides a flexible schema that enables many disconnected and unstructured data sources to be aggregated into a singularly connected graph. Paired with very effective data import techniques through many native connectors to existing databases as well as standard transport formats such as CSV, Neo4j can be kept up to date through batch, streaming and ad hoc query integrations to ensure the latest a in your data lake is averrable for real-time use by your business applications and business analysts.

Neo4j is great at dealing with big data integrations because it values reliability first and foremost. If you’ve dealt with large volumes of varying types of data for any period of time then you’ll appreciate not needing to worry about whether or not two nodes always agree on the state of the relationship between them. As a fully ACID compliant, native graph database built from the ground up to guarantee referential integrity, Neo4j will keep your relationships in a consistent state, which is one major reason why we’ve preferred Neo4j over other pluggable graph layers and graph-document hybrid databases.

Maturity of the Data Lake

A data lake begins with raw data and it will only mature when that data is continuously connected and accessible for real-time interactions by personnel and algorithms. By introducing the Neo4j graph database on top a data lake, enterprise domains can gradually and independently mature. Enterprise users can see across all areas of concern — not restricted by rigid schema or organizational silos.

The risk of introducing Neo4j into your data architecture is actually very low because of the way it plugs in right alongside your data lake and existing databases. This non-invasive integration alone should make it a prime consideration in your discussions around driving better business insights from your data.

If you’re interested in evaluating what it would take to effectively perform this integration of Neo4j into your data lake within your organization, contact our experts at GraphGrid to help you plan and execute your integration.