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Keeping Your Data Current and Flowing into Neo4j

January 29, 2016

SimpleDataPipelineFor an enterprise to excel today a key aspect centers around utilization of the data-based business assets. To grow and succeed as a whole an enterprise must enable the usability, quality, and constant flow of its data into a connected state. Sometimes, an enterprise with a data architecture may have to deal with a complex life cycle while undergoing varying transformation processes. This makes it difficult to track the origin and flow of data as well as managing changes, audit trails, history, and a host of other critical processes.

Distributed Graph Database Platform with Neo4j

The dynamics of an increasingly distributed and connected world are shining the spotlight on a new generation of database focused on more efficiently modeling, storing and querying the connected nature of the data enterprises deal with in the real world. But as graph database usage grows, solving the issue of handling large volumes of read and write operations at scale will pose a serious challenge for the growing market.

Graph databases like Neo4j are perfect aggregation and landing place for data across the enterprise because it effectively deals with challenges presented with variations in data. As a leading graph database, enterprises are relying on Neo4j to effectively connect data for usage by real-time enterprise applications. The big challenge though is efficiently and continuously flowing data into your Neo4j graph database.

To do this effectively data connectors need to be utilized to perform ETL. The data extraction will come from your existing data source such as a MySQL database. This extracted data is then transformed to Cypher or a CSV format for use with LOAD CSV; both which can be routed and flowed effectively into Neo4j with transaction size optimizations. The transformed data is then loaded into Neo4j.

GraphGrid Data Platform

We’ve personally experience this common pain point across our Neo4j enterprise solutions, which is why we’ve made sure GraphGrid has a wide range of data import/export and routing abilities for utilizing Neo4j efficiently within any modern data architecture. The core framework enables data integration and job processing.

The GraphGrid Data Platform offers a full-bodied data pipeline for optimizing a high write throughput data flow to Neo4j from different input sources. It handles batch operations for incoming data with strategies for throttling, sizing, and transactional integration. A majority of batch operations work well with concurrent write processes.

Being able to efficiently route and flow your ever growing data into a connected state will provide competitive business advantages and allow you to more quickly unlock business value from your most untaped business asset. The enterprise path to graph can be very efficient and effective with the right tooling and expertise leading the way.