With Precisely Connect, Symphony Health achieved the parallel processing for which Hadoop is designed
Symphony Health provides data science for the Healthcare industry. Using Precisely Connect, Amazon Redshift, and Hadoop, they built an optimally efficient process to minimize data latency, reduce costs, and provide usable data to data scientists, and results to customers. Now, instead of analysts waiting days, data will be available for analysis within minutes of its arrival.
Symphony Health needed to minimize data latency, reduce costs, and fully leverage Hadoop to increase the value of its analysis.
Fresh data is always important, but when your business is data analytics, it is critical. Symphony Health was constrained by a legacy solution that loaded data into Oracle databases typically once a day, and into a data warehouse weekly.
In addition to data being delayed before it was available for analysis, performing new types of analysis against data in the Oracle databases took longer than it should have. If analysts needed a new schema, they had to request it from a database administrator. The request then went into a work queue, and the analysts waited for the schema to be created. This process could delay new analyses.
The delays in data availability, early data discovery, and analysis were unacceptable, and a new approach was required.
Symphony Health transformed its data management and analytics processes by moving to Hadoop, which has a number of benefits. For one, analysts can easily define their own data schemas in Hadoop, eliminating the need to wait for a database administrator to do it for them.
In addition, the more data Symphony Health stored in the Hadoop Distributed File System (HDFS), the less it had to store in a high-cost, proprietary RDBMS. They could use industry standard commodity hardware, rather than having to buy bigger, more expensive servers, so storage costs were drastically reduced. In fact, some industry reports state that open source data management on industry standard hardware can be as much as 90% less expensive than traditional relational databases.