Analytics are in great demand. Today’s requirements are putting new pressures on existing data infrastructures. Performing real-time analytics across operational and stored data is typically critical to success but often challenging to implement. InfoWorld brought this interesting information to our attention in their article, “How to do real-time analytics across historical and live data.”
Many organizations developing big data initiatives use Hadoop to store a copy of their operational data in a data lake, where data scientists can access the data for various analyses. When it comes to real-time analytics, the traditional infrastructure can quickly become a stumbling block.
Real-time analytics allows businesses to react without delay. They can seize opportunities or prevent problems before they happen. Business intelligence insights from real-time analytics can allow businesses to get ahead of the curve.
To be immediately useful, real-time analytics applications should have high availability and low response times. They should also be able to handle large amounts of data, up to and including terabytes. Yet they should still return answers to queries within just seconds. Therein lies the challenge.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
