The Emergence of Operational Data Warehouses


Monica Multer

Communications Coordinator at SingleStore

The Emergence of Operational Data Warehouses

Last month, we announced that SingleStore received the highest score in the Gartner Critical Capabilities Report for the “Operational Data Warehouse Use Case”.

While the findings Gartner shared are gratifying, this deserves a bit of a deeper dive.

For starters, let’s examine how Gartner defines an Operational Data Warehouse: “This use case manages structured data that is loaded continuously in support of embedded analytics in applications, real-time data warehousing, and operational data stores. This use case primarily supports reporting and automated queries to support operational needs, and will require high-availability and disaster recovery capabilities to meet operational needs. Managing different types of users or workloads, such as ad hoc querying and mining, will be of less importance as the major driver is to meet operational excellence.”

In light of these observations, we expect the adoption rates for Operational Data Warehouses to climb rapidly. Several megatrends point directly to a growing need for Operational Data Warehouses that take full advantage of real-time transaction processing and big data analytics in an in-memory optimized architecture.

Take the explosive global sensor market that BCC Research predicts will reach \$154.4 billion by 2020. Image, flow, level, biosensor and chemical sensors will generate a data deluge of information across a wide range of parameters. Organizations that can efficiently analyze these data flows in real time to adapt to constantly changing market conditions will be at a distinct advantage over their competitors who do not possess this capability.

To fully appreciate the extent of the data deluge we are talking about, consider forecasts about the volume of data captured by the Internet of Things (IoT). According to ABI Research, 1.6 zettabytes of IoT data will be collected by 2020. This staggering volume of data will tax latency issues already affecting enterprises in terms of data loading and query execution. Batched loading takes hours to implement in the absence of real-time ingestion capabilities causing slow query responses, reporting and applications.

So what is the net effect? These challenges threaten the ability to detect and respond to business changes as soon as they occur, compromising the delivery of real-time analytics to applications with growing user bases.

Throw mobile computing, risk management, personalization, portfolio tracking, clean energy and geospatial trends into the mix, and it’s easy to see why the demand for real-time analytics is ramping up quickly. The challenge of course is how to efficiently ingest copious and continuous data loads in order to support operational business intelligence queries to rapidly iterate on the greatest number of analytic models possible. What is clear is that analytic flexibility is essential to business adaptation.

Interestingly, when it comes to Operational Data Warehouses, Gartner weights:

  1. Operational BI queries
  2. Repetitive queries
  3. Continuous data loading and
  4. System availability as the top ranked critical capabilities

High marks for SingleStore in these strategic areas account for an overall score of 3.77 (out of a possible 5), ahead of IBM, Teradata, MongoDB and Oracle.

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