Operational Data Lakes…a Contradiction in Terms?
Powering Operational Data Lakes with Splice Machine
Splice Machine is a relational DBMS that leverages HDFS, HBase and Spark to deliver the economics and horizontal scaling of a Hadoop Data Lake, while offering full ANSI SQL, ACID transactions, and real-time analytics to power even the most demanding operational applications.
The result is that Splice Machine can continuously and concurrently ingest large amounts of data from source systems, while supporting transactional applications such as customer service operations and operational reporting, as well as real-time analytical workloads to discover trends that require immediate action.
For a detailed description of the Splice Machine architecture, see “How It Works”.
"As an expanding company, our wealth management technology platform experienced rapid data growth, and we needed additional tools to quickly access our growth in analytic data to guide strategic decisions and optimize our business processes. Moving to an Enterprise Data Hub powered by Splice Machine resulted in significant performance improvements."Mohan Gurupackiam CTO, Cetera Financial Group
Sample Operational Data Lake Use Cases
Replace Operational Data Stores
An operational data lake has the following additional benefits over an ODS:
- Based on modern scale-out technology. Compared to older RDBMSs like Oracle, operational data lakes can be 5-10x faster with 75% less cost
- Handle semi-structured and unstructured data. When part of a larger Hadoop-based data lake, you can now analyze structured, semi-structured, and unstructured data together
Offloading Reporting and Analytics Tasks from SQL Databases
As the amount of data in traditional databases grows, their performance on reporting and analytical workloads suffers and negatively impacts their transactional duties.
- Using Splice Machine allows you to run reports and analytics faster, cheaper and without the impact on performance of the source systems
- Offloading reporting and analytics can delay, or even avoid, investments in expensive scale-up expansions from traditional database vendors