A ten-fold increase in worldwide data by 2025 is one of many predictions about big data. And this data today is coming with great velocity from a variety of sources be it social media, operational data or transactional data and all from different source systems. Data lake adoption is expected to rise over the next couple of years from about 30% to 90% of organizations worldwide using them predicts IDC.
As organizations struggled to manage the ingest of rapidly changing structured operational data, next generation data lake models evolved that leverage streaming data via Kafka based Information Hubs. These Kafka based Information Hubs go well beyond feeding the data lake by seamlessly delivering continuously changing data in real time model for downstream data integration with everything from the Cloud to AI environments.
While organizations are in a frenzy to create Information Hubs or data lakes to optimize the data available to their organizations, success is often dependent on dynamic real-time delivery of the most current operational data managed by DB2, Oracle, SQL Server, and even legacy IMS and VSAM systems.
If you are swimming across currents in your data lake or just wondering why organizations like yours are flocking to a Kafka based Information Hub, then join this webcast from IBM to have a sneak peek at a best in class data replication technology for dynamic, real time, incremental delivery of transactional data to Kafka, Hadoop and the rest of the enterprise. We will also discuss some of the challenges inherent in replicating transactionally consistent operational data into the potentially unstructured, unordered Big Data world.
Join us to learn how to build a comprehensive information integration platform based on dynamic, real time, incremental delivery of transactional data to your Data Lake or Information Hub and how to keep that data fresh for all of your data consumers.