Big Data Lakes are often a random collection of large volumes of data for uncertain use cases. Because of this, many Big Data solutions struggle to keep up with large data volumes and the need to drive clear value. With batch integration and after-the-fact analytics, Big Data solutions cannot discover urgent and perishable operational insights that high-velocity data offers. Using real-time data integration and streaming analytics, you can feed pre-processed streaming data into enterprise data lakes while getting the maximum value from your high-velocity, high-volume data with context-rich, real-time insights.
Feed your Hadoop and NoSQL solutions continuously with real-time, pre-processed data from enterprise databases, log files, messaging systems, and sensors to support operational intelligence. By loading and storing up-to-date, filtered, transformed, and enriched data in enterprise data lakes, you gain insights faster and easier, while better managing limited data storage capacity. Striim also integrates easily with 3rd-party machine learning solutions to automate operational decisions where appropriate using Big Data insights.
The leading aerospace and defense manufacturer chose Striim to support its modernization of analytics solutions. The company moved to a Hadoop-based Big Data environment to provide richer and more timely analytics to its employees and partners. Striim integrates its HP NonStop OLTP systems with their Hadoop ecosystem by delivering transactional data to HDFS, Kafka, and HBase in real time. With the ability to contain up-to-date airplane parts and schema data in the Hadoop environment, the company moved operational reporting processes from HP NonStop to Hadoop.
Offloaded operational reporting from transactional systems to Hadoop, reducing overhead on the OLTP systems.
Supports critical operational decision-making for production and supply-chain management using real-time airplane parts and schema data.
Hadoop environment serves a large ecosystem including suppliers and partners, with timely, operational data.
Striim ingests changed data continuously from a wide variety of sources including transactional databases, messaging systems, log files, and sensors. For transactional databases, Striim uses non-intrusive change data capture (CDC) to minimize the impact on source systems. After performing filtering, transformation, aggregation, enrichment, and analytics on data-in-motion via continuous queries, it delivers the streaming data to Hadoop, Kafka, and NoSQL environments – on-premise or in the cloud – with sub-second latency. Striim can also feed real-time data to other targets, and easily integrates with machine learning solutions to bring Big Data insights into real life. Striim uses a SQL-based development language and a wizards-based, drag-and-drop UI for fast development and easy modification.