The timely loading of real-time data from your on-site or cloud-based mission-critical operational systems to your cloud-based analytical systems is assured by a continuous streaming ETL solution. The data loaded for making crucial operational decisions should be reliable due to continuous data flow. By supplying an efficient, end-to-end data integration between the source and the target systems, Striim can guarantee the dependability of the stream ETL solutions. To ensure data reliability and send it to the target systems, the data from the source can be transformed in the real-time data pipeline. Striim applications can be created for a variety of use cases.
A power company called Glitre Energi manages the power grid, retails electricity, and offers broadband services. About 90,000 people receive electricity from Glitre Energi. The organization oversees the power lines that pass through heavily populated areas.
Getting the most unique identifier from the target table is made possible by a memory-based cache of non-real-time historical or reference data that was obtained from an external source.
External Cache The need for data prompts Striim to query an external database. In order to determine whether the incoming data is present in the target table already or not, Striim queries the same data when it joins with real-time data.
By limiting the data set by a specific number of events, period, or both, Windows will aggregate, join, or perform calculations on the data. This aids in bringing the target database data and real-time data together in one location where the downstream pipeline can carry out the transformations.
A continuous query that can be used to filter, aggregate, join, enrich, and transform the events specifies the logic of an application. A query facilitates the logic in the data pipeline and the combining of data from various sources.
Read About Our Success: How we were able to assist one of the biggest manufacturing companies involved setting up an ETL process using PySpark to move sales data from a MySQL on-premises database, which was then obtained from several different ERP systems to Redshift on the AWS cloud.
The use case’s high-level representation is shown in the image below:
Using continuous query components has made it simple to compare event data in real-time load to reach decisions. With the aid of windows and cache components, efficient data that must be retrieved from external sources has been planned out very well. The beauty of Striim allows data to be joined wherever it is needed and the desired output to be obtained, assisting Glitre Energi in achieving the normalization of their metering events in their relational systems.
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By Ankit Kumar Ojha
By Uma Raj