Cloud object storage lets you affordably store large amounts of semi-structured and streaming data, such as logs, clickstreams, and IoT sensor data. However, preparing complex and continuous data for analytics using Apache Spark presents a number of challenges.
This intricate, error-prone work requires extreme care, plus expensive and scarce big data engineers. It can take months to design, test, debug, and deploy these continuous pipelines reliably at scale.
Upsolver makes creating real-time pipelines as easy as S-Q-L. It reduces your most difficult data engineering challenges to an SQL query combined with automation based on data lake best practices. Upsolver customers build powerful pipelines quickly, without needing “big data” skills, and run them reliably at scale, even when dealing with complex data such as nested and array fields arriving as semi-structured event streams.
Upsolver lets you combine streaming data at high volume (e.g. 100,000s of events per second) with historical batch data, and perform stateful transformations such as high cardinality joins, window or sessionization operations. Output data is always up-to-date due to incremental aggregations via data lake upserts and exactly once consistency guarantees. And performance is ensured through unique data lake indexing.
Complex data types such as nested data and arrays can be challenging to work with. Upsolver auto-detects the schema on read and visualizes it in an IDE, making it easy to flatten rich data types into a more workable format.
No need to hand code ingestion as Upsolver comes with connectors for a variety of data source types:
Upsolver pipelines output to a variety of platforms via connectors, so you can distribute data as you see fit.