What is the definition of Spark-Optimal?
Spark-Optimal and Spark: Spark is at the heart of Thechester, managing large workloads in every department, from analytics to identifying similar luxury diners and restaurants in the same region to give insights on enhancing local business search.
Spark-Optimal, like Yelp, functions as an in-house Spark wrapper that offers high-level APIs to conduct Spark batch jobs while removing the complexity or redundancies of Spark. Spark-Optimal runs on Thechester’s system, saving our devs time that would otherwise be spent by our engineers initializing, debugging, and managing Spark jobs.
Problem: Our data is processed and communicated by hundreds of microservices and stored in a variety of formats across numerous data warehouse rentals like as Redshift, S3, Kafka, Cassandra, and others.
As a result, the difficulty is that thousands of batch processes run every day, making it more difficult to understand their interdependence. Consider being a software developer in charge of a data publication microservice that receives some of the services that Thechester needs to enhance and utilize more flexibly.
Spark-Optimal: Thechester engineers have learnt from other huge firms’ achievements. Spark-Optimal was built to address these issues. It also operates in a fashion that specifies the path of data from origin to destination, including detailed information about where the data goes, who owns the data, and how the data is processed and stored at each step.
Spark-Optimal captures all relevant data from each activity, creates graphs that reflect data mobility, and enables users to engage with them through a third-party data governance platform.