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Qubole Pipeline Services

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Qubole Pipelines Service - A Complete Stream Processing Service DATA SHEET Manage streaming ETL pipelines with zero overhead of installation, integration or maintenance When it comes to data analytics, lowering end-to-end time-to- decision matters. Building pipelines to simply dump all data to a data lake and then indexing and warehousing is relatively easy but that, by itself, does not reduce the time-to-decision as there are additional processing steps required. Today, organizations are moving towards real-time processing solutions which help them deliver insights in a matter of seconds to minutes. Although this brings in significant business benefits it is poses additional challenges. Why Qubole Pipelines Service? Entire event processing SDLC made easy. Lower time to value with rich user experience, REST APIs and connectors to real- time systems to quickly move from experimentation to testing to deployment to the management of long-running business-critical pipelines. Data engineers and data scientists leverage the power of Apache Spark to build, train, and deploy MLlib models and create consumer applications that take advantage of derived inferences in real-time. For a long-standing job where a steady-state is the norm rather than the exception, provide insights on optimizing Spark configurations to minimize cost without sacrificing SLA. For handling ebb and flow in data loads, the cluster will auto-scale based on workload. There are significant improvements in fault tolerance and performance when building stateful streaming applications such as de-duplication, pattern detection etc. Lower time-to-market with productivity gains Advanced machine learning platform completely under one roof High Performance / Lower TCO Qubole Pipelines Service solves the complexities in data processing that arise when dealing with data streaming. Qubole Pipelines Service is an enterprise-grade stream processing platform built on the highly fault-tolerant, scalable and performant Apache Spark Structured Streaming. It is supported on Apache Spark 2.3.x and above. Currently it is supported on AWS and Google Cloud.

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