A data processing engine is a distributed software framework that enables running large-scale data workloads for transformation, mining, and analysis. They are designed to efficiently process huge volumes of structured and unstructured data in parallel across clusters of commodity machines.
Examples include open source engines like Apache Spark and commercial cloud services like Databricks, Amazon EMR, and Google Cloud Dataflow. Data processing engines are commonly used with data orchestrators and data warehouses.
A data processing engine transparently distributes computation for data workflows across clusters while handling parallelization, fault tolerance and other complexities.
It provides APIs and runtimes to express data processing logic for ETL, SQL queries, machine learning, graph processing, etc. This allows running programs and scripts to filter, analyze, and model datasets in a scalable way.
Data processing engines unlock large-scale analytics on massive datasets to drive insights for business intelligence, data science, IoT workloads.
They are a critical technology for big data platforms including data lakes, warehouses, streaming systems. Use cases span metrics analysis, ETL, data mining, machine learning, and other data-intensive domains like finance, e-commerce, and social analytics.
Unlike databases focused on storage, data processing engines are optimized for complex analytical computations on data leveraging parallel distributed runtimes.
Data processing engines excel at complex analytics on large, distributed datasets:
However, data processing engines also face complexities in performance, operations, and debugging:
A data orchestrator manages and coordinates the execution of individual tasks in a large data workflow, ensuring they run in the correct order, handling dependencies, and providing oversight on job lifecycles. It often integrates with various services, data sources, and data processing engines (sometimes multiple such engines). On the other hand, data processing engines focus on the actual computation and transformation of data, performing specific operations such as querying or aggregating. While orchestrators dictate the flow and order of tasks, processing engines are concerned with the granular execution of those tasks.
A data warehouse is a centralized data management system designed to enable business reporting, analytics, and data insights.
Read more ->A data lake is a scalable data repository that stores vast amounts of raw data in its native formats until needed.
Read more ->A data orchestrator is a middleware tool that facilitates the automation of data flows between diverse systems such as data storage systems (e.g. databases), data processing engines (e.g. analytics engines) and APIs (e.g. SaaS platforms for data enrichment).
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