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Apache Spark is a high-performing large-scale analytics and data processing engine that offers high batch and interactive processing performance.
Apache spark streamlines data processing objectives with in-memory computing capabilities and super-fast queries.
Spark-based real-time predictive analytics solutions for enterprises to gain faster and more practical business insights.
With managed workloads, organizations can work on reducing costs and maximizing workload efficiency.
Apache Spark is a fast cluster computing platform developed for performing computations and stream processing activities. Understand how it assists in building and processing data applications.
Utilizing Apache Spark's built-in libraries and APIs for data processing, machine learning, and streaming analytics.
Monitoring Solutions
Unified Memory Management
Collocated Data Engine
RDD in Apache Spark
Streamlined Performance
24x7 monitoring and support solutions for promoting efficient and faster workload performance.
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Apache Spark comes with a shared memory pool for execution and caching, further empowering self-managing and optimization abilities.
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Apache Spark engine is present in the operational database, which means there is no need for extracting, transforming, and loading into a new environment.
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Resilient Distributed Datasets driven solutions for extending Immutability, Fault Tolerance, and partitioning capabilities.
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Apache Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing, running application to run up to 100x faster.
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Enterprise solutions for building scalable data deployment and management strategy.
Empower scalable and reliable applications with optimized query execution and implementation solutions.
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Facilitate and build completely managed parallel and integrated operations with Dataproc.
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Enterprises can utilize HD insight abilities for easy administration and configuration of Spark clusters.
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Explore our Data Processing related Insights
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