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Amazon OpenSearch Service Under the Hood : OpenSearch Optimized Instances(OR1)

AWS Big Data

Amazon OpenSearch Service recently introduced the OpenSearch Optimized Instance family (OR1), which delivers up to 30% price-performance improvement over existing memory optimized instances in internal benchmarks, and uses Amazon Simple Storage Service (Amazon S3) to provide 11 9s of durability.

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Optimization Strategies for Iceberg Tables

Cloudera

It offers several benefits such as schema evolution, hidden partitioning, time travel, and more that improve the productivity of data engineers and data analysts. This blog discusses a few problems that you might encounter with Iceberg tables and offers strategies on how to optimize them in each of those scenarios.

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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

Compaction is the process of combining these small data and metadata files to improve performance and reduce cost. Systems of this nature generate a huge number of small objects and need attention to compact them to a more optimal size for faster reading, such as 128 MB, 256 MB, or 512 MB.

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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

datapine

Table of Contents 1) Benefits Of Big Data In Logistics 2) 10 Big Data In Logistics Use Cases Big data is revolutionizing many fields of business, and logistics analytics is no exception. These applications are designed to benefit logistics and shipping companies alike. Did you know?

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Real-time cost savings for Amazon Managed Service for Apache Flink

AWS Big Data

When running Apache Flink applications on Amazon Managed Service for Apache Flink , you have the unique benefit of taking advantage of its serverless nature. This means that cost-optimization exercises can happen at any time—they no longer need to happen in the planning phase. per GB per month.

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Materialized Views in Hive for Iceberg Table Format

Cloudera

Queries containing joins, filters, projections, group-by, or aggregations without group-by can be transparently rewritten by the Hive optimizer to use one or more eligible materialized views. Subsequently, these snapshot IDs are used to determine the delta changes that should be applied to the materialized view rows.

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Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

Impala Optimizations for Small Queries. We’ll discuss the various phases Impala takes a query through and how small query optimizations are incorporated into the design of each phase. Query optimization in databases is a long standing area of research, with much emphasis on finding near optimal query plans.