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

AWS Big Data

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. As of this writing, only the optimize-data optimization is supported. Note the last four newly added configurations in the following statement.

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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.

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

datapine

You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. According to studies, 92% of data leaders say their businesses saw measurable value from their data and analytics investments.

Big Data 275
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15 Supply Chain Metrics & KPIs You Need For A Successful Business

datapine

That’s why it’s critical to monitor and optimize relevant supply chain metrics. While there are numerous KPI examples you can select for your assessment and optimization, we have focused on a list that will enable you to identify potential bottlenecks and ensure sustainable development. Freight Bill Accuracy.

Metrics 260
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Improve your Amazon OpenSearch Service performance with OpenSearch Optimized Instances

AWS Big Data

Amazon OpenSearch Service introduced the OpenSearch Optimized Instances (OR1) , deliver price-performance improvement over existing instances. For more details about OR1 instances, refer to Amazon OpenSearch Service Under the Hood: OpenSearch Optimized Instances (OR1). OR1 instances use a local and a remote store.

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Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 1

AWS Big Data

Internally, Apache Flink uses clever mechanisms to maintain exactly-once state consistency, while also optimizing for throughput and reduced latency. Each of the distributed components of an application asynchronously snapshots its state to an external persistent datastore. The default behavior works well for most use cases.

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Monitoring Apache Iceberg metadata layer using AWS Lambda, AWS Glue, and AWS CloudWatch

AWS Big Data

Despite their advantages, traditional data lake architectures often grapple with challenges such as understanding deviations from the most optimal state of the table over time, identifying issues in data pipelines, and monitoring a large number of tables. It is essential for optimizing read and write performance.

Metadata 126