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Building end-to-end data lineage for one-time and complex queries using Amazon Athena, Amazon Redshift, Amazon Neptune and dbt

AWS Big Data

Amazon Redshift , optimized for complex queries, provides high-performance columnar storage and massively parallel processing (MPP) architecture, supporting large-scale data processing and advanced SQL capabilities. The solutions flexible and scalable architecture effectively optimizes operational costs and improves business responsiveness.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. However, none of these layers help with modeling and optimization. This approach is not novel. Enter the software development layers.

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Apply Modern CRM Dashboards & Reports Into Your Business – Examples & Templates

datapine

With a powerful dashboard maker , each point of your customer relations can be optimized to maximize your performance while bringing various additional benefits to the picture. Whether you’re looking at consumer management dashboards and reports, every CRM dashboard template you use should be optimal in terms of design.

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Improve operational efficiencies of Apache Iceberg tables built on Amazon S3 data lakes

AWS Big Data

When you build your transactional data lake using Apache Iceberg to solve your functional use cases, you need to focus on operational use cases for your S3 data lake to optimize the production environment. The following examples are also available in the sample notebook in the aws-samples GitHub repo for quick experimentation.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. To avoid look-ahead bias in backtesting, it’s essential to create snapshots of the data at different points in time. Tag this data to preserve a snapshot of it.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

Determining optimal table partitioning Determining optimal partitioning for each table is very important in order to optimize query performance and minimize the impact on teams querying the tables when partitioning changes. The following diagram illustrates the solution architecture. Orca addressed this in several ways.

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Amazon Managed Service for Apache Flink now supports Apache Flink version 1.19

AWS Big Data

In every Apache Flink release, there are exciting new experimental features. This flexibility optimizes job performance by reducing checkpoint frequency during backlog phases, enhancing overall throughput. However, in this post, we are going to focus on the features most accessible to the user with this release.