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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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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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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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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. This allowed them to focus on SQL-based query optimization to the nth degree. They ingest data in snapshots from operational systems. What is Presto?

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Build a multi-Region and highly resilient modern data architecture using AWS Glue and AWS Lake Formation

AWS Big Data

The utility for cloning and experimentation is available in the open-sourced GitHub repository. The on-demand mode is a batch replication that takes a snapshot of the metadata at a specific point in time and uses it to synchronize the metadata. These mechanisms can be customized for your organization’s processes.