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How Gupshup built their multi-tenant messaging analytics platform on Amazon Redshift

AWS Big Data

Moreover, no separate effort is required to process historical data versus live streaming data. E.g., use the snapshot-restore feature to quickly create a green experimental cluster from an existing blue serving cluster. Apart from incremental analytics, Redshift simplifies a lot of operational aspects.

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

AWS Big Data

Some of the important non-functional use cases for an S3 data lake that organizations are focusing on include storage cost optimizations, capabilities for disaster recovery and business continuity, cross-account and multi-Region access to the data lake, and handling increased Amazon S3 request rates.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. To avoid look-ahead bias in backtesting, it’s essential to create snapshots of the data at different points in time.

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

AWS Big Data

The Orca Platform is powered by a state-of-the-art anomaly detection system that uses cutting-edge ML algorithms and big data capabilities to detect potential security threats and alert customers in real time, ensuring maximum security for their cloud environment. Why did Orca choose Apache Iceberg?

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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

With this architecture pattern, you capture not only inserts and updates, but also deletes committed to the data lake, and then merge those captured changes into the data warehouses. or later supports change data capture as an experimental feature, which is only available for Copy-on-Write (CoW) tables. Delta Lake 2.0.0

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

AWS Big Data

This post explains how to create a design that automatically backs up Amazon Simple Storage Service (Amazon S3), the AWS Glue Data Catalog, and Lake Formation permissions in different Regions and provides backup and restore options for disaster recovery. These mechanisms can be customized for your organization’s processes.

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Unleashing the power of Presto: The Uber case study

IBM Big Data Hub

The technical value of Presto at Uber Analyzing complex data types with Presto As a digital native company, Uber continues to expand its use cases for Presto. For traditional analytics, they are bringing data discipline to their use of Presto. They ingest data in snapshots from operational systems.

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