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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Introducing AWS Glue crawler and create table support for Apache Iceberg format

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. With each crawler run, the crawler inspects each of the S3 paths and catalogs the schema information, such as new tables, deletes, and updates to schemas in the Data Catalog.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. AWS Glue can interact with streaming data services such as Kinesis Data Streams and Amazon MSK for processing and transforming CDC data.

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Load data incrementally from transactional data lakes to data warehouses

AWS Big Data

Data lakes and data warehouses are two of the most important data storage and management technologies in a modern data architecture. Data lakes store all of an organization’s data, regardless of its format or structure.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

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Estimating Scope 1 Carbon Footprint with Amazon Athena

AWS Big Data

In this blog, we will walk through how we can apply existing enterprise data to better understand and estimate Scope 1 carbon footprint using Amazon Simple Storage Service (S3) and Amazon Athena , a serverless interactive analytics service that makes it easy to analyze data using standard SQL.