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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Manage your Iceberg table with AWS Glue You can use AWS Glue to ingest, catalog, transform, and manage the data on Amazon Simple Storage Service (Amazon S3). With AWS Glue, you can discover and connect to more than 70 diverse data sources and manage your data in a centralized data catalog.

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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

A CDC-based approach captures the data changes and makes them available in data warehouses for further analytics in real-time. usually a data warehouse) needs to reflect those changes in near real-time. This post showcases how to use streaming ingestion to bring data to Amazon Redshift.

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Synchronize your Salesforce and Snowflake data to speed up your time to insight with Amazon AppFlow

AWS Big Data

To achieve this, they combine their CRM data with a wealth of information already available in their data warehouse, enterprise systems, or other software as a service (SaaS) applications. One widely used approach is getting the CRM data into your data warehouse and keeping it up to date through frequent data synchronization.

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Dimensional modeling in Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model.

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

AWS Big Data

AWS Glue for ETL To meet customer demand while supporting the scale of new businesses’ data sources, it was critical for us to have a high degree of agility, scalability, and responsiveness in querying various data sources. The data is partitioned on InputDataSetName, Year, Month, and Date.

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How Tricentis unlocks insights across the software development lifecycle at speed and scale using Amazon Redshift

AWS Big Data

Additionally, the scale is significant because the multi-tenant data sources provide a continuous stream of testing activity, and our users require quick data refreshes as well as historical context for up to a decade due to compliance and regulatory demands. Finally, data integrity is of paramount importance.

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Join a streaming data source with CDC data for real-time serverless data analytics using AWS Glue, AWS DMS, and Amazon DynamoDB

AWS Big Data

You will also want to apply incremental updates with change data capture (CDC) from the source system to the destination. To make data-driven decisions in a timely manner, you need to account for missed records and backpressure, and maintain event ordering and integrity, especially if the reference data also changes rapidly.