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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

Data Lake 104
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Use Apache Iceberg in a data lake to support incremental data processing

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. and later supports the Apache Iceberg framework for data lakes. The snapshot points to the manifest list. AWS Glue 3.0

Data Lake 120
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How Gupshup built their multi-tenant messaging analytics platform on Amazon Redshift

AWS Big Data

It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. Additionally, extract, load, and transform (ELT) data processing is sped up and made easier. Moreover, no separate effort is required to process historical data versus live streaming data.

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Manage your data warehouse cost allocations with Amazon Redshift Serverless tagging

AWS Big Data

Amazon Redshift Serverless makes it simple to run and scale analytics without having to manage your data warehouse infrastructure. For Filter by resource type , you can filter by Workgroup , Namespace , Snapshot , and Recovery Point. For this post, we don’t include any tag filters, so we can view all the resources across our account.

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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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Configure monitoring, limits, and alarms in Amazon Redshift Serverless to keep costs predictable

AWS Big Data

To centralize monitoring, you can add these metrics to an existing CloudWatch dashboard or a new dashboard. On the Actions menu, choose Add to dashboard. Let’s take an example where you have to create a serverless workgroup for your dashboards. You know that dashboard queries typically complete in under a minute.

Metrics 83
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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices. All these architecture patterns are integrated with Amazon Kinesis Data Streams. The raw data can be streamed to Amazon S3 for archiving.

Analytics 115