adesso Blog

In today’s data-driven world, small businesses and enterprises still rely on data warehousing solutions to ingest, process, and analyze great volumes of data. AWS provides a large list of services built to meet the demands of modern data warehousing, providing scalability, flexibility, and cost-effectiveness. In this first part of the blog post, we’ll dive into the building blocks of modern data analytics architectures on AWS, focusing on the key data services like Redshift, Glue, and S3 focusing on the data pipeline design and the data warehousing capabilities of Redshift.

In today's digital landscape, the efficient management and processing of data is crucial to the success of applications and services. This blog post introduces the powerful caching solution DynamoDB Accelerator and explains why it is a suitable solution for lightning-fast database queries.

If your infrastructure utilizes EventBridge and its archive feature, you may easily encounter situations where modifications to your event bus are necessary. This could involve creating a new archive or event bus and subsequently transferring events from the old archive to the new one as there is no way to attach an existing archive to a new event bus, so creating a new event bus requires creating a new archive for it.

Dagger is a fairly new tool, which has already gained traction in the tech community as a promising replacement for your regular CI/CD pipelines. In this post we are going to take a look at how you can use Dagger to write your CI/CD pipeline as code and to deploy your AWS CDK stacks.

The 2023 AWS re:Invent gave developers, analysts, and business owners many innovations to consider. One of the new services included was the generative-AI based chat tool called Amazon Q.

AWS announced the General Availability (GA) of DataZone on October 4, 2023. DataZone is a data management service (essentially a managed data mesh) designed to streamline data management across various sources. This blog post will briefly examine its capabilities and what I learned when I evaluated it two weeks ago.

Even if you are a seasoned AWS CDK/CloudFormation veteran, it has most likely happened to you that you tried to work with a resource that was not yet available in CDK/CloudFormation (looking at you Lake Formation). Luckily if the resource and the accompanying operations are available through the AWS API then you are in good luck as AWS has a way to incorporate these API calls into your templates/code and bridge the gap created by the unavailability of some resources.

In this blog post, I present a solution that utilizes Athena's new Iceberg functionality for achieving such minimal ETL pipelines.

This guide will walk you through the process of reading Athena views in Glue ETL using PySpark.