Explore cloud insights, engineering trends and tech deep dives.
Let's take a look at the architectural changes which have made EC2 the fundamental part of most environments launched in AWS.
Time is more valuable than money, but cake trumps both. With automated (pre)baking of your AMIs you can have your cake — and eat it too.
Read about our client's real-world migration case supported by this full-stack observability platform. Find out all the key benefits we have identified.
Have you ever thought about moving buckets between accounts/regions?
Some key takeaways from the "Cloud is more efficient than your own DC" debate.
A recap of thoughts after spending one week on one of the most influential conferences in the world.
A short tale of trying to outsmart AWS and tackle its hard limits in Cognito and IAM.
Leverage your cybersecurity strategy by implementing the zero trust model.
Prisma Cloud is one of the cutting-edge tools that can help protect all aspects of a cloud operation.
How do we ensure the security of serverless applications in AWS?
Disasters usually happen at an inconvenient time, with no warning. Invest your time!
Security, as one of the top priorities, cannot rely on merely a single service.
Using Amazon Cognito to secure applications, APIs and other resources without any coding.
In this article, we answer why they are useful, what problems they solve and demonstrate basic usage based on the Apache Iceberg table format.
IaC is necessary for most cloud projects, data engineering included — but it can get tricky in some setups. Here's how to solve one of those.
A tutorial on how to reuse dashboards for multiple customers using Amazon QuickSight templates.
Large language models such as ChatGPT recently made headlines in nearly every corner of the world. Why is the matter important and how can you benefit from it?
A general guide to how Amazon SageMaker can be used in larger organizations by multiple teams in a secure, repeatable manner without constant reinvention of the wheel.
A comparison between three AWS technologies used to automate workflows in machine learning projects.
A showcase of various methods and features of SageMaker Inference in the context of deploying machine learning models for Software as a Service applications.
In this part, we will characterize the oldest Amazon’s offering - AWS CodePipeline.
Get to know more about the AWS Step Functions service in the context of machine learning.
A practical introduction to Amazon SageMaker Pipelines, AWS Step Functions and AWS CodePipeline
How we put together a serverless application running AWS Lambda under the baton of AWS Step Function.
Step into the world of testing Step Functions, Python wrappers and Lambda function aliases and versions.
Let's look deeper into AWS serverless services and try to make our S3 copy tool.
AWS RDS integrity testing tool based on a serverless approach.
We’re going to walk through the automated flow using DynamoDB streams, AWS CI/CD services and Lambda.
This is a continuation of ‘Don't panic, organize (part 1 of 2)'.
Closer look at one of such services like AWS Organizations. How it can be used?
Hitting resource limits in your AWS CloudFormation template? I'll show a method that worked!
Tools, AWS services and third-party frameworks that speed up different parts of the process.
Finishing touches to our implementation, with all of its configuration steps, in the last chapter of the series.
More about client requirements, current situation and limitations
A way to get full transparency of your cloud storage bills.
Failures — same story over and over again.
Native AWS services in context of CI/CD and other tools for managing resource configuration
