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Tired of manually provisioning and managing your infrastructure? Well, then it's time to adopt best practices and treat your infrastructure as code. In this blog post, we’ll be diving into the world of Infrastructure as Code (IaC) using one of the most popular tools available - Terraform.

By the end of this post, you’ll have a better understanding of how to leverage Terraform to deploy your AWS Glue Pyspark jobs, giving you a more automated and scalable infrastructure. So, let’s get started and spark your infrastructure!

AWS Glue, the serverless ETL service of AWS, supports two types of jobs: Spark and Python shell. In this article, we'll focus on Python shell jobs and explain how you can make optimal use of your S3 Data Lake using Athena within Python shell jobs.

As your traditional BI environment bumps up against its limits, a lightweight cloud analytics lab can serve as a complement and as a first step in building a future-proof modern data platform. Check out three reasons to level up your current data architecture.

We should use AI to make our organization smarter! Chances are you’ve recently heard this statement and see organizations acting to it. Leading companies are using AI across departments to increase productivity. Customer care organizations are using chatbots and speech recognition in their customer contacts. Marketing departments are predicting churn and segmenting their customer base.

Venturing into the field of AI may seem daunting at first. While AI has been hyped immensely in the last decade, it is a deeply technical field. Gartner even made a prediction that 80% of AI will remain alchemy run by wizards whose talents won’t scale in the organization. Your organization probably doesn’t have the scale and technological know-how of Google, Apple or Facebook. This might lead you to conclude that developing AI models isn’t for you. You are right, partially.