Systems | Development | Analytics | API | Testing

July 2021

What is Change Data Capture in SQL Server?

For more than three decades, Microsoft SQL Server has helped countless organizations store and manage their enterprise data, and it’s still one of the most widely used software applications on the planet. According to the DB-Engines database ranking, SQL Server remains the third most popular database management system, just behind Oracle and MySQL. Change data capture (CDC) is essential functionality for many businesses, especially those with real-time ETL use cases.

How to do data transformation in your ETL process?

Working with raw or unprocessed data often leads to poor decision-making. This explains why data scientists, engineers, and other analytic professionals spend over 80% of their time finding, cleaning, and organizing data. Accordingly, the ETL process - the foundation of all data pipelines - devotes an entire section to T, transformations: the act of cleaning, molding, and reshaping data into a valuable format.

The Future of the Modern Data Stack

The Modern Data Stack is quickly picking up steam in tech circles as the go-to cloud data architecture, and although its popularity has been quickly rising, it can be ambiguously defined at times. In this blog post we’ll discuss what it is, how it came to be, and where we see it going in the future. Regardless of whether you’re new to the modern data stack or have been an early adopter, there should be something of interest for everyone.

When to Use Change Data Capture

Automated ETL (extract, transform, load) and data integration workflows are essential for the modern data-driven organization, and they can swiftly and efficiently migrate data from sources to a target data warehouse or data lake. But ETL must run at regular intervals — or even in real-time — so how can you know which information is fresh and which information you’ve already ingested? Solving this problem is the goal of change data capture (CDC) techniques.