Thanks to its user-friendliness and popularity in the field of data science, Python is one of the best programming languages for ETL. Still, coding an ETL pipeline from scratch isn’t for the faint of heart — you’ll need to handle concerns such as database connections, parallelism, job scheduling, and logging yourself. The good news is that Python makes it easier to deal with these issues by offering dozens of ETL tools and packages.

Every organization wants to stay on the cutting edge of technology, making smart and data-driven decisions. However, ensuring that company information and data integration remains up to date can be a very time-consuming process. That is where CDC can make all the difference. Change data capture or CDC allows for real-time data set changes, ensuring that company data is always up to date. Change data capture can transform the way companies make data-driven decisions.

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.

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.

ETL security is essential for safeguarding your customer's data and complying with regulations. Learn about the Fivetran approach.

Building a Heroku ETL pipeline can seem like a daunting task. The process seems complicated, and it's hard to know where to start. This post will show how easy it is to build an ETL pipeline with Heroku and Hadoop — and how Xplenty can help simplify the process.

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.

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.

The verdict is in: The more you analyze your customer data, the better chance you have of outperforming your business rivals, attracting new prospects and providing excellent service. For example, a report by McKinsey & Company has good news for companies who are "intensive users of customer analytics:" Their chances of excelling at new customer acquisition and being highly profitable are 23 and 19 times more likely, respectively, than those of their competitors.