What’s the difference between knowledge and wisdom? Knowledge is knowing Excel is Turing-complete. Wisdom is not using Excel as your database. The best data engineering tools make your life easier. Speed up processes, simplify complex operations, give you insights into the machinery, and maybe save some $ along the way. In this article, we’ll give you an overview of the 7 best data tools for data engineering use cases.

Data integration can be a daunting task, and data engineers usually prefer open-source ETL solutions because of their transparency (you can always inspect the code), flexibility (tinker with the tool), and price performance (no vendor licenses, no maintenance fees). But there are many “gotchas!” with open-source tools you need to consider before picking the best tool for the job.

How do you link BigQuery with Google Sheets? Importing a table from Google BigQuery to Google Sheets is quite easy. In fact, you can connect BigQuery tables directly to the Google Sheets spreadsheet with a couple of clicks using Connected Sheets, the new Google BigQuery data connector (check the full instructions here). But what if you want to move data the other way around? That is, how do you send Google Sheets data to your Google BigQuery data warehouse? Now your life becomes a bit more complicated.

Keboola is proud to announce the addition of dbt to the transformation layer. Now anyone who knows SQL can build production-grade data pipelines by embracing the software engineering approach.

A data extraction tool can help you speed up one of the most error-prone engineering processes: collecting raw data from different sources. In this article, we are going to analyze the following 6 market leaders in data extraction: Before we dive in, let’s look at all the problems you can avoid by implementing a data extraction tool.

Snowflake is a cloud-based software-as-a-service (SaaS) data warehouse. It offers separate and on-the-fly scalable services for data storage, compute, and analytics.

The data transformation process reshapes data from a raw mess to a business goldmine by: Using a data transformation tool streamlines the entire process and saves you time and energy on these valuable but tedious tasks. In this article, we’ll explore the 7 best tools on the market for data transformations. Each tool will be evaluated with Pros, Cons, and a clear decision for who this tool is best for. Are you in a hurry?

When you look closer at the Modern Data Stack (MDS) you need to brace yourself. The number of tools companies use for their databases, user administration, data extraction, data integration, security, machine learning, and a myriad of other use cases has grown astronomically. Matt Turck, VC at FirstMark, composes a yearly infographic of the hot tools in the datascape: And this is just a shortlist of both the most popular and fastest-growing tools.

Gaining insights from your data can be time-consuming. Or as simple as a few clicks. Depending on if you want to do everything by yourself, or if you let us help. For example, next time one of your stakeholders asks: “Can you deliver the dashboards by the end of next week?” You can say yes with confidence, and in this blog we are going to show you why and how it is done.

Oh boy, it’s been more than a year again since my last HKBFUKC article (yep, that’s a new standard abbreviation). This is the fourth article in the series. You can always check out the first, second and third on our blog. Again, loads of stuff has happened since the last time. I made the top 16 at the 4 Seasons MTG Legacy tournament in Bologna, I visited Lego House in Billund and I got married!