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Think of a data professional (data scientist/data engineer/business analyst/…), and guess what they do all day. Design big data algorithms? Build state-of-the-art, scalable pipelines? Discover insights that drive business growth? Wrong. Data professionals spend over 40% of their time preparing data before they even start using it for their job. The data preparation process is the most time-consuming task in a data operative’s schedule.

After another round of G2 awards, Keboola is shining brighter than ever. This season, Keboola struck gold where it really counts: in customer service and satisfaction. With a world-class platform, we provide our users with the product and user experience they need to excel in their industry. Here’s a breakdown of the G2 awards featuring Keboola.

Česká spořitelna is the biggest Czech retail bank with 4.5 million clients across 400 branches. Running a bank of this size brings its own data challenges from strict regulatory compliance via a wide range of data management needs, to almost limitless product possibilities within the data-rich environment.

The best ETL tools help you extract, transform, and load data from multiple data sources to the data destination of your choice. But not all ETL tool providers are created equal. Some are built for high-performance big data analytics, while others offer more intuitive and cost-effective workflows for the non-technical user.

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.

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?