In a world where 2.5 quintillion bytes of data are created every day, it’s not surprising that organizations want to harness the power of being data-driven. In our 2022 Data Health Barometer, 99% of companies surveyed recognized that data is crucial for success — but 97% said they face challenges in using data effectively. Perhaps in response to those challenges, 65% of companies reported that they'd started a data literacy program.

In this new exciting development, Keboola is launching no-code data transformations for everybody on the platform. No-code transformations empower users without the technical know-how to build robust, feature-rich applications without having a degree in computer science or waiting for the IT department to develop the apps for them.

Data engineers who work with huge amounts of data know that “big data” is not just an overhyped term. When the volumes of data get into petabytes the best data engineering tools start to break down. This is when you need devoted big data technologies that are fault-tolerant, scalable, and offer high performance even when amounts of data test the limits of your data platform. This article won’t be just another listicle. Instead, we’ll showcase the best big data tools by use case.

CIOs are fed up with having a plethora of BI and analytics tools with business units seemingly chasing the shiniest new solution. And although most industry surveys show data and analytics budgets continuing to grow despite a faltering economy, there is closer scrutiny and belt tightening to rid teams of overlapping capabilities. Here’s a look at how BI tool portfolios have become such a mess and how to streamline them.

Much like Apple people tend to be all Apple, all the time, Microsoft Dynamics ERP users tend to prefer Microsoft products for all their computing needs. It’s not hard to understand why. Using products from the same ecosystem prevents compatibility issues and saves time in learning multiple systems.

A data extraction tool can help improve the accuracy of data by automating the extraction process and reducing the risk of human error. This can lead to more reliable and consistent data that can be used to make better business decisions. Moreover, data extraction tools can help you increase productivity and improve the quality of your data as they automate the process of retrieving data from multiple sources.