Star schemas and snowflake schemas are the two predominant types of data warehouse schemas. A data warehouse schema refers to the shape your data takes - how you structure your tables and their mutual relationships within a database or data warehouse. Since the primary purpose of a data warehouse (and other Online Analytical Processing (OLAP) databases) is to provide a centralized view of all the enterprise data for analytics, data warehouse schemas help us achieve superior analytic results.

Don’t sacrifice scalability for savings - have it both ways When left unchecked, the cumulative costs of your company data can ramp up fast. From training CPU-intensive machine learning algorithms that aren’t used in production to supporting enormous databases storing every minute event “just in case”. Letting your data operating costs run without checks and balances can quickly cause costs to bloat beyond your allocated budgets.

Whether you are trying to establish a new niche, carve out a bigger share of a mature and competitive market, or increase the value of your existing products or services, data can be used to gain a competitive advantage and increase your revenue. In this article, we will establish what data monetization is, how it translates to business use cases, showcase how it impacts business performance, and offer guidance on how to start monetizing data today.

Since the beginning, Keboola has been designed to be a world-class data platform as a service. Based on the results of this season’s G2 statistics, we have succeeded yet again. The purpose of the Keboola platform is to make our customers’ data processing simple, reliable, and transparent throughout the company. We love our customers and value the feedback they've given our teams over the years.

We previously wrote how the data mesh architecture rose as an answer to the problems of the monolithic centralized data model. To recap, in the centralized data models, ETL or ELT data pipelines collect data from various enterprise data sources and ingest it into a single central data lake or data warehouse. Data consumers and business intelligence tools access the data from the central storage to drive insights and inform decision-making.

The modern enterprise taps into over 400 different data sources to extract the insights that sharpen its competitive edge. The complexity, though, does not stop at the origin, where data is generated. To get valuable insights from raw data enterprises must extract data from its source, transform the data (clean and aggregate it), and finally load the data into a data warehouse or BI tool, where it is served to data scientists for analysis.

Running your business without data is like flying blindfolded. You might get lucky or you might crash without warning. Telemetry can help you spot issues before they develop and hurt your bottom line. In this article, we’ll dive deep into what telemetry is, how to set it up (with best practices and common pitfalls to avoid), and equip you with the tools to automate it.

Rohlik is a data-driven e-commerce that bridges the gap between food producers on the one hand and retail consumers on the other. By delivering the best quality food blazingly fast without compromising on quality, Rohlik has expanded across the European continent, operating multiple brands including Knuspr, Gurkerl, Sezamo, and Kifli, and growing 53% year-over-year. Becoming the first Czech unicorn company in 2021. Rohlik used Keboola to drive data-driven processes from day one.

Stuck manually updating data for that Excel report? There is a better way to spend your time at the office. Automated data processing can do the heavy-lifting for you and liberate your schedule for more productive and creative work.

Mass data fragmentation causes companies multiple problems - from missed business opportunities to increased risks of security breaches. To better appreciate the risks, we need to better understand what is the nature of data fragmentation, what causes it, and how to solve the problems of data fragmentation.