Experimentation and data drive growth. But the question is, how can you set up a self-driving experimentation machine that helps you run marketing tests. And is that the best use of your marketing team’s time?

Follow along to go from zero to search in minutes. In this tutorial, we will guide you step-by-step through your Keboola and ThoughtSpot integration.

Keboola is proud to announce the launch of its new feature: the Flow builder.

Understand the tradeoffs to make the best choice “Why are you still doing ETL pipelines?” “The Data Warehouse is the only way you can keep data quality high, despite the extra data modeling needed.” “Have you not heard of data mesh beforehand? It solves all centralization problems.” When it comes to data management, there are as many opinions as there are data managers. This is not to say there aren’t any good answers or right principles to follow.

Companies move as fast as their slowest insight. In the era of big data, information is often produced faster than it can be consolidated and analyzed throughout the Enterprise. Analysts battle for the limited engineering resources, so they can construct data sets for their analytics endeavors to ultimately provide insights to fast-growing enterprises.

Data integration is the data engineering process of combining data from disparate sources into a single unified view of the data. The process begins with data ingestion from different source systems. This includes data extraction from disparate sources, data transformations or cleaning, and loading the data into a single repository - anything from Excel data sets to Enterprise data stores.