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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.

Google Data Studio is a beautiful visualization tool that turns your data into compelling story-telling reports. But before you can visualize your data, you have to collect it, clean it, and validate it. This is where Keboola comes in. Keboola is the Data Stack as a Service (DaaS) platform that helps you with all your data operations - from building and automating ETL pipelines to data governance.

In the era of big data, the world is producing more information than it can consume. Every minute of the day: Smart companies took notice of the growth in data and turned it into an opportunity for company growth. But having a lot of data is just part of the recipe. You also need to have technical data experts, who can turn the raw data into manageable operations that deliver revenue-generating insights. This led to the job roles of the data engineers and data scientists, that joined data teams.

Data analytics is invaluable to companies that want to drive growth. Research from giants like McKinsey, the Financial Times, and Google confirms it: Companies that rely on data analytics to drive business decision-making grow 2.5x faster than their lagging competitors. So how does descriptive analytics fit into the wider frame of data analytics?

We’ve got some exciting news! Are you ready? Drum roll, please… We are proud and excited to announce our partnership with ThoughtSpot! You can now design and deploy your end-to-end data stack and analytics in a few clicks. It’s a major shift from how much time and resources it used to take to get from data to insights. This partnership unlocks your data capabilities and supercharges your growth. Keboola gets all your data nice and clean and delivers it to the destination of your choice.

Yes, I’ve said it. The modern data stack is a pain to work with. But it wasn’t always like that. As companies realized they can leverage data to accelerate growth new data tools were invented. From NoSQL databases that specialize in processing specific data structures (graph anyone?) to the Python-Pandas-like Spark ecosystem that allows you to run queries on Big Data (capital B, mind you). But with every new tool added to the data stack, the complexity increased.