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Is your business growing to the point where it’s critical to start learning from your data? It’s no easy feat figuring out where to start, especially if you don’t have a data team or the resources to hire one. While we all know that data has become the lifeblood of businesses, providing valuable insights that fuel decision-making and growth, getting started on the data journey is easier said than done.

If you haven’t been tuned in, AI products are the new “it girl” on the block, turning heads and transforming the way businesses operate. This seismic shift isn't merely a trend; it's the new norm. AI has massive implications for Keboola’s mission - helping companies create and ship better data projects faster. We’ve been helping build AI-powered products for years, but the recent innovations in the space have accelerated our efforts even further.

As the pace of data grows, modern companies struggle to get a unified view and extract insights across all their disparate sources. Heck, just accessing all the enterprise datasets can be hard and requires too many email chains with the IT department. Luckily, integrating data from different systems and external sources can be simplified with the right data integration technique.

Can you put a number to how much bad data is costing your business? This includes everything from marketing losing 21 cents of every advertising dollar due to misinformed business decisions to data engineers wasting more than 3h daily resolving data quality issues. Luckily, you can guarantee high-quality data with 9 simple checks. In this article, we’ll look at.

Achieving seamless alignment between business objectives and IT strategies can be a formidable challenge in today's rapidly evolving business landscape. Yet, without this business-IT alignment, companies can’t fully leverage technology as a driving force for growth and innovation.

Artificial intelligence (AI) is propelling us toward a more autonomous future. Just recently, ChatGPT passed another Bar exam and DALL-E 2 won an art competition. But AI is not just about self-driving cars and hopeful futurism. Some are pessimistic about the techno consequences of the new inventions. For instance, Elon Musk called for a pause in AI R&D due to concerns about its dangers.

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.

Keboola has once again taken first place in multiple categories, as announced by the world’s leading software peer-to-peer review marketplace, G2. As a data stack as a service, Keboola has one core goal - to make data accessible across all departments in your business. We want to empower teams everywhere to use data and build data products effortlessly, regardless of their technical background. Keboola’s success in the G2 Spring Report is gratifying proof that our users continue to love us.

Are you executing data replications and backups? Building ETL data pipelines? Ensuring the data ingestion is running smoothly? Or simply building an ad hoc dataset for your machine learning task? Using a data mapping tool can shave hours off your engineering work in all four use cases. In this article, you will learn.