Systems | Development | Analytics | API | Testing

Artificial intelligence (AI) is widely used today, from voice assistants to Netflix recommendations, but AI models do not always behave as intended. Testing an app before it is released is standard practice, and similarly, AI models should be thoroughly tested. Testing an AI model can verify that the model’s decisions are accurate, fair, and safe.

With vibe coding, you can build complex applications using powerful AI tools that offer a conversational experience, letting AI handle the heavy lifting. But what happens after you've built your app? You might have a three-tier web application connected to a database running locally, but taking it live—ensuring it’s connected, secure, and production-ready—can be a complex process that disrupts your flow. What if deploying your app was just as easy as building it?

Industries like manufacturing and smart cities rely on connected devices to generate data streams for predictive maintenance, automation, and efficiency. But moving this data between systems can be slow, insecure, and inefficient. Here's the solution: smart data flows powered by edge computing and automated APIs.

Joe Reis, author of Fundamentals of Data Engineering, known for practical education and his YouTube content — formerly CEO/co-founder of Ternary Data — is reviewing our Keboola MCP (Model Context Protocol) Server with Claude to list Shopify his tables, run exploratory analysis, export data to BigQuery, and generate a star schema. And you can do the same in minutes! We will go through everything here from one-click setup to best propmts to use MCP with.

DevOps stands for Development and Operations. The term is a combination of: DevOps originated from the growing need to connect the silos between software development and IT operations. The adoption of agile practices in software development teams enabled developers to release software faster and more reliably. As developers sped up their processes, the Operations side of the organization started to struggle with the impact of agile on deployment, maintenance and stability.

For many engineering leads and executives, reviewing quality assurance dashboards and automation reports may feel like trying to solve a complex puzzle, one where the pieces keep changing, and the full picture only comes into focus after it’s too late.

Before jumping into AI data cleaning directly, let’s first understand data cleaning itself. Data cleaning, also known as data scrubbing, is a critical data preparation step where organizations remove inconsistencies, errors, and anomalies to make datasets ready for analysis. The cleaning process may involve actions like removing null values, correcting formatting, fixing syntax errors, eliminating duplicate data, or merging related fields like City and Postal Code.