Systems | Development | Analytics | API | Testing

GitHub Copilot has been the subject of some controversy since Microsoft announced it in the Summer of 2021. Whatever your feelings about the matter, Copilot is likely here to stay. So that got me thinking — perhaps there are more important questions to ask about Copilot. If developers are going to use an AI-assisted code generation tool, it would be more productive to think about how to improve it rather than contemplating its right to exist.

Codemagic has recently decreased its prices thanks to Apple M1 machines. How is it possible for us to provide Apple M1 VMs to everybody, including those on a Free plan, and lower the prices at the same time? Codemagic’s CTO Mikhail Tokarev took some time to share the details, including the technical aspects behind our recent changes.

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD).

So much relies on data these days that it might be safe to say that most companies can’t thrive without it. Yet, gathering data is just the start — using it to make better company decisions or improve business processes is the end goal. Unfortunately, collecting large quantities of data for your business might seem futile if that data remains cut off from other systems. As such, it has become vital for developers and decision-makers to unify data for practical use.

What does this mean for those of us managing or trying to understand fast-growing companies?

This is a guest post with exclusive content by Bill Inmon, Mary Levins, and Georgia Burleson. Bill “is an American computer scientist recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” -Wikipedia.

The data landscape is exploding with tools. As data professionals we have at our fingertips specialized tools for anything: from specialized databases (graph, geo, you name it) to tools for SQL-driven transformations (looking at you, dbt). Yet, a lot of data work is about provisioning, selecting, administering, and just maintaining those tools. Which is just a pain. As Pavel Dolezal, CEO and co-founder of Keboola said: The answer is in how the Modern Data Architecture is built.

Data integration is the data engineering process of combining data across all the different sources in a company (CRM, SaaS apps like Salesforce, APIs, …) into a single unified view. The data integration process includes data extraction, data cleansing, data ingestion, data validation, modeling, and exposing ready-to-be-consumed data sets to other users and applications for business intelligence or data-driven activities.

Unravel now pulls in data quality checks from external tools into its single-pane-of-glass full-stack observability view.