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

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.

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.

Artificial Intelligence (AI) is quite powerful and is constantly evolving and currently knows no bounds. It is focused on outperforming its limits using the power of Machine Learning (ML). AI is empowering computers to do things that human beings are unable to do efficiently and effectively and machine learning is aiding the computers to do so by breaking the rules of traditional programming.

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).

Testing for quality assurance (QA) is a fundamental and essential component of software development. Not only is it necessary to ensure that all the critical elements of software projects are in place to be delivered on schedule, but it also contributes to raising the project’s overall quality. This is why QA candidates need to have a strong understanding of various concepts and be able to answer tricky questions.

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.

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.

Starting a company that works in any field is an exciting journey. We start with probably ten people and almost no user base in the initial days. But if our services are user-centric and our product solves a good problem, we will grow in both these dimensions. When our user base grows, and we expand our services, we generally ask our engineers to make our systems even better for them.

Discover how mobile teams building finance apps rate themselves across the five key Mobile DevOps metrics.