Platform Engineering vs. DevOps: While they both share similar goals, here are 3 key differentiating principles. See how they apply to your tech stack.

If your business deals in large volumes of data, chances are that you are at least somewhat familiar with PostgreSQL, or you may use it actively in your data management strategy. PostgreSQL is a powerful relational database system, and it is completely open source. As of 2019, PostgreSQL was the second-most-common database solution for professionals. Thirty-four percent of more than 75,000 Respondents reported that it was their first choice.

Microservices architecture has emerged as a popular approach for developing software applications in recent years. Instead of building monolithic applications, microservices architecture involves breaking down an application into smaller, independent services that can be developed, deployed, and scaled independently. This approach offers a number of benefits, including increased flexibility, better scalability, and improved fault tolerance.

Customer services now constitute one of the key distinguishing factors of banks in an ever-changing environment. With increasing customer requirements, banks must adopt and incorporate novel technological methods to provide customized services. A very promising technology in this regard is generative AI. Generative AI in core banking systems will enable banks to transform their customer services by increasing efficiencies and building meaningful customer relationships.

Unit tests are performed on a single unit of code, usually on a specific function, variable or flow. They allow us to test the components in isolation and validate each single component’s reliability in turn. In this article we’ll introduce the concept of Swift unit testing, and explain, at a high level, why you should carry them out, how you can start developing your iOS apps in a test driven development methodology and how you can write Swift unit test cases.

Kipi.bi and Hevo Data have been bringing increased efficiency and maturity into enterprise organizations’ data stacks for years, and both teams are thrilled to formalize their partnership! Both organizations share a keen dedication to enabling data-driven decision-making by allowing businesses to leverage the power of modern data solutions.

It’s a milestone moment for Snowflake to have achieved FedRAMP High authorization on the AWS GovCloud (US-West and US-East Regions) . This authorization, from the Federal Risk and Authorization Management Program (FedRAMP), is one of the most rigorous security endorsements a cloud service provider (CSP) can achieve.

You need metrics to do your job well as a marketer but getting clear, meaningful metrics is a huge challenge. While digital advertisers and paid media professionals are on the hook to build ample sales pipeline and maximize return on ad spend (ROAS), they’re also expected to deliver personalized advertising content while navigating evolving privacy requirements and adhering to consumer expectations—all while extracting insights from siloed ad platforms.

At Integrate.io, we work with companies that build data pipelines. Some start cloud-native on platforms like Amazon Redshift, while others migrate from on-premise or hybrid solutions. What they all have in common is the one question they ask us at the very beginning: And so that’s why we decided to compile and publish a list of publicly available blog posts about how companies build their data pipelines.

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.