Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.

At Koyeb, our mission is to provide the fastest way to deploy applications globally. We are building a platform allowing developers and businesses to easily run applications, a platform where you don't need to think and deal with the resiliency and scalability of your servers: a serverless platform. Ironically, a serverless platform is actually full of servers. As a cloud service provider, we operate the infrastructure for you and abstract it as much as possible.

If you’ve been performing on-premise testing in your organization, you know the rules already. But for the uninitiated, on-premise testing is a form of testing where testers perform tests on local machines, systems, or devices set up at an office. As the name suggests, it happens on-premise. On-premise testing comes with a lot of responsibility. From the maintenance and monitoring of the machines and software to upgrading and installations — you’ll need all hands on deck.

The application landscape is swiftly moving from monolithic architectures towards next-gen applications running on microservices. It’s all about fast delivery of applications and services to stay competitive in the market. Making this change gives businesses the agility and flexibility to quickly launch innovative new digital services.

Knowing best practices for Amazon Web Services (AWS) data pipelines is essential for modern companies handling large datasets and requiring secure ETL (Extract, Transform, Load) processes. In this article, we discuss AWS data pipeline best practices to ensure top performance and streamlined processes — without complications that can impede the execution of data transfer.

We all know that what customers see in the market is, in fact, only a small percent of the shifts happening within our organizations. Recently, Time Magazine stated that “Every Company is a Tech Company… The Disruption is Just Beginning.” We’re seeing it in the way we wait in lines, find places to stay when traveling and work from anywhere. The disruption is distribution, and it impacts how we live and build applications.

Across organizations large and small, ML teams are still faced with data silos that slow down or halt innovation. Read on to learn about how enterprises are tackling these challenges, by integrating with any data types to create a single end-to-end pipeline and rapidly run AI/ML with Azure Synapse Analytics with Iguazio.