Systems | Development | Analytics | API | Testing

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.

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.

The data mesh approach has gained popularity over the last couple of years as organizations look for reliable ways to break down data silos. At first, data lakes looked like a good way to improve data management and make information more discoverable. Unfortunately, data lakes — and data warehouses — don’t always conform to business needs. They’re often slow and even unresponsive to queries. Potentially even worse, they can still lead to data silos.

In the competitive landscape of iOS app development, ensuring the quality and functionality of your application is crucial. That’s where automated testing tools come in, enabling developers to streamline their testing processes and deliver robust, bug-free applications. In this comprehensive overview, we dive into the world of automated testing tools for iOS, exploring the top contenders that can revolutionize your testing efforts.

Natural language processing and large language models to help BigQuery process dataframes.

The Apigee API Gateway is a tool that enables the management, analysis, and security of APIs. This API gateway enables organizations to optimize their digital ecosystems and deploy user-friendly products. It is particularly valuable for developers, IT professionals, and SaaS businesses interested in enhancing their API infrastructure, streamlining development workflows, and ensuring secure and seamless connectivity.

With its simplicity, flexibility, and cost-efficient characteristics, Amazon Simple Storage Service (Amazon S3) cloud object storage has become the preferred platform for collecting, analyzing, and retaining today’s growing mountain of diverse and disjointed enterprise data. And as Amazon Web Services (AWS) continues to grab market share in the hyperscale IaaS/PaaS/SaaS marketplace, organizations of every size are leveraging Amazon S3 to underpin a variety of use cases, such as.