Systems | Development | Analytics | API | Testing

From DIY opportunity cost and pipeline maintenance to moving and transforming data, here’s how to judge the cost-effectiveness of ELT.

If you have managed a cloud data platform, you have undoubtedly gotten that call. You know the one, it's usually from finance or the office of the CFO, inquiring about your monthly spend. And it usually comes in one of two forms: While both are clear and present dangers to cloud data platform owners, they don’t have to be.

Deciding where to run your enterprise workloads is one of the biggest business decisions you’ll make. That’s because your chosen deployment model influences how you access software and systems, scale your business, and safeguard sensitive data. There are three main deployment methods you need to know about: Self-hosted, on-premises, and cloud. All three models have pros and cons, so it’s important to do your research before investing in new infrastructure and/or services.

We recently rolled out our very own GPU autoscaler in Collaboration with Genesis Cloud and it has been quite a success. Also recently, YOLOv8 by Ultralytics was unveiled, the new king of object detection, segmentation and classification. In this blogpost we’ll see that you can train a computer vision model using the ClearML/Genesis Cloud autoscaler at a fraction of the cost of competing cloud services like AWS or GCP. And it even runs 100% off of green energy! 😎

As announced at Snowflake Summit 2022, Iceberg Tables combines unique Snowflake capabilities with Apache Iceberg and Apache Parquet open source projects to support your architecture of choice. As part of the latest Iceberg release, we’ve added catalog support to the Iceberg project to ensure that engines outside of Snowflake can interoperate with Iceberg Tables.

The concept of the "digital double" isn’t new, but it’s gaining importance as more and more of our lives are lived in the digital world. Asanka Abeysinghe, WSO2's chief technology officer and the creator of this concept, was a guest on a recent KuppingerCole videocast hosted by Martin Kuppinger, founder and principal analyst at KuppingerCole. During the discussion, Asanka spoke about this unique concept, its benefits, and how it relates to customer identity and access management (CIAM).

Show a responsive, highly-customizable feedback form when a user encounters an error—and let them describe the issue in their own words!

Are you considering venturing into the world of analytics engineering? Analytics engineers are the newest addition to data teams and sit somewhere between data engineers and data analysts. They are technical, business savvy, and love to learn. A huge part of an analytics engineer’s role is learning new modern data tools to implement within data stacks.

The open source ML tooling ecosystem has become vast in the last few years, with many tools both overlapping in their capabilities as well as complimenting each other nicely. In part because AI/ML is a still-immature practice, the messaging around what all these tools can accomplish can be quite vague. In this article, we’ll dive into three tools to better understand their capabilities, and how they fit into the ML lifecycle.