Systems | Development | Analytics | API | Testing

Organizations are constantly seeking more efficient ways to extract, transform, and load (ETL) data into their data warehouses. Snowflake, is one of the leaders in cloud data warehousing, has traditionally recommended ETL/ELT processes for data ingestion and extraction. However, for organizations building internal data products, there is a new kid on the block: API generation. Here’s the key things to know from this article.

When ChatGPT first hit the market its human-like responses were astonishing, yet somewhat eerie. Many thought, “It communicates just like me.” It follows that the next logical thought was, “It’s going to take over my job.” However, the fear that AI-driven tools will replace human software testers is unfounded. AI will certainly impact testing jobs, including market expectations, skillsets, and required knowledge.

We’re constantly bombarded by data points and it takes real effort to make sense of them. While having a lot of information is a good thing, it’s easy to get overwhelmed and miss what’s really important. Businesses especially need to be able to sort the wheat from the chaff and assess their data accurately. But, how do you go about this exactly?

Hanami is a relatively new full-stack Ruby web framework. Unlike Rails, which has many default assumptions about how an app should be built, Hanami promises developer freedom by not imposing too many such defaults. The framework is also blazingly fast due to its low memory footprint and focus on minimalism. Combine that with a focus on strict abstractions, and you get a fully-featured Ruby framework that could rival Rails for building some applications, such as APIs and micro-services.

Geospatial data can give a business a competitive edge — especially when it’s combined with the company’s own data resources. Considering a new store location? You’ll want to analyze not just where your nearest competitors and potential customers are, but also retail footfall numbers, historical traffic patterns, distance from distribution centers, environmental factors, potential delivery times to customers and more. You need geospatial data to make it all happen.