Modern data architecture allows you to have your cake and eat it, too
How data technologies circumvent ugly tradeoffs and satisfy competing priorities.
How data technologies circumvent ugly tradeoffs and satisfy competing priorities.
The insurance industry is quickly moving into a digital-first future where emerging technologies will reshape the way policies are sold, claims are processed, and risks are managed, and where market leaders are automating critical workflows to deliver seamless experiences, enhance customer satisfaction, and unlock profitability.
The wider adoption of Agile and DevOps practices in recent years have led to a significant shift in the way software is developed and tested. 2 emerging concepts are shift left testing (moving testing to the left on the project timeline) and shift right testing (performing testing in production). Both carry their own advantages, and in this article we’ll explore primarily the concept of shift right testing and explore the differences between shift left vs. shift right.
For more than a decade now, the Hive table format has been a ubiquitous presence in the big data ecosystem, managing petabytes of data with remarkable efficiency and scale. But as the data volumes, data variety, and data usage grows, users face many challenges when using Hive tables because of its antiquated directory-based table format. Some of the common issues include constrained schema evolution, static partitioning of data, and long planning time because of S3 directory listings.