Having frequently worked with governments around the world over the course of my career, I’ve had all kinds of discussions about the global impact of generative AI. Today, I’m publicly wading into those waters to deliver my perspective, and my opinion is that … it’s incredibly hard to predict the future. Done. Wrapped up this entire post in a single sentence.

Javascript Object Notation (JSON) is becoming the standard log format, with most modern applications and services taking advantage of its flexibility for their logging needs. However, the great flexibility for developers quickly turns into complexity for the DevOps and Data Engineers responsible for ingesting and processing the logs. That’s why we developed JSON FLEX: a scalable analytics solution for complex, nested JSON data.

Business process management solutions often tout their analysis and optimization capabilities, but few provide the full set of tools needed to build a process, measure its performance, and identify and implement improvements. And when you’re looking for information on how to optimize a process, it’s easy to quickly get lost in the information-overload about the best methodologies and tools. That’s likely because the answer to process improvement isn’t straightforward.

Intelligent automation happens when robotic process automation (RPA) meets artificial intelligence (AI), bringing simple actions and cognitive tasks together for lightning-fast processing. Intelligent automation in insurance is a powerful tool that reduces both human error and the need to perform repetitive tasks manually. Imagine AI extracting data from an invoice then a bot entering that data into a software program.

Headline: The transition to Codemagic made our CI builds faster and more reliable and positively impacted the experience of contributors contributing to our open-source project, Tuist. Thanks to Codemagic’s support, we can bring new free goods to the Swift community and the ecosystem of app developers.

In this blog series, I am exploring the “sliding doors”, or divergent paths, for creating value with data across different use cases, practices, and strategies. In this post, I want to discuss how to generate value with Data Products. As I reviewed in my last blog, grabbing the door to the better path for managing your data isn’t just about solving your particular use case: it’s ultimately about delivering value for your business.

According to Gartner’s Top Priorities for Customer Service Leaders -2024 – Three priority areas are Self Service, Gen AI, and Customer Journey Analytics. When delved into the primary concern, it became apparent that many customers abandon product or service issues rather than seek agent assistance if online solutions are unavailable. Additionally, a notable proportion of Millennials and Gen Z adhere to a “self-service or no service” mindset.

In this article, we present some best practices and key concepts for using Confluent Schema Registry.

Considering BigQuery vs. Redshift for your data warehousing needs? This guide is for you. Both BigQuery and Redshift stand as leading cloud data warehouse solutions each offering a multitude of features catering to multiple use cases. Google’s BigQuery offers seamless scalability and performance within its cloud platform, while Amazon’s Redshift provides great parallel processing and tuning options.

Introducing new BigQuery SQL features for performing two of the most common time-series operations: windowing and gap filling.