Last year was significant for us, so let's reflect briefly. As one of the key milestones, we released Xray Enterprise - a comprehensive solution for test management challenges, blending powerful features with user-friendly functionality. It is especially beneficial for larger companies with high scale and complexity of the development operations. Here are other noteworthy numbers/metrics.

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.

You can now unpack the Christmas present that was promised to you at the end of 2023! As you may have already read in the Flamegraph Feature Preview, this exciting new feature of the Profiler is now the focus of our first release in 2024. The Flamegraph complements the Timeline and Callgraph features and visualizes Aggregated Traces. The goal was to provide profiling information across many traces and thereby gaining new and deeper insights into performance.

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.

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 a study by Statista, the cloud storage market was valued at $90.17 billion in 2022 and will reach a value of $472.47 billion by 2030. These figures indicate a growing shift toward cloud computing and data storage solutions. A typical scenario in modern data management involves data transfer from cloud storage to cloud-based computing platforms. Amazon’s Simple Storage Service (S3) is among the go-to options for the former, and businesses trust Snowflake for the latter.

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.

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.

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.

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