Systems | Development | Analytics | API | Testing

One of the fastest-growing trends in the datasphere today is the development and use of data products. As we move deeper into 2025 and beyond, data product usage is going to explode. In fact, according to the Gartner 2024 Hype Cycle for Data Management, Data Products are currently at the Peak of Inflated Expectations, signaling strong interest and rapid evolution in this space. You can download the entire report here. The advantages and impact of this new paradigm extend beyond data engineers.

If you’re building AI systems—whether for personalization, fraud detection, or predictive analytics—you already know the brutal truth: your models are only as good as the data you feed them. Enter Countly, an analytics platform that doesn’t just collect user data but transforms it into rocket fuel for AI. This isn’t about explaining what Countly does (AI can do that for you).

Centralized data allows data teams to dedicate their effort to last-mile analytics and AI challenges.

Open table formats offer enterprises the capabilities of data warehouses and the flexibility of data lakes.

Lenses 6.0 (code-named Panoptes after the Greek mythical character with many eyes) is generally available. It brings a multi-Kafka developer experience and complete autonomy to streaming data.

Everyone associated with Business Intelligence (BI) applications is talking about their Artificial Intelligence (AI) journey and the integration of AI in analytics. Artificial intelligence encompasses a broad spectrum of categories, including machine learning, natural language processing, computer vision, and automated insights. ThoughtSpot has been a leader in augmented analytics, leveraging AI to automate insights and empower users to make data-driven decisions.

An engineering Key Performance Indicator (KPI) or metric is a clearly defined quantifiable measure that an engineering firm uses to gauge its success over time. With engineering being a very broad field, KPIs are employed in a variety of ways, ranging from company-wide analysis to project specific performance metrics. Company wide KPIs can be used to compare companies in the same industry, while project specific KPIs should be used internally to evaluate project performance.

As data-driven decision-making becomes more complex, businesses need tools that don’t just analyze data but actively guide smarter, faster decisions. At Databox, we are embracing the shift. In 2024, AI became one of our top strategic priorities. Now, we are taking an even more significant step: transitioning into an AI-first company.

Data visualization is an elementary component of human learning and understanding. It's also a significant capability of business intelligence (BI) and analytics solutions today. So, how did data visualization first come to be? The humble origins and gradual evolution of data visualization tools has led to an interesting timeline.

ClearML, Latent AI, and Carahsoft recently teamed up to talk about how teams can innovate, adapt, and collaborate in service of their mission goals by utilizing emerging AI technologies. If you couldn’t attend, you’re in luck!