Systems | Development | Analytics | API | Testing

Enterprise automation didn’t start with AI agents, but they’ve had a much bigger impact than earlier automation methods, such as software scripts or bots. Modern AI agents can do a lot more than tackle repetitive tasks. They can reason through complicated workflows, choose the best course of action, and access tools to execute said action. But to do all this, AI agents require interoperability. They need to be able to connect to numerous tools, databases, services, and APIs.

Before we get started on WebSocket integration, it’s worth quickly explaining how building real-time mobile apps used to work – and why issues with lag and latency led engineers to turn to WebSockets instead. Engineers building real-time Android apps struggled to make sure updates were reflected immediately when a user sent them. To solve this, they tried polling, which meant firing off a GET request to the server, say every five seconds, to a /messages endpoint.

In today's competitive landscape, data warehouses and data lakes are the essential platforms for business intelligence, analytics, and AI. While immensely powerful, these systems were traditionally designed for batch data processing, often leading to insights based on data that is hours or even days old. The primary challenge has always been the complexity of bridging the gap between real-time data streams, typically flowing through Kafka, and these analytical systems.

I was reading about the gRPC recently and was wondering what all this about? Believe me, I was in the same boat not too long ago. I didn’t even know what exactly gRPC means before this. If you’re curious about how to inspect gRPC traffic, check out this guide on capturing gRPC traffic going out from a server. In this blog, I will walk you through everything I have learned about the game changing technology that is changing the world of distributed system.

The evolving market landscape is driving an urgent need for a unified enterprise performance management (EPM) solution, as finance teams face increasing pressures from several fronts. Rapid technological advancements, heightened competition, and the growing complexity of global markets have made financial agility and real-time decision-making critical to maintaining a competitive edge.

Every year, Data Summit attracts data professionals to Boston for three days to exchange ideas and innovation. In 2025, the event was dedicated to data management trends, Big Data, business intelligence (BI), analytics, as well as emerging innovation in AI and machine learning. At the Data Summit, Sami Akbay, insightsoftware’s Head of Data Analytics Projects, hosted a session on the evolution of BI, the rise of generative AI, and the shift toward insight-driven workflows.

BigQuery ML TimesFM model is now in preview, and forecasts datasets through the use of generative AI.

BigQuery has three new categories of advanced aggregation functions: group by extensions, user-defined and approximate aggregate functions.

Organizations have mastered collecting and storing vast amounts of data in cloud data warehouses like Snowflake. This central repository has become the single source of truth for analytical insights, business intelligence, and reporting. However, the true potential of this data remains trapped if it's confined to the warehouse, creating a disconnect between rich analytical insights and real-time operational systems.

True embedded analytics isn't about dashboards; it's about giving your users the confidence to act. It's about turning your application into the one place where data becomes a story, and differentiating it from those that lack a cohesive narrative. This guide explores how smoothly or painfully different BI platforms let you connect, embed, and share analytics within your product.