Systems | Development | Analytics | API | Testing

Embedded Analytics ROI: Quantified. Visualized. Justified.

Most companies wait for perfect data before investing, but by then, it’s too late. In this video, you'll discover how to model real, measurable gains from embedded analytics, whether it’s saving time, reducing churn, or boosting engagement. Learn how small efficiency improvements compound into big results and why early investment in analytics helps teams build stronger, more resilient products. See how to quantify value, justify spend, and move faster with confidence.

Ensuring Data Consistency in Sharded APIs with High Latency

When dealing with sharded APIs, scaling is easier, but maintaining data consistency becomes a challenge, especially in high-latency environments. Here's the core problem: as data gets spread across multiple shards (or databases), operations like updates, reads, and transactions can lag or fail, leading to stale data, conflicts, or inconsistent states. This is especially problematic for critical applications like financial systems or e-commerce platforms.

Orchestrating Multi-Agent Workflows with MCP & A2A

Multi-agent workflows are the latest technological gen AI advancements. In this blog, we explore how to develop such systems, overcome operational challenges, improve system observability, and enable seamless collaboration between agents in complex AI pipelines. We’ll cover architecture, A2A and MCP protocols and introduce Google Cloud’s agentic marketplace.

Why Design IP Is Important: IP Integration in SoCs

Intellectual property (IP) in semiconductor design refers to reusable design components that can be integrated into a larger chipan IC, SoC, or chiplet. These design blocks may be developed in-house or licensed from third-party vendors and are used in system-on-chip (SoC) design and production. With growing SoC complexity, increased market demand, and the rapid pace of innovation, adopting an IP-centric design approach is critical for staying competitive.

The Rise of the Data Operator: Why the Future of AI Depends on Them

We are entering a new era in enterprise data: the era of the Data Operator. As AI becomes core to every business process, every team is being asked to move faster, act smarter, and operate with real-time data. But the old stack isn't built for that. It's built for centralization. For gatekeeping. For data engineers and IT teams to own every flow, sync, and transformation. That model is breaking down. Why? Because the need for data has exploded at the edge of the business. Customer teams. RevOps.

Data Sovereignty Is Everyone's Problem

Data sovereignty isn’t just a niche consideration anymore – it’s a central requirement in everything from cloud computing and analytics to software development. The environment of 2025 is significantly different from that of 2015, and even more so from 2005. What was once a patchwork of guidance documents, data privacy laws, and local regulations has given way to massive EU-wide regulations, multinational frameworks, and a greater focus by users on digital identity.

Demo Days: How SaaS Companies Build & Scale Their Data Stack

Scaling your SaaS product? Your data stack needs to keep up. As your SaaS business grows, so does the complexity of your data with more tools, more teams, and more questions to answer. But without a strong data foundation, analytics slows down, insights are delayed, and scaling becomes a constant struggle. Whether you're laying down your first modern data stack or trying to fix one that’s straining under growth, this webinar will show you what actually works. You’ll see a real-world demo, unpack proven strategies, and learn from SaaS companies who’ve been through it.