Systems | Development | Analytics | API | Testing

Welcome to the latest entry in Yellowfin Japan’s long-running ‘How to?’ blog series. This series of blogs aims to provide a step-by-step guide of how to get the most out of your Yellowfin BI experience, such as how to create your own Yellowfin dashboard, and how to use Yellowfin's rich data visualization tools. Catch up on the series below.

Let's talk about a problem that might sound simple but gets complex quickly: copying data from one Kafka cluster to another. As our Kafka usage grows, many of us find ourselves managing multiple clusters and needing to share data between them. Or worst still, sharing data to an external cluster. During a London meetup, we explored why this happens, what existing solutions offer, and why we decided to build our own Kafka replicator. Here's what we learned.

Reverse ETL delivers personalized experiences, smarter campaigns, and better decisions with operationalized data.

The demand for AI-driven applications is surging, and enterprises have reached an inflection point where they can no longer afford fragmented, siloed development. Traditionally, AI development is done by data scientists or machine learning experts with deep expertise in multiple tools and frameworks.

Every day, finance teams using Oracle ERPs find themselves bogged down by the inefficiencies of manual processes—relying on spreadsheets, juggling data extracts, and waiting on IT for access to the information they need. Even with Excel as the go-to tool, managing large datasets and producing accurate reports becomes a time-consuming ordeal, pulling focus away from strategic analysis and decision-making. Enter Spreadsheet Server from insightsoftware.

Modern companies generate large volumes of data, but often the internal users who need that data to do their jobs—data engineers, managers, business analysts, and developers—can find it challenging to quickly figure out answers to their questions. Apache Kafka is a powerhouse for real-time data processing of high-throughput workloads, and many organizations use Kafka to enable self-service access to data streams.

As your business grows, so does your data. And if you’re running Microsoft SQL Server, you’ve likely faced the challenge of managing storage in a way that doesn’t break the bank but keeps your applications running smoothly. It’s a balancing act that many IT professionals and business leaders struggle with daily. We hear from numerous mid-sized companies that hit a wall with their Microsoft SQL Server database.

At ThoughtSpot, we’re on a mission to empower every business user to become a data champion. Over the past year, I’ve witnessed firsthand how organizations across Australia and New Zealand are embracing this vision, transforming the way they work, make decisions, and serve their customers. Today, I’m excited to share some of the incredible momentum we’re seeing in the region and to celebrate the forward-thinking organizations leading the charge.

Gen AI call center co-pilots can provide enterprises with operational visibility and insights while automating repetitive tasks, to improve the customer experience. In this session, we’ll show how a large health insurance provider implemented an agentic co-pilot designed scale across multiple call centers and environments. To dive deep into the architecture and see a demo of the co-pilot, you can watch the webinar this blog is based on.