As organizations increasingly seek to enhance decision-making and drive operational efficiencies by making knowledge in documents accessible via conversational applications, a RAG-based application framework has quickly become the most efficient and scalable approach. As RAG-based application development continues to grow, the solutions to process and manage the documents that power these applications need to evolve with scalability and efficiency in mind. Until now, document preparation (e.g.

Every business handles numerous document types—contracts, purchase orders, reports, invoices—you name it. And the thing about documents? They never look the same. One day, you’ve got a well-organized PDF with neatly labeled sections, making it easy to find what you need. The next, you’re stuck with a document that’s all over the place—random tables, text scattered everywhere, or even a scanned image that doesn’t fit the mold.

Every device, transaction, and interaction in our digital world generates an endless stream of data. By 2025, the amount of global data is expected to reach a mind-boggling 180 zettabytes. So, how do we extract and make sense of this growing data? That’s exactly where generative AI proves its value. This blog explains generative AI applications for document extraction and how this technology helps cut through the noise and zero in on exactly what you need.

Throughout my career in enterprise technology, I've witnessed numerous transformations play out across the Asia-Pacific (APAC) region. But the shift we're seeing now with data streaming is truly unprecedented. What was once a supportive technology is rapidly becoming the very foundation of modern business in our region.

We are now in the final chapter of Apache Kafka’s multi-year journey to remove Apache ZooKeeper and fully transition to self-managed metadata in KRaft. Many Kafka users and customers are beginning to migrate to KRaft and are eager to understand its performance characteristics in production environments.

If innovation is the ultimate goal in business and technology today, then consider generative AI (gen AI) the vehicle taking us there — and a strong data strategy, the fuel. Despite all its promise of productivity gains and new discoveries, gen AI alone can't do it all. The technology needs a "very ready" data foundation to feed on, something the vast majority of businesses today (78%) do not possess, according to a new report by MIT Technology Review Insights, in partnership with Snowflake.