A constant flow of breaking news from the data lakehouse space is making notable tech headlines this week. On Tuesday, Databricks announced that it will acquire Tabular, a data management company founded by the creators of Apache Iceberg, Ryan Blue, Daniel Weeks, and Jason Reidfor. The deal was for an unconfirmed sum, but some reports suggest that amount to be between $1B and $2B (and allegedly outbidding Snowflake).

Recently, Cloudera, alongside OCBC, were named winners in the“Best Big Data and Analytics Infrastructure Implementation” category at The Asian Banker’s Financial Technology Innovation Awards 2024. This recognition underscores the importance of trusted data when building AI and generative AI (GenAI) models and serves as a testament to the impact that reliable data can have in real world use cases.

It’s not a surprise that in today’s challenging economic landscape, rising costs pose a significant threat to the telecommunications industry. Consider that in 2022, Bain Capital was predicting that Telcos would grapple with increased personnel and escalating operating costs due to inflation. And here we are.

We are excited to announce a tech preview of Cloudera AI Inference service powered by the full-stack NVIDIA accelerated computing platform, which includes NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform for generative AI. Cloudera’s AI Inference service uniquely streamlines the deployment and management of large-scale AI models, delivering high performance and efficiency while maintaining strict privacy and security standards.

In an era where artificial intelligence (AI) is reshaping enterprises across the globe—be it in healthcare, finance, or manufacturing—it’s hard to overstate the transformation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to value for AI applications.

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission-critical, large-scale data analytics and AI use cases—including enterprise data warehouses. Nearly two years ago, Cloudera announced the general availability of Apache Iceberg in the Cloudera platform, which helps users avoid vendor lock-in and implement an open lakehouse. With an open data lakehouse powered by Apache Iceberg, businesses can better tap into the power of analytics and AI.

The financial services industry is undergoing a significant transformation, driven by the need for data-driven insights, digital transformation, and compliance with evolving regulations. In this context, Cloudera and TAI Solutions have partnered to help financial services customers accelerate their data-driven transformation, improve customer centricity, ensure compliance with regulations, enhance risk management, and drive innovation.

Behind every business decision, there’s underlying data that informs business leaders’ actions. As the market landscape across verticals from financial services to healthcare and manufacturing grows increasingly competitive, those decisions need to happen ever faster and to make them, businesses need to rely on data to reveal insights quickly, as near-real-time as possible.

In modern enterprises, the exponential growth of data means organizational knowledge is distributed across multiple formats, ranging from structured data stores such as data warehouses to multi-format data stores like data lakes. Information is often redundant and analyzing data requires combining across multiple formats, including written documents, streamed data feeds, audio and video. This makes gathering information for decision making a challenge.

Hybrid cloud plays a central role in many of today’s emerging innovations—most notably artificial intelligence (AI) and other emerging technologies that create new business value and improve operational efficiencies. But getting there requires data, and a lot of it. More than that, though, harnessing the potential of these technologies requires quality data—without it, the output from an AI implementation can end up inefficient or wholly inaccurate.