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Latest Posts

Do You Know Where All Your Data Is?

In spite of diligent digital transformation efforts, most financial services institutions still support a loose patchwork of siloed systems and repositories. These dis-integrated resources are “data platforms” in name only: in addition to their high maintenance costs, their lack of interoperability with other critical systems makes it difficult to respond to business change.

One Big Cluster Stuck: Platform Health

Clearly environmental health and high performance are dependent on the proper implementation, tuning, and use of CDP, hardware, and microservices. Ideally you have Visibility and Transparency into existing high priority problems in your environment. The links below will carry you to regions within the Cloudera Community where you will find best practices to properly implement and tune hardware and services.

Beyond Monitoring: Introducing Cloudera Observability

Increased costs and wasted resources are on the rise as software systems have moved from monolithic applications to distributed, service-oriented architectures. As a result, over the past few years, interest in observability has seen a marked rise. Observability, borrowed from its control theory context, has found a real sweet spot for organizations looking to answer the question “why,” that monitoring alone is unable to answer.

Generative AI for the Enterprise

Riding the wave of the generative AI revolution, third party large language model (LLM) services like ChatGPT and Bard have swiftly emerged as the talk of the town, converting AI skeptics to evangelists and transforming the way we interact with technology. For proof of this megatrend look no further than the instant success of ChatGPT, where it set the record for the fastest-growing user base, reaching 100 million users in just 2 months after its launch.

Projects in SQL Stream Builder

Businesses everywhere have engaged in modernization projects with the goal of making their data and application infrastructure more nimble and dynamic. By breaking down monolithic apps into microservices architectures, for example, or making modularized data products, organizations do their best to enable more rapid iterative cycles of design, build, test, and deployment of innovative solutions.

Running Ray in Cloudera Machine Learning to Power Compute-Hungry LLMs

Lost in the talk about OpenAI is the tremendous amount of compute needed to train and fine-tune LLMs, like GPT, and Generative AI, like ChatGPT. Each iteration requires more compute and the limitation imposed by Moore’s Law quickly moves that task from single compute instances to distributed compute. To accomplish this, OpenAI has employed Ray to power the distributed compute platform to train each release of the GPT models.