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.
By now, almost everyone across the tech landscape has heard of the Zero Trust (ZT) security model, which assumes that every device, application, or user attempting to access a network is not to be trusted (see NIST definitions below). But as models go, the idea is easier than the execution.
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.
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.
Data asset standardization is the purposeful and carefully planned consolidation of redundant, contradictory reports, processes, and databases into enterprise standards.
Out of the box Cloudera Data platform (CDP) performs superbly but over time, if data architecture, data engineering, and DevOps best practices are not maintained, you can get stuck maintaining the wild, wild west. In this six-part series, we’re focused on improving the health of your environment.
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.
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.
Data is core to decision making today and organizations often turn to the cloud to build modern data apps for faster access to valuable insights. With cloud operating models, decision making can be accelerated, leading to competitive advantages and increased revenue. Can you achieve similar outcomes with your on-premises data platform? You absolutely can.
Unwelcome … are platform instability, downtime, hardware failure, poor performance, cluster resource contention, repeated process failures, runaway live queries, critical services alarms, invisibility into alarm cacophony… the list goes on. If those are ailments you would like to remedy …
