Once upon an IT time, everything was a “point product,” a specific application designed to do a single job inside a desktop PC, server, storage array, network, or mobile device. Point solutions are still used every day in many enterprise systems, but as IT continues to evolve, the platform approach beats point solutions in almost every use case. A few years ago, there were several choices of data deduplication apps for storage, and now, it’s a standard function in every system.

With the release of CDP Private Cloud (PvC) Base 7.1.7, you can look forward to new features, enhanced security, and better platform performance to help your business drive faster insights and value. We understand that migrating your data platform to the latest version can be an intricate task, and at Cloudera we’ve worked hard to simplify this process for all our customers.

As businesses look to scale-out storage, they need a storage layer that is performant, reliable and scalable. With Apache Ozone on the Cloudera Data Platform (CDP), they can implement a scale-out model and build out their next generation storage architecture without sacrificing security, governance and lineage. CDP integrates its existing Shared Data Experience (SDX) with Ozone for an easy transition, so you can begin utilizing object storage on-prem.

Today’s organizations face rising customer expectations in a fragmented marketplace amidst stiff competition. This landscape is one that presents opportunities for a modern data-driven organization to thrive. At the nucleus of such an organization is the practice of accelerating time to insights, using data to make better business decisions at all levels and roles.

In our previous blog, we talked about the four paths to Cloudera Data Platform. If you haven’t read that yet, we invite you to take a moment and run through the scenarios in that blog. The four strategies will be relevant throughout the rest of this discussion. Today, we’ll discuss an example of how you might make this decision for a cluster using a “round of elimination” process based on our decision workflow.

With hackers now working overtime to expose business data or implant ransomware processes, data security is largely IT managers’ top priority. And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. Requirements, however, have changed significantly in recent years.

Historically, maintenance has been driven by a preventative schedule. Today, preventative maintenance, where actions are performed regardless of actual condition, is giving way to Predictive, or Condition-Based, maintenance, where actions are based on actual, real-time insights into operating conditions. While both are far superior to traditional Corrective maintenance (action only after a piece of equipment fails), Predictive is by far the most effective.

January 2020 is a distant memory, but for most, the early days of the pandemic was a time that will be ingrained in memories for decades, if not generations. Over the last 18 months, supply chain issues have dominated our nightly news, social feeds and family conversations at the dinner table. Some but not all have stemmed from the pandemic.

Recently, I worked with a large fortune 500 customer on their migration from Apache Storm to Apache NiFi. If you’re asking yourself, “Isn’t Storm for complex event processing and NiFi for simple event processing?”, you’re correct. A few customers chose a complex event engine like Apache Storm for their simple event processing, even when Apache NiFi is the more practical choice, cutting drastically down on SDLC (software development lifecycle) time.

With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.