Tapping into the benefits of an open data lakehouse for enterprise AI

What does it take to implement and maximize the value of AI within an organization? In short, it takes data—and a lot of it. As it stands, many large organizations find themselves relying on a mix of solutions, platforms, and architectures to handle the volume of structured and unstructured data that has been created as their operations have expanded. But that approach can lead to complications in terms of data access, visibility, manageability, and ease of use for analytics and AI.

Instead, organizations should look toward solutions like an open data lakehouse that can provide a unified architecture for data storage, processing, and analytics.

Challenges to Effective Data Management

Among the issues that surround data management and effective AI, one of the most common comes in the form of data silos that result from sprawling systems and operations. A large enterprise typically has a vast pool of data to store and manage. Having that data spread across the organization and a variety of environments can lead to pockets of data that are difficult to access, misused, and even unaccounted for in broader data management and unification efforts.

There can also be serious issues surrounding data governance, security, and trustworthiness. Data can sometimes fall into the wrong hands either purposefully or accidentally – from a malicious hacker, or an unknowing employee who transferred data to a personal device that was later stolen. The scenarios are numerous. If an organization is going to achieve truly impactful, real-time outputs from analytics and AI, it needs to ensure that all data—including structured and unstructured—is properly governed and managed even as the scale of data grows rapidly.

The Value of an Open Data Lakehouse

An open data lakehouse provides a number of advantages that go beyond what other data architectures are capable of. As the name suggests, an open data lakehouse brings together the flexibility that comes with a data lake and merges it with the performance of a data warehouse, enabling better, quicker analytics to run on all relevant data. And in the case of an open data lakehouse, like Cloudera, it brings the ability to operate both on-premises and in the public cloud. That means it can be built once and run anywhere, saving time and ensuring portability on all clouds.

Acting as a unified data platform means an open data lakehouse can do away with the silos that are hampering analytics and AI initiatives. Having that level of unification between data also means organizations can encourage data democratization and self-service analytics that allow users enterprise-wide to generate impactful insights from their data.

We’ve talked a lot about the open data lakehouse to this point, but what do we mean when we say “open”? In this case, it means interoperability and compatibility with a variety of data processing frameworks, programming languages, and analytics tools. By establishing this openness, the data lakehouse enables deep collaboration and innovation, allowing data teams to leverage their preferred tools and methodologies.

Both business needs and technological capabilities are constantly evolving. As those changes take hold, data platforms need to be flexible and future-proofed enough to keep up. An open data lakehouse architecture makes it easier to support modern analytics workloads, SQL-based querying, and advanced analytics frameworks. Whatever form the workload or analytics use case comes in, an open data lakehouse offers a robust platform to adapt and scale.

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Learn more about how Cloudera’s open data lakehouse can support your AI and analytics initiatives.