8/25/2023 0 Comments Oracle lakehouse architecture![]() As the performance and functionality gap between open data lakehouses and proprietary data warehouses continues to close, the lakehouse starts to compete with the warehouse for more workloads, while providing choice of tooling and optimal price-performance. There are two major technology trends that have driven investments in analytics repositories recently: one, a move from on-premises to SaaS, and two, the proliferation and preference for open-source technologies over proprietary. Many of our customers have analytics repositories such as analytics appliances on-premises, cloud data warehouses and data lakes. And AI, both supervised and unsupervised machine learning, is the best and sometimes only way to unlock these new insights at scale. New insights are found in the combination of new data with existing data, and the identification of new relationships. A lakehouse should make it easy to combine new data from a variety of different sources, with mission critical data about customers and transactions that reside in existing repositories. Join us virtually at IBM watsonx Day The analytics repositories market landscapeĬurrently, we see the lakehouse as an augmentation, not a replacement, of existing data stores, whether on-premises or in the cloud. Let’s dive into the analytics landscape and what makes watsonx.data unique. To help organizations scale AI workloads, we recently announced IBM watsonx.data, a data store built on an open data lakehouse architecture and part of the watsonx AI and data platform. The data lakehouse architecture combines the flexibility, scalability and cost advantages of data lakes with the performance, functionality and usability of data warehouses to deliver optimal price-performance for a variety of data, analytics and AI workloads. It is comprised of commodity cloud object storage, open data and open table formats, and high-performance open-source query engines. Previous attempts at addressing some of these challenges have failed to meet their promise. Against this challenging backdrop, the sense of urgency has never been higher for businesses to leverage AI for competitive advantage. Moreover, increased regulatory requirements make it harder for enterprises to democratize data access and scale the adoption of analytics and artificial intelligence (AI). The proliferation of data silos also inhibits the unification and enrichment of data which is essential to unlocking the new insights. There’s no debate that the volume and variety of data is exploding and that the associated costs are rising rapidly.
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