Organizations need data management—ingestion, transformation, storage and serving—to fully leverage their data. Separate from relational database management systems, which are optimized for online transaction processing (such as inserting, updating or deleting single rows), data platforms are optimized for analysis. As the complexity of data management has grown, organizations have adopted a variety of different data platform architectures to make sure they are getting the full value of the data they collect, but these systems are often fraught with issues that include reliability, data staleness, high Total Cost of Ownership, and limited support for AI and ML. With the ever-growing volume of unstructured data and use of ML, the need for an optimal solution is greater than ever.
As a component of Noblis’ AI Stack, the Noblis Data Lakehouse has a scalable data infrastructure that provides replication, consistency and reliability; an efficient and scalable engine for data processing; and support for faster analytic queries and the ML development lifecycle. We have developed this capability by bringing together a combination of open-source and custom-developed software solutions and through development of ingestion pipelines for multiple Noblis-owned and publicly available data sources.