Many of the Nordic companies currently exploring techniques like AI and machine learning experience how essential the data foundation is. Learn how a cloud-based “lakehouse” can provide a scalable, cost-effective platform for both machine learning and data analytics workloads.
As businesses use machine learning across more areas of their business to make informed decisions (like this Nordic insurance company that we helped) we see them facing four significant challenges:
Meeting the data needs
So far, the extensive data needs that come with machine learning have been met by data warehouses and data lakes (like for this Nordic-Baltic banking group). To fully address these challenges, however, businesses need to combine the best of data warehouses and data lakes into a new data architecture. The “lakehouse architecture” is gaining support from leading hyperscalers, in partnership with their technology and service provider partners.
Lakehouses evolved to overcome the limits of data delivery platforms such as data warehouses and data lakes, which are often too expensive to maintain and cannot support the types and amount of data required by today’s machine learning systems. A lakehouse aims to combine the best features of data warehouses and data lakes by providing:
Databricks on Google Cloud is the latest example of leading hyperscalers offering the lakehouse architecture. Delta Lake on Databricks enables data engineering, cloud data processing, data science and analytics workloads on a unified data platform. This makes it easier, faster and more cost-effective for any user – from business analyst to data scientist – to discover and deliver insights quickly to the enterprise and use machine learning in production.
Here is how a lakehouse architecture can help meet the four critical enterprise ML needs.
The lakehouse effect
As the market evolves, look for hyperscalers and their partners to deliver improved self-service capabilities for data engineering and access; higher-performance lakehouse-based platforms so they match that of data warehouses; improved ACID (atomicity, consistency, isolation, durability) capabilities; and simpler containerization and deployment of production-ready ML models.
Remember to make proper data governance and data management the foundation of your lakehouse strategy, as the “garbage-in/garbage-out” rule is more important than ever when it comes to the data required to build machine learning models. Also, learn more in this blogpost about Building a strong data foundation.