Unlock the power of your data, with a modern Cloud lakehouse architecture on AWS

Companies look to leverage their data to drive better decisions, as quickly as possible. Doing so quickly often requires moving all of their data located in various silos to be moved to a single location, often referred to as a ‘data lake’, from where they can perform analytics and ML. In parallel, companies also store data in purpose-built datastores that support specific use cases with relevant performance, scale and cost advantages; for example data warehouses offer optimized complex analytics on structured data, Elasticsearch is suitable for search or log analysis applications.

A single datastore is no longer sufficient, to maximize the value of data and these purpose-built datastores, companies need to be able to easily and seamlessly move data between them. For example, real-time bidding data can be stored in a data lake, and a portion of it can be moved to a data warehouse for reporting, or leveraged by an ML platform as a dataset for training. Sometimes, there is a need to move data in the other direction, Such as, moving datahouse analytics results to the data lake to train product recommendation ML models. Finally, there may be a need to move data directly between these purpose-built datastores. For example, copying catalog data from a relational operational database to a search service to enable super-fast text based search while offloading it from the database.

Lakehouse architecture supports common use cases: ⏩ E-commerce: Usage analytics, search services, analyze & predict user intent from clickstream data ⏩ Ad-Tech: Measure campaign effectiveness and train ML models to better target bidding ⏩ Finance: Real-time reports and continuous training of models for better fraud detection ⏩ Manufacturing: Real-time insights from mass IoT data and predictive maintenance models

Adopting lakehouse architecture for the above sample use cases enables these organizations to enrich their product offerings, reduce time to market, increase cost-performance and address new markets and customers.

We can help you, wherever you are at with your implementation:

• New to Data Lake -Customers with no data lake but existing data sources who are ready to modernize their analytics capabilities.
• Existing Data Lake-Customers who are already using a data lake and want to shift to a lakehouse architecture.
• Open Lakehouse Engine-Customers who are ready to implement a Cloud data lakehouse with Upsolver, for more features and use cases.