Emily Sergent
The data lakehouse is fast becoming one of the most popular analytics data architectures today. Based on distributed object storage and open table formats such as Apache Iceberg and Delta Lake, it is considered a practical and scalable alternative to traditional data warehouses. Organizations who turn their data lakehouse dream into a successful data platform reality are using an "augmented" approach, choosing technologies which don't just manage the technical nuts-and-bolts of lakehouse integration, but also address the larger data management needs of the enterprise, such as simplified governance, advanced self-service capabilities, predictable costs, and real-time access to data outside the data lake.
In this TechTalk, Emily Sergent, Technical Sales Director at Denodo, will present the Denodo Embedded MPP, a component based on the high-performance, distributed SQL query engine Presto, and explain how it works and how it complements the core Denodo platform in a data lakehouse architecture. Through customer use cases, she will explain how to position the Embedded MPP in your data architecture, whether you're extending a traditional data warehouse, moving data lake workloads from an existing SQL engine, or building a new data lakehouse from the ground up.
Attend & Learn:
- What the Embedded MPP engine has in common with other lakehouse engines and what sets it apart
- The role of the Embedded MPP in both on-premises and cloud-based deployments
- How to build a lakehouse "from scratch"
- When to add the Embedded MPP to existing architectures
- How to facilitate governance while opening the data lakehouse to new usages
