Building a Real-Time Lakehouse with Data Streaming

Data lakehouses are a strategic platform for unification of enterprise decision support, advanced analytics, and machine learning.

If engineered for streaming and other low-latency workloads, data lakehouses can enable companies to deliver trusted data-driven insights and sophisticated analytics in real time to internal operations as well as to customer-facing applications.

Delivering on the promise of the real-time cloud data lakehouse demands careful attention to a wide range of architecture, deployment, governance, and performance issues. Please join TDWI’s senior research director James Kobielus on this on-demand webinar, in which he, Databricks’ Spencer Cook, and Striim’s John Kutay, discuss how enterprises can incorporate change data capture and data streaming in a cloud-based lakehouse to drive real-time data-driven insights, operations, and customer engagement. After brief presentations, Kobielus, Cook, and Kutay engage in a roundtable discussion focused on the following issues:

• What are the core business use cases for a real-time enterprise cloud data lakehouse?
• What are the essential ingredients of a real-time enterprise cloud data lakehouse?
• How should enterprises deploy change data capture, streaming, and other low-latency infrastructure within a real-time cloud data lakehouse?
• What challenges do enterprises face when migrating schemas, data, and workloads from legacy data warehouses to a cloud data lakehouse?
• How can enterprises future-proof their investments in real-time cloud data lakehouses?