Apache Spark™ — The Unified Engine for All Workloads
On-Demand Webinar
Current data management architectures are a complex combination of siloed, single-purpose tools. There are data lakes for low cost storage, but are difficult to use for data discovery, data warehouses that are reliable and optimized for fast queries, but come at a cost when having to scale, and various streaming and batch systems to shuffle data between them, often times resulting in data integrity issues.
Businesses have to create a patchwork of different tools, skillsets, and expertise just to solve one fundamental problem: How can I make data-driven decisions faster?
Join this webinar to learn how Databricks Delta — a new unified data management system — takes advantage of the the scale of a data lake, the reliability and performance of a data warehouse, and the low-latency updates of a streaming system, all in a unified and fully managed fashion.
This webinar will cover:
- How the need to process batch and streaming data creates challenges for enterprises with complex data architectures.
- How Databricks Delta takes the best of data warehouses, data lakes and streaming systems to provide a highly scalable, performant, and reliable data management system.
- A live demonstration of Databricks Delta to showcase how easy it is to cost-efficiently scale without impacting query performance.
Presenters
Tony Baer
Principal Analyst - Ovum
Tony Baer leads Ovum’s Big Data research area. Over his 25 years in the industry, he has studied issues of data integration, software and data architecture, middleware, and application development. Having tracked the emergence of BI and data warehousing back in the 1990s, Baer sees similar parallels emerging in the world of Big Data today. His coverage focuses on how Big Data must become a first-class citizen in the data center, IT organization, and the business.