According to the Sixth Annual Gartner Chief Data Officer Survey, CDOs who successfully increased data sharing led data and analytics (D&A) teams that were 1.7 times more effective at showing demonstrable, verifiable value to D&A stakeholders.
Data products abstract away the complexity of data storage for consumers. For data engineers, however, it makes sense to take advantage of AWS tools and capabilities to optimize for speed and efficiency. Utilizing the power of Starburst Galaxy, you can operationalize an AWS data lake and manage it for the purpose of data analytics. Starburst Galaxy provides fast access and flexible data product management without adding the complexity of data movement.
Implementing a data lake house architecture with Starburst Galaxy on AWS capitalizes on the low-cost object storage of Amazon Simple Storage Service (Amazon S3) and the ability to load all types of data, while implementing the data warehousing principles of performance, reliability, and ease of use. A data lakehouse allows you to optimize your data architecture to meet specific organizational needs through the balance of cost-based optimizations and scalability, while also implementing a reporting structure to operationalize your analytics. At the same time, because Starburst can connect to and query multiple modern and legacy enterprise sources, it allows data lake users to only pay for what they use and minimize data duplication.
In this webinar, we’ll cover:
Head of Data Products at Starburst
AWS Senior Partner Solutions Architect
Up to $500 in usage credits included