On-demand webinar

Building a flexible modern data architecture:

How to integrate your data lake and data warehouse as a single semantic layer for analytics

On-demand webinar

In the data world, change seems to be the one constant. In the past few years, on-prem data architectures have been stretched by the scale and complexity of today’s data volume and structure. And now even the cloud data warehouse is challenged by similar factors: 

  • There is too much data to ETL all of it into a cloud data warehouse
  • There are newer, more modern tools, file formats, table formats and governance solutions that data teams want to productionalize
  • Speed to insight and time to production are not slowing down
  • Everyone is tired of never-ending data migrations

So how do data managers maximize architectural flexibility to free their business and development partners to take advantage of the latest data evolution while minimizing migrations? 

At Starburst, we promote flexible data architectures by creating a single access point across all company-wide data centers of gravity; from on-prem to cloud data warehouse to cloud lakehouse. With a single SQL-based semantic platform, data managers can connect and manage all of their data sources to focus on governing and securing data rather than attempting to run, operate and manage numerous data infrastructures.   

In this webinar, Monica Miller demonstrates how to utilize Starburst to get the most out of your cloud data lake and your cloud data warehouse in a single platform. 

In this on-demand session, you will learn:

  • How to convert your data swamp into a highly functioning modern data lake
  • Best practices for utilizing both warehouse and lake solutions 
  • Strategies to empower your analysts to self-serve their data requests and reduce the backlog of your data engineering teams

On-demand webinar

Speakers

Monica Miller

Developer Advocate

Read more

Monica is a former data engineer turned developer advocate. As a data engineer, she spent her time primarily developing and supporting ETL pipelines for both near-real time analytics and batch processing. Monica now works to improve the lives of other data engineers by creating informational resources, speaking at conferences, and writing about her experiences in the data space.

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