Maturity Model: Cloud Data Management for Cloud Data Warehouses and Data Lakes 

Best Practices for Cloud Data Warehouse/Data Lake Implementation & Design

Available On-Demand

The cloud makes everything easy—maybe too easy. Even with first-hand experience integrating enterprise data for analytics, it’s easy to forget the lessons of the past. For instance, you may be tempted to code data pipelines in Python and R, but research and experience show that success depends not just on the quality of code, but how often it gets reused, how understandable it is, and how many tests are built around the code. Experts also understand that things like data quality, data governance, data lineage, data privacy, master data, DataOps, data preparation, data architecture, and curated metadata (i.e., data catalogs) spell the difference between success and failure of an enterprise data project.

This webinar will provide a checklist of items that every enterprise should consider when designing and implementing a cloud data warehouse and/or data lake. To help you plan your cloud data management strategy, we’ll present a maturity model to gauge your capabilities against industry best practices and recommend next steps.