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, freshly minted developers want to code data pipelines in Python and R, but experts know that success depends not 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 provides 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 present a maturity model to gauge your capabilities against industry best practices and recommend next steps.

You Will Learn:

- The range of cloud data management capabilities

- Best practices in cloud data management

- How to assess your cloud data management maturity

- How to improve your cloud data management capabilities

Sponsor: Informatica

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