Everyone is looking to be more agile as they move to the cloud, but there has never been more data than there is now and data is growing exponentially everyday. Data is also more shared and distributed than ever across an increasingly complex technology landscape.
Data scientists’ time is valuable. Computing resources are expensive. With only 87% of projects ever making it to production (Source: VentureBeat), organizations often overcommit to costly projects that bear little fruit. Data science teams need a way to assess project feasibility without diving head first.
Many data teams worry that automation won’t work on their specific data and technology stack. They’ve learned the hard way that automation doesn’t always stand up to the complexity of different source data models, taxonomies and tech stack components.
Join this webinar to understand how Data Vault 2.0 is designed to focus on models and logic, not complex code, so that it’s rapidly becoming the DWH standard.
We’ll explain how Data Vault has taken the best of the more traditional modeling approaches, such as Inmon or Kimball, to provide the level of abstraction, quality and agility that automation requires.
Leveraging business data as a valuable asset is no longer a debated concept – it’s a broadly adopted, competitive undertaking that’s part and parcel to cloud modernization. Today, if there’s one thing that defines competitive advantage in the data analytics arena it’s streaming data platforms. Older approaches employing batch-only analytics, brittle ETL pipelines, and the latency they can introduce just don’t cut it anymore. Cloud-Modernized Analytics are poised to step in and take over.