Scattered and disparate data environments are the norm for most enterprise BI
Vinay Samuel |
CEO, Zetaris
December 16, 2019
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Hybrid Cloud Data Warehouse

Scattered and disparate data environments are the norm for most enterprise BI architectures. Customers seeking to move to a modern cloud data warehouse platform (like Snowflake) experience major difficulties due to business logic complexity, transformation logic handling, lack of automated exception processing and other data structuring challenges. Zetaris simplifies this by providing a single location for connecting, modelling and querying all the data. Our Intelligent Semantic Engine and Business Data Fabric builder enables the organisation and preparation of data in different places while supporting complex query workloads in real-time. In turn, this makes migration to Snowflake a non-disruptive business agenda.

Autonomous Snowflake Infrastructure Management

For customers desiring to change to a hybrid-cloud (data centre connected to a cloud data warehouse – Snowflake) or multi-cloud, our analytical data virtualisation technology automates and simplifies the migration journey while supporting a “lights on” business continuity scenario during the project. Data in different places can be accessed, modelled and queried to support the present tools and users guided and supported through automation. Business rules and logic can be extracted and stored in an infrastructure independent mode so that the target database (Snowflake) remains pliable and adaptive to the varying nature of the project.

Zetaris Networked Data Platform

Zetaris makes Snowflake multi-cloud query ready by:

  1. Querying data in Snowflake against data in your data centre, data lake, data warehouse, partner data centre or anywhere across the internet during the same query pass and connect the view to your tools.
  2. Automating the extraction, conversion and implementation of business logic.
  3. Automating the transformation of data for the target (Snowflake) source using Zetaris TFA (Transformational Functional Automation) technology.
  4. Creating virtual data structures (Data Fabrics) rather than costly physical data files or database across the organisation while raw data remains or moves to Snowflake.
  5. Connecting Snowflake to any other cloud data warehouse during the one same query and enabling the answer to drop into Snowflake.
  6. Optimising workloads within Snowflake to maximise a multi-cloud join.
  7. Reducing compute and storage cost in the data centre or cloud by matching queries to the right Snowflake compute in real-time.
  8. Reducing the duplication of data in Snowflake and across your organisation.
  9. Reducing cost of data retrieval from the cloud.
  10. Building a hybrid virtual data warehouse or virtual data marts with data in different clouds (or data centres) to reduce cloud vendor lock-in and risk; using Snowflake as the raw storage for an Intelligent Data Hub in Zetaris.
  11. Moving of your business logic away from vendor infrastructure, thereby enabling a “no lock-in” architecture for your data platforms.
  12. Reducing your electricity consumption for data management to help save the planet.

Get rid of old school ETL to modern Data Fabric migration of data.

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