Networked Cloud Data Warehouse | Zetaris

Scattered and disparate data environments are the norm for most enterprise BI architectures. Customers seeking to move to a modern cloud data warehouse platform, such as Snowflake, experience major difficulties due to business logic complexity, transformation logic handling, lack of automated exception processing, and other data structuring challenges.

Zetaris: The Networked Data Platform simplifies this by providing a single location for connecting, modelling, and querying all the data. Our Intelligent Semantic Engine and Business Data Mesh builder enable the organisation and preparation of data in different places while supporting complex query workloads in real time. This makes migration to Snowflake, or any other modern cloud platform, 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) or multi-cloud environment, our Analytical Data Virtualisation (ADV) 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 are guided and supported through automation. Business rules and logic can be extracted and stored in an infrastructure independent mode so that the target database remains pliable and adaptive to the varying nature of the project.


Zetaris: The 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 Transformational Functional Automation (TFA) technology.
  4. Creating virtual data structures (Data Mesh) rather than costly physical data files or databases across the organisation while raw data remains or moves to Snowflake.
  5. Connecting Snowflake to any other cloud data warehouse with the one 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 Networked Data Warehouse or Virtual Data Marts with data in different clouds (or data centres) to reduce cloud vendor lock-in and risk and by using Snowflake as the raw storage for an Intelligent Data Hub in Zetaris.
  11. Moving 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 Mesh migration of data.
Vinay Samuel

Vinay Samuel

Founder & CEO of Zetaris

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