I feel that a lot of people in the BI, Data (warehouse) and Analytics Industry are not aware that Teradata has undertaken and successfully implemented a significant transformation: from best in class Enterprise Data Warehouse vendor with powerful appliances for every pattern of BI and Data Warehouse workload to a cloud-first software company with a best of breed Data Management Solution for Analytics (DMSA) and Integrated Data and Analytics Platform named Teradata Vantage. Teradata Vantage can be:
- Deployed Anywhere – Teradata Vantage provides analytic processing across flexible deployment options, including public clouds, multi-cloud and hybrid-cloud environments.
- Bought Any Way – Teradata Vantage empowers companies to purchase software in more accommodating ways based on specific use cases through simplified pricing bundles, subscription-based licenses, as-a-service and elastic billing options.
- Moved Anytime – Teradata Vantage future-proofs buying decisions with Teradata software license portability that provides flexibility to move analytics across deployment options.
Whilst I appreciate this can be interpreted as marketing strategy, it should not divert attention from what Teradata Vantage really is: a DMSA and Data Integration Platform
. Those deployment, purchasing and licensing options are great, but we need to look at capabilities. Teradata Vantage addresses the capabilities the modern data-driven enterprise demands.
Teradata Vantage: the Data Management Solution for Analytics
A DMSA is defined as: “A complete software system that supports and manages data in one or more file management systems (usually databases). DMSAs include specific optimizations to support analytical processing. This includes, but is not limited to, support for relational processing, non-relational processing (such as graph processing), machine learning and programming languages such as Python and R. Data is not necessarily stored in a relational structure, and multiple models can be used — for example, relational, XML, JSON, key-value, text, graph and geospatial.”
Teradata has evolved its architecture to be able to incorporate additional data engines next to a ‘plain’ SQL engine; introducing Machine Learning and Graph engines with more engines (such as a SAS Viya engine) on the roadmap. All these engines are connected to the Teradata Bynet – the interconnect that allows the various components of Vantage to communicate. Furthermore, Teradata has extended this ‘plain’ SQL engine with a ton of analytical functions for in-database processing of analytical workloads: the NewSQL engine.
Teradata Vantage: the data integration platform
The need for data integration capabilities in enterprises is immense and still growing. The Data Warehousing Institute (www.TDWI.org
) defines three main techniques used for integrating data: consolidation, propagation, and federation.
Data Consolidation captures data from multiple source systems and integrates it into a single persistent data store. This data store may be used for reporting and analysis as in data warehousing, or it can act as a source of data for downstream applications as in an operational data store.
Data Propagation applications copy data from one location to another. These applications usually operate online and push data to the target location, (i.e., they are event-driven). Updates to a source system may be propagated asynchronously or synchronously to the target system. Synchronous propagation requires that updates to both source and target systems occur in the same physical transaction. Regardless of the type of synchronization used, propagation guarantees the delivery of the data to the target. This guarantee is a key distinguishing feature of data propagation.
Data Federation provides a single virtual view of one or more source data files. When a business application issues a query against this virtual view, a data federation engine retrieves data from the appropriate source data stores, integrates it to match the virtual view and query definition, and sends the results to the requesting business application. By definition, data federation always pulls data from source systems on an on-demand basis.
Teradata Vantage fully supports all three of these data integration techniques. The most well-known Teradata supported technique undoubtedly is data consolidation. Integrating data from a very diverse set of data sources into a single persistent data store is what Teradata has been doing since the company was founded and the business value of such an integrated view
is as great, if not greater, now as it was in the beginning.
Teradata Vantage can also be positioned on the producing (sending) and consuming (receiving) end when using the Data Propagation technique, but what may be less well-known is that Teradata also supports the Data Federation technique.
Teradata’s QueryGrid fully supports Data Federation by enabling the definition of foreign servers (pointers to servers with data sources) and foreign tables (pointer to as specific data source) in the Teradata database so they can be accessed as being just normal tables in the Teradata database. The Teradata optimizer gathers information from those foreign servers and tables and finds the best way to access the data, either by pushing down execution to another technology or by pulling data into Vantage. This even works with more cloud-based storage options like Azure Blob Store and Amazon S3.
I hope that by reading this article people will change their perception of Teradata being the Enterprise Data Warehouse to Teradata Vantage being a flexible, cloud-first solution for a very large set of data management capabilities.
The platform fits seamlessly into your data landscape and if you have a negative experience with getting value out of your Hadoop investment, Teradata Vantage can turn this into a more positive one. Also, your Cloud migration does not have to be a one-way path.
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