Learn Teradata

Teradata is the world’s largest company focused on analytic data solutions through integrated data warehousing, big data analytics, and business applications. Only Teradata gives organizations the advantage to transform data across the organization into actionable insights empowering leaders to think boldly and act decisively for the best decisions possible.
Teradata acts as a single data store that can accept large numbers of concurrent requests from multiple client applications.

Datawarehousing

A data warehouse is a relational/multidimensional database that is designed for query and analysis rather than transaction processing. It usually contains historical data derived from transaction data, but it can include data from other sources.
Subject Oriented:
Data warehouses are designed to help you analyze data. For example a telecom company would like to know more and more about its subscribers, a sales company would want to expand its sales. This ability to define a data warehouse by subject matter makes the data warehouse subject oriented.
Integrated:
Since data in data warehouse is usually obtained from multiple asynchronous sources running on various platforms and architecture, the data warehouse must present this data in a consistent and integrated format for analytical reporting.
Nonvolatile:
Data in the warehouse is not updated in real-time but is refreshed from operational systems on a regular basis. New data is always added as a supplement to the database, rather than a replacement.
Time Variant:
In order to discover trends in business, analysts need large amounts of data. This is very much in contrast to Online Transaction Processing systems, which focuses on current data and older data is archived. A data warehouse’s focus on change over time is what is meant by the term time variant.
Data Granularity:
An Operational System is a Point-of-Sale System where Data is usually kept at the Lowest Level of Detail. Whereas, a Data Warehouse stores Summarized Operational Data.It is NOT efficient to store Data in the Data Warehouse at the Lowest Level of Detail as that may lead to huge amount of data in the Data Warehouse. The Analysis of Data in the Data Warehouse begins at a High-Level and Drills-down to Lower-Levels of Detail.
Benefits of Data Warehouse:
  • It has high return on investment if implemented correctly
  • Provides competitive advantage
  • Increased productivity of corporate decision-makers             
Enterprise Data Warehouse - An enterprise data warehouse provides a central database for decision support throughout the enterprise.
ODS (Operational Data Store) - This has a broad enterprise wide scope, but unlike the real enterprise data warehouse, data is refreshed in near real time and used for routine business activity. One of the typical applications of the ODS (Operational Data Store) is to hold the recent data before migration to the Data Warehouse. Typically, the ODS are not conceptually equivalent to the Data Warehouse albeit do store the data that have a deeper level of the history than that of the OLTP data.
Data Mart - Datamart is a subset of data warehouse and it supports a particular region, business unit or business function.
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