Teradata Training Tutorial - Teradata Parsing Engine - BigClasses

What is Teradata?
Teradata is a database that is used to maintain huge volume of data .Teradata accepts large number of concurrent request from multiple clients because it allows parallel processing and it is designed for analytical query processing like selecting data within the fraction of seconds and the performance is very high with respect to huge amount of data.
RDBMS:
RDBMS stands for relational database management system and It stores data in the form of related tables which has the relation with the existing tables. It is basically satisfy all the rules of DBMS. DBMS is database management system the relation between the data in the form of rows and columns. It is used to store, manage and fetching data from database.
Why Teradata for Data warehouse?
We are having two different kinds of system they are OLTP, OLAP
OLTP:
OLTP stands for online transaction processing, this system provides source data information to Data ware house. In OLTP accessing data from data warehouse is very fast, accurate, processing query quickly with dynamic environment.
Eg: day to day transactions in banking environment.
OLAP:
OLAP stands for online Analytical processing, this system used to analyze the data that is stored in the form of tables by using OLAP methodologies. User can easily access, view and extract data from different points of view in the sense of different databases.
Where Teradata persists into the OLAP model?
OLAP contains huge amount of data and it stores history of the information in the data warehouse. Teradata can actually select the data within the fraction of seconds even a billion of rows, because it is designed with analytical data purposing. Teradata is used to manage large data warehouse operations easily.
Architecture of Teradata:
Teradata mainly consists of four components:
1. Parsing Engine (PE)
2. BYNET
3. AMP (Access Module Processor)
4. Disk storage
Parsing Engine:
Parsing engine is common for all databases. Parsing engine will take entire query and it will check the syntax of the query and execute it. Parsing engine extract data from disk storage and it will gives query results back to the user response that is what parsing engine does in real time.

Parsing Engine consists four parts for data processing.
Session controller: it controls the session. The Session Control component verifies the request for session authorization (user names and passwords), and either allows or disallows the request.
Parser: it checks syntax of the query. Interprets the query received from the user. Verifies query requests for the proper syntax and evaluates them semantically.
Optimizer: it optimizes the plan in the sense based on the primary index that you define on the table, parsing engine will give explain plan to the optimizer.
Dispatcher: once the plan is ready then it send the request to corresponding amps through the BYNET.
BYNET: it is a communication mechanism for Parsing Engine and AMP, BYNET searching is based on index and it checks corresponding data in AMP and getting data from amp and sending to the Parsing Engine. It acts as interface between the Parsing Engine and AMP.
AMP: it is used to access data from the disk storage. AMPS work in parallely to distribute the data operation they did not share any resource.
Disk storage: it is used to store the data, whenever client request is processed then it gives data to the AMP.

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