Getting started with Teradata
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Teradata is a massively parallel processing (MPP) relational database management system known for its ability to handle large datasets. It provides scalability, parallel processing, and can execute complex queries involving up to 256 joins. Teradata uses a scalable architecture with nodes that contain parsing engines, access module processors, and virtual processors to distribute data and query processing across hardware components for high performance. Data is stored across access module processors and retrieved in parallel to support analytics on large datasets.
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Calcite has been used to empower many Big-Data platforms such as Hive, Spark, Drill Phoenix to name some.
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This Edureka PPT on "RDD Using Spark" will provide you the detailed and comprehensive knowledge about RDD, which are considered to be the backbone of Apache Spark. You will learn about the various Transformations and Actions that can be performed on RDDs. This PPT will cover the following topics:
Need for RDDs
What are RDDs?
Features of RDDs
Creation of RDDs using Spark
Operations performed on RDDs
RDDs using Spark: Pokemon Use Case
Blog Series: http://bit.ly/2VRogGx
Complete Apache Spark and Scala playlist: http://bit.ly/2In8IXD
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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- Writing testable, serializable code with static methods and avoiding non-serializable objects like database connections.
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Kubernetes Certification: https://www.edureka.co/kubernetes-certification
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- 7. www.edureka.co/teradata Teradata when compared to other RDMBS
- 8. www.edureka.co/teradata Teradata Competitive Advantages Linear Scalability : Large volume of data and a number of users are present. Adding nodes will increase the capacity/performance of the system Unlimited Parallelism : Teradata is based on Massively Parallel Architecture and it is designed to be parallel since beginning. MPP systems divide large task into smaller tasks and run them in parallel Mature Optimizer : Teradata Optimizer has been fine tuned throughout its history. It can handle up to 64 joins in a query Low CTO : Easy setup, administration and maintenance Load & Unload utilities : Teradata provides robust load & unload utilities to move data into/from Teradata system
- 10. www.edureka.co/teradata Teradata Components Demo Node is the basic unit in a Teradata system. It consists of below components Gateway » Gateways are used to connect sessions Parsing Engine » Parses the queries and prepares the execution plan » Dispatches the plan to AMPs AMP (Access Module Processor) » Data Management and Data Conversion » I/O Processing and Locks VProcs » Virtual Processors
- 12. www.edureka.co/teradata Parsing Engine Demo Session Manager - When users logon to the system, they establish a session. Each PE can manage 120 sessions Parser - User submits SQL requests to the DB server. The PE will parse the SQL statements and checks for syntax errors. Performs the security check to validate if the user has required privileges. Verifies if the columns used in the query actually exists in the table Optimizer - PE optimizes the query to come up with an execution plan Dispatcher - Passes the execution plan to the AMPs through BYNET
- 13. www.edureka.co/teradata BYNET Demo BYNET is a software and hardware component that provides networking capability. A communication layer that handles the communications in Teradata database Supports point-to-point, multi-cast and broadcast communication For each node added, 960 MB of network bandwidth is added PE dispatches the execution steps to the AMPs through Message passing Layer. AMPs return the result set to the Parsing Engine through Message Passing Layer » Point-to-point – Message is routed to a single AMP » Multi-cast – Message is routed to multiple AMPs » Broadcast – Message is routed to all AMPs
- 14. www.edureka.co/teradata Access Module Processor(AMP) Demo AMPs are the database execution engine virtual processors(Vprocs) within Teradata system. The responsibilities of Access Module Processor(AMP) are 1. Manages a portion of the database 2. Manages a portion of each table 3. Does all the work associated with generating result set such as sort, aggregation, join and conversion 4. Lock management 5. Space management
- 16. www.edureka.co/teradata Data storage and retrieval
- 17. www.edureka.co/teradata Teradata Storage Architecture Demo 1. The Parsing Engine dispatches request to insert a row 2. The Message Passing Layer sends the row to appropriate AMP 3. The AMP stores the row on its associated (logical) disk
- 18. www.edureka.co/teradata Teradata Retrieval Architecture Demo 1. The Parsing Engine dispatches request to retrieve rows 2. The Message Passing Layer sends the request to appropriate AMP(s) 3. The AMP(s) locate the rows and retrieves them in parallel 4. The Message Passing Layer returns the rows to the PE 5. The Parsing Engine returns the rows to the client
- 19. www.edureka.co/teradata Multiple Tables Storage Demo Each AMP stores a portion of each table
- 21. www.edureka.co/teradata Thank You Questions/Queries/Feedback Recording and presentation will be made available to you within 24 hours