This article is shared from the link
http://www.pdwtutorial.com/2014/06/parallel-data-warehouse-introduction.html
Parallel Data Warehouse Introduction
http://www.pdwtutorial.com/2014/06/parallel-data-warehouse-introduction.html
Parallel Data Warehouse Introduction
The SQL Server 2008 R2 Parallel Data Warehouse (PDW) edition is its first product in the Massively Parallel Processor (MPP) data warehouse space.
PDW uniquely combines MPP software acquired from DATAllegro, parallel compute nodes, commodity servers, and disk storage.
PDW lets you scale out enterprise data warehouse solutions into the hundreds of terabytes and even petabytes of data for the most demanding customer scenarios. In addition, because the parallel compute nodes work concurrently, it often takes only seconds to get the results of queries run against tables containing trillions of rows. For many customers, the large data sets and the fast query response times against those data sets are game-changing opportunities for competitive advantage.
The simplest way to think of PDW [Parallel Data Warehouse Tutorial] is a layer of integrated software that logically forms an umbrella over the parallel compute nodes. Each compute node is a single physical server that runs its own instance of the SQL Server 2008 relational engine in a shared-nothing architecture. In other words, compute node 1 doesn't share CPU, memory, or storage with compute node 2.
The smallest PDW will take up two full racks of space in a data center, and you can add storage and compute capacity to PDW one data rack at a time. A data rack contains 8 to 10 compute servers from vendors such as Bull, Dell, HP, and IBM, and Fiber Channel storage arrays from vendors such as EMC, HP, and IBM. The sale of PDW includes preconfigured and pretested software and hardware that's tightly configured to achieve balanced throughput and I/O for very large databases. Microsoft and these hardware vendors provide full planning, implementation, and configuration support for PDW.
The collection of physical servers and disk storage arrays that make up the MPP data warehouse is often referred to as an appliance. Although the appliance is often thought of as a single box or single database server, a typical PDW appliance is comprised of dozens of physical servers and disk storage arrays all working together, often in parallel and under the orchestration of a single server called the control node. The control node accepts client query requests, then creates an MPP execution plan that can call upon one or more compute nodes to execute different parts of the query, often in parallel. The retrieved results are sent back to the client as a single result set.
This introduction taken from http://sqlmag.com/sql-server-2008/getting-started-parallel-data-warehouse
For your Reference
Parallel Data Warehouse Introduction
SQL Server Parallel Data Warehouse Overview
Microsoft Parallel Data Warehouse
Microsoft Parallel Data Warehouse : Getting Started with Parallel Data Warehouse
Server 2012 Parallel Data Warehouse (PDW)
SQL Server Parallel Data Warehouse Part 2 – Architecture Components
SQL Server Parallel Data Warehouse Part 3 – Hardware Components
SQL Server 2012 Parallel Data Warehouse (PDW) – POC Experiences
Parallel Data Warehouse (PDW) POC – lessons learned
Parallel Data Warehouse – POC lessons learned-1
Parallel Data Warehouse (PDW) POC – lessons learned Part 2
Parallel Data Warehouse – POC lessons learned Part 3
How Does SQL Server Parallel Data Warehouse (PDW) Deliver the Performance that it Does?
Microsoft Parallel Data Warehouse:Is SQL Server Parallel Data Warehouse 2012 an EDW Game Changer?
Microsoft Parallel Data Warehouse: Connecting and Configuring SQL Server Parallel Data Warehouse (PDW) Clients
parallel Data WareHouse:Simplifying Management of PDW Appliances with System Center
parallel Data WareHouse : BIG Data
Microsofts Big Data Appliance
Microsoft ships CTP of Hadoop Connectors for SQL Server and Parallel Data Warehouse
Microsoft parallel Data WareHouse: Modern Data Warehouse with Big Data Analytics (aka APS)
SSIS Performance Tuning–Monitoring & Data Collection
SSIS Performance Tuning – Methodology and general approach
Design SSIS for Performance and Scale
Design SSIS for Performance and Scale – Parallelism strategies, Part 1
Design SSIS for Performance and Scale – Baseline tests
Microsofts Big Data Appliance
SQL Server 2012 Parallel Data Warehouse (PDW) – What’s new?
Building a data warehouse in SQL Server: Tips to get started
SQL Server Parallel Data Warehouse Overview
Microsoft Parallel Data Warehouse
Microsoft Parallel Data Warehouse : Getting Started with Parallel Data Warehouse
Server 2012 Parallel Data Warehouse (PDW)
SQL Server Parallel Data Warehouse Part 2 – Architecture Components
SQL Server Parallel Data Warehouse Part 3 – Hardware Components
SQL Server 2012 Parallel Data Warehouse (PDW) – POC Experiences
Parallel Data Warehouse (PDW) POC – lessons learned
Parallel Data Warehouse – POC lessons learned-1
Parallel Data Warehouse (PDW) POC – lessons learned Part 2
Parallel Data Warehouse – POC lessons learned Part 3
How Does SQL Server Parallel Data Warehouse (PDW) Deliver the Performance that it Does?
Microsoft Parallel Data Warehouse:Is SQL Server Parallel Data Warehouse 2012 an EDW Game Changer?
Microsoft Parallel Data Warehouse: Connecting and Configuring SQL Server Parallel Data Warehouse (PDW) Clients
parallel Data WareHouse:Simplifying Management of PDW Appliances with System Center
parallel Data WareHouse : BIG Data
Microsofts Big Data Appliance
Microsoft ships CTP of Hadoop Connectors for SQL Server and Parallel Data Warehouse
Microsoft parallel Data WareHouse: Modern Data Warehouse with Big Data Analytics (aka APS)
SSIS Performance Tuning–Monitoring & Data Collection
SSIS Performance Tuning – Methodology and general approach
Design SSIS for Performance and Scale
Design SSIS for Performance and Scale – Parallelism strategies, Part 1
Design SSIS for Performance and Scale – Baseline tests
Microsofts Big Data Appliance
SQL Server 2012 Parallel Data Warehouse (PDW) – What’s new?
Building a data warehouse in SQL Server: Tips to get started