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Sep
16
Sql Server Parallel Data Warehouse (PDW)
Posted by Thang Le Toan on 16 September 2015 03:28 PM

Sql Server Parallel Data Warehouse (PDW)

Fari Payandeh

Fari Payandeh

8/26/2015

zcolorsmp

zcolormpp

Unlike Hadoop and NoSql Databases, MPP is not a new technology. Yet, it is a strong contender in the “Big Data” space. Sql Server PDW appliance boasts up to 100x performance gains over legacy data warehouses. Moreover, it is a fault tolerant, horizontally scalable, high capacity RDBMS. Simply put, it is an excellent solution for companies that are wholly vested in RDBMS but need to break free of its constraining factors. We will narrow down our comparison by juxtaposing Sql Server SMP with Sql Server MPP.

pdwmatrix

Performance (Velocity)
Scalability: Throwing more hardware at Sql Server SMP will eventually hit a point of diminishing return as the size of the data sets grow. By contrast, Sql Server MPP architecture is horizontally scalable and performance grows linearly as we add more nodes (Physical Servers) to the appliance– Up to 100x performance gains over legacy data warehouses.
CPU Utilization: A Database task in Sql Server SMP is bound to only one Cpu whereas a task runs on multiple Cpu’s in Sql Server MPP
Resource Sharing: Sql Server MPP has a shared nothing architecture which allows each node to dedicate its resources to processing queries thereby avoiding resource contention and I/O bottlenecks that are caused by resource sharing.
Distributed Queries: Query execution time is reduced significantly. Each query is broken down into pieces and fed to different nodes enabling parallel processing.
Data Distribution: Sql Server MPP automatically distributes the data among different nodes. Each node processes its own data set before sending the output to the control process which in turn merges the results.
Parallel Load: Data is automatically loaded in parallel.
In-Memory Operations and Columnar Data Store : Both Sql Server SMP and Sql Server MPP support In-Memory Operations and Columnar Data Stores.

Capacity (Volume)
Sql Server SMP can handle a few Terabytes whereas Sql Server MPP can linearly scale-out to 6 Petabytes.

High Availability
Sql Server MPP is fault tolerant. Redundancy is applied to all hardware and software components of the appliance. Moreover, the appliance runs on Microsoft Hyper-V which gives the nodes failover capabilities.

Analytics Capabilities (Variety and Variability)
PDW is part of Microsoft Analytics Platform System, which supports connectivity and query access to Hadoop and unstructured data via PolyBase data querying technology.

Maintainability
Sql Server SMP Databases are simpler and less costly to maintain.
Sql Server MPP: Upgrades are not seamless and may require down time. Patches are applied by Microsoft.

smp-v1

zmpp


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Sep
16
The Best Of Open Source For Big Data
Posted by Thang Le Toan on 16 September 2015 03:22 PM

Originally posted on Data Science Central

https://bigdatahome.files.wordpress.com/2013/08/bigdata-opensource-final5.jpg

It was not easy to select a few out of many Open Source projects. My objective was to choose the ones that fit Big Data’s needs most. What has changed in the world of Open Source is that the big players have become stakeholders; IBM’s alliance with Cloud Foundry, Microsoft providing a development platform for Hadoop, Dell’s Open Stack-Powered Cloud Solution, VMware and EMC partnering on Cloud, Oracle releasing its NoSql database as Open Source.

“If you can’t beat them, join them”. History has vindicated the Open Source visionaries and advocates.

Hadoop Distributions

Hortonworks

Cloud Operating System

Cloud Foundry -- By VMware

OpenStack -- Worldwide participation and well-known companies

Storage

fusion-io -- Not open source, but very supportive of Open Source projects; Flash-aware applications.

Development Platforms and Tools

REEF -- Microsoft's Hadoop development platform

Lingual -- By Concurrent

Pattern -- By Concurrent

Python -- Awesome programming language

Mahout -- Machine learning programming language

Impala -- Cloudera

R -- MVP among statistical tools

Storm -- Stream processing by Twitter

LucidWorks -- Search, based on Apache Solr

Giraph -- Graph processing by Facebook

NoSql Databases

MongoDB, Cassandra, Hbase

Sql Databases

MySql -- Belongs to Oracle

MariaDB -- Partnered with SkySql

PostgreSQL -- Object Relational Database

TokuDB -- Improves RDBMS performance

Server Operating Systems

Red Hat -- The defacto OS for Hadoop Servers

BI, Data Integration, and Analytics

Talend

Pentaho

Jaspersoft

See Big Data Studio

 

 


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