Introduction to Windows Azure HDInsight Jon Tupitza Principal, Solution Architect [email protected]

Introduction to Windows Azure HDInsight

Jon TupitzaPrincipal, Solution Architect

[email protected]

2© 2014 JT IT Consulting, LLC

Agenda

•What is Big Data? …and how does it effect our business?

•What is Hadoop? …and how does it work?

•What is Windows Azure HDInsight? …and how does it fit into the Microsoft BI Ecosystem?

•What tools are used to work with Hadoop & HDInsight?

•How do I get started using HDInsight?


What is Big Data?

• Explosion in social media, mobile apps, digital sensors, RFID, GPS, and more have caused exponential data growth.

Volume (Size)

• Traditionally BI has sourced structured data, but now insight must be extracted from unstructured data like large text blobs, digital media, sensor data, etc.

Variety (Structure)

• Sources like Social Networking and Sensor signals create data at a tremendous rate; making it a challenge to capture, store, and analyze that data in a timely or economical manner.

Velocity (Speed)

Datasets that, due to their size and complexity, are difficult to store, query, and manage using existing data management tools or data processing applications.


Key Trends Causing Data Explosion

Device Explosion

Ubiquitous Connection

Social Networks

Sensor Networks

Cheap Storage

InexpensiveComputing

5.5 Billion+ devices with over 70% of the global population using them.

Web traffic to generate over 1.6 Zettabytes of data by 2015

Over 2 Billion users worldwide

Over 10 Billion networked sensors

In 1990 1MB cost $1.00Today 1MB costs .01 cent

1980: 10 MIPS/Sec2005: 10M MIPS/Sec


$Big Data is Creating Big Opportunities!

Big Data technologies are a top priority for most institutions: both corporate

and government

Currently, 49% of CEO’s and

CIO’s claim they are undertaking

Big Data projects

Software estimated to

experience 34% YOY compound

growth rate: 4.6B by 2015

Services estimate to

experience 39% YOY compound

growth rate: 6.5B by 2015


Uses for Big Data technologies

Data Warehousing:

• Operational Data: New User Registrations, Purchasing, New Product offering

• Data Exhaust: by-products like log files have low density of useful data but the data they do contain is very valuable if we can extract it at a low cost and in a timely manner.

Devices and the Internet of things:

• Trillions of Internet-connected devices• GPS data• Cell phone data• Automotive engine

performance data

Collective Intelligence

• Social Analytics: What is the social sentiment for my product(s)?

• Live Data Feed/Search: How do I optimize my services based on weather or traffic patterns? How do I build a recommendations engine (Mahout)?

• Advanced Analytics: How do I better predict future outcomes?


What is Hadoop and How does it Work?• Hadoop Distributed Architecture:

• Storage Layer (HDFS)

• Programming Layer (Map/Reduce)

• Schema on Read vs. Schema on Write• Traditionally we have always brought

the data to the schema and code

• Hadoop sends the schema and thecode to the data

• We don’t have to pay the cost or livewith the limitations of moving the data: IOPs, Network traffic.

Task Tracker

Task Tracker

Task Tracker

Name Node

Data NodeData Node

Map/R

educe

La

yer

HD

FS L

ayer

Implements a Divide and Conquer Algorithm to Achieve Greater Parallelism


HDFS (Hadoop Distributed Files System)•Fault Tolerant:

• Data is distributed across each Data Node in the cluster (like RAID 5).

• 3 copies of the data is stored in case of storage failures.

• Data faults can be quickly detected and repaired due to data redundancy.

•High Throughput• Favors batch over interactive operations to support streaming large

datasets.

• Data files are written once and then closed; never to be updated.

• Supports data locality. HDFS facilitates moving the application code (query) to the data rather than moving the data to the application (schema on read).


Map/Reduce

• Like the “Assembly” language of Hadoop / Big Data: very low level

• Users can interface with Hadoop using higher-level languages like Hive an Pig

• Schema on Read; not Schema on Write

• Moving the Code to the Data:• First, Store the data

• Second, (Map function) move the programming to the data; load the code on each machine where the data already resides.

• Third, (Reduce function) collects statisticsback from each of the machines.


Map/Reduce: Logical Process

Key Value

A X=2, Y=3

B X=1, Z=2

C X=3

A Y=1, Z=4

Key Value

E Y=3

A X=1, Z=2

A Z=5

D Y=2, Z=1

Key Value

A X=2, Y=3

A Y=1, Z=4

Key Value

A X=1, Z=2

A Z=5

Key Value

A X=3, Y=4, Z=11

Map

Reduce

Map

1. SELECT WHERE Key=A

2. SUM Values of Each Property

The MAP function runs on each data node, extracting data that matches the query.

The REDUCE function runs on one node, combining the results from all the MAP components into the final results set.


Yet Another Resource Manager (YARN)•Second generation

Hadoop• Extends capabilities

beyond Map/Reduce; beyond batch processing

• Makes good on the promise of (near) real-time Big Data processing

Node Manager

Resource Manager

App Master

Container

Node ManagerApp

MasterContaine

r

Node Manager

ContainerContaine

r

Client

Client


Data Movement and Query Processing RDBMS require a

schema to be applied when the data is written:

The data is transformed to

accommodate the schema

Some information hidden in the data

may be lost at write-time

Hadoop/HDInsight applies a schema

only when the data is read:

The schema doesn’t change the

structure of the underlying data

The data is stored in its original (raw) format so that all

hidden information is retained

RDBMS perform query processing

in a central location:

Data is moved from storage to a central

location for processing

More central processing capacity is required to move data and execute

the query

Hadoop performs query processing at each storage

node:

Data doesn’t need to be moved across

the network for processing

Only a fraction of the central

processing capacity is required to

execute the query


Major Differences: RDBMS vs. Big DataFeature Relational Database Hadoop / HDInsight

Data Types and Formats Structured Semi-Structured or Unstructured

Data Integrity High: Transactional Updates Low: Eventually Consistent

Schema Static: Required on Write Dynamic: Optional on Read & Write

Read and Write Pattern Fully Repeatable Read/Write Write Once; Repeatable Read

Storage Volume Gigabytes to Terabytes Terabytes, Petabytes and Beyond

Scalability Scale Up with More Powerful HW Scale Out with Additional Servers

Data Processing Distribution Limited or None Distributed Across Cluster

Economics Expensive Hardware & Software Commodity Hardware & Open Source Software


Solution Architecture: Big Data or RDBMS?

• Perhaps you already have the data that contains the information you need, but you can’t analyze it with your existing tools. Or is there a source of data you think will be useful, but you don’t yet know how to collect it, store it, and analyze it?

Where will the source data come from?

• Is it highly structured, in which case you may be able to load it into your existing database or data warehouse and process it there? Or is it semi-structured or unstructured, in which case a Big Data solution that is optimized for textual discovery, categorization, and predictive analysis will be more suitable?

What is the format of the data?

• Is there a huge volume? Does it arrive as a stream or in batches? Is it of high quality or will you need to perform some type of data cleansing and validation of the content?

What are the delivery and quality characteristics

of the data?

• If so, do you know where this data will come from, how much it will cost if you have to purchase it, and how reliable this data is?

Do you want to combine the results with data from

other sources?

• Will you need to load the data into an existing database or data warehouse, or will you just analyze it and visualize the results separately?

Do you want to integrate with an existing

BI system?


What is Windows Azure HDInsight?

• Windows Azure: Microsoft’s online storage and compute services

• HDInsight: Microsoft’s implementation of Apache Hadoop (Hortonworks Data Platform) as an online service

Makes Apache Hadoop readily available to the Windows community

• Enables Windows Azure subscribers to quickly and easily provision an HDInsight cluster across Windows Azure’s pool of storage and compute resources.

• Also enables them to quickly de-provision those clusters when they’re not needed.

• Allows subscribers to continuously store their data for later use.

Exposes Apache Hadoop services to the Microsoft programming ecosystem

• SQL Server: Analysis Services• PowerShell and the Cross-platform

Command-line tools• Visual Studio: CLR (C#, F#, etc.)• JavaScript• ODBC / JDBC / REST API• Excel Self-Service BI (SSBI): PowerQuery,

PowerPivot, PowerView and PowerMap


HDInsight Data Storage• Azure Blob Storage System: The default storage system for HDInsight

• Enables you to persist your data even when you’re not running an HDInsight cluster

• Enables you to leverage your data using HDInsight, Azure SQL Server Database & PDW

Name Node

Data Node

Data Node

Stream Layer

Partition Layer

Front End

Azure Storage (ASV)

Azure Blob Storage

Hadoop Distributed File System (HDFS) API


Azure Storage Vault (ASV)

• The default file system for the HDInsight Service

• Provides scalable, persistent, sharable, highly-available storage

• Fast data access for compute nodes residing in the same data center

• Addressable via:• asv[s].<container>@<account>.blob, core.windows.net/path

• Requires storage key in core-site.xml:<property>

<name>fs.azure.account.key.accountname</name>

<value>keyvaluestring</value>

</property>


The Zoo: HDInsight / Hadoop Ecosystem

Distributed Processing(Map/Reduce)

Distributed Storage(HDFS)

Scripting(Pig)

Query(Hive)

Metadata(HCatalog)A

ctive D

irecto

ry(S

ecu

rity)

Mon

itorin

g &

D

eplo

ym

ent

(Syste

m C

ente

r)

World’s Data(Azure Data Marketplace)

Azure StorageVault (ASV)

NoSQ

L D

ata

base

(HB

ase

)

Pipelin

e / W

orkfl

ow

(Oozie

)Lo

g fi

le A

ggre

gatio

n(Flu

me)

C#, F#.NET

JavaScript

Stats processin

g(RHadoop

)

Graph(Pegasus)

Machine Learning(Mahout)

Data

Inte

gra

tion

(OD

BC

/ SQ

OO

P / R

ES

T)

Busin

ess In

tellig

ence

(E

xcel, Po

wer V

iew

, SS

AS

)

Relational(SQL Server)

Event Driven Processing

Para

llel D

ata

Ware

house

(PD

W) /

Plo

ybase


Programming HDInsight

Since HDInsight is a service-based implementation, you get immediate access to the tools you need to program against HDInsight/Hadoop

• Hive, Pig, Sqoop, Mahout, Cascading, Scalding, Scoobi, Pegasus, etc.Existing Ecosystem

• C#, F# Map/Reduce, LINQ to Hive, .Net Management Clients, etc..NET

• JavaScript Map/Reduce, Browser-hosted Console, Node.js management clientsJavaScript

• PowerShell, Cross-Platform CLI ToolsDevOps/IT Pros:


Microsoft’s Vision

Broader Access• Easy installation

of Hadoop on Windows

• Simplified programming via integration with .Net and JavaScript.

• Integration with SQL Server Data Warehouses

Enterprise Ready• Choice of

deployment on Windows Server or Windows Azure

• Integration with other Windows components like Active Directory and System Center

Insights• Integrate with

the Microsoft BI Stack:• SQL Server• SharePoint• Excel &

PowerBI

To provide insight to users by activating new types of data…

And contribute back to the community distribution of Hadoop.


How do I get started with HDInsight?

1. Create an Windows Azure account (subscription)

2. Create an Azure Storage account

3. Create an Azure Blob Storage node

4. Provision an HDInsight Service cluster

5. Install Windows Azure PowerShell

6. Install Windows Azure HDInsight PowerShell

7. Setup Environment


Architectural Models

Standalone Data Analysis &

Visualization

Experiment with data sources to discover if they provide useful information. Handle data that can’t be processed

using existing systems.

Data Transfer, Cleansing or ETL

Extract and transform data before loading it into existing databases. Categorize, normalize, and extract summary

results to remove duplication and redundancy.

Data Warehouse or Data Storage

Create robust data repositories that are reasonably inexpensive to maintain. Especially useful for storing and

managing huge data volumes.

Integrate with Existing EDW and

BI Systems

Integrate Big Data at different levels; EDW, OLAP, Excel PowerPivot. Also, PDW enables querying HDInsight to integrate Big Data with existing dimension & fact data.


Resources

• http://www.windowsazure.com

• http://hadoopsdk.codeplex.com

• http://nugget.org/packages?q=hadoop

• PolyBase – David DeWitt http://www.Microsoft.com/en-us/sqlserver/solutions-technologies/data-warehousing/polybase.aspx

• PDW Website: http://Microsoft.com/en-us/sqlserver-solutions-technologies/data-warehousing/pdw.aspx

• http://blogs.technet.com/b/dataplatforminsider/archive/2013/04/25/insight-through-integration-sql-server-2012-parallel-data-warehouse-polybase-demo.aspx

http://nugget.org/packages?q=hadoop

http://nugget.org/packages?q=hadoop

http://www.microsoft.com/en-us/sqlserver/solutions-technologies/data-warehousing/polybase.aspx

http://www.microsoft.com/en-us/sqlserver/solutions-technologies/data-warehousing/polybase.aspx

http://blogs.technet.com/b/dataplatforminsider/archive/2013/04/25/insight-through-integration-sql-server-2012-parallel-data-warehouse-polybase-demo.aspx




Tools

•http://azurestorageexplorer.codeplex.com


Place title here

• HDInsight clusters can be provisioned when needed and then de-provisioned without loosing the data or metadata they have processed.

• Azure Storage Vault allow you to maintain that state; paying only for the storage and not the cluster(s).

• Since stream data often arrives in massive bursts, HDInsight can provide a buffer between that data generation and existing data warehouse/BI infrastructures.

Documents

Introduction to Windows Azure HDInsight Jon Tupitza Principal, Solution Architect [email protected]