This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
This presentation discusses the follow topics
What is Hadoop?
Need for Hadoop
History of Hadoop
Hadoop Overview
Advantages and Disadvantages of Hadoop
Hadoop Distributed File System
Comparing: RDBMS vs. Hadoop
Advantages and Disadvantages of HDFS
Hadoop frameworks
Modules of Hadoop frameworks
Features of 'Hadoop‘
Hadoop Analytics Tools
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
In this session you will learn:
HIVE Overview
Working of Hive
Hive Tables
Hive - Data Types
Complex Types
Hive Database
HiveQL - Select-Joins
Different Types of Join
Partitions
Buckets
Strict Mode in Hive
Like and Rlike in Hive
Hive UDF
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. The core of Apache Hadoop consists of a storage part (HDFS) and a processing part (MapReduce).
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
In this session you will learn:
HIVE Overview
Working of Hive
Hive Tables
Hive - Data Types
Complex Types
Hive Database
HiveQL - Select-Joins
Different Types of Join
Partitions
Buckets
Strict Mode in Hive
Like and Rlike in Hive
Hive UDF
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Presentation slides of the workshop on "Introduction to Pig" at Fifth Elephant, Bangalore, India on 26th July, 2012.
http://fifthelephant.in/2012/workshop-pig
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
Back in summer of 2014, we launched the results of a survey on Java 8, which shared a lot of information we were looking for, but also contained a small golden nugget of data that we didn’t expect: that out of more than 3000 developers surveyed, a shocking 17% of them reported using Apache Spark in production.
So we did another survey with 2100+ respondents drilling down into what developers, data scientists, executives and organizations are looking forward to with Apache Spark. You can download the full version of the report for the whole story, but here is a sneak peak into the findings that we discovered.
The full version is at: http://typesafe.com/blog/apache-spark-preparing-for-the-next-wave-of-reactive-big-data
This was a presentation on my book MapReduce Design Patterns, given to the Twin Cities Hadoop Users Group. Check it out if you are interested in seeing what my my book is about.
This is the presentation I made on JavaDay Kiev 2015 regarding the architecture of Apache Spark. It covers the memory model, the shuffle implementations, data frames and some other high-level staff and can be used as an introduction to Apache Spark
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Content presented at a talk on Aug. 29th. Purpose is to inform a fairly technical audience on the primary tenets of Big Data and the hadoop stack. Also, did a walk-thru' of hadoop and some of the hadoop stack i.e. Pig, Hive, Hbase.
Introduction to Apache Hadoop. Includes Hadoop v.1.0 and HDFS / MapReduce to v.2.0. Includes Impala, Yarn, Tez and the entire arsenal of projects for Apache Hadoop.
deck from talk at YOW Data in Sydney, covers VariantSpark, custom Apache Spark Machine Learning library and also GT-Scan2 using AWS Lambda architecture for bioinformatics
VariantSpark - a Spark library for genomicsLynn Langit
VariantSpark a customer Apache Spark library for genomic data. Customer wide random forest machine learning algorithm, designed for workloads with millions of features.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
3. What is Hadoop?
Open-source data storage and processing API
Massively scalable, automatically parallelizable
Based on work from Google
GFS + MapReduce + BigTable
Current Distributions based on Open Source and Vendor Work
Apache Hadoop
Cloudera – CH4 w/ Impala
Hortonworks
MapR
AWS
Windows Azure HDInsight
4. Why Use Hadoop?
Cheaper
Scales to Petabytes or
more
Faster
Parallel data processing
Better
Suited for particular types
of BigData problems
5. What types of business problems for Hadoop?
Source: Cloudera “Ten Common Hadoopable Problems”
19. So, what’s the problem?
“I can just use some ‘SQL-like’ language to query Hadoop, right?
“Yeah, SQL-on-Hadoop…that’s what I want
“I don’t want learn a new query language and….
“I want massive scale for my shiny, new BigData
22. What is Hive?
a data warehouse system for Hadoop that
facilitates easy data summarization
supports ad-hoc queries (still batch though…)
created by Facebook
a mechanism to project structure onto this data and query the data using a
SQL-like language – HiveQL
Interactive-console –or-
Execute scripts
Kicks off one or more MapReduce jobs in the background
an ability to use indexes, built-in user-defined functions
23. Is HQL == ANSI SQL? – NO!
--non-equality joins ARE allowed on ANSI SQL
--but are NOT allowed on Hive (HQL)
SELECT a.*
FROM a
JOIN b ON (a.id <> b.id)
Note: Joins are quite different in MapReduce, more on that coming up…
25. Common Hadoop Shell Commands
hadoop fs –cat file:///file2
hadoop fs –mkdir /user/hadoop/dir1 /user/hadoop/dir2
hadoop fs –copyFromLocal <fromDir> <toDir>
hadoop fs –put <localfile>
hdfs://nn.example.com/hadoop/hadoopfile
sudo hadoop jar <jarFileName> <method> <fromDir> <toDir>
hadoop fs –ls /user/hadoop/dir1
hadoop fs –cat hdfs://nn1.example.com/file1
hadoop fs –get /user/hadoop/file <localfile>
Tips
-- ‘sudo’ means ‘run as administrator’ (super user)
--some hadoop configurations use ‘hadoop dfs’ rather than ‘hadoop fs’ – file paths to hadoop differ for the former, see the link
included for more detail
28. Understanding MapReduce – P1/3
Map>>
(K1, V1)
Info in
Input Split
list (K2, V2)
Key / Value out
(intermediate values)
One list per local
node
Can implement local
Reducer (or
Combiner)
29. Understanding MapReduce – P2/3
Map>>
(K1, V1)
Info in
Input Split
list (K2, V2)
Key / Value out
(intermediate values)
One list per local
node
Can implement local
Reducer (or
Combiner)
Shuffle/Sort>>
30. Understanding MapReduce – P3/3
Map>>
(K1, V1)
Info in
Input Split
list (K2, V2)
Key / Value out
(intermediate values)
One list per local
node
Can implement local
Reducer (or
Combiner)
Reduce
(K2, list(V2)
Shuffle / Sort phase
precedes Reduce phase
Combines Map output
into a list
list (K3, V3)
Usually aggregates
intermediate values
(input) <k1, v1> map <k2, v2> combine <k2, v2> reduce <k3, v3> (output)
Shuffle/Sort>>
36. Ways to run MapReduce Jobs
Configure JobConf options
From Development Environment (IDE)
From a GUI utility
Cloudera – Hue
Microsoft Azure – HDInsight console
From the command line
hadoop jar <filename.jar> input output
42. Where is your Data coming from?
On premises
Local file system
Local HDFS instance
Private Cloud
Cloud storage
Public Cloud
Input Storage buckets
Script / Code buckets
Output buckets
44. Demo – Other Types of MapReduce
Tip: Review the Java MapReduce code in these samples as well.
45. Methods to write MapReduce Jobs
Typical – usually written in Java
MapReduce 2.0 API
MapReduce 1.0 API
Streaming
Uses stdin and stdout
Can use any language to write Map and Reduce Functions
C#, Python, JavaScript, etc…
Pipes
Often used with C++
Abstraction libraries
Hive, Pig, etc… write in a higher level language, generate one or more
MapReduce jobs
49. Using AWS MapReduce
Note: You can select Apache or MapR Hadoop Distributions to run your MapReduce job on the
AWS Cloud
50. What is Pig?
ETL Library for HDFS developed at Yahoo
Pig Runtime
Pig Language
Generates MapReduce Jobs
ETL steps
LOAD <file>
FILTER, JOIN, GROUP BY, FOREACH, GENERATE, COUNT…
DUMP {to screen for testing} STORE <newFile>
64. Demo – Unit Testing MapReduce
Using MRUnit + Asserts
Optionally using ApprovalTests
Image from http://c0de-x.com/wp-content/uploads/2012/10/staredad_english.png
65. A note about MapReduce 2.0
Splits the existing JobTracker’s roles
resource management
job lifecycle management
MapReduce 2.0 provides many benefits over the existing MapReduce
framework, such as better scalability
through distributed job lifecycle management
support for multiple Hadoop MapReduce API versions in a single cluster
66. What is Mahout?
Library with common machine learning algorithms
Over 20 algorithms
Recommendation (likelihood – Pandora)
Classification (known data and new data – spam id)
Clustering (new groups of similar data – Google news)
Can non-statisticians find value using this library?
68. Setting up Hadoop on Windows
For local development
Install from binaries from Web Platform Installer
Install .NET Azure SDK (for Azure BLOB storage)
Install other tools
Neudesic Azure Storage Viewer
71. Clients (Visualizations) for HDFS
Many clients use Hive
Often included in GUI console tools for Hadoop distributions as well
Microsoft includes clients in Office (Excel 2013)
Direct Hive client
Connect using ODBC
PowerPivot – data mashups and presentation
Data Explorer – connect, transform, mashup and filter
Hadoop SDK on Codeplex
Other popular clients
Qlikview
Tableau
Karmasphere
78. Comparing: RDBMS vs. Hadoop
Traditional RDBMS Hadoop / MapReduce
Data Size Gigabytes (Terabytes) Petabytes (Hexabytes)
Access Interactive and Batch Batch – NOT Interactive
Updates Read / Write many times Write once, Read many times
Structure Static Schema Dynamic Schema
Integrity High (ACID) Low
Scaling Nonlinear Linear
Query Response
Time
Can be near immediate Has latency (due to batch processing)
79. Microsoft alternatives to MapReduce
Use existing relational system
Scale via cloud or edition (i.e. Enterprise or PDW)
Use in memory OLAP
SQL Server Analysis Services Tabular Models
Use “productized” Dremel
Microsoft Polybase – status = beta?
80. Looking Forward - Dremel or Apache Drill
Based on original research from Google
http://www.cloudera.com/content/dam/cloudera/Resources/PDF/cloudera_White_Paper_Ten_Hadoopable_Problems_Real_World_Use_Cases.pdf Also -- http://gigaom.com/2012/06/05/10-ways-companies-are-using-hadoop-to-do-more-than-serve-ads/
Image from http://curiousellie.typepad.com/.a/6a0133ec911c1f970b0168ebe6a2e4970c-500wi
http://hadoop.apache.org/docs/r1.1.2/streaming.html How to run and compile a Hadoop Java program -- https://sites.google.com/site/hadoopandhive/home/how-to-run-and-compile-a-hadoop-program Sample code to compile a JAVA class: javac –classpath ~/hadoop/hadoop-core-1.0.1.jar;commons-cli-1.2.jar –d classes <nameOfJavaFile>.java && jar –cvf <nameOfJarFile>.jar –C classes/
The key and value classes have to be serializable by the framework and hence need to implement the Writable interface. Additionally, the key classes have to implement the WritableComparable interface to facilitate sorting by the framework.
Tips from Cloudera -- http://blog.cloudera.com/blog/2009/12/7-tips-for-improving-mapreduce-performance/ & http://www.slideshare.net/Hadoop_Summit/optimizing-mapreduce-job-performance
Download local Hadoop via the Web Platform InstallerAlso download the Azure .NET SDK for VS 2012Link to download Windows Azure storage explorerhttp://azurestorageexplorer.codeplex.com/LInk for downloading .NET SDK for Hadoophttp://hadoopsdk.codeplex.com/wikipage?title=roadmap&referringTitle=Home
Image from - http://bluewatersql.files.wordpress.com/2013/04/image12.png