This document serves as an introduction to Hadoop and big data, explaining the challenges of processing large datasets and the necessity for distributed computing solutions. It covers key components of Hadoop, including HDFS and MapReduce, along with tools like Pig and Hive that facilitate data processing and analysis. The document emphasizes the advantages of Hadoop for handling vast amounts of data while discussing its limitations and the ecosystem surrounding it.

1. Hadoop for the Absolute
Beginner
Ike Ellis, MVP

2. Agenda
• What is Big Data?
• Why is it a problem?
• What is Hadoop?
– MapReduce
– HDFS
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Pig
Hive
Sqoop
HCAT
The Players
Maybe data visualization (depending on time)
Q&A

3. What is Big Data?
• Trendy?
• Buzz words?
• Process?
• Big data is “a collection of data sets so large and complex
that it becomes difficult to process using on-hand
database management tools or traditional data processing
applications” – Wikipedia
• So how do you know your data is big data?
• When your existing data processing methodologies are no
longer good enough.

4. Traditional Data Warehouse Stack

5. There are a lot of moving pieces back there…
• Sometimes, that‟s our biggest challenge
– Simple question – massive data
• Do we really need to go through the pain of that huge
stack?

6. Big Data Characteristics
• Volume
– Large amount of data
• Velocity
– Need to be processed quickly
• Variety
– Excel, SQL, OData feeds, CSVs, web downloads, JSON
• Variability
– Different semantics, in terms of meaning or context
• Value

7. Big Data Examples
• Structured Data
– Pre-defined Schema
– Highly Structured
– Relational
• Semi-structured Data
– Inconsistent structure
– Cannot be stored in rows and tables in a typical database
– Logs, tweets, data feeds, GPS coordinates
• Unstructured Data
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Lacks structure
Free-form text
Customer feedback forms
Audio
Video

8. The Problem
• So you have some data

9. The Problem
• And you want to clean and/or analyze it

10. So you use the technology that you know
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Excel
SQL Server
SQL Server Integration Services
SQL Server Reporting Services

11. But what happens if it’s TONS of data
• Like all the real estate transactions in the US for the last ten
years?
• Or GPS data from every bike in your bike rental store?
• Or every swing and every pitch from every baseball game
since 1890?

12. Or what happens when the analysis is very complicated?
• Tell me when earthquakes happen!
• Tell me how shoppers view my website!
• Tell me how to win my next election!

13. So you use SQL Server, and have a lot of data, so….
• YOU SCALE UP!
• But SQL can only have so much RAM, CPU, Disk I/O,
Network I/O
• So you hit a wall, probably with disk I/O
• So you….

14. Scale Out!
• Add servers until the pain goes away….
All analysis is done away from the data servers

15. But that’s easier said than done
• What‟s the process?
• You take one large task, and break it up into lots of smaller
tasks
– How do you break them up?
– Once it‟s broken up and processed, how do you put them back together?
– How do you make sure you break them up evenly so they all execute at the
same rate?
– And really, you‟re breaking up two things:
• Physical data
• Computational Analysis
– If one small task fails, how to you restart it? Log it? Recover from failure?
– If one SQL Server fails, how do you divert all the new tasks away from it?
– How do you load balance?
• So you end up writing a lot of plumbing code….and even
when you get done….you have one GIANT PROBLEM!

16. Data Movement
Data moves to achieve fault tolerance, to segment data, to reassemble data, to derive data,
to output data, etc, etc….and network (and disk) is SLOW..you’ve saturated it.

17. Oh, and another problem
• In SQL, the performance between a query over 1MB of
data and 1TB of data is significant
• The performance of a query over one server and over 20
servers is also significant

18. So to summarize and repeat
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Drive seek time….BIG PROBLEM
Drive channel latency…BIG PROBLEM
Data + processing time…BIG PROBLEM
Network Pipe I/O saturation…BIG PROBLEM
Lots of human problems
– Building a data warehouse stack is a difficult challenge
• Semi-structured data is difficult to handle
– When data changes, it becomes less structured and less valuable as
it changes
– Flexible structures often give us fits

19. Enter Hadoop
• Why write your own framework to handle fault tolerance,
logging, data partitioning, heavy analysis when you can
just use this one?

20. What is Hadoop?
• Hadoop is a distributed storage and processing technology
for large scale applications
– HDFS
• Self-healing, distributed file system. Breaks files into blocks and
stores them redundantly across cluster
– MapReduce
• Framework for running large data processing jobs in parallel
across many nodes and combining results
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Open Source
Distributed Data Replication
Commodity hardware
Disparate hardware
Data and analysis co-location
Scalability
Reliable error handling

21. Hadoop Ecosystem

22. Under the covers
Hadoop works by keeping the compute next to the data (to minimize network I/O costs)

23. MapReduce

24. Segmentation Problem

25. MapReduce Process – Very simple example

26. Programming MapReduce
• Steps
– Define the inputs
• Usually some files in HDFS/HBase (Or Azure Blob Storage)
– Write a map function
– Write a reduce function
– Define outputs
• Usually some files in HDFS/HBase (Or Azure Blob Storage)
• Lots of options for both inputs and outputs
• Functions are usually written in Java
– Or Python
– Even .NET (C#, F#)

27. Scalability
• Hadoop scales linearly with data size
– Or analysis complexity
– Scales to hundreds of petabytes
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Data-parallel or computer-parallel
Extensive machine learning on <100GB of image data
Simple SQL queries on >100TB of clickstream data
Hadoop works for both!

28. Hadoop allows you to write a query like this
Select productname, sum(costpergoods)
From salesorders
Group by productname
• Over a ton of data, or a little data, and have it perform
about the same
• If it slows down, throw more nodes at it
• Map is like the GROUP BY
• While reduce is like the aggregate

29. Why use Hadoop?
• Who wants to write all that plumbing?
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Segmenting data
Making it redundant and fault tolerant
Overcoming job failure
Logging
All those data providers
All the custom scripting languages and tooling
Synchonization
Scale-free programming model
• Wide adoption
• You specify the map() and reduce() functions
– Let the framework do the rest

30. What is Hadoop Good For?
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Enormous datasets
Log Analysis
Calculating statistics on enormous datasets
Running large simulations
ETL
Machine learning
Building inverted indexes
Sorting
– World record
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Distributed Search
Tokenization
Image processing
No fancy hardware…good in the cloud
And so much more!

31. What is Hadoop Bad For?
• Low latency (not current data)
• Sequential algorithms
– Recursion
• Joins (sometimes)
• When all the data is structured and can fit on one database
server with scaling up
– It is NOT a replacement for a good RDBMS

32. Relational vs Hadoop

33. Another Problem
• MapReduce functions are written in Java, Python, .NET, and
a few other languages
• Those are languages that are widely known
• Except by analysts and DBAs, the exact kind of people who
struggle with big data
• Enter Pig & Hive
– Abstraction for MapReduce
– Sits over MapReduce
– Spawns MapReduce jobs

34. What MapReduce Functions look like
function map(String name, String document):
// name: document name
// document: document contents
for each word w in document:
emit (w, 1)
function reduce(String word, Iterator partialCounts):
// word: a word
// partialCounts: a list of aggregated partial counts
sum = 0
for each pc in partialCounts:
sum += ParseInt(pc)
emit (word, sum)

35. Introduction to Pig
• Pig – ETL for big data
– Structure
– Pig Latin
• Parallel data processing for Hadoop
• Not trying to get you to learn Pig. Just want you to want
to learn it.

36. Here’s what SQL looks like
Select customername, count(orderdate) as totalOrders
From salesOrders so
Join customers c
On so.custid = c.custid
Group by customername

37. Pig
Trx = load „transaction‟ as (customer, orderamount);
Grouped = group trx by customer;
Ttl = foreach grouped generate group, sum(trx.orderamount)
as tp;
Cust = load „customers‟ as (customer, postalcode);
Result = join ttl by group, cust by customer;
Dump result;
Executes on step at a time

38. Pig is like SSIS
• One step at a time. One thing executes, then the next in
the script, acting on the variable declarations above it

39. How Pig Works
• Pig Latin goes to pre-processor
• Pre-processor creates MapReduce jobs that get submitted
to the JobTracker

40. Pig Components
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Data Types
Inputs & Outputs
Relational Operators
UDFs
Scripts & Testing

41. Pig Data Types
• Scalar
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Int
Long
Float
Double
CharArray
ByteArray
• Complex
– Map (key/value pair)
– Tuple (fixed-size ordered collection)
– Bag(collection of tuples)

42. Pig: Inputs/Outputs
• Load
– PigStorage
– TextLoader
– HBaseStorage
• Store
– PigStorage
– HBaseStorage
• Dump
– Dumps to console
– Don‟t dump a ton of data…uh oh…

43. Pig: Relational Operators
• Foreach – projection operator, applies expression to every
row in the pipeline
– Flatten – used with complex types, PIVOT
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Filter – WHERE
Group, Cogroup – GROUP BY (Cogroup on multiple keys)
ORDER BY
Distinct
JOIN (INNER, OUTER, CROSS)
LIMIT – TOP
Sample – Random sample
Parallel – level of parallelism on the reducer side
Union

44. Pig: UDFs
• Written in Java/Python
• String manipulation, math, complex type operations,
parsing

45. Pig: Useful commands
Describe – shows schema
Explain – shows the logical and physical MapReduce plan
Illustrate – runs a sample of your data to test your script
Stats – produced after every run and includes start/end
times, # of records, MapReduce info
• Supports parameter substitution and parameter files
• Supports macros and functions (define)
• Supports includes for script organization
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46. Pig Demo

47. Introduction to HIVE
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Very popular
Hive Query Language
Defining Tables, Views, Partitioning
Querying and Integration
VERY SQL-LIKE
Developed by FaceBook
Data Warehouse for Hadoop
Based on SQL-92 specification

48. SQL vs Hive
• Almost useless to compare the two, because they are so
similar
• Create table Internal/External
• Hive is schema on read
– It defines a schema over your data that already exists in the
database

49. Hive is not a replacement for SQL
• So don‟t throw out SQL just yet
• Hive is for batch processing large data sets that may span
hundreds, or even thousands, of machines
– Not for row-level updates
• Hive has high overhead when starting a job. It translates
queries to MR so it takes time
• Hive does not cache data
• Hive performance tuning is mainly Hadoop performance
tuning
• Similarity in the query engine, but different architectures
for different purposes
• Way too slow for OLTP workloads

50. Hive Components
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Data Types
DDL
DML
Queries
Views, Indexes, Partitions
UDFs

51. Hive Data Types
• Scalar
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TinyInt
SmallInt
Int
BigInt
Boolean
Float
Double
TimeStamp
String
Binary
• Complex
– Struct
– Array(Collection)
– Map(key/value pair)

52. What is a Hive Table?
• CREATE DATABASE NewDB
– LOCATION „hdfshuaNewDB‟
• CREATE TABLE
• A Hive table consists of:
– Data: typically a file in HDFS
– Schema: in the form of metadata stored in a relational database
• Schema and data are separate
– A schema can be defined for existing data
– Data can be added or removed independently
– Hive can be pointed to existing data
• You have to define schema if you have existing data in
HDFS that you want to use in Hive

53. How does Hive work?
• Hive as a Translation Tool
– Compiles and executes queries
– Hive translates the SQL Query to a MapReduce job
• Hive as a structuring tool
– Creates a schema around the data in HDFS
– Tables stored in directories
• Hive Tables have rows and columns and data types
• Hive Metastore
– Namespace with a set of tables
– Holds table definitions
• Partitioning
– Choose a partition key
– Specify key when you load data

54. Define a Hive Table
Create Table myTable (name string, age int)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY „;‟
STORED AS TEXFILE;

55. Loading Data
Use LOAD DATA to import data into a Hive table
LOAD DATA LOCAL INPATH „input/mydata/data.txt‟
INTO TABLE myTable
The files are not modified in Hive – they are loaded as is
Use the word OVERWRITE to write over a file of the same
name
• Hive can read all the files in particular directory
• The schema is checked when the data is queried
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– If a row does not match the schema, it will be read as null

56. Querying Data
• SELECT
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WHERE
UNION ALL/DISTINCT
GROUP BY
HAVING
LIMIT
REGEX
• Subqueries
• JOIN
– INNER
– OUTER
• ORDER BY
– Reducer is 1
• SORT BY
– Multiple reducers with a sorted file from each

57. Hive Demo

58. Pig Vs Hive
• Famous Yahoo Blog Post
– http://developer.yahoo.com/blogs/hadoop/pig-hive-yahoo464.html
• PIG
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ETL
For preparing data for easier analysis
Good for SQL authors that take the time to learn something new
Unless you store it, all data goes away when the script is finished
• Hive
– Analysis
• When you have to answer a specific question
– Good for SQL authors
– Excel connectivity
– Persists data in the Hadoop data store

59. Sqoop
• SQL to Hadoop
– SQL Server/Oracle/Something with a JDBC driver
• Import
– From RDBME into HFDS
• Export
– From HDFS into RDMBS
• Other Commands
– Create hive table
– Evaluate import statement

60. HUE
• Hadoop User Experience

61. HCatalog
• Metadata and table management system for Hadoop
• Provides a shared schema and data type mechanism for
various Hadoop tools (Pig, Hive, MapReduce)
– Enables interoperability across data processing tools
– Enables users to choose the best tools for their environments
• Provides a table abstraction so that users need not be
concerned with how data is stored
– Presents users with a relational view of data

63. HCatalog DDL
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CREATE/ALTER/DROP Table
SHOW TABLES
SHOW FUNCTIONS
DESCRIBE
Supports a subset of Hive DDL

64. Why do we have HCat?
• Tools don‟t tend to agree on
– What a schema is
– What data types are
– How data is stored
• HCatalog solution
– Provides one consistent dta model for various Hadoop tools
– Provides shared schema
– Allows users to see when shared data is available

65. HCatalog – HBase Integration
• Connects HBase tables to HCatalog
• Uses various Hadoop tools
• Provides flexibility with data in HBase or HDFS

66. HCat Demo

67. HBase
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NoSQL Database
Modeled after Google BigTable
Written in Java
Runs on top of HDFS
Features
– Compression
– In-memory operations
– Bloom filters
• Can serve as input or output for MapReduce jobs
• FaceBook‟s messaging platform uses it

68. Yarn
• Apache Hadoop Next Gen MapReduce
• Yet aNother Resource Negotiator
• Seperates resource management and processing
components
– Breaking up the job tracker
• YARN was born of a need to enable a broader array of
interaction patterns for data stored in HDFS beyond
MapReduce

69. Impala
• Cloudara
• Real-time queries for Hadoop
• Low-latency Queries using SQL to HDFS or HBase

70. Storm
• Free and open source distributed real-time computation
system
• Makes it easy to process unbounded streams of data
• Storm is fast
– Million tuples processed per second per node

71. The Players
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HortonWorks
Cloudara
MapR
Microsoft HDInsight
Microsoft PDW
IBM
Oracle
Amazon
Rackspace
Google

72. The Future
• Hadoop features will push into RDBMS systems
• RDBMS features will continue to push into Hadoop
• Tons of 3rd party vendors and open source projects have
applications for Hadoop and RDBMS/Hadoop integration
• Lots of buy-in, lots of progress, lots of changes

73. How to Learn Hadoop
• Lots of YouTube videos online
• HortonWorks, MapR, and Cloudara all have good videos
for free
• HortonWorks sandbox
• Azure HDInsight VMs
• Hadoop: The Definitive Guide
• Tons of blog posts
• Lots of open source projects

74. Ike Ellis
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www.ikeellis.com
SQL Pass Book Readers – VC Leader
@Ike_Ellis
619.922.9801
Microsoft MVP
Quick Tips – YouTube
San Diego TIG Founder and Chairman
San Diego .NET User Group Steering Committee Member