using Big data with Hadoop

2. 1- Introduction .
2- What is big data?
3- The characters of big data.
4- The handling with data.
5- Building a Successful Big Data Management.
6- Big data applications .
7- History of Hadoop.
8- The Core of Apache Hadoop .
9- Workflow and data movement .
10- Apache hadoop Ecosystem .
2

3. INTRODUCTION
“big data” became more than just a technical term for scientists,
engineers, and other technologists.
The term entered the main stream on a myriad of fronts,
becoming a household word in news ,business , health care, and
people’s personal lives.
The term became synonymous with intelligence gathering and
spy craft ,
3

4. These days, increased data generation rate so as to increase the
sources that generate such data. thus , the data becomes huge ”Big
data”.
The traditional data were generated from employee , now in the era of
massive data become from:
- Employee.
- Users .
- Machines .
are all generate large and different type of data Constantly.
4

5. What is big data?
Big data is a broad term for data sets so large or complex that
traditional data processing applications are inappropriate.
5

6. Example
Every day, we create 2.5 quintillion bytes of data — so much that
90% of the data in the world today created in last two years alone.
- data comes from everywhere: sensors used to gather climate
information, posts to social media sites, digital pictures and videos,
purchase transaction records, and cell phone GPS signals to name a
few. This data is big data.
6

7. Big data is not a single technology but a combination of old and new
technologies that helps companies gain actionable insight.
- big data the capability to manage a huge volume
- at the right speed
- in right time frame to allow real-time analysis and reaction.
Why the Big Data is important?
7

8. QUESTIONS
1- What are the examples of machines that generate big data ?
2- What are the examples of employees that generate big data ?
3- What are the examples of users that generate big data ?
8

9. The examples of machines that generate Big data
1- GPS Data:
records the exact position of a device at a
specific moment in time. GPS events can
be transformed easily into position and
movement information.
EX: vehicles on a road network rely on
the accurate and sophisticated
processing of GPS information.
9

10. 2- Sensor Data:
The availability of low cost, intelligent
sensors, coupled with the latest 3G and 4G
wireless technology has driven a dramatic
increase in the volume of sensor data, but the
need to extract operational intelligence in
real-time from the data.
Ex: include industrial automation plants, smart metering.
10

11. The examples of employees that generate Big data
11

12. The examples of users that generate Big data
12

13. THE CHARACTERS OF BIG DATA
13

14. Big Data is typically broken down by three
characteristics:
✓ Volume: How much data
✓ Velocity: How fast that data is processed
✓ Variety: The various types of data
Big Data
14

15. The Variety
- Big data combines all data.
- structured data.
- unstructured data .
- semi structure data.
This kind of data management requires that companies
leverage both their structured and unstructured data.
15

16. The characters of big data
16

17. Structure Unstructured
Analog Data
Big data
Semi-structure
Variety
XML
Enterprise
system(CRM,
ERP.. etc)
Data
warehouses
Audio/video
streams
GPS tracking
information
Databases
EDI
E-Mail
17

18. Looking at semi-structured data
Semi-structured data is a kind of data
- falls between structured and unstructured data.
- Semi-structured data not necessarily conform to a fixed schema.
- but may be self-describing and may have simple label/value pairs.
18

19. Looking at semi-structured data
For example, label/value pairs might include:
<family>=Jones, <mother>=Jane, and
<daughter>=Sarah.
Examples of semi-structured data include:
EDI, SWIFT, and XML.
You can think of them as sort of payloads for processing complex
events.
19

20. Traditional data & Big data
Traditional Data
- Documents.
- Finances .
- Stock records.
- Personal files.
Big Data
- Photographs .
- Audio & video .
- 3D models .
- Simulation .
- Location data.
20

21. Real time
Near real time
Hourly
Daily
Weekly
monthly
Batch And so on ….
v
e
l
o
c
i
t
y
v
o
l
u
m
e
Megabytes
Gigabytes
Terabytes
Petabytes
And more …
velocityvolume
21

22. The benefit gained from the ability to process large amounts of
information is the main attraction of big data analytics.
This volume presents the most immediate challenge to
conventional IT structures.
It calls for scalable storage, and a distributed approach to querying.
The volume
22

23. The Velocity
- It’s not just the velocity of the incoming data.
- it’s possible to stream fast-moving data into bulk storage
for later batch processing.
- The velocity of big data, coupled with its variety.
- cause a move toward real-time observations.
- allowing better decision making or quick action .
23

24. - The importance lies in the speed of the feedback loop, taking data
from input through to decision.
- A commercial from IBM makes the point that you wouldn’t cross the
road if all you had was a five-minute old snapshot of traffic location.
There are times when you simply won’t be able to wait for a report to
run or a Hadoop job to complete. The importance lies in the speed of
the feedback loop.
Example
24

25. Product categories for handling streaming data divide into :
1- Established proprietary products such as :
- IBM’s InfoSphere Streams and the lesspolished
2- Still emergent open source frameworks originating in the web industry:
- Twitter’s Storm and Yahoo S4.
Categories for handling streaming data
“Velocity ”
25

26. Practice example on Big data
Example 1
Example 2
Example 3
- These are good web sites to absorb how much of data generated
in the world
26

27. Different approaches to handling data exist
based on whether
- It is data in motion.
- It is data at rest.
Different approaches
To
handling data
27

28. - Data at rest would be used by a business analyst to better understand
customers’ current buying patterns based on all aspects of the customer
relationship, including sales, social media data, and customer service
interactions.
Here’s a quick example of each:-->
- Data in motion would be used if a company is able to analyze the quality
of its products during the manufacturing process to avoid costly errors.
28

29. Managing Big data
With Big data, now possible to virtualize data.
- stored efficiently, utilizing cloud-based storage.
- more cost- effectively .
- improvements network speed .
- reliability have removed other physical limitations to manage massive
amounts of data at an acceptable pace.
29

30. Building a Successful Big Data Management
- capture.
- organize.
- Integrate.
- analyze.
- act .
Big data management should beginning with:
The cycle of big data management.
30

31. - data must first be captured.
- Then organized and integrated.
- After this phase is successfully implemented.
- data analyzed based on problem being addressed.
Finally, management takes action based on the outcome of that analysis.
Building a Successful
Big Data
Management
31

32. The importance of Big data
In
our world & The our future
Big data provides a competitive advantage for organizations .
- helps to make decisions are thus increasing efficiency and
profit and loss reduction.
- extend benefit to including energy, education, health and
huge scientific projects like” the human genome project”
(the entire genetic material for the study of human beings).
32

33. - Healthcare.
- Manufacturing.
- Management .
- traffic management .
Big data applications
Some of the emerging applications are in areas such as :
They rely on huge volumes, velocities , and varieties data to transform
the behavior of a market.
33

34. In healthcare: a big data application might be able to monitor
premature infants to determine when data indicates when
intervention is needed.
In manufacturing, a big data application can be used to prevent a
machine from shutting down during a production run.
Example 1
Example 2
34

35. Let’s summarize some benefits of Big data
Some of benefit of big data are:
- Increase of storage capacity. = scalable.
- Increase processing power. =real-time.
- Availability of data . = full tolerant.
- less cost . = commodity hard ware
35

36. Hadoop was created by Doug Cutting
and Mike Cafarella in 2005. Cutting,
who was working at Yahoo! at the time,
named it after his son's toy elephant. It
was originally developed to support
distribution for the Nutch search engine
project.
History of
36

37. Hadoop is designed to process huge amounts of structured,
unstructured data (terabytes to petabytes) and is implemented
on racks of commodity servers as a Hadoop cluster.
Hadoop is designed to parallelize data processing across computing
nodes to speed computations and hide latency.
37

38. Apache Hadoop is a set of algorithms.
- Open source software framework written in Java.
- Distributed storage.
- Distributed processing .
- Built from commodity hardware.
- Files are replicated to handle hardware failure
- Detect failures and recovers from them
What is ?
38

39. Some of Hadoop users
- Facebook.
- IBM.
- Google.
- Yahoo!.
- New York Times.
- Amazon/A9.
- And there are others
39

40. The Core of Apache Hadoop
At its core, Hadoop has two primary components:
1- storage part
- Hadoop Distributed File System <HDFS>.
# can support petabytes of data.
2- processing part
- MapReduce.
#computes results in batch.
40

41. HDFS:
- Stores large files across a commodity cluster.
- typically in the range of gigabytes to terabytes .
- Scalable, and portable file-system .
- written in Java for the Hadoop framework .
- Replicating data across multiple hosts.
Hadoop Distributed File System HDFS
41

42. - Default replication value, 3, data is stored on three nodes:
- two on the same rack, and one on a different rack.
- Data nodes can talk to each other:
- to rebalance data.
- to move copies around .
- to keep the replication of data high.
HDFS:
42

43. Question
- Why we need to replicate files in HDF ?
To achieve reliability .
if any failure occur on any node we can continue in
processing
43

44. HDFS works by breaking large files into smaller pieces called blocks.
- The blocks are stored on data nodes.
- NameNode responsibility to know what blocks on which data
nodes make up the complete file.
“keeps track of where data is physically stored”.
- NameNode acts as a “traffic cop,” managing all access to the files .
HDFS
44

45. How a Hadoop cluster is mapped to hardware.45

46. The responsibility of NameNode
1- reads data blocks on the data nodes.
2- writes data blocks on the data nodes.
3- creates data blocks on the data nodes.
4- deletes data blocks on the data nodes.
5- replication of data blocks on the data nodes.
- NameNode acts as a “traffic cop,” managing all access to the files
including :
46

47. NameNode and data nodes :
- they operate in a “loosely coupled” fashion
that allows the cluster elements to behave
dynamically:
- adding (or subtracting) servers as the
demand increases (or decreases).
How a Hadoop cluster is mapped to hardware.
The Relationship
Between
NameNode & DataNodes
47

48. Are DataNodes also smart?
NameNode is very smart
Data nodes are not very smart
48

49. Are DataNodes also smart?
- Data nodes are not very smart, but the NameNode is.
- Because the DataNodes constantly ask the NameNode whether there
anything for them to do.
- tells NameNode what data nodes out there and how busy they are.
- The NameNode so critical for correct operation of the cluster, can and
should be replicated to guard against a single point failure.
49

50. Map : distribute a computational
problem across a cluster .
Reduce : master node collects the
answers to all the sub- problems
and combines them .
master
copy
copy
copy
Map Reduce
50

51. An example of an inverted index being created in MapReduce

52. 52
public static class Map
extends Mapper<LongWritable, Text, Text, Text> {
private Text documentId;
private Text word = new Text();
@Override
protected void setup(Context context) {
String filename =
((FileSplit) context.getInputSplit()).getPath().getName();
documentId = new Text(filename);
}
@Override
protected void map(LongWritable key, Text value,
Context context)
throws IOException, InterruptedException {
for (String token :
StringUtils.split(value.toString())) {
word.set(token);
context.write(word, documentId);}}}
shows the mapper code

53. 53
public static class Map
extends Mapper<LongWritable, Text, Text, Text> {
When you extend the MapReduce mapper class you specify the key/value
types
for your inputs and outputs. You use the MapReduce default InputFormat for
your job, which supplies keys as byte offsets into the input file, and values as
each line in the file. Your map emits Text key/value pairs.
The following shows the mapper code

54. 54
To cut down on object creation you create a single Text object, which you’ll reuse
private Text word = new Text();
private Text documentId;
A Text object to store the document ID (filename) for your input

55. - InputFormat decides how file going to be
broken into smaller pieces for processing
using a function called InputSplit.
- It then assigns a RecordReader to
transform the raw data for processing by
the map.
- Then the map requires two inputs: a key
and a value.
Workflow and data movement
in a small Hadoop cluster
Hadoop MapReduce
55

56. - Your data is now in form acceptable to map.
- For each input pair, a distinct instance of map
is called to process data.
- map and reduce need to work together to
process your data.
- OutputCollector collects output from
independent mappers.
The mapping begin
56

57. - A Reporter function provides information
gathered from map tasks.
- to know when or if map tasks are
complete.
- All this work is being performed on
multiple nodes in the Hadoop cluster
simultaneously.
The mapping begin. Cont.
57

58. - Some of output may on a node different from the node where
reducers for that specific output will run.
- a partitioner and a sort gather and shuffle of intermediate
results.
- Map tasks deliver results to specific partition
- as inputs to the reduce tasks.
Workflow and data movement
- After all the map tasks are complete.
- the intermediate results are gathered in the partition.
- reduce shuffle ,sorting output for optimal processing.
58

59. Reduce & Combine
- For each output pair, reduce is called to perform its task.
- Reduce gathers its output while all the tasks are processing.
- Reduce can’t begin until all the mapping is done, and
- It isn’t finished until all instances are complete.
- The output of reduce a key and a value.
- OutputFormat takes the key-value pair .
- organizes the output for writing to HDFS.
- RecordWriter takes OutputFormat data and writes it to HDFS.
59

60. The Benefits of MapReduce
- Hadoop MapReduce is the heart of the Hadoop system.
- MR provides capabilities you need to break big data into
manageable chunks.
- MR process data in parallel on your cluster.
- MR makes data available for user consumption.
- MapReduce does all works in a highly resilient, fault-tolerant
manner.
60

61. 61

62. Apache Hive
Apache Pig
Apache HBase
SQL-like language and
metadata repository
High-level language for expressing
data analysis programs
The Hadoop database. Random,
real -time read/write access
Hue
Browser-based desktop interface
for interacting with Hadoop
Oozie
Server-based workflow engine
for Hadoop activities
Sqoop Apache Whirr
Library for running
Hadoop in the cloud
Flume
Apache Zookeeper
Highly reliable distributed
coordination service
Distributed service for collecting
and aggregating log and event
data
Integrating Hadoop
with RDBMS
The major Utilities of Hadoop
62

63. - distributed, nonrelational columnar) database that utilizes HDFS
as its persistence store.
- Modeled after Google BigTable.
- Layered on Hadoop clusters .
- Capable of hosting very large tables (billions of columns/rows).
- Provides random, real-time read/write access .
- Highly configurable, providing flexibility to address huge of data
efficiently.
63

64. 64

65. Mining Big Data with Hive
Hive is a batch-oriented, data-warehousing layer .
- built on the core elements of Hadoop (HDFS and MapReduce).
- It provides users who know SQL with HiveQL.
- Hive queries can take several minutes or hours depending on complexity.
- Hive best used for data mining and deeper analytics.
- relies on Hadoop foundation.
- very extensible, scalable, and resilient.
65

66. Hive uses three mechanisms for data organization:
✓ Tables .
✓ Partitions .
✓ Buckets .
supports multitable queries and inserts by
sharing input data within a single HiveQL
statement.
66

67. Hive tables same as RDBMS tables consisting rows and columns.
- Tables are mapped to directories in the file system.
- Hive supports tables stored in other native file systems.
Tables
67

68. - A Hive table can support one or more partitions.
- partitions mapped to subdirectories in underlying file system & represent
the distribution of data throughout the table.
For example:
If a table is called autos, with a key value of 12345 and a maker value Ford,
the path to the partition would be /hivewh/autos/kv=12345/Ford.
Partitions
68

69. - data may be divided into buckets.
- Buckets stored as files in partition directory in underlying file system.
- The buckets based on hash of a column in table.
In the preceding example, you might have a bucket called Focus, containing all
the attributes of a Ford Focus auto.
Buckets
69

70. Pig and Pig Latin
Pig make Hadoop more approachable and usable by non-
developers.
- interactive, or script-based,
- execution environment supporting Pig Latin.
- language used to express data flows.
- Pig Latin language supports loading, processing input data
transform the input data and produce the desired output.
70

71. Pig execution environment has two modes :
✓ Local mode: All scripts are run on a single machine.
Hadoop MapReduce and HDFS are not required.
✓ Hadoop: Also called MapReduce mode, all scripts are
run on a given Hadoop cluster.
Pig and Pig Latin
71

72. - Pig Latin language abstract way to get answers from big data .
- focusing on data and not structure of custom software program.
- Pig makes prototyping very simple.
For example
you can run a Pig script on small representation of your big data
environment
to ensure getting desired results before commit to processing all data.
Pig and Pig Latin. cont.
72

73. - Pig programs run in three different ways, all compatible with local
and Hadoop mode:
✓ Script .
✓ Grunt .
✓ Embedded .
Pig and Pig Latin. cont.
73

74. ✓ Script: Simply a file containing Pig Latin commands
- identified by .pig suffix (for example, file.pig or myscript.pig).
- commands interpreted by Pig, executed in sequential order.
Script
74

75. ✓ Grunt: Grunt is a command interpreter.
- can type Pig Latin on grunt command line .
- Grunt execute command on your behalf.
- very useful for prototyping and “what if” scenarios.
✓ Embedded: Pig programs executed as part of Java program.
Grunt & Embedded
75

76. Pig Latin has rich syntax. It supports operators for following
operations:
✓ Loading and storing of data .
✓ Streaming data .
✓ Filtering data .
✓ Grouping and joining data .
✓ Sorting data .
✓ Combining and splitting data .
Pig Latin supports wide variety of types, expressions, functions, diagnostic
operators, macros, and file system commands.
Pig and Pig Latin. cont.
76

77. 77

78. Apache Sqoop
- Sqoop (SQL-to-Hadoop)
- tool that offers capability to extract data from non-
Hadoop data stores.
- transform data into form usable by Hadoop.
- then load data into HDFS.
- This process is called ETL, for Extract, Transform, and
Load.
- Sqoop commands executed one at a time.
78

79. Features of keys in Sqoop
✓ Bulk import:
- Sqoop import individual tables or entire databases into HDFS.
- data stored in native directories & files in HDFS file system.
✓ Direct input:
- Sqoop import , map SQL databases directly into Hive and HBase.
79

80. ✓ Data interaction:
- Sqoop generate Java classes .
- you interact with data programmatically.
✓ Data export:
Sqoop export data directly from HDFS into a relational database.
Features of keys in Sqoop .Cont.
80

81. The Apache Sqoop working
- Sqoop works by looking at database you want to import .
- Then selecting appropriate import function for source data.
- Then recognizes input, then reads metadata for table or database .
- Then creates a class definition of your input requirements.
81

82. 82

83. - Zookeeper Hadoop’s way coordinating all elements of distributed
applications.
- simple, but its features are powerful.
- managing groups of nodes in service to single distributed application.
- best implemented across racks.
83

84. Some of the capabilities of Zookeeper are as follows:
✓ Process synchronization .
✓ Configuration management .
✓ Self-election .
✓ Reliable messaging .
The capabilities of Zookeeper
Zookeeper
84

85. The capabilities of Zookeeper. Cont.
✓ Process synchronization:
- coordinates starting and stopping of multiple nodes in cluster.
- This ensures all processing occurs in intended order.
- When entire process group complete, processing occur.
85

86. ✓ Configuration management:
- used to send configuration attributes to any or all nodes in
cluster.
- processing dependent on particular resources being available
on all nodes.
- ensures consistency of configurations.
The capabilities of Zookeeper. Cont.
86

87. ✓ Self-election:
- Zookeeper understands makeup of cluster.
- can assign “leader” role to one of nodes.
- leader/master handles all client requests on behalf of
cluster.
- if leader node fail, another leader will be elected from
remaining nodes.
The capabilities of Zookeeper. Cont.
87

88. ✓ Reliable messaging: Even though workloads in Zookeeper
- Loosely coupled.
- Zookeeper offers a publish/subscribe capability.
- allows creation of queue.
- queue guarantees message delivery even in case of node failure.
The capabilities of Zookeeper. Cont.
88

89. The Benefits of Hadoop
- represented most pragmatic that allow companies to manage huge
volumes of data easily.
- Allowed big problems to broken down into smaller elements .
- Analysis done quickly and cost-effectively.
- Big data processed in parallel.
- Process information and regroup small pieces to present results.
89

90. 90

91. Any Questions?
91

92. 92