The document outlines the role of Hadoop in modern data architecture, highlighting its significance in handling big data through various components like MapReduce, Hive, and HBase. It emphasizes the growing need for efficient data processing and storage due to the rapid increase in data volume, variety, and velocity driven by technologies such as IoT and social media. Key use cases including data warehouse optimization demonstrate how Hadoop can enhance data insights and cost-effectiveness.

1. Presented by
Stephen Peter
The Hadoop Data Access Layer

2. Stephen Peter
E-Mail: Stephen.peter@gmail.com
LinkedIn - https://in.linkedin.com/in/stephenepeter
Hortonworks Certified Trainer.
Hortonworks Certified Developer (Apache Pig & Hive)
Digital Badge : http://bcert.me/sxohnqiq
Professional Experience: Over 20 years of IT experience with
specialization in Business Intelligence , Data warehousing and Big Data.
Worked in organizations such as HCL Tech, Oracle , Cisco Systems.
Presently working as Hadoop trainer at Spring People.
Area of interest: coexistence of Enterprise DW and Hadoop
Introduction

3. • The motivation for Hadoop
▫ The need for ingesting, storing and analyzing big data.
▫ Use cases on the value of Big Data.
• Hadoop as an integral part of Modern Data Architecture.
• The HDP (Hortonworks Data Platform) reference architecture.
▫ HDP Data Access Layer.
 The different components its functions and application.
• Use case – Data warehouse Optimization using Hadoop.
▫ to achieve better insight and cost effectiveness.
Agenda

4. Emerging Data landscape
• In the past the world’s data doubled every
century, now its every 2 years.
• The flood of data is driven by IOT, mobile
devices, server logs, geo location coordinates,
social media and sensor data.
• Big data is characterized by:
 Velocity – 90% of world’s data created in the
last two years.
 Volume – from 8 ZB in 2015 expected to grow
to 40 ZB by 2020.
 Variety – 80% of enterprise data unstructured
ranging from docs, emails, images, web logs,
sensor data, geospatial coordinates and server
logs.

5. Big Data Use Cases
Source: https://hortonworks.com

6. Hadoop – An integral part of modern Data Architecture
Source: https://hortonworks.com

7. Hortonworks Hadoop Platform - HDP
www.hortonworks.com

8. • Batch Processing using Map Reduce Framework
• Interactive SQL Query using Hive on Tez framework.
• Apache Pig scripting language can run on MR or Tez.
• Low latency data access via NoSQL database Hbase.
• Apache Storm processes and analyze streams of data
in real time as it flows into HDFS
• Apache Spark is a fast, in-memory data processing
engine that enables batch, real-time, and advanced
analytics on the Apache Hadoop platform.
HDP - Data Access Layer
www.hortonworks.com

9. Ingest Data into HDFS using Scoop

10. ▫ The primary use case:
 Stream log entries from multiple machines
 Aggregate them to a centralized, persistent
store such as the Hadoop Distributed File
System
 Log entries can be analyzed by other Hadoop
tools.
▫ Flume is not limited to log entries.
 Flume is used to collect many types of
streaming data.
 Examples include network traffic data, social
media generated data, machine sensor data, and
email messages.
▫ Flume is not the best choice where data is not
regularly generated.
Ingest Data into HDFS using Flume

11. • Use the Twitter streaming API as the source
• Create a twitter application
• Configure the flume agent by modifying the flume
configuration.
▫ Configure the source, channel and sink.
▫ Source type:
org.apache.flume.source.twitter.TwitterSource
▫ Channel type: MemChannel
▫ Sink type : HDFS
• Run the flume command to extract data from
twitter.
for example
$ flume-ng agent --conf ./conf/ -f conf/twitter.conf
Importing Twitter data into HDFS

12. Query Data using Hive

13. Example Hive QL commands
 Create a Hive managed table:
CREATE TABLE stockinfo (symbol STRING, price FLOAT,
change FLOAT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ‘,’;
 Create a Hive external table:
CREATE EXTERNAL TABLE salaries (gender string, age int, salary
double,zip int
) ROW FORMAT DELIMITED
FIELDS TERMINATED BY ',‘
LOCATION '/user/train/salaries/';
 Load data from file in HDFS:
LOAD DATA INPATH ‘/user/me/stockdata.csv’
OVERWRITE INTO TABLE stockinfo;
 View everything in the table:
SELECT * from stockinfo;

14. Performance tuning in Hive
• Hive Partition table
• Hive Buckets
• Use Optimized Row Columnar (ORC) Format storage
• Cost Based SQL Optimization
• Using Hive on Tez for low latency query

15. Use cases for Apache Pig
• Pig can extract data from multiple sources, transform it and store it in HDFS.
• Research raw data.
• Iterative data processing
database
data
log
data
sensor
data
transform HDFS
extract transform load
Hive
other
tools
PIG
analysis
tools

16.  Load data from a file and apply a schema:
stockinfo = LOAD ‘stockdata.csv’ using PigStorage(‘,’) AS
(symbol STRING, price FLOAT, change FLOAT) ;
 Display the data in stockinfo:
DUMP stockinfo;
 Filter the stockinfo data and write the filtered data to HDFS:
IBM_only = FILTER stockinfo BY (symbol == ‘IBM’);
STORE IBM_only INTO ‘ibm_stockinfo’;
 Load data from a file without applying a schema
a = LOAD ‘flightdelays’ using PigStorage(‘,’);
 Apply schema on read
c = foreach a generate $0 as year:int, $1 as month:int,
$4 as name:chararray;
Example Pig Statements

17. Create workflow using Apache Oozie
email
distcp
MapReduce
Hive
PigSqoop
Oozie workflow example
data data
Apache Oozie is a server-based workflow engine
used to execute Hadoop jobs.
Used to build and schedule complex data
transformations by combining MapReduce,
Apache Hive, Apache Pig, and Apache Sqoop
jobs into a single, logical unit of work.
Oozie can also perform Java, Linux shell,
distcp, SSH, email, and other operations.
Oozie runs as a Java Web application in
Apache Tomcat.

18. Use Case -Data warehouse Optimization with Hadoop