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Project 2: Building a Data Pipeline with Hadoop Ecosystem

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In this project, you will learn how to build a data pipeline using various tools from the Hadoop ecosystem. This project will cover the end-to-end process of data ingestion, storage, processing, and analysis.

Objectives

  • Understand the components of a data pipeline.
  • Learn how to use Apache Sqoop for data ingestion.
  • Use HDFS for data storage.
  • Process data using Apache Hive and Apache Pig.
  • Schedule and manage workflows with Apache Oozie.

Prerequisites

  • Basic understanding of Hadoop and its ecosystem.
  • Familiarity with HDFS, Hive, Pig, and Oozie.
  • A working Hadoop environment.

Steps to Build the Data Pipeline

Step 1: Data Ingestion with Apache Sqoop

Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.

Example: Importing Data from MySQL to HDFS

sqoop import \
--connect jdbc:mysql://localhost/employees \
--username root \
--password password \
--table employees \
--target-dir /user/hadoop/employees

Explanation:

  • --connect: JDBC URL to connect to the MySQL database.
  • --username and --password: Credentials for the database.
  • --table: The table to import.
  • --target-dir: The HDFS directory where the data will be stored.

Step 2: Storing Data in HDFS

HDFS is the primary storage system used by Hadoop applications. The data imported using Sqoop is now stored in HDFS.

Verify Data in HDFS

hdfs dfs -ls /user/hadoop/employees

Step 3: Data Processing with Apache Hive

Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

Example: Creating a Hive Table

CREATE EXTERNAL TABLE employees (
  emp_no INT,
  birth_date STRING,
  first_name STRING,
  last_name STRING,
  gender STRING,
  hire_date STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE
LOCATION '/user/hadoop/employees';

Explanation:

  • CREATE EXTERNAL TABLE: Creates an external table in Hive.
  • ROW FORMAT DELIMITED FIELDS TERMINATED BY ',': Specifies the format of the data.
  • STORED AS TEXTFILE LOCATION: Specifies the storage format and location in HDFS.

Example: Querying Data in Hive

SELECT * FROM employees WHERE gender = 'M';

Step 4: Data Processing with Apache Pig

Apache Pig is a high-level platform for creating programs that run on Hadoop. The language for this platform is called Pig Latin.

Example: Processing Data with Pig

employees = LOAD '/user/hadoop/employees' USING PigStorage(',') AS (emp_no:int, birth_date:chararray, first_name:chararray, last_name:chararray, gender:chararray, hire_date:chararray);
male_employees = FILTER employees BY gender == 'M';
DUMP male_employees;

Explanation:

  • LOAD: Loads data from HDFS.
  • FILTER: Filters the data based on the condition.

Step 5: Workflow Management with Apache Oozie

Apache Oozie is a workflow scheduler system to manage Hadoop jobs.

Example: Creating an Oozie Workflow

Create a workflow XML file (workflow.xml):

<workflow-app name="data-pipeline" xmlns="uri:oozie:workflow:0.5">
    <start to="sqoop-node"/>
    <action name="sqoop-node">
        <sqoop xmlns="uri:oozie:sqoop-action:0.2">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <command>import --connect jdbc:mysql://localhost/employees --username root --password password --table employees --target-dir /user/hadoop/employees</command>
        </sqoop>
        <ok to="hive-node"/>
        <error to="fail"/>
    </action>
    <action name="hive-node">
        <hive xmlns="uri:oozie:hive-action:0.5">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <script>hive-script.q</script>
        </hive>
        <ok to="end"/>
        <error to="fail"/>
    </action>
    <kill name="fail">
        <message>Workflow failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name="end"/>
</workflow-app>

Explanation:

  • <workflow-app>: Defines the workflow application.
  • <start>: Defines the start node.
  • <action>: Defines an action node.
  • <sqoop>: Defines a Sqoop action.
  • <hive>: Defines a Hive action.
  • <kill>: Defines a kill node for error handling.
  • <end>: Defines the end node.

Step 6: Running the Oozie Workflow

oozie job -oozie http://localhost:11000/oozie -config job.properties -run

Explanation:

  • -oozie: Oozie server URL.
  • -config: Configuration file for the job.
  • -run: Runs the job.

Conclusion

In this project, you have learned how to build a data pipeline using various tools from the Hadoop ecosystem. You have covered data ingestion with Apache Sqoop, data storage in HDFS, data processing with Apache Hive and Apache Pig, and workflow management with Apache Oozie. This project provides a comprehensive understanding of how to build and manage data pipelines in a Hadoop environment.

Summary

  • Data Ingestion: Used Apache Sqoop to import data from MySQL to HDFS.
  • Data Storage: Stored data in HDFS.
  • Data Processing: Processed data using Apache Hive and Apache Pig.
  • Workflow Management: Managed workflows using Apache Oozie.

Next Steps

  • Explore more advanced features of each tool.
  • Experiment with different data sources and processing techniques.
  • Implement additional workflows and scheduling strategies with Oozie.
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