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Table of contents
1.
Introduction
2.
Getting Started
3.
Using Hadoop YARN to Manage Resources and Applications
4.
HBase for Big Data Storage
5.
Mining Big Data with Hive
6.
Using the Hadoop Ecosystem
6.1.
Pig and Pig Latin
6.2.
Sqoop
6.3.
Zookeeper
7.
Frequently Asked Questions
7.1.
What is the foundation of big data?
7.2.
In the context of big data, what role does Hadoop play?
7.3.
What is the difference between Apache Spark and Hadoop?
8.
Conclusion
Last Updated: Mar 27, 2024

Building the Big Data Foundation with Hadoop

Author Palak Mishra
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Ashwin Goyal
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Introduction

Open-source and commercial developers worldwide have been developing and testing solutions to boost Hadoop acceptance and usability for some years and will continue to do so for the foreseeable future. 

Many people are working on different aspects of the ecosystem and contributing their improvements to the Apache project. This continuous supply of fixes and enhancements aids in the controlled and secure advancement of the entire ecosystem.

This blog will look at the many technologies that make up the Hadoop ecosystem.

Getting Started

It's like attempting to empty a river with a spoon. Suppose you try to tackle Big data challenges without a toolbox full of technology and services. Hadoop MapReduce and HDFS are great starting points and constantly improve as core components, but you need more. The Hadoop ecosystem offers a growing set of tools and technologies to make the development, deployment, and support of big data solutions more accessible. Let's talk about the Hadoop ecosystem and its role in the big data stage before we get into the critical components of the ecosystem.


Without foundation, no structure can stand. Stability is an essential criterion in a building, but it is not the only one. Each component of the structure must contribute to the overall goal. The walls, floors, stairs, electricity, plumbing, and roof must all work together to support and integrate the foundation. The Hadoop ecosystem is the same way. MapReduce and HDFS serve as the foundation. They provide the basic structure and integration services required to support the core requirements of big data solutions. The rest of the ecosystem provides the components you'll need to create and manage real-world purpose-driven big data applications.


Developers, database administrators, system and network administrators, and others would be responsible for identifying and agreeing on technologies to build and deploy big data solutions if the ecosystem did not exist. When companies want to adapt to new and emerging technological trends, this is frequently the case. It's a daunting task to piece together technologies in a new market. That's why the Hadoop ecosystem is so important for big data success. It's the most comprehensive set of tools and technologies currently addressing significant data challenges. The ecosystem helps businesses and organizations find new ways to use big data.

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Using Hadoop YARN to Manage Resources and Applications

Hadoop MapReduce includes job scheduling and tracking as standard features. Early versions of Hadoop had a rudimentary job and task tracking system, but the scheduler couldn't keep up as the types of work supported by Hadoop shifted. The old scheduler, in particular, couldn't handle non-MapReduce jobs and couldn't optimize cluster utilization. As a result, a new capability was created to address these flaws and provide greater flexibility, efficiency, and performance.
 

YARN (Yet Another Resource Negotiator) is a Hadoop core service that provides two essential benefits:

✓ Global resource management (ResourceManager)

✓ Per-application management (ApplicationMaster)
 

Each node in a Hadoop cluster has a ResourceManager, a master service that controls the NodeManager.The ResourceManager's Scheduler component is in charge of assigning system resources to specific running applications (tasks), but it doesn't monitor or track their status.
 

A Resource Container holds all of the necessary system information. It includes detailed CPU, disk, network, and other resource information required to run applications on the node and cluster.

Each node in the cluster has a NodeManager slaved to the cluster's global ResourceManager. The NodeManager reports to the ResourceManager on the application's CPU, disk, network, and memory usage. There is an ApplicationMaster for each application that runs on the node.

If more resources are required to support the running application, the ApplicationMaster notifies the NodeManager, who then negotiates for the additional capacity on behalf of the application with the ResourceManager (Scheduler). The NodeManager is also in charge of keeping track of the status and progress of jobs within its node.

HBase for Big Data Storage

HBase is a nonrelational (columnar) distributed database that uses HDFS for persistence. Because it is layered on Hadoop clusters of commodity hardware, it can host huge tables (billions of columns/rows). HBase allows users to access big data in a random, real-time manner. HBase is a highly configurable database that gives you many options for dealing with large amounts of data. Examine how HBase can assist you in overcoming your significant data challenges.
 

Because HBase is a columnar database, all data is organized into tables with rows and columns, much like relational database management systems (RDBMSs).

A cell is a point where a row meets a column. The versioning of HBase and RDBMS tables is a significant distinction. A "version" attribute, which is nothing more than a timestamp that uniquely identifies the cell, is included in each cell value. Versioning keeps track of changes in the cell and allows you to go back to any previous version if needed. Because HBase stores data in cells in decreasing order (based on the timestamp), a read will always return the most recent values.
 

In HBase, columns are grouped into families. The prefix family name is used to identify members of its family in the queue. The fruits column, for example, includes fruits: apples and fruits: bananas. Because HBase implementations are tuned at the column family level, it's crucial to think about how you'll access the data and how large the columns will be.

A key is also associated with each row in an HBase table. The key's structure is highly adaptable. A computed value, a string, or another data structure can be used. The key is used to restrict access to the cells in the row, which are organized from low to high value.
 

The schema is made up of all of these features. Before any data can be stored, the schema must be defined and created. Once the database is up and running, tables can be changed, and new column families added.

This extensibility is extremely useful because you don't always know the variety of your data streams when dealing with big data.
 

Mining Big Data with Hive

Hive is a batch-oriented data-warehousing layer based on Hadoop's core components (HDFS and MapReduce). Users familiar with SQL can use HiveQL, a simple SQL-lite implementation, without sacrificing access via mappers and reducers. With Hive, you can enjoy the best of both worlds. 

MapReduce provides SQL-like access to structured data and sophisticated big data analysis.

Unlike most data warehouses, Hive isn't built to respond to queries quickly. Depending on the complexity of the question, it could take several minutes or even hours to complete. As a result, Hive is best suited to data mining and deeper analytics that do not necessitate real-time behavior. It is very extensible, scalable, and resilient because it is built on the Hadoop foundation, which is something that the average data warehouse is not.

Hive organizes data using three mechanisms:

  • Tables: Hive tables are made up of rows and columns, just like RDBMS tables. Tables are mapped to directories in the file system because Hive is built on top of Hadoop HDFS. Hive can also read tables from native file systems.
     
  • Buckets: Data can also be divided into buckets. Buckets are stored as files in the underlying file system's partition directory. The buckets are based on the hash of a table column. You might have a bucket called Focus in the preceding example, which contains all of the attributes of a Ford Focus vehicle.
     
  • Partitions: One or more partitions can be supported by a Hive table. These partitions represent the data distribution across the table and are mapped to subdirectories in the underlying file system. The path to the partition would be /hivewh/autos/kv=12345/ Ford if the table is called autos and has a crucial value of 12345 and a maker value of Ford.
     

The "metastore" is where Hive metadata is kept externally. The Hive metastore is a relational database that stores detailed descriptions of the Hive schema, such as column types, owners, key and value data, table statistics, etc. The metastore can synchronize catalog data with other Hadoop metadata services.
 

Hive supports HiveQL, which is a SQL-like language. HiveQL, including select support many SQL primitives, join aggregate, union all, etc. Multitable queries and inserts are also endorsed by sharing the input data within a single HiveQL statement. User-defined aggregation, column transformations, and embedded MapReduce scripts can all be added to HiveQL.

Using the Hadoop Ecosystem

You can interact with the Hadoop ecosystem in various ways, including writing programs and using specialty query languages. Infrastructure management teams must be able to control Hadoop and the big data applications built on top of it. Non-technical professionals will want to use big data to solve business problems as it becomes more mainstream. 

Pig and Pig Latin

Pig was created to make Hadoop more approachable and usable for people who aren't programmers. Pig is a script-based interactive execution environment that supports Pig Latin, a language for expressing data flows. The Pig Latin programming language allows you to load and process data using a set of operators that transform the data and produce the desired output.

There are two modes in the Pig execution environment:

All scripts are run on a single machine in local mode. HDFS and Hadoop MapReduce are not required.

Hadoop: Also known as the MapReduce model, Hadoop runs all scripts on a single Hadoop cluster.

Pig creates a set of maps and reduces jobs under the hood. The user is relieved of the burden of writing code, compiling it, packaging it, submitting it, and retrieving it. Pig is similar to SQL in the world of RDBMS in many ways.

Pig programs can be run in three ways, all of which are compatible with Hadoop and local mode:

  • Script: A file that contains Pig Latin commands and is identified by the suffix. Pig (for example, file.pig or myscript.pig). Pig interprets the commands and executes them in the correct order.
  • Grunt (command interpreter): Grunt is a command interpreter. Pig Latin can be typed into the grunt command line, and Grunt will run the command for you. This is great for "what if" scenarios and prototyping.
  • Embedded: Pig programs can be run alongside Java programs.
     

The syntax of Pig Latin is exceptionally complex. It includes operators for the following tasks: 

  • Data loading and storage
  • Data in real-time
  • Data filtering
  • Combining and grouping data
  • Sorting information
  • Data combining and slicing
     

Pig Latin also has a lot of different types, expressions, functions, diagnostic operators, macros, and file system commands to choose from.

Sqoop

Sqoop (SQL-to-Hadoop) is a tool that allows you to extract data from non-Hadoop data stores, transform it into a Hadoop-friendly format, and load it into HDFS. The acronym ETL stands for Extract, Transform, and Load. While getting data into Hadoop is critical for MapReduce processing, getting data out of Hadoop and into an external data source for other applications is also vital. This is something that Sqoop can do as well.

Sqoop is a command-line interpreter like Pig. Sqoop commands are typed into the interpreter and executed one at a time. 

Sqoop has four key characteristics:

  • Bulk Import: Individual tables or entire databases can be bulk imported into HDFS using Sqoop. The data is saved in the HDFS file system's native directories and files.
     
  • Direct Input: Direct SQL (relational) database import and mapping: Sqoop can import and map SQL (relational) databases directly into Hive and HBase.
     
  • Data interaction: Sqoop can generate Java classes so you can programmatically interact with the data.
     
  • Data export: Using a target table definition based on the target database's specifics, Sqoop can export data directly from HDFS into a relational database.
     

Sqoop analyzes the database you want to import and chooses an appropriate import function for the source data. It then reads the metadata for the table (or database) and creates a class definition for your input requirements after recognizing the input. Instead of doing complete information and then looking for your data, you can force Sqoop to be very selective so that you only get the columns you want before you input. This can help you save a lot of time. A MapReduce job created behind the scenes by Sqoop performs the actual import from the external database to HDFS.

Zookeeper

Although Zookeeper is a simple technology, its features are compelling.

Arguably, creating resilient, fault-tolerant distributed Hadoop applications without it would be difficult, if not impossible. The following are some of Zookeeper's features:

  • Process Synchronization: Zookeeper coordinates the start and stops of multiple nodes in a cluster. This ensures that everything happens in the correct order. Only after an entire process group has been completed can subsequent processing begin.
     
  • Configuration management: Zookeeper can send configuration attributes to any or all of the cluster's nodes. When processing relies on specific resources being available on all nodes, Zookeeper ensures that the configurations are consistent.
     
  • Self-election: The Zookeeper can assign a "leader" role to one of the nodes based on the cluster's makeup. This group's leader/master is in charge of all customer requests. The remaining nodes will elect a new leader if the leader node fails.
     
  • Reliable messaging: Even though Zookeeper workloads are loosely coupled, the distributed application still requires communication between and among the nodes in the cluster.
     

Zookeeper has a publish/subscribe feature that allows you to create a queue. Even if a node fails, this queue ensures that messages are delivered.

Zookeeper is best implemented across racks because it manages groups of nodes for a single distributed application. This differs significantly from the cluster's requirements (within racks). The rationale is straightforward: At a level higher than the cluster itself, Zookeeper must perform, be resilient, and be fault-tolerant.

Frequently Asked Questions

What is the foundation of big data?

The CCC Big Data Foundation Certification (CCC-BDF) is required for anyone working with big data. This certification provides an overview of data mining and its capabilities and an understanding of Big Data and potential data sources that can be used to solve real-world problems.

In the context of big data, what role does Hadoop play?

Apache Hadoop is a free and open-source framework for storing and processing large datasets ranging from gigabytes to petabytes. Rather than storing and processing data on a single large computer, Hadoop allows clustering multiple computers to analyze massive datasets in parallel.

What is the difference between Apache Spark and Hadoop?

It's a top-level Apache project that focuses on parallel data processing across a cluster, but the main difference is that it runs in memory. Unlike Hadoop, which reads and writes files to HDFS, Spark processes data in RAM using the RDD (Resilient Distributed Dataset) concept.

Conclusion

In this article, we have extensively discussed Big data. The Hadoop ecosystem, as well as the commercial distributions that are supported, are constantly evolving. New tools and technologies are introduced, old ones are enhanced, and some are phased out in favor of a (hopefully better) replacement.

The knowledge never stops, have a look at more related articles: Data WarehouseMongoDB, AWS, and many more. To learn more, see Operating SystemUnix File SystemFile System Routing, and File Input/Output.

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