Introduction to Hadoop Architecture and Its Components

This article was published as a part of the Data Science Blogathon.

Introduction

Hadoop is an open-source, Java-based framework used to store and process large amounts of data. Data is stored on inexpensive asset servers that operate as clusters. Its distributed file system enables processing and tolerance of errors. Developed by Doug Cutting and Michael J. Cafarella, Hadoop uses the MapReduce editing model to quickly store and retrieve data from its nodes. The framework is owned by the Apache Software Foundation and licensed under the Apache License 2.0.

For years, while the processing capacity of application servers has grown exponentially, data information has lagged behind due to its limited capacity and speed. However, today, as many applications produce large amounts of data to be processed, Hadoop plays a key role in providing much-needed conversion to the database.

From a business perspective, too, there are direct and indirect benefits. By using open source technology on less expensive cloud servers (and sometimes on-premises), organizations gain significant cost savings.

Additionally, the ability to collect large amounts of data, as well as the information obtained from compiling this data, results in better business decisions in the real world — such as the ability to focus on the right consumer sector, remove weeds or fix faulty processes, and improve the environment. tasks, provide relevant search results, perform predictable analysis, and more.

How Hadoop Develops In Traditional Archives?

Hadoop solves two important challenges with common information:

1. Power: Hadoop stores large volumes of data.

By using a distributed file system called HDFS (Hadoop Distributed File System), data is fragmented and stored in all clusters of asset servers. Since these assets are built on simple computer hardware configurations, they are economical and easy to grow as data grows.

2. Speed: Hadoop stores and retrieves data quickly.

Hadoop uses the MapReduce editing model to perform the same processing across all data sets. Therefore, when a query is sent to a website, instead of managing the data sequentially, the tasks are split and simultaneously applied to all distributed servers. Finally, the output of all the functions is collected and returned to the application, greatly improving processing speed.

5 Benefits of Hadoop Big Data

With big data and statistics, Hadoop is a life saver. Data collected by people, processes, materials, tools, etc. it is useful only when sensible patterns emerge, which in turn lead to better decisions. Hadoop helps to overcome the challenge of big data size:

1. Resilience

The data stored on any node is duplicated in other clusters nodes. This ensures tolerance for mistakes. When one node goes down, there is always a backup copy of the data available in the collection.

2. Stability

Unlike traditional systems that limit data retention, Hadoop is measurable because it operates in a distributed environment. As demand increases, the set can be easily expanded to include more servers that can end up with more petabytes of data.

3. Low Cost

Since Hadoop is an open source framework, without a license to be purchased, costs are much lower compared to related database programs. The use of inexpensive hardware hardware also works with your harvest to keep the solution economical.

4. Speed

Hadoop Distributed File System, Simultaneous Processing, and MapReduce model make it easy to run complex queries in just seconds.

5. Data Diversity

HDFS has the ability to store different data formats such as those that are not formatted (e.g. videos), built-in (e.g. XML files), and formatted. While storing data, you do not need to verify against the previously defined schema. Instead, data can be discarded in any format. Later, when retrieved, the data is sorted and added to any schema as needed. This provides flexibility for obtaining different data using the same data.

Hadoop Ecosystem: Key Parts

Hadoop is not just a single application, rather it is a platform with various key components that allow the storage and processing of distributed data. These components combine to form the Hadoop ecosystem.

Some of these are key elements, forming the basis of the framework, while others are additional components that bring additional functionality to the Hadoop world.

The main components of Hadoop are:

HDFS: Stored Distributed File System

HDFS is a Hadoop pillar that maintains a distributed file system. It makes it possible to store and duplicate data across multiple servers.

HDFS has NameNode and DataNode. DataNodes are server servers where the data is actually stored. NameNode, on the other hand, contains data metadata that is stored in different locations. The application only works with NameNode, which connects data nodes as required.

YARN: Yet Another Resource Negotiator

YARN stands for Yet Another Resource Negotiator. Manages and organizes resources, and determines what should happen in each data area. The central master node that manages all processing requests is called the Resource Manager. Resource Manager works with Node managers; every slave datanode has its Node Administrator to perform tasks.

MapReduce

MapReduce is the editing model that Google first used to showcase its search functions. It is a concept used to divide data into smaller sets. It works on the basis of two functions – Map () and Minimize () – which analyzes data in a fast and efficient way.

First, Map task groups, filters, and filter multiple data sets in conjunction to produce tuples (key, value pairs). Then, the Minimize function combines data from these tuples to produce the output you want.

Hadoop Ecosystem: Additional Features

The following are a few of the more widely used components in the Hadoop ecosystem.

Hive: Data Warehousing

Hive is a data storage system that helps query large databases in HDFS. Prior to Hive, developers faced the challenge of creating complex MapReduce tasks to query Hadoop data. Hive uses HQL (Hive query Language), which is similar to SQL syntax. With most developers coming from the SQL domain, Hive is easy to find on board.

The advantage of Hive is that the JDBC / ODBC driver acts as a visual interface between the application and HDFS. It exposes the Hadoop file system as tables, converts HQL into MapReduce functions, and vice versa. So while developers and site managers benefit from processing large data sets, they can use simple, common questions to accomplish that. Founded by the Facebook group, Hive is now an open source technology.

Pig: Reduce Mapreduce functions

Pig, launched by Yahoo !, is like Hive in that it eliminates the need to create MapReduce jobs to query HDFS. Similar to HQL, the language used – here, called “Pig Latin” – is closer to SQL. “Pig Latin” is a layer of high-quality data flow language at the top of MapReduce.

Pig also has an operating time area that meets HDFS. Texts in languages ​​such as Java or Python can also be embedded in a pig.

Hive Versus Pig

Although Pig and Hive have similar functions, one can work better than another in different situations.

Pig is useful in the data preparation phase, as it can make complex joining and queries easier. It also works well with various data formats, including semi-structured and unstructured. Pig Latin is close to SQL but also varies from SQL enough to have a learning curve.

The Hive, however, works well with organized data and as a result works best during data storage. Used on the server side of the collection.

Researchers and programmers often use Pig on the client side of the collection, while business intelligence users like data analysts find Hive as a good fit.

Flume: Importing Big Data

Flume is a great data import tool that serves as a courier service between multiple data sources and HDFS. Collects, compiles, and transmits large amounts of distributed data (e.g. log files, events) generated by applications such as social networking sites, IoT applications, and ecommerce sites in HDFS.

Flume is Rich in Features, which:

• It has a distributed structure.

• Ensures reliable data transfer.

• He is tolerant of mistakes.

• Able to collect data in batches or in real time.

• It can be measured horizontally to handle additional traffic, as needed.

Data sources communicate with Flume agents – each agent has a source, channel, and sink. The source collects data from the sender, the channel temporarily stores the data, and finally, the sink transfers the data to the destination, which is the Hadoop server.

Sqoop: Relationship Database

Sqoop (“SQL,” to Hadoop) is another data import tool like Flume. Although Flume operates on informal or less structured data, Sqoop is used to send data from and import data into related databases. Since most business data is stored on a related website, Sqoop is used to import that data into Hadoop for analysts to review.

Webmasters and developers can use the command line interface to export and import data. Sqoop converts these commands into MapReduce format and sends them to HDFS using YARN. Sqoop is also able to tolerate mistakes and perform simultaneous tasks like Flume.

Zookeeper: Integration of Distributed Applications

Zookeeper is a service that integrates distributed applications. In the Hadoop framework, it acts as an administrator with a central register with information about the set of distributed servers they manage. Some of its key functions are:

• Storage of configuration information (shared configuration data status)

• Composition service (naming per server)

• Sync service (handling deadlocks, race status, and data incompatibility)

• Leader selection (selecting leader between servers consistently)

The collection of servers that the Zookeeper service operates in is called “cluster.” The group chooses a leader among the group, with some acting as fans. All writing tasks from clients need to be delivered to the leader, and reading can go directly to any server.

Zookeeper provides high reliability and durability with unsafe synchronization, atomicity, and message editing.

Kafka: Fast Data Transfer

Kafka is a widely used messaging system used by Hadoop for faster data transfer. The Kafka collection consists of a group of servers that act as a liaison between manufacturers and consumers.

In the context of big data, the manufacturer’s example could be a sensor that collects temperature data for transmission back to the server. Buyers are Hadoop servers. Manufacturers publish a message on a topic and consumers draw messages by listening to the topic.

One topic can be divided into sections. All messages with the same key arrive somewhere. The buyer may listen to one or more portions.

By combining messages under one key and making the buyer face certain parts, multiple buyers can listen to the same topic at the same time. Therefore, the title is consistent, which increases system output. Kafka is widely accepted for its speed, durability, and durability.

HBase: Non-Relationship Database

HBase is a column-based, unrelated site that resides over HDFS. One of the challenges with HDFS is that it can only do batch processing. So with simple interaction questions, the data still has to be processed in clusters, leading to high delays.

HBase solves this challenge by allowing single-line queries across all large tables with low latency. It achieves this by using hash tables inside. Made with Google BigTable lines that help access the Google File System (GFS).

HBase is measurable, has support for failure as the site descends, and is good for informal and centralized data. Therefore, it is best to inquire in big data stores for analytical purposes.

Hadoop Challenges

Although Hadoop is widely regarded as the key to enabling large data, there are still some challenges to consider. These challenges stem from the nature of its complex ecosystem and the need for advanced technical knowledge to perform Hadoop operations. However, with the proper integration platform and tools, the complexity is greatly reduced and therefore, makes working with it even easier.

1. A steep learning curve

To inquire about the Hadoop file system, program editors must write MapReduce tasks in Java. This is incorrect, and it involves a steep learning curve. Also, there are many components that make up an ecosystem, and it takes time to get used to them.

2. Different data sets require different methods

There is no ‘one size fits all’ solution in Hadoop. Many of the additional components discussed above are designed to address the gap that needed to be filled.

For example, Hive and Pig provide an easy way to query data sets. Additionally, data entry tools such as Flume and Sqoop help collect data from multiple sources. There are many other components and it takes experience to make the right choice.

3. Limits of Mapreduce

MapReduce is an excellent planning model for processing large data sets. However, it has its limitations.

Its file-based approach, with a lot of reading and writing, is not well suited for real-time, analytical data analysis or repetitive tasks. In such operations, MapReduce does not work well enough, and it leads to high delays. (There are strategies to work on this problem. Apache is another way of closing the MapReduce space.)

4. Data Security

As big data is transferred to the cloud, sensitive data is dumped on Hadoop servers, creating the need to ensure data security. The great ecosystem has so many tools that it is important to ensure that each tool has the right data access rights. There needs to be proper verification, provisioning, data encryption, and regular auditing. Hadoop has the potential to meet this challenge, but it is a matter of skill and caution in practice.

Although many tech giants used the Hadoop components mentioned here, they are still relatively new in the industry. Many challenges arise from this start, but a large solid data integration platform can solve or alleviate all of them.

Hadoop vs Apache Spark

The MapReduce model, despite its many advantages, does not work well in interactive queries and real-time data processing, as it relies on disk writing during each processing phase.

Spark is a data processing engine that solves this challenge through memory retention. Although started out as a Hadoop sub-project, it has its own collection technology.

Usually, Spark is used over HDFS to use the final Hadoop feature. With the processing algorithm, it uses its libraries that support SQL queries, streaming, machine learning, and graphs.

Data scientists make the most of Spark with its lightning-fast, and beautiful, rich APIs that make working with large data sets easier.

Although Spark may seem limited over Hadoop, both can work together. Depending on the need and the type of data set, Hadoop and Spark are compatible. Spark does not have its own file system, so it must rely on HDFS, or other such solutions, in order to be saved.

The real comparison is actually between Spark processing and the MapReduce model. If RAM is a barrier, as well as night operations, MapReduce is a good fit.

Frequently Asked Questions

Conclusion

Hadoop is a software framework that can be used to process large amounts of data quickly. The framework contains Hadoop Common, Map-Reduce algorithm, Yet Another Resource Negotiator, and Hadoop Distributed File System. It differs in many ways from the relative comparable website. It should be decided basically what is the best way to process and store data.

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Chetan

Data Analyst who love to drive insights by visualizing the data and extracting the knowledge from it. Automating various tasks using python & builds Real time Dashboard's using tech like React and node.js. Capable of Creaking complex SQL queries to fetch the accurate data.