We all store and use data. Data is basically facts or figures or information which is collected and stored in the computer or used by it. Over the years, and particularly in the last few years, there have been tremendous technological advancements. Multiple devices like smartphones have been introduced that both consume and produce data. There is a data revolution around the world. With more and more electronic devices being used, there is a huge increase in the amount of data being produced by the users of these devices through the internet. The amount of data being produced and the rate at which it is growing is enormous. The huge quantity of data that is being produced at a fierce speed from all types of electronic devices is now termed Big Data. But it is not practical to store such a large amount of data on the systems that have been traditionally used over the last so many years. Traditional processors do not have the needed power to compute such extensive data. A more complex structure having multiple components that can handle different operations is required to handle such huge data. So, this has resulted in the development of a software platform called Hadoop. 

What is Hadoop?

Hadoop is an open-source framework used for storing huge amounts of data. It manages Big Data by using distributed storage and parallel processing. Hadoop runs the application on clusters of commodity software. It has a large facility for storing data and vast computational power. Hadoop's main advantage is its ability to handle different types of data like structured data, unstructured data, and semistructured data in huge volumes. This makes the collection, processing, and analysis of data a lot easier and more flexible, a characteristic that traditional processors did not have. Industries and organizations that are required to handle a large amount of data or data sets that are sensitive and need to be handled efficiently are using Hadoop and benefiting from it. 

Hadoop ecosystem and its components?

Let us first see what the Hadoop ecosystem is. The need for a framework having several components to handle various operations for handling Big Data has been fulfilled by the Hadoop ecosystem. Hadoop consists of many components. So, simply speaking, Hadoop together with its components is called the Hadoop ecosystem. It is basically a framework or platform that provides many services related to solving the problems of Big Data. Its services include ingesting, storing, analyzing, and maintaining the data inside it. The Hadoop ecosystem comprises Apache Open Source projects and many other commercial tools and solutions. 

What are the main components of the Hadoop ecosystem?

There are multiple components of the Hadoop ecosystem. However, it has four main components.  They are Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop common. There are other components and tools also but most of them support these four major components. All these components combine to provide the ingestion, storage, analysis, and maintenance services that we discussed earlier. 

Now that we have learned about Hadoop, the Hadoop ecosystem, let us know about the different components of the Hadoop ecosystem in detail. There are so many components in the Hadoop ecosystem that it may become difficult to remember the role of each component and understand it. Therefore, let us discuss each of these tools in detail for a clear understanding.

Hadoop ecosystem components

As we mentioned earlier, there are four major components of the Hadoop ecosystem. But there are other components. And all these components collectively provide services and solutions for Big Data. Here they are.

1. Hadoop Distributed File System (HDFS):

It is the primary and the most important component of the Hadoop ecosystem. It is the storage component of the Hadoop ecosystem that stores data in a distributed manner in the form of files. The file system is based on java and provides scalable, reliable, and cost-effective data storage. It runs on commodity hardware. It has a master-slave type of architecture and there are further two components of HDFS which are the Name node and the Data node. There is only one name node but there can be multiple data nodes. Let us know more about these two components of HDFS.

Name node: 

The name node is also called the Master node. Actual data or data set is not stored here. In fact, it is a Daemon that maintains and operates all Data nodes. All the metadata like multiple blocks and their locations are stored here along with the identification of which rack has stored which Data node and the other information. The Name node includes files and directories. Moreover, all changes to the metadata are recorded here. The name node keeps on receiving block reports from the data nodes in the cluster to make sure that they are working fine.

Data node:

 It is also called the slave node and stores data blocks. There is more than one for each cluster. The main job of the data node is to read, write, process, and replicate the data. It, therefore, retrieves the data whenever required. Basically, the data nodes work as storage devices. The data node's replica block has two files on the file system. One for data and the other for recording metadata of blocks. The data node's work is to create a block replica, its deletion, and replication as per the instructions given by the name node. 

2. Hadoop MapReduce:

It is the processing unit of Hadoop. In the Hadoop ecosystem, MapReduce works as a basic component because it enables the logic of processing. In simple terms, it is a software framework to facilitate writing applications that process large data sets of both structured and unstructured data in a Hadoop environment in the files stored in HDPS, by distributed and parallel algorithms. In MapReduce, a single task is divided into multiple tasks which are processed on different machines. In MapReduce, the processing is done at the data node and the results are sent to the name node. Since it has a parallel processing feature, MapReduce helps in analyzing Big data by using multiple machines in the same cluster. 

There are two functions or two phases of processing data in MapReduce - Map and Reduce. The Map function takes data, filters it, sorts it out, organizes it into groups or clusters, and produces key-value pairs. Key-value pairs are generated when each task of the map then works on a group of data in parallel on different machines. The output of the map function acts as the input for the reduce function. As is evident from its name, the Reduce function takes data from the map function and summarizes the data generated by aggregating it. A good characteristic of MapReduce is its simplicity. Here, applications can be run in any language like C++, Java, and Python. Its scalability is also a plus point as it can process data in petabytes. Moreover, problems that may usually take days to solve, can be solved in hours or even minutes through parallel. 

3. Yet Another Resource Negotiator (YARN):

 This is the next component of the Hadoop ecosystem. YARN is its resource management unit. YARN not only manages resources in clusters but also manages the applications over Hadoop. To put it in a simpler way, YARN schedules and allocates resources for the Hadoop system. Data processing engines like real-time streaming and batch processing can handle the data stored on a single platform through YARN. So, it also becomes one of the major components of the Hadoop ecosystem. It is also called the operating system of Hadoop. YARN again has two components namely the Resource manager and the Node manager. The resource manager acts at the level of the cluster and is responsible for operating the master machine. The track of the heartbeats from the node manager is stored here. Node manager, on the other hand, as you might have guessed from its name, works on the node level and runs on slave machines. The node manager also monitors the log management and node health. There is constant communication between the node manager and the resource manager to provide updates. YARN ensures that a single machine is not overburdened and the load is fairly distributed and its scheduling function makes sure that the tasks are scheduled at the right place. YARN has some really beneficial features. The first of which is its flexibility that facilitates custom-built data processing models like interactive and streaming. It also helped in increasing the efficiency of Hadoop since multiple applications run on the same cluster. And last but not least is that the shared operational services provided by it across platforms are stable, reliable, and secure.

4. Apache Pig:

Apache Pig was developed by Yahoo and is a high-level language platform. As a component of the Hadoop ecosystem, pig works on a pig Latin language that is similar to SQL. It is used for querying and analyzing massive datasets that are stored in HDFS. Its work comprises loading the data, applying the required filters, and dumping it in the required format. Pig needs a java runtime environment for running the programs and pig engine works as an execution engine for running pig Latin. It substitutes java programming for MapReduce and automatically produces MapReduce functions. Pig executes the commands and all the work of MapReduce is handled in the background. Pig Latin scripts are translated into MapReduce by Pig. So, MapReduce runs on YARN and processes and stores the data in the HDFS cluster. Let us look at some salient features of Apache Pig. Pig is extensible which means users can build their own functions if they want to execute some special-purpose processing. It also provides opportunities for optimization. The system can optimize automatic execution. Also, Pig can analyze any kind of data, both structured and unstructured. Apache Pig is best used where complex use cases requiring multiple data operations need to be solved. 

5. Hive:

Hive, the next component in the Hadoop ecosystem is built on Hadoop and is an open-source data warehouse system. Its function is to query and analyze the large datasets stored in the Hadoop files. Hive reads and writes large datasets with the help of SQL and an interface. But the language it uses for query is called HQL (Hive Query Language). The three main functions that Hive performs are summarizing the data, querying it, and analyzing it. HQL acts as a translator for translating SQL queries to MapReduce tasks to be carried out on Hadoop. Hive has four main components. They are:

6. Metastore:

As the name suggests, metastore acts as a storage device for metadata. Information like the location and schema of every table is stored here. It tracks the data and replicates it thus working as a backup in case data is lost.

7. Driver:

The driver acts as a controller as it receives instructions from HQL. It builds sessions through which it monitors the progress and lifecycles of the tasks being executed. Whenever the HQL carries out an action, the driver stores the data that is produced as a result of that execution. 

8. Query compiler:

The function of the Query compiler is to convert the HQL query to MapReduce input. A driver is so designed that it can carry out such functions which are necessary for getting HQL output as per the requirement of MapReduce.

9. Hive server:

It provides a thrift interface and JDBC/ODBC server.

10. HBase:

This component of the Hadoop ecosystem runs on top of Hadoop and is a NoSQL database because of this and because it is scalable and distributed, it is considered a Hadoop database. HBase has been built to store structured data in tabular form with millions of rows and columns. You can get real-time access to HBase for reading or writing data on HDFS. Since it supports all types of data, HBase is capable of handling anything within the Hadoop ecosystem. There are two main components of HBase. They are:

HBase Master:

Even though it is not a part of the actual data storage, it still manages all the activities pertaining to load balancing across Regional Servers (we will discuss these next). Its other functions include maintaining and monitoring the Hadoop cluster and performing administration. This provides an interface for creating, deleting, and updating tables. Apart from this, the HBase master handles the DDL operations.

Regional Server:

A worker node, Regional Server handles requests from the customers related to reading, writing, and deleting data. It runs on every node of the Hadoop cluster and runs on HDFS data nodes. 

HBase is designed after Google Big table, a distributed storage system built to handle large data sets, so it provides the capabilities of Google Big table. HBase is written in java language but HBase applications can be written in Avro, REST, and Thrift APIs.

So, we have discussed what is Hadoop ecosystem and its components in detail. This is not an exhaustive list of all the components of the Hadoop ecosystem but we have tried to include all the important components here. Knowing or understanding one or two components within the Hadoop ecosystem would not be sufficient to create a solution. You need to learn about a set of components that can work together to build a solution. Each component of the Hadoop ecosystem is unique in its own way and makes its contribution and performs its functions whenever its turn comes. All these components of the Hadoop ecosystem provide power to the Hadoop functionality. 

Simpliaxis is one of the leading professional certification training providers in the world offering multiple courses related to DATA SCIENCE. We offer numerous DATA SCIENCE related courses such as Data Science with Python Training, Python Django (PD) Certification Training, Introduction to Artificial Intelligence and Machine Learning (AI and ML) Certification Training, Artificial Intelligence (AI) Certification Training, Data Science Training, Big Data Analytics Training, Extreme Programming Practitioner Certification  and much more. Simpliaxis delivers training to both individuals and corporate groups through instructor-led classroom and online virtual sessions.