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Hadoop Ecosystem and Hadoop Components for Crunching Big Data Problems

Hadoop ecosystem

If you are curious about Hadoop and want to learn Hadoop on the right track, you have anchored at the ideal place. In this article, you will gain an understanding of Hadoop concepts. You may have questioned why we need Hadoop? In simple words, the Hadoop ecosystem is the solution to Big Data Problems.

Due to the source of new technologies, devices, and social networking sites, the amount of data produced by humanity is rising rapidly every second. To handle this Big Data, our traditional system (RDBMS) has failed to store the data, hence the Hadoop ecosystem comes into the picture. Apache introduced the Hadoop ecosystem as an open-source framework for storing and analyzing huge volumes of data in a distributed environment. This distributed environment is a cluster of machines that work closely together to give an appearance of a single working machine.

Components of the Hadoop ecosystem.



• Hadoop Distributed File System is the backbone or core component of the Hadoop Ecosystem. HDFS helps store structured, unstructured, and semi-structured data in a large amount. • HDFS works as a single unit, as HDFS creates an abstraction over the resources. HDFS maintains the log files about the metadata.
• Files in HDFS break block-sized chunks. Each file is divided into blocks of 128MB (configurable) and stored on different machines in the cluster.

HDFS follows a master/slave architecture

This architecture has two main components NameNode and DataNode. A single NameNode works as a master and multiple DataNodes perform the role of a slave. Both NameNode and DataNode are capable enough to run on entity machines.


Name node:

• The NameNode is the master node that only has one per cluster node. The NameNode doesn't store the actual data. It contains metadata, just like a log file or a table of content. Therefore, it requires less storage and high computational resources. It's a duty to know where each block belonging to a file is fibbing in the cluster. HDFS handles the file system namespace by performing an operation like opening, renaming, and closing the files. As it is a single node, it may evolve the cause for single-point failure.

Data node:

• DataNode is a slave node and, all data is stored on it, hence it requires more storage resources. The DataNodes are the hardware like laptops and desktops in the distributed environment. And that makes Hadoop solutions cost-effective. Its task is to retrieve the data as and when required.



YARN or Yet Another Resource Manager serves all processing activities by allocating resources and scheduling tasks. YARN allows data stored in HDFS to be processed and run by different data processing engines such as batch processing, stream processing, interactive processing, graph processing, and more.

Following are the components of YARN:

  1. ResourceManager is used in the processing department to receive the processing requests, and then gives the parts of requests to connected NodeManagers consequently, where the actual processing happens.
  2. NodeManagers are installed on every DataNode. It is responsible for executing the tasks on every single DataNode.
  3. Schedulers: According to application resource requirements, Schedulers conduct scheduling algorithms and allocate the resources to the application.
  4. ApplicationsManager simply accepts the job submission, negotiates to containers for executing the application-specific, and monitors the progress.
  5. ApplicationMasters are the daemons that live on DataNode and communicate to containers to execute tasks on each DataNode.

Map Reduce


To handle Big Data, Hadoop relies on the MapReduce algorithm introduced by Google in 2004. Map Reduce is the core component of the Hadoop Ecosystem that helps in the processing, which means it provides the logic of processing. In simple words, MapReduce is a software framework of distributed and parallel algorithms inside the Hadoop environment that helps to write applications that process large data sets. Map Reduce works in a divide-and-conquer manner.

To handle the huge amount of data in a parallel and distributed form, the data follows the following flow from various phases:

Working flow of Map Reduce


- Input reader:
The task of the input reader is to read the coming data. The input data can be in any form, then split into the data blocks of the fitting size means 64 MB to 128 MB. Every data block is associated with a Map function (). Once the input reader reads the data, it generates the corresponding <key, value> pairs, and the keys will not be unique in this case.

- Map function:
The map function () helps to process the coming <key-value> pairs and generates the corresponding output <key-value> pairs. The Map function executes actions like sorting, filtering, and grouping.

- Partition function:
The partition function helps to assign the output of each Map function to the fitting reducer and returns the index of reducers.

- Shuffling and sorting:
The data is shuffled between/within nodes, and it rolls out from the map to get ready to process further reduced functions. The sorting operation performs on input data for the Reduce function. Here, the data comparison happens, using a comparison function and then arranged in a sorted form.

- Reduce function:
Reduce function aggregates and summarizes the result. The Reduce function assigns each unique key and these keys were already arranged in sorted order. The values associated with the keys can iterate the Reduce and generate the corresponding output.

- Output writer:
The Output writer executes once the data flow from all the above phases. The main task of the Output writer is to write the Reduce output to the stable storage.

Apache Pig


The pig was developed to analyze large datasets. Pig helps to overcome the difficulty of writing maps and reduces functions. Pig has two parts: Pig Latin is the Scripting Language like SQL. Pig runtime is the execution engine on which Pig Latin runs. Internally, the code written in Pig converts into MapReduce functions and helps programmers who are not familiar with Java.

Apache Hive


Hive is a distributed data warehouse system developed by Facebook which is used to analyze structured data. Hive is built on the top of Hadoop and operates on the server-side of a cluster. The hive is commonly used by Data Analysts for creating reports. Hive help to perform reading, writing, and managing large data sets in a distributed environment using a SQL-like interface. Hive supports Data Definition Language (DDL), Data Manipulation Language (DML), and User-Defined Functions (UDF). Hive is not capable of handling real-time data and online transaction processing. Hive has two components:

  • Hive Command-Line interface runs HQL commands
  • Java Database Connectivity (JDBC)/(Object Database Connectivity (ODBC) driver establishes connections from data storage.

Apache Mahout


Apache Mahout is an open-source project, providing an environment for creating machine learning applications that are scalable. Apache Mahout performs Recommendation, classification, and clustering machine learning techniques.

Apache Spark


Apache spark framework is used for real-time data analytics in a distributed computing environment. Spark is written in Scala and developed at the University of California, Berkeley, and supports R, SQL, Python, Scala, Java, and more. Apache spark is 100x faster than map Reduce for large-scale data processing as it performs in-memory computations and other optimizations.


Further, Spark has its ecosystem:

  • Spark core: It is an execution engine for Spark and other APIs built on top of it.
  • Spark SQL API: It allows for querying structured data stored in DataFrames or Hive tables.
  • Streaming API: It enables Spark to handle real-time data. It integrates with data sources like Flume, Kafka, and Twitter.
  • MLlib: It is a scalable machine learning library, enabling data science tasks.
  • GraphX is a graph computation engine.



HBase is a Column-based NoSQL database and runs on top of HDFS to handle any kind of data. HBase allows for real-time processing. The HBase is written in Java, and HBase applications can be written in REST, Avro, and Thrift APIs.

Apache Drill


It’s an open-source application to analyze large data sets in distributed environments. It supports different NoSQL databases such as Azure Blob Storage, Google Cloud Storage, HBase, MongoDB, MapR-DB HDFS, MapR-FS, Amazon S3, Swift, NAS, and local files. It follows the ANSI SQL and combines a variety of data stores in a single query. The primary purpose behind Apache Drill is to provide scalability which helps to process petabytes and exabytes of data efficiently.

Apache Zookeeper


Apache Zookeeper is an open-source that coordinates with various services in a distributed environment. It was challenging and time-consuming to coordinate between different services in the Hadoop Ecosystem. To solve these problems, Zookeeper was introduced. Zookeeper preserves the entire time by performing synchronization, configuration maintenance, grouping, and naming.

Apache Oozie


Oozie is a workflow scheduler to schedule Hadoop jobs and bind these together as one logical work. Oozie helps in job scheduling in advance and creating a pipeline of individual jobs to be executed sequentially or in parallel to achieve a sizable task.

Apache Flume


The Flume is a service that helps to ingest streaming unstructured and semi-structured data into HDFS. The flume agent has a source, sink, and channel.

  1. Source: it takes the data from the incoming streamline and stores data in the channel.
  2. Channel: It is local storage and acts as temporary storage between the source data and persistent data in HDFS.
  3. Sink: It collects the data from the channel and commits the data in HDFS permanently.

Apache Sqoop


Sqoop is a structured data ingesting service that can import and export structured data from RDBMS or Enterprise data warehouses to HDFS or vice versa. It performs with nearly all relational databases like MySQL, Postgres, SQLite, and more. When we submit the sqoop command, the main task converts internally into MapReduce tasks that execute over HDFS.

Apache Solr & Lucene


Apache Solr and Apache Lucene are two services. To understand the relationship between both is that of a car and its engine. We are incapable of driving an engine, but you can drive a car. In the same way, Lucene is a programmatic library that may not use as-is, whereas Solr is a complete application that you can use out-of-box.

Apache Ambari


Apache Ambari is an open-source administration tool deployed on top of the Hadoop cluster and is responsible for keeping track of running applications and their status. In other words, consider an open-source web-based management tool that manages, monitors, and provisions the health of Hadoop clusters.

I hope you enjoyed and understand the Hadoop ecosystem and its components to handle big data problems.



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