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Table of contents
Distributed Computing  
Introduction to Hadoop 
What is Hadoop? 
Working of Hadoop 
Frequently Asked Questions
What is Data Mining?
What is big data?
What do you understand about data warehouses?
Last Updated: Mar 27, 2024

Getting the performance Right

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Having a faster computer isn't enough to assure adequate performance when dealing with large amounts of data. You'll need to be able to spread your big data service's components over several nodes. 

  • A node is an element housed inside a cluster of systems or within a rack in distributed computing. 
  • A node usually consists of a CPU, memory, and a disc. On the other hand, a node can be a blade CPU and memory that is dependent on adjacent storage within a rack.



  • These nodes are frequently grouped in a big data environment to offer scale. For instance, you may begin with an extensive data analysis and gradually add more data sources. 
  • An organization simply adds additional nodes to a cluster to accommodate the expansion, allowing it to scale out to meet new demands. However, increasing the number of nodes in the cluster isn't enough. Instead, the ability to transfer parts of the extensive data analysis to diverse physical contexts is critical. Where you assign these tasks and how you handle them determines whether they are successful or not.

Distributed Computing  

There is no one distributed computing model today, and computer resources can be dispersed in a number of ways. You can, for example, distribute a group of programs on the same physical server and use messaging services to allow them to communicate and transmit data amongst each other. To solve the same problem, numerous different systems or servers can be combined, each with its own memory.


To reach the speed of analysis necessary in some complicated circumstances, you may need to run many algorithms in parallel, even inside the same cluster. Why would you use the same rack to run several big data algorithms simultaneously? The closer the function distributions are, the faster they can run. Although you may deploy big data analysis across networks to use available capacity, you must do it depending on performance needs. Processing speed takes a back place in some cases. In other cases, though, immediate findings are required. You'll want to make sure that the networking functions are close to one other in this instance. The big data environment should be suited for this sort of analytics activity.

  • As a result, scalability is critical to successfully implementing big data.
  • Although it is theoretically conceivable to run a big data environment within a single large environment, this is not feasible. Look at cloud scalability and grasp both the criteria and the technique to understand the demands for scalability in big data. 
  • Big data, like cloud computing, need fast networks and low-cost hardware clusters that can be stacked in racks to boost performance. 
  • Software automation allows for dynamic scaling and load balancing in these clusters.


MapReduce's concept and implementations illustrate how distributed computing can make massive data operationally transparent while also improving performance. In essence, we are at one of computing's rare turning points, where technical concepts collide at the right time to tackle precisely the right challenges. Data management is evolving dramatically due to the combination of distributed computing, enhanced hardware systems, and practical solutions like MapReduce and Hadoop.

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Introduction to Hadoop 

What is Hadoop? 

Apache Hadoop is a free and open-source platform for storing and processing huge datasets ranging in size from gigabytes to petabytes. Hadoop allows clustering several computers to analyze big datasets in parallel, rather than requiring a single large computer to store and analyze the data.

Hadoop is made up of four major modules:

  • HDFS (Hadoop Distributed File System) is a distributed file system that runs on low-end or basic hardware. HDFS outperforms traditional file systems in terms of data performance, fault tolerance, and native support for huge datasets.
  • YARN (Yet Another Resource Negotiator) is a tool for managing and monitoring cluster nodes and resource utilization. It keeps track of jobs and tasks.
  • MapReduce is a framework that aids programs with parallel data processing. The map job turns input data into a dataset that can be computed using key-value pairs. Reduce tasks consume the output of the map task in order to aggregate it and produce the required result.
  • Hadoop Common — Provides a set of shared Java libraries that may be used by all Hadoop modules.

Working of Hadoop 

Hadoop makes it easy to make use of all of a cluster server's storage and processing capability, as well as to run distributed operations on massive volumes of data. Hadoop provides the foundation for the development of other services and applications.

By connecting to the NameNode via an API call, applications that collect data in multiple formats can place data into the Hadoop cluster. The NameNode, which is duplicated among DataNodes, keeps track of the file directory structure and placement of "chunks" for each file. Provide a MapReduce job made up of several maps and reduce jobs that run on the data in HDFS scattered over the DataNodes to run a job to query the data. Reducers operate on each node to collect and organize the final output, and map tasks are done on each node against the input files supplied.

Because of its extensibility, the Hadoop ecosystem has evolved tremendously over time. The Hadoop ecosystem now comprises a variety of tools and applications for collecting, storing, processing, analyzing, and managing large amounts of data. The following are some of the most popular applications

  • Spark: Spark is a widely-used open source distributed processing engine for big data applications. Apache Spark provides general batch processing, streaming analytics, machine learning, graph databases, and ad hoc queries, and it leverages in-memory caching and optimized execution for fast performance.
  • Presto: Presto is a distributed SQL query engine that is optimized for low-latency, ad-hoc data analysis. Complex queries, aggregations, joins, and window functions are all supported by the ANSI SQL standard. Presto can process data from a variety of sources, including the Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3).
  • Hive — provides a SQL interface for leveraging Hadoop MapReduce, allowing for massive-scale analytics as well as distributed and fault-tolerant data warehousing.
  • HBase: HBase is a non-relational, versioned open-source database that works on Amazon S3 (through EMRFS) or the Hadoop Distributed File System (HDFS). HBase is a massively scalable, distributed big data store designed for real-time access to tables with billions of rows and millions of columns in a random, rigorously consistent manner.
  • Zeppelin: It is an interactive notebook that allows you to explore data in real-time.

Frequently Asked Questions

What is Data Mining?

It includes looking for and detecting hidden, relevant, and possibly valuable patterns in massive data sets, also known as KDD (Knowledge Discovery in Database). The discovery of previously discovered links among data is a crucial objective of data mining. Data mining may extract insights utilized for marketing, fraud detection, and scientific discoveries.

What is big data?

Big Data is a massive collection of data that continues to increase dramatically over time. It is a data set that is so huge and complicated that no standard data management technologies can effectively store or process it. Big data is similar to regular data, except it is much larger.

What do you understand about data warehouses?

Compiling and arranging data from diverse sources into a single database is required to provide significant business insights. Data is cleansed, merged, and aggregated in a data warehouse to assist management decision-making. A data warehouse may hold object-oriented, integrated, time-varying, and nonvolatile data.


This article discussed getting the performance right and how performance is needed for scalability. 

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