Introduction to Distributed Systems (Part -1)

Accredian Publication
4 min readMar 10, 2022


by Pronay Ghosh and Hiren Rupchandani

A distributed system is a computing environment in which diverse components are dispersed across a network of computers (or other computing devices).

  • These devices split up the work and coordinated their efforts to complete the task more quickly than if it had been assigned to a single device.
  • Because of this a bulk amount of data is generated from which large-scale business decisions are made.

Why Is Data So Important?

  • Terms like data and quantitative analysis may be frightening if you work in human services because you despise math.
  • Don’t be frightened! Data does not have to be difficult to understand.

Simply said, data is information that you collect to help you make better decisions and develop a better plan for your company.

  • The following is a list of reasons why data is important.
  • We will also consider what you can do with it, and how it pertains to the field of human services.

1. With data, we can make informed decisions:

  • Knowledge is equal to data.
  • Anecdotal evidence, assumptions, or abstract observation provide incontrovertible evidence.
  • Taking action based on an inaccurate conclusion may result in a waste of resources.

2. Obtain the Results You Desire

  • Organizations can use data to assess the effectiveness of a strategy.
  • When strategies are put in place to overcome a difficulty, gathering data allows you to see how effectively your solution is working.
  • It also says whether it needs to be altered or changed in the long run.

3. Back Up Your Claims

  • Data is an important part of systems advocacy.
  • Data will aid in presenting a compelling case for system change.
  • Using data to illustrate your point will allow you to demonstrate why changes are needed.
  • Whether you’re pushing for additional money from public or private sources or making the case for regulatory reforms.

What is Big Data?

Big Data is a massive collection of data that continues to grow dramatically over time.

  • It is a data set that is so huge and complicated that no typical data management technologies can effectively store or process it.
  • Big data is similar to regular data, but it is much larger.
  • However, as we can see as everyday data usage grows so grows the challenges.
  • Hence, we will list down the top 3 challenges with Big Data.

Common Problems with Big Data

1. Professionals with insufficient knowledge:

  • Companies require trained data specialists to run these latest technologies and massive data tools.
  • To work with the technologies and make sense of massive data sets.
  • These experts will include data scientists, data analysts, and data engineers.
  • A lack of enormous Data professionals is one of the Big Data Challenges that any company faces.
  • This is frequently due to the fact that data processing tools have advanced rapidly, but most experts have not.
  • To close the gap, concrete efforts must be taken.

2. Massive Data is not properly understood:

  • Companies fail to succeed in their Big Data projects due to a lack of understanding.
  • Employees may not understand what data is, how it is stored, processed, and where it comes from.
  • Others may not have a clear picture of what’s going on, even if data professionals do.
  • Employees who do not understand the need for knowledge storage, for example, may not be able to preserve a backup of sensitive material.
  • They were unable to correctly save data in databases.
  • As a result, when this critical information is needed, it is difficult to locate.

3. When it comes to choosing a Big Data tool, there is a lot of confusion:

  • When it comes to selecting the simplest tool for huge projects, businesses are frequently perplexed.
  • Data storage and analysis Is HBase or Cassandra the easiest data storage technology? Is Hadoop MapReduce sufficient, or will Spark be a vastly superior data analytics and storage solution? Companies are bothered by these problems, and they are sometimes unable to find answers.
  • They are prone to making poor selections and utilizing ineffective technology.
  • As a result, resources such as money, time, effort, and work hours are squandered.


  • So far in this article, we covered an overview of what is Distributed Systems.
  • In the next article, we will learn in-depth about how does a distributive system works, and then we will dive into the Foundations of Hadoop.

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