Introduction to Distributed Systems (Part -1)

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.

Conclusion:

• 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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