Introduction to Distributed Systems (Part -1)
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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