Big Data Vs Data Science- What Is The Difference?

Big Data vs Data Science

Data is everywhere. The amount of digital data that exists is rapidly increasing, doubling every two years, and changing the manner in which we live. Information is all over the place. Till the year 2020,about1,7 megabytes of new information will be generated every second for every human being. 

Here we will differentiate, big data and data science with various parameters. Before we start Big data vs Data science, let us see each one in detail.

What Is Big Data?

Big data is a humongous volume of data which cannot be effectively processed with the traditional apps that exist. The processing of Big Data starts with the raw data that isn’t aggregated and is generally difficult to store in the memory of a single computer. A popular expression that is utilized to describe massive volumes of data, both unstructured and structured, Big Data inundated a business on an everyday premise. Big Data is something that can be used to examine insights that can prompt better decisions and strategic business moves. By the definition- Big data is high-volume and high velocity or  high variety information asset that demand cost-effective, innovative forms of information processing that allow enhanced insights, decision making and process automation.

What Is Data Science?

Managing unstructured and structured data, Data Science is a field that involves all that is related to data cleansing, preparation, and analysis. Data science is a combination of programming, problem-solving, statistics, mathematics, the ability to look at things in a different way, getting data in ingenious ways, data cleaning, aligning and preparing. In simple words, it is an umbrella of techniques that use to extract insights and information from data.

Big Data Vs Data Science- What Is The Difference?

1. Perception-

Generally, big data is generated from multiple data sources and so it can be called a collective dataset. As the data set is made with data from multiple sources, each data type and data format is possible to add in big data. Big data can be Structured or unstructured or semi-structured datasets. Basically, a company or organization creates real time that insures the current status of an event and encourages them to work in a way to achieve the goal.

Data science includes multiple tools and techniques to analyse the dataset. Main goal of data science is to simplify the complexity of big data. Basically it is a concept made to reduce the difficulties in taking decisions for an organization. Considering big data vs data science, big data are unstructured and need to be simplified, whereas data science is a quick solution to it.

2. Platforms-

Big data is produced from each conceivable history that can be made in an event. The operation of producing data is started on platforms like DOMO, Hortonworks, Cloudera, Microsoft Machine Learning Server, Vertica, Kofax insight, AgileOne and so on. 

Data science works for the improvement of an organization through data analysis, process, preparation, and so on. Knowing the use and importance of data science, scientists started to work on it for the creation of detailed and accurate data science platform. After some attempts, many platforms are created and those are MATLAB, TIBCO statistica, Anaconda, H2O, R-Studio, Databricks Unified Analytics platforms and so on.

3. Tools-

Big data was introduced in 2005 and since then there has been developed many new and interesting tools that process data. These tools are Apache Spark, Apache Cassandra that work for SQL, graph processing, scalability etc. Hadoop by Apache can distributes huge amounts of data on different computers. 

Data science eases the decision making process for companies. Data scientists have developed the topic data science with different tools. Python programming, R programming, Tableau, Excel are some common examples with what data science can be explained. Statistical explanation and exponential development curves with the probability of an event can also be appeared with these tools.

4. Data Filtering-

Big data is expanding at a higher rate and never stops growing. But, it can assist with identifying the data which are important and which are less important. And it is called a data cleansing process. Dataset consists of huge data so it becomes so difficult to find out the detected data and analyze it by ownself. Although it is a harder process, big data helps in data cleaning through error data detection.

Data science is used to find the error and clean it. When data science is applied to big data, it helps to process, analyze and get the final result. From this, the summary of big data comes out and unwanted data remains  untouched. This remaining data will not be needed in future and it can be cleaned. In this way data science helps to keep internet clean by removing unnecessary data and finding out errors.

5. Relation With Cloud Computing-

The goal of big data is to serve as CEO and achieve business success whereas the goal of cloud computing is to serve as CIO in convenient and accurate IT solutions. When big data and cloud computing work together, business and IT-related success come rapidly and the efficiency becomes more rapid and smooth.  Big data can be stored on a cloud because cloud computing provides more storage and big data needs storage to get stored too.

When you work with data science, to find out accurate results, there is a need to apply algorithms. Clouds are advantageous with high computational needs and data storage. Data science requires more storage to store the analyzed data. Cloud computing is an easy solution for this.

Applications Of Big Data-

  1. Financial services
  2. Telecommunications
  3. Optimizing business processes
  4. Performance optimization
  5. Health and sports
  6. Improving commerce
  7. Research and Development
  8. Security and law enforcement

Applications Of Data Science-

  1. Internet Search
  2. Digital advertisements
  3. Search recommenders
  4. Speech/ Image recognition
  5. Fraud, risk detection
  6. Web development

Final Words-

Big data and data science is developing rapidly so there is need to know the difference between these two before you go for the one. The above points can give you the clear idea of BIg data and Data Science. If you are still confused to choose the one for your organization, consult with solace experts. We are here to help you through consultation and development. Connect with Solace and get a free quote for big data development. We will be happy to help you.

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