What is the difference between Big Data and Data Analytics
Data has become a part and parcel of every business sector. Hence, to deal with huge data, different tools, techniques, and skills have evolved. Some of the tools and techniques used to manage a large set of data are big data, data analytics, data science, machine learning, and AI. Even though these domains sound similar, they have a major difference. Through this article, we will be highlighting the key differences between big data and data analytics.
Big Data, as the name implies, is a large and complex set of data that is derived from new data sources. The big sets of data are complex in nature and cannot be dealt with using the traditional methods of Data Science techniques. Whereas, the term Data Analytics implies analyzing the raw data to find new patterns or trends for a specific purpose.
What is Big Data?
Big Data is large and complex sets of data that are beyond the capability of traditional tools to collect, organize and analyze to produce meaningful insights. Volume, Variety, and Velocity are the three attributes of Big Data.
Volume refers to the size of data that is generated every day. The size or volume of data is considered while processing big data.
Variety means different sources of data, including structured and unstructured data. Sources of traditional data sets were limited, but with growth in tech, the data comes from a variety of sources; the sources can be in the form of PDFs, emails, videos, etc.
Velocity, as the name suggests, means at what pace data is generated and processed. This characteristic of big data is crucial in processing a large volume of data quickly and efficiently.
The above-mentioned characteristics of big data play a key role in determining the decision of any business by reducing the cost of storing and processing a huge volume of data, thereby, helping the business to speed up the work and produce new products and services for the customers.
What is Data Analytics?
Data Analytics is the method to process and analyze raw data to get coherent information. Data Analytics helps in making informed decisions, provides better customer information and interest, and gives actionable insights for better marketing.
Some of the popular Data Analytics tools are Python, R, Tableau, Apache Spark, Statistical Analysis Software, etc. Nowadays, Data Analytics applications are used in several sectors such as healthcare, logistics, retail, banking, manufacturing, and others.
Big Data vs Data Analytics: Key Differences
| Characters | Big Data | Data Analytics |
|---|---|---|
| Programming Knowledge Required | The domain of Big Data demands certain programming skills like Data Mining, Data Visualization, and Machine Learning among others. | To get into the field of data analytics one does not need to master an advanced level of programming skills. Just a basic knowledge of Python, R, and other query languages is sufficient. |
| Coding Language | Python is the most popular coding language for Big Data, however, Java, R, Scala, and SQL are also used as coding languages for Big Data projects. | SQL is the most common coding language along with Python and R. |
| Scope | Big Data works at a macro level and analyzes massive data to improve the decision-making process. | The use of Data Analytics is restricted to find specific information in any business. |
| Goal | The goal of Big Data is to collect a huge volume of data and analyze it to get valuable insights. | Data Analytics aims to collect raw data and process and find out new patterns and trends for business purposes. |
| Tools | Some of the well-known Big Data tools are Hadoop, Storm, HPCC, Cassandra, Stats iQ, CouchDB, Pentaho and Flink. | Popular Data Analytics tools are Apache Spark, Excel, Python, R, TensorFlow, Amazon QuickSight, Amazon Kinesis, etc. |
Big Data vs Data Analytics: Job Profiles
Big Data jobs require experts but the flow of skilled workers is very low. In fact, big companies are looking for professionals to deal with a huge volume of data and are providing various roles with a handsome package. Some of the job profiles to make a career in Big Data are mentioned below.
| Job Profiles | Job Description | Average Annual Salary |
|---|---|---|
| Big Data Analyst | A Big Data Analyst studies the market and analyses data to help and guide and make future business decisions. | INR 4 - 15 LPA |
| Big Data Developer | Big Data Developers code and program Hadoop applications using different databases and programming languages such as Java, C++, Ruby, and more | INR 4 - 14 LPA |
| BI Specialist | BI Specialists manage data retrieval and analyse it for an organization. The duties of a BI Specialist include organizing data points, analyzing data to determine an organization’s need, and communicating the results between upper management and the IT department. | INR 6.5 - 24 LPA |
| Data Scientist | Data Scientists extract, analyze, and interpret large sets of data to work closely with the decision making of an organisation. Data Scientists use algorithms, data structures, AI, ML, etc to present data. | INR 10 - 25 LPA |
Data Analytics jobs demand highly professionals to deal with critical tasks. Due to its critical work, the demand is high. In fact, the package and the perks that come with it are quite decent. The Data Analytics industry provides the following job roles.
| Job Profiles | Job Description | Average Annual Salary |
|---|---|---|
| Data Analyst | Data Analysts use statistical tools to solve problems pertaining to a business’s customers. Data Analysts use raw data and turn them into an insight to help make better decisions. | INR 7 - 15 LPA |
| Data Architect | Data Architects maintain a company’s database and identify structural and instal solutions. Data Architects are responsible to create database solutions, evaluate requirements, and prepare design reports. | INR 8 - 21 LPA |
| Machine Learning Engineer | ML Engineers build Artificial Intelligence systems to handle and leverage huge data sets to generate algorithms and make better predictions. | INR 7.5 - 22 LPA |
| Database Administrator | A Database Administrator is responsible to improve the performance, integrity and security of a database. DBAs stay updated with policies and procedures to protect any kind of data loss. | INR 8 - 23 LPA |
Responsibilities in Big Data vs Data Analytics
Big Data responsibilities depend on the job profile of the person. But the basic responsibilities in the field of Big Data are mentioned below.
- They are responsible for analyzing and monitoring real-time situations.
- Extracting results from sets of data to make better decisions.
Some of the basic responsibilities in the field of Data Analytics are
- Collecting the data, and processing and summarizing it to make the data understandable.
- Analyze new patterns and trends.
- Extracting valuable information.
Big Data vs Data Analytics: Core Skills Required
Both Big Data and Data Analytics require core skills to carry out the tasks. Big Data includes the following core skills to get a good job:
- Programming languages
- Data Mining
- Big Data Tools
Any person willing to get into the Data Analytics industry should gain the following skills:
- Data Visualization
- Data Wrangling
- Mathematical and Statistical knowledge
- Programming knowledge like R and Python
Conclusion
In recent times, data generation has grown exponentially. Big tech companies, healthcare, and businesses are generating huge data which require experts to handle. Hence, different methods have been taken up to deal with real-world issues; for which Big Data, Data Analytics, and others are aiding humans to make their work easy. Conclusively, both Big Data and Data Analytics are used by professionals to handle enormous data sets and get valuable information out of those. Since the world is getting more data-centric and growing exceptionally, both Big Data and Data Analytics are equally important in the present day.
.jpeg?tr=w-306,h-153,c-force)
