Which is best Big Data or Data Science: Overview & Comparison
Big data and data science are the two important hot technologies that create tremendous job opportunities for freshers and working professionals. Data plays a crucial role in global businesses as it enables data-driven decision-making for an accurate business move. Demand is increasing exponentially for professionals with the right skills and hands-on exposure to handling big data and data science processing in top companies. In this blog, we are explaining big data and data science to make you understand them clearly for choosing the best one that satisfies your career goal.
What are big data and data science?
Big Data is the large and complex collection of data while data science is the multidisciplinary field that focuses to produce better insights from the raw data. Both the technologies have similar learning benefits in two separate domains. Big data will be used to store large sets of data and data science techniques are used to extract meaningful insights from datasets.
Overview of Big Data
Big data refers to large data sets that combined with the complexity and evolving nature of datasets that has enabled them to utilize the capacities and facilities of traditional data management tools. Big data is efficiently handled by data warehouses and data lakes to utilize the potential of traditional databases. Stock market data, social media data, sporting events and games, and scientific and research data are considered big data.
The 5 Vs of Big Data
There are five major characteristics of Big Data and they are volume, variety, velocity, veracity, and value.
Volume: Big data is immeasurable that utilizes normal data storage and processing methods.
Variety: Big data contains various kinds of data from tabular databases to images and audio data irrespective of data structure.
Velocity: Big data is continuously generated and added faster to the datasets frequently.
Veracity: Big data should be managed and processed properly as its enormity and complexity.
Value: The worthiness of big data is evaluated according to the unique business goals.
Types of Big Data
Big data is categorized into three types and they are structured data, semi-structured data, and unstructured data.
Structured data is the data set with a specific structure. The structured data set will be processed more easily compared to other data types. It can be easily identified by users and the best example is a distributed RDBMS that contains data with organized tables.
Semi-structured data is the type of data that doesn’t have any particular structure but remains some kind of observable structure like grouping and organized hierarchy. Some examples of the semi-structured data types are XML, emails, and web pages.
Unstructured data contains data that doesn’t have any similar schema or structure. It is the most common type of data that deals efficiently with big data. Examples of the unstructured data types are text, pictures, video, audio, and so on.
Structured data is difficult and affordable to collect and process, limited insights, purpose-driven, active participation, and transparency with privacy. Unstructured data is easy to collect, costlier, has infinite insights, is reusable, requires presence, and lacks transparency and privacy.
Big Data Systems and Tools
There are numerous solutions available to store and process the data sets for managing big data. Cloud providers like Azure, AWS, and GCP are offering data warehousing and data lake implementations. They are AWS Redshift, GCP BigQuery, Azure SQL Data Warehouse, Azure Synapse Analytics, and Azure Data Lake. Big data is handled by some specialized providers like Snowflake, Apache Hadoop, Databricks, Openrefine, Apache Storm, etc that offers robust big data solutions for any kind of hardware and commodity hardware.
Overview of Data Science
Data Science is an integrative approach that extracts insights from data by combining methods like scientific methods, maths and statistics, programming, advanced analytics, machine learning, artificial intelligence, and deep learning. The main purpose of data science is to deal with everything like analyzing complex data, creating new analytics algorithms, generating tools for processing and purification, and developing powerful visualizations.
Tools and Technologies used in Data Science
Data science utilizes programming languages like R, Python, and Julia for creating new algorithms, ML methods, and AI processes for managing big data platforms like Apache Spark and Apache Hadoop. Data processing and purification tools are Data ladder, Winpure, and data visualization tools like Microsoft power platform, Google data studio, etc. Data visualization frameworks like Tableau, PowerBI, and frameworks like Matplotlib and Plotly are considered data science tools.
Comparison between Big Data and Data Science
| Data Science | Big Data |
| Data Science is an area that is about the collection, processing, analyzing, and utilizing of data with various operations that are more conceptual. | Big data is a technique to collect, maintains, and process huge information and it is about extracting the crucial and valuable information from a big amount of the data. |
| Data science is a field of study like computer science, applied mathematics, and applied statistics. | The Big data technique is to track and discover trends of complicated datasets. |
| The purpose of big data is to make data more significant and usable by extracting only important insights from the huge data within historical traditional aspects. | The purpose of data is to develop data-dominant products for a progress |
| Data Science is using tools like SAS, R, Python, etc. | Big data is utilizing tools like Hadoop, Flink, Spark, etc. |
| Data Science is a superset of big data that contains data scrapping, visualization, cleaning, statistics, and so on. | Big data is a subset of data science that includes mining activities that is a pipeline of data science. |
| Data science is used for scientific purposes | Big data is used for business purposes and customer satisfaction |
| Data Science focuses on the science of data. | Big data involves the process of handling a large and voluminous amount of data. |
Conclusion
Big data refers large and complex collection of data while data science aims to produce broader insights for better decision-making. The learning of data science and big data will be the right choice for the learners who are freshers and working professionals. It makes learners become competent in processing data using Hadoop for big data and R or Python for Data Science processing. Learn the best Data Science Training in Chennai at SLA to discover the wide range of opportunities in Big Data and Data Science domains.