1. Big Data: Big data refers to the large volume of data and also the data is increasing with a rapid speed with respect to time. It includes structured and unstructured and semi-structured data which is so large and complex and it cant not be managed by any traditional data management tool. Specialized big data management tools are required to store and process the data. Volume, velocity, and variety represents the primary characteristics of big data. Stock exchanges, Data warehouses, Sensors, social media sites, jet engines, etc are the different sources of Big data.
Application of Big data :
- Big Data for Financial Services
- Big Data in Communications
- Communications, Media and Entertainment
- Big Data for Retail
- Banking and Securities
Benefits of Big Data :
- Diversify revenue streams
- Big Data is Secure
- Authoritive and Actionable
- Product price optimization
- Greater innovations
2. Data Analytics : Data Analytics refers to the process of analyzing the raw data and finding out conclusions about that information. It helps in taking raw data and uncovering patterns by examining it to extract valuable insights from it. The aim behind data analytics is to enhance productivity and business gain. It helps companies to better understand their customers, planning strategies accordingly and develop products. Descriptive, Diagnostic, Predictive, and Prescriptive are the four basic types of data analytics.
Application of Data Analytics:
- Healthcare
- For travelling
- Gaming
- Energy Managementand aRisk detection and management
Benefits of Data Analytics :
- Improved Performance
- Better decision making
- Maintaining quality and consistency
- Data-driven marketing
- Real-time forecasting and monitoring
How does big data fit into data analysis solutions?
- Big data plays a crucial role in data analysis solutions by providing organizations with large amounts of data that can be used to uncover insights and support decision-making.
- Data analytics solutions, such as Hadoop, Spark, and NoSQL databases, are specifically designed to handle big data and can be used to process, store, and analyze large amounts of data in a distributed and parallel manner.
- Additionally, big data can be integrated with other data sources such as structured data, semi-structured data, and unstructured data, to form a holistic view of the organization’s data landscape, which can lead to more accurate predictions, better decision-making, and more effective outcomes.
- In summary, big data is a crucial input for data analysis solutions, and data analytics solutions are specifically designed to handle big data and extract valuable insights from it.
Difference between Big Data and Data Analytics :
| S.No. | BIG DATA | DATA ANALYTICS |
|---|---|---|
| 01. | Big data refers to a large volume of data and also the data is increasing at, modeling rapid speed with respect to time. | Data Analytics refers to the process of analyzing the raw data and finding out conclusions about that information. |
| 02. | Big data includes Structured, Unstructured and Semi-structured the three types of data. | Descriptive, Diagnostic, Predictive, Prescriptive are the four basic types of data analytics. |
| 03. | The purpose of big data is to store huge volume of data and to process it. | The purpose of data analytics is to analyze the raw data and find out insights for the information. |
| 04. | Parallel computing and other complex automation tools are used to handle big data. | Predictive and statistical modelling with relatively simple tools used to handle data analytics. |
| 05. | Big data operations are handled by big data professionals. | Data analytics is performed by skilled data analysts. |
| 06. | Big data analysts need the knowledge of programming, NoSQL databases, distributed systems, and frameworks. | Data Analysts need the knowledge of programming, statistics, and mathematics. |
| 07. | Big data is mainly found in financial services, Media and Entertainment, communication, Banking, information technology, retail, etc. | Data analytics is mainly used in business for risk detection and management, science, travelling, health care, Gaming, energy management, and information technology. |
| 08. | It supports in dealing with huge volumes of data. | It supports in examining raw data and recognizing useful information. |
| 09. | It is considered as the first step as first big data generated and then stored. | It is considered as second step as it performs analysis on the large data sets. |
| 10. | Some of the big data tools are Apache Hadoop, Cloudera Distribution for Hadoop, Cassandra, MongoDB etc. | Some of the data analytics tools are Tableau Public, Python, Apache Spark, Excel, RapidMiner, KNIME etc. |