Evolution of Big Data and its Impact on Database Management Systems

Evolution of Big Data:

If we see the last few decades, we can analyze that Big Data technology has gained so much growth. There are a lot of milestones in the evolution of Big Data which are described below:

1. Data Warehousing:
   In the 1990s, data warehousing emerged as a solution to store and analyze large volumes of structured data.
2. Hadoop:
   Hadoop was introduced in 2006 by Doug Cutting and Mike Cafarella. Distributed storage medium and large data processing are provided by Hadoop, and it is an open-source framework.
3. NoSQL Databases:
   In 2009, NoSQL databases were introduced, which provide a flexible way to store and retrieve unstructured data.
4. Cloud Computing:
   Cloud Computing technology helps companies to store their important data in data centers that are remote, and it saves their infrastructure cost and maintenance costs.
5. Machine Learning:
   Machine Learning algorithms are those algorithms that work on large data, and analysis is done on a huge amount of data to get meaningful insights from it. This has led to the development of artificial intelligence (AI) applications.
6. Data Streaming:
   Data Streaming technology has emerged as a solution to process large volumes of data in real time.
7. Edge Computing:
   dge Computing is a kind of distributed computing paradigm that allows data processing to be done at the edge or the corner of the network, closer to the source of the data.

Overall, big data technology has come a long way since the early days of data warehousing. The introduction of Hadoop, NoSQL databases, cloud computing, machine learning, data streaming, and edge computing has revolutionized how we store, process, and analyze large volumes of data. As technology evolves, we can expect Big Data to play a very important role in various industries.

Impact of Big Data on Database Management System:

In recent years, Big Data has become increasingly important in various industries, and this has led to huge changes in the way we manage data. Database Management Systems (DBMS) have evolved to handle the growing demand for data storage, processing, and analysis. In this article, we will discuss the impact of Big Data on DBMS and the changes that have taken place in the field.

Scalability:

The main impact of Big Data on DBMS has been the need for scalability. Big data requires a DBMS to handle large volumes of data. Traditional DBMSs were not designed to handle the amount of data that Big Data generates. As a result, DBMSs must be able to scale horizontally and vertically to meet the growing demand for data storage and processing.

Distributed Architectures:

This architecture helps the organizations to manage their vast amount of data which are clustered into different nodes. This provides better fault tolerance, availability, and scalability.

Distributed Architectures can be categorized into two types: shared-nothing and shared-disk.

• In shared-nothing architectures, each node in the cluster is independent and has its own storage and processing power.
• In shared-disk architectures, all nodes share the same storage, and each node has its own processing power.

Both types of architecture have their advantages and drawbacks, and the choice of architecture depends on the need of the application.

NoSQL Databases:

The growth of Big Data has led to the emergence of NoSQL databases. NoSQL databases provide a flexible way to store and retrieve unstructured data.NoSQL database does not have any fixed structure or schema like other DBMS have. This makes them ideal for handling Big Data, which often has a variable schema. NoSQL databases can be categorized into four types: document-oriented, key-value, column-family, and graph. Each type of database has its advantages and disadvantages, and the choice of the database depends on the specific requirements of the application.

Real-time Processing:

Big data requires DBMSs to provide real-time processing of data. Real-time Processing allows applications to process data as it is generated. This requires DBMSs to support in-memory data processing and streaming data processing. In-memory data processing allows applications to store data in memory instead of on disk, which provides faster access to the data. Streaming data processing allows applications to process data as it is generated, which provides real-time insights into the data.

Advanced Analytics:

DBMSs must be able to handle advanced analytics such as data mining, machine learning, and artificial intelligence. This requires DBMSs to provide support for these types of algorithms and tools.

• Data Mining is a way of discovering patterns in data.
• Machine learning is the way in which a computer learns itself from the given data.
• Artificial intelligence is the way in which machines do the work, which is not possible without the human brain.

Conclusion:

In conclusion, Big Data has driven significant changes in the DBMS landscape. DBMSs must now be able to handle large volumes of data, provide real-time processing, and support advanced analytics. The rise of Distributed Architectures and NoSQL databases has provided new opportunities for managing big data. We can expect further evolution in DBMSs as Big Data grows in importance. Organizations which have better management of Big Data will be able to grow their business in a better way, and decision-making power will be better.