The volumes of data collected by contemporary organizations are so huge that they are beyond the ability of typical DBMS to capture, store, and analyze. These big data may be unstructured or semi-structured and are not suitable for relational DBMS products. What are the new technologies developed to handle the big data

Some of the new technologies developed to handle the big data are as follows:

1. In-memory Databases

2. Blockchain

3. NoSQL Databases

4. Edge Computing

5. Streaming Analytics.

6. Artificial Intelligence

Explanation:

Some of the new technologies developed to handle the big data are explained as follows:

1. In-memory Databases: This allows for rapid and easy access to data and analytics, which can be used to inform a variety of business choices. In-memory computing provides the information required to improve marketing, finances, and operations efficiency.

2. Blockchain: Blockchain is the distributed database technology that underpins the Bitcoin digital currency, and it's a darling among forward-thinking analysts and venture capitalists. The distinctive feature of a blockchain database is that data can't be erased or changed after it's been written. Furthermore, it is extremely secure, making it a perfect solution for sensitive industries such as retail, insurance, health care, banking, and among others.

3. NoSQL Databases: NoSQL databases are used to store unstructured data and offer quick performance. This implies it can handle a wide range of data formats in high volumes while remaining flexible. MongoDB, Redis, and Cassandra are examples of NoSQL databases.

4. Edge Computing: Edge computing systems evaluate data very near to where it was originated — at the network's edge — rather than sending it to a centralized server for analysis. An edge-computing system has the advantage of minimizing the quantity of data that must be transferred over the network, therefore lowering network traffic and expenses.

5. Streaming Analytics: By analyzing data as it is created, Streaming Analytics allows for faster and faster access to insights. Streaming Analytics can also take data from a variety of sources, evaluate it, and offer insights almost instantly.

6. Artificial Intelligence: Artificial intelligence has the ability to transform data analytics by learning (from previous analytic activities) and improving the speed and accuracy of outputs over time. Artificial intelligence has the ability to make accurate future predictions based on current events with vast amounts of data to draw from, as well as analytic results from previous inquiries.

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explanation:

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