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Improvements in NoSQL databases over last decade

NoSQL databases, also known as Non-Relational databases, have seen a dramatic improvement over the past few years. With the exponential growth of big data, the demand for flexible, scalable and high-performance data storage solutions has increased. This has driven the evolution of NoSQL databases, and as a result, they have become more sophisticated and feature-rich. In this article, we will explore some of the key improvements in NoSQL databases and how they have become a more viable option for organizations of all sizes.

Transactional Support

One of the major improvements in NoSQL databases is the increased support for transactions. Transactions are essential for ensuring data consistency and integrity in applications. In the past, NoSQL databases were known for their lack of transactional support, but now many NoSQL databases provide transactional support. This makes NoSQL databases a better option for use cases that require strong consistency, such as financial applications.

For example, MongoDB is a NoSQL database that supports transactions. With the introduction of multi-document transactions in MongoDB 4.0, developers can now ensure that their data is consistent even in the face of failures or errors. This makes MongoDB a better choice for use cases that require transactional support, such as e-commerce applications.

Data Modeling and Query Capabilities

Another improvement in NoSQL databases is the increased focus on data modeling and query capabilities. In the past, NoSQL databases were criticized for their lack of query capabilities, but now many NoSQL databases support sophisticated query languages and offer improved data modeling capabilities. This makes it easier for developers to manage and access data.
For example, Apache Cassandra is a NoSQL database that provides a flexible data model and sophisticated query capabilities. With Cassandra, developers can define data models that match the needs of their applications, and the database supports a rich set of query capabilities, including secondary indexes, materialized views, and more.

Performance

The performance of NoSQL databases has also improved significantly. With advancements in hardware and software, NoSQL databases can now handle big data and deliver fast, scalable performance. In addition, many NoSQL databases have added indexing and caching capabilities to improve query performance.

For example, Redis is a NoSQL database that is known for its fast performance. Redis uses an in-memory data store, which allows it to deliver fast performance even with large datasets. In addition, Redis supports a variety of data structures, including lists, sets, and hashes, making it a flexible solution for a wide range of use cases.

Flexibility in Deployment Options

NoSQL databases have also become more flexible in their deployment options. Many NoSQL databases can now be deployed on-premises, in the cloud or as a hybrid solution, providing organizations with the flexibility to choose the deployment model that best meets their needs.

For example, Amazon DynamoDB is a NoSQL database that can be deployed in the cloud. DynamoDB provides a scalable, highly available data store that can be used for a wide range of use cases. With DynamoDB, organizations can take advantage of the scalability and performance of the cloud, and pay only for the resources they use.

User-Friendliness

Finally, NoSQL databases have become more accessible and user-friendly. With the increased use of NoSQL databases, many vendors have added features and functionalities that make it easier for developers to use NoSQL databases and integrate them with other systems.

For example, CouchDB is a NoSQL database that is known for its user-friendliness. CouchDB provides a simple REST API that makes it easy for developers to access data, and the database supports a variety of programming languages, including Java, Python, and Ruby
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