The 9 Fastest NoSQL Databases Every Data Science Professional Should Know About

Data is all around us and extremely plentiful. Just about everything that a consumer does generates data now. From posting to social media to purchasing groceries online for in-store pick up, modern day databases need to be prepared to cope with these extreme data volumes.

The best way to cope with large data volumes is to use a distributed database capable of operating different nodes to partition data accordingly. That way, if one node goes down or is overloaded, the system can continue to run using other nodes with no problems. 

Answering these challenges means using a fast NoSQL database that can handle partition tolerance seamlessly to create a great experience for customers, while housing the information data scientists want.

What Is a NoSQL Database?

A NoSQL database does not mean that there is no relationship between data at all. NoSQL actually stands for “Not Only SQL.” This means that information stored in this type of database is not divided into various tables. That way, you can see all related data without specific restrictions.

NoSQL databases allow data scientists to view data in one structure. This enables the data processing to experience greater speeds and fewer performance lags. Completing multiple queries at once with these databases should be no problem and you won’t need to run joins.

Using a NoSQL database continues to grow in popularity because they scale extremely well and are ideal for distributed environments. Your team can rely on these databases to perform well under heavy workloads time and time again. 

Are NoSQL Databases Faster?

Yes, NoSQL databases are faster and designed for high-performance data processing. Developers created these non-relational databases out of a need for greater agility and performance while also scaling daily to meet the needs of ever-increasing data processing and storage.

NoSQL databases can help with real-time predictive analytics and meet the needs of billions of users.

As you go about your daily routines of surfing the internet and using mobile applications, you’re probably engaging with these lightning-fast databases. Some common uses for NoSQL databases include:

• Social applications
• Online ads
• Data archiving

Why Are NoSQL Databases Faster?

The biggest reason that these databases are faster is that they “focus on using a very small set of database functionality,” according to Cameron Purdy, who used to work at Oracle.

Ultimately, the speed of your database will depend on how you’re using and querying that data. Some software engineers develop SQL applications that can act and function similarly to NoSQL, but it still leaves the question of the scalability of such ad hoc creations. And you’ll need to plan for longer development timelines to accommodate such engineering.

9 Fast NoSQL Databases

If you’re looking to increase the speed, reliability and scalability of your database solutions, here’s a look at the nine fastest NoSQL databases available.

1. BangDB

BangDB is an ideal solution for the following database uses:

• Key-Value stores
• Document storage
• Handling large files
• Graph storage
• Transactional database
• Rich query

It’s the only product on the market that uses native AI. The company offers its product for free with up to three licenses. To get access to resources like deep learning and information extraction, you’ll need to upgrade to a paid plan.

2. MongoDB

MongoDB is an excellent database for storing documents in JSON objects. Large companies like Uber and eBay use its services. Its ideal use cases include the following.

1. Integration of hundreds of data sources with a unified data view
2. The need for large read and write operations
3. Storing clickstream data to analyze customer behavior

The company offers extensive online training and certification programs to help its users learn the database and use it to its fullest. 

3. Cassandra

Cassandra is an open-source NoSQL database. Facebook initially developed Cassandra but now it’s widely available and many companies use it because of its scalability. 

Cassandra is well-known for its ability to handle petabytes of information. It is also a great product for responding to thousands of requests at once.

Data scientists often choose Cassandra in the following scenarios.

• Situations where they have more write operations than read operations
• Greater availability needs than consistency needs. Facebook built it to meet the needs of social networking, but the application would not do as well for banking
• Fewer joins and aggregation database queries
• Some examples of applications that lend themselves well to using Cassandra include weather data, order tracking, health tracking

4. Elasticsearch

Elasticsearch offers one of the best full-text search databases. It is open-source and highly scalable. You can use Elasticsearch when you need fuzzy matching.

Some major companies like Slack and Medium use Elasticsearch. The database is ideal if you’re looking to accomplish the following functionality.

• Full-text search use cases
• Chatbots to resolve queries, especially using fuzzy matching in the case of misspellings or poor syntax
• Storing and analyzing log data

5. Amazon DynamoDB

Amazon’s product is not open source, but it is still highly scalable even up to 10 trillion daily requests. It’s no surprise that many large companies like Snapchat and Samsung use Amazon DynamoDB.

This NoSQL database has two major use cases.

1. Simple key-value queries with high volumes
2. OLTP workloads that require highly consistent data, such as online banking or ticket booking

6. HBase

HBase is an open source and highly scalable NoSQL database. Developers wrote this database in JAVA on top of the Hadoop Distributed File System (HDFS). Major companies like Pinterest and HubSpot use HBase as their tech stack.

HBase is ideal for the following use cases.

1. Large data volume of at least petabytes
2. Random and real-time data access
3. Real-time message storage for billions of users

7. Redis

Redis is an open source database enabling applications to handle millions of real-time requests per second. 

Redis is popular in the following industries.

1. Gaming
2. Ad-tech
3. Healthcare
4. Internet of things (IoT)
5. Financial services

The database powers services like caching, real-time leaderboards, analytics, ride-hailing, session management, chat and media streaming. Redis is known for its flexible data structures and ease of use. 

8. NEO4J

NEO4J is a native graph database, meaning it stores data in the format that you whiteboard it. You can use pointers to traverse and navigate your graphed data. The database supports ACID (atomicity, consistency, isolation and durability) rules.

Some of the benefits of NEO4J include:

• Does not require joins to retrieve data
• High availability for large applications requiring real-time data
• Easy to learn query language commands
• Clear representation of semi-structured data
• Simple tuning

The one drawback to NEO4J is that it does not support sharding.

9. RavenDB

RavenDB is an open source distributed database. It offers an incredibly fast NoSQL database with high availability. RavenDB can help you eliminate the need for addons or other tools to boost developer productivity.

You can use RavenDB on-premises or in the cloud on any major cloud platform. The database has the capacity for up to 1.5 million reads per second. Plus it can complete up to 150,000 writes per second.

Scalable, Reliable Database Solutions

NoSQL databases offer some of the fastest, most reliable and scalable solutions. Download BangDB now to start building your modern data app.