The Best 9 Database Platforms Catering to Big Data Requirements


The database management systems are presently expanding and coming up with new features! Today, several database platforms cater to Big Data requirements. These platforms help to manage vast pools of unstructured and structured data, and that provides essential Big Data perspectives.

Recently, businesses and companies have begun depending on open source solutions for instance, MongoDB and Cassandra, which is perfect for catering to Big Data loads. Another tool, for example the OrientDB, can save as much as 150,000 documents per second. Now, the most prominent brands such as Boeing, depend on open source databases.

Several databases cater to the big data world. It is always a good start with essential know-how. Users have an online application to seek transaction assistance along with concurrency control. This application provides a balanced update and can process the data size of terabytes. The names to count on are:

  1. Relational Database Management System (RDMS)

The platform makes use of a B-Tree structure as data engine storage. The index and data get arranged with B-Tree concepts and writes/reads with logarithmic time. Based on close to a million records, the platform takes about 20 comparisons in the B-Tree finding the data in the index. If there are necessary optimizations, then the comparisons can reduce to three.

2. MongoDB

You can use this platform if you need to de-normalize tables. It is apt if you want to resort to documents that comprise all the allied nested structures in a single document for maintaining consistency. This platform makes use of the indexes along with the B-Tree architecture, and the file mappings get stored in the memory. It helps with fast access to data. The writing takes place, and then the memory data gets flushed at regular intervals. While reading the data back, the question selects the data from memory. Usually, the NoSQL database platforms make use of the memory in comparison to the RDMS. The MongoDB query capacity is rich. The platform can manage terabytes of data. It performs best with online applications that need scalability and consistency. To know more about this, you can check out

3. Cassandra

This database platform is perfect for upfront queries and fast writing. However, the query performance is slightly less, and that makes it ideal for Time-Series data. Cassandra uses the Long-Structured-Merge-Tree format in the storage engine. Here it starts to write against the memory and then flushes the same to the disk. Cassandra helps in faster writing in comparison to the RDMS and a couple of NoSQL database platforms. The database doesn’t require any extra space for inserting information and can append. In the case of the queries, the best performance is attained when the question hits a single node depending on PK. Also, the query doesn’t scan multiple nodes. Furthermore, Cassandra stores data values in a column format. Hence, it is necessary to slice the data and then spread in columns before you save it to this database management platform.

4. Apache HBASE

This data management platform has similarities with Cassandra in its formatting. HBase also comes with the same performance metrics as Cassandra. Have you already invested in Hadoop? If yes, then you can use this database platform. The primary use of this platform is the lookups.

5. OpenTSDB

Do you require added analytics on Cassandra or Apache HBase? If yes, you can opt-in for OpenTSDB. The platform is perfect for IoT user-cases where the information gathers thousands within seconds. The collected questions are needed for the dashboards. One of the OpenTSDB alternatives is Druid, and it utilises real-time roll-ups for calculating the aggregates in IOT dimensions.

6. NewSQL

You can use of the MemSQL and VoltDB, which are NewSQL in-memory databases. As the name suggests, the information is stored in memory. The NewSQL has been designed to avert the RDMS drawbacks and offer the NoSQL performance. It means this platform comprises the best of both the worlds.

Furthermore, NewSQL backs up ACID transactions. It also writes and reads the analytics in the restricted joins. The MemSQL is used more for analytics. On the other hand, the Volt DB gets placed as a CEP since it backs up the procedures stored in Java. The objective is to operate custom logic and directly use up the feeds using connectors. It is interesting to note that the in-memory database platforms are always quicker as compared to the RDMS. It comes with extra RAM owing to its revolutionary approach to the database platform design.

Similarly, Kudu is yet another disk-oriented solution which addresses the HTAP requirements as the storage engine. However, it needs another query engine, for instance, the Impala that has issues with the increased concurrency workloads. You will also come across the disks that get designed on the New SQL methods, which shares similarity with the capacities of Parquet and HBase.

7. Infinispan

This database platform comes from JBoss and is known to be highly scalable. Also, Infinispan is called an accessible data grid platform. This platform is Java-based and got designed for its multi-core stricture. It provides disseminated cache capacities.

8. Infobright Community Edition 

You can best describe this as a scalable data warehouse. Simply put, Infobright helps to store as much as 50TB. The database platform also provides data compression to about 40:1 for enhanced performance. The commercial products that get fashioned using similar technology are available on the corporate website. The operating system apt for this database platform is Linux and Windows.

9. Hive

Hive is the data warehouse from Hadoop. It provides seamless and straightforward data summarization along with ad-hoc queries. That aside, there is other Big Data analysis. For questions, this database platform makes use of SQL language, such as HiveQL. The operating systems for this OS Independent.

These are the leading database platforms for Big Data. The use and consumption of data is increasing. Hence, companies must choose the best database platforms. If you want to cater to your Big Data demands and requirements, you can select any of the database mentioned above and pick platforms that you find best.

Kristen Smith is a blogger and content writer who focuses on Web Design, Social Media and Technology. She enjoys reading new things on the internet. She spends a lot of time on social media.


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