Which Database is Used for Big Data? (2023)

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Last Updated on January 4, 2023 by Ashish

Introduction

Big data is a term that refers to the large and complex datasets that are generated by businesses, governments, and other organizations in the digital age. These datasets can be challenging to store and analyze due to their size, complexity, and speed at which they are generated. Choosing the right database to manage big data is therefore critical for organizations that want to extract value from their data and make informed decisions.

In this blog, we will explore the different types of databases that are commonly used for big data and discuss the key features and use cases of each. We will also look at some of the popular databases within each category and consider their strengths and limitations. Whether you are a data scientist, a developer, or a business leader, this blog should provide you with a solid understanding of the options available for storing and analyzing big data. So, let’s get started!

What is big data?

Big data refers to datasets that are too large, complex, and fast-changing to be managed and analyzed using traditional database technologies. These datasets often have one or more of the following characteristics:

Volume

Big data datasets can be extremely large, with terabytes or even petabytes of data. Traditional databases may need help to handle such large volumes of data efficiently.

Variety

Big data datasets can include a wide range of data types, such as structured data (e.g., rows and columns in a spreadsheet), semi-structured data (e.g., JSON documents), and unstructured data (e.g., text, audio, and video). Traditional databases may not be well-suited to storing and querying such diverse data types.

Velocity

Big data datasets can be generated at very high speeds, such as in real-time or near-real-time. Traditional databases may not be able to keep up with the fast-changing nature of such datasets.

The challenges that big data poses for traditional databases have led to the development of specialized database technologies that are better equipped to handle large, complex, and fast-changing datasets. In the next section, we will discuss the main categories of databases that are commonly used for big data.

Types of databases for big data

There are several categories of databases that are commonly used for big data, each with its own unique features and characteristics. These categories include:

NoSQL databases

NoSQL (short for “Not Only SQL”) databases are designed to handle large and complex datasets that don’t fit the traditional relational model. They are often used for storing and querying unstructured or semi-structured data. Some well-known NoSQL databases include MongoDB, Cassandra, and HBase.

NewSQL databases

NewSQL databases are a hybrid of traditional relational databases and NoSQL databases. They combine the scalability and performance of NoSQL databases with the transactional support and SQL querying capabilities of traditional databases. Popular NewSQL databases include Google Cloud Bigtable and Amazon Aurora.

Distributed databases

Distributed databases are databases that are spread across multiple servers or machines. They are designed to handle very large volumes of data and provide high availability and scalability. Popular distributed databases include Apache Hadoop and Apache Spark.

In the next few sections, we will take a closer look at each of these categories of databases and discuss some of the popular databases within each category.

NoSQL databases

NoSQL databases are designed to handle large and complex datasets that don’t fit the traditional relational model. They are often used for storing and querying unstructured or semi-structured data, such as documents, graphs, and key-value pairs. NoSQL databases are known for their flexibility, scalability, and ability to handle large volumes of data.

Some popular NoSQL databases include:

MongoDB

MongoDB is a popular document-oriented NoSQL database that is designed for scalability and flexibility. It stores data in JSON-like documents and supports various data types, including strings, numbers, and arrays. MongoDB is often used for storing and querying large datasets in real-time, such as logs, social media feeds, and sensor data.

Cassandra

Cassandra is a distributed NoSQL database that is designed for high scalability and availability. It uses a column-oriented data model optimized for fast writes and reads. Cassandra is often used for storing large amounts of data that needs to be accessed quickly, such as in real-time analytics and recommendation engines.

HBase

HBase is a distributed NoSQL database that is based on the Google Big Table design. It is designed for storing and processing large amounts of data in real-time and is often used in conjunction with Apache Hadoop. HBase is known for its fast read and write performance and is often used for storing and querying large datasets in real-time, such as social media feeds and sensor data.

In the next section, we will discuss NewSQL databases and how they differ from traditional relational databases and NoSQL databases.

NewSQL databases

NewSQL databases are a hybrid of traditional relational databases and NoSQL databases. They aim to combine the scalability and performance of NoSQL databases with the transactional support and SQL querying capabilities of traditional databases. NewSQL databases are designed for handling large volumes of data and high levels of concurrency and are often used for real-time analytics and online transaction processing (OLTP) applications.

Some popular NewSQL databases include:

Google Cloud Bigtable

Google Cloud Bigtable is a distributed NoSQL database that is designed for storing and processing large amounts of data in real-time. It is based on the design of Google’s Big Table database and is optimized for fast reads and writes. Google Cloud Bigtable is often used for storing and querying large datasets in real time, such as social media feeds, sensor data, and financial data.

Amazon Aurora

Amazon Aurora is a distributed NewSQL database that is designed for high performance and scalability. It is compatible with MySQL and PostgreSQL and supports ACID transactions, stored procedures, and triggers. Amazon Aurora is often used for powering real-time analytics and OLTP applications, such as e-commerce platforms and customer relationship management (CRM) systems.

In the next section, we will discuss distributed databases and how they differ from traditional databases.

Distributed databases

Distributed databases are databases that are spread across multiple servers or machines. They are designed to handle very large volumes of data and provide high availability and scalability. Distributed databases often use a shared-nothing architecture, which means that each node in the database cluster operates independently and stores a portion of the data.

Some popular distributed databases include:

Apache Hadoop

Apache Hadoop is an open-source framework for storing and processing large amounts of data in a distributed manner. It consists of two main components: the Hadoop Distributed File System (HDFS), which stores data across a cluster of machines, and MapReduce, which processes data in parallel. Hadoop is often used for storing and analyzing large datasets, such as web logs and social media data.

Apache Spark

Apache Spark is an open-source distributed computing system that is designed for the fast processing of large datasets. It uses an in-memory data processing model and supports a wide range of data sources, including HDFS, Amazon S3, and Cassandra. Spark is often used for real-time data processing and analytics, such as machine learning and stream processing.

Conclusion

In this blog, we have explored the different types of databases that are commonly used for big data, including NoSQL, NewSQL, and distributed databases. We have also discussed some of the popular databases within each category and considered their key features and use cases. Choosing the right database for your big data needs will depend on your specific requirements and the type of data you are working with. It is important to carefully evaluate the available options and choose a database that is well-suited to your needs.