Big data databases store petabytes of unstructured, semi-structured and structured data without rigid schemas. They are mostly NoSQL (non-relational) databases built on a horizontal architecture, which enable quick and cost-effective processing of large volumes of big data as well as multiple concurrent queries.
Even though non-relational databases have proved to be better for high-performance and agile processing of data at scale, such solutions as Amazon Redshift and Azure Synapse Analytics are now optimized for querying massive data sets, which makes them sufficient when dealing with big data.
Types of Databases Used for Big Data
Big Data involves large volumes of information generated at high velocity and in a variety of formats. Handling such massive and complex data sets requires specialized databases designed to support scalability, flexibility, and efficient data processing. Here’s an overview of the most common types of databases used for Big Data:
NoSQL Databases
NoSQL databases are a popular choice for Big Data. They differ from traditional relational databases by allowing more flexible data models. These databases can handle structured, semi-structured, and unstructured data and are designed to scale horizontally, making them ideal for applications that process and store vast amounts of information.
- Document Databases: These databases store data in JSON-like documents, making them suitable for applications that require a flexible schema. Examples include MongoDB and CouchDB.
- Key-Value Stores: These databases store data in key-value pairs, which provide fast and efficient retrieval. Examples include Redis and Amazon DynamoDB.
- Column-Family Stores: These databases use a column-oriented storage approach, optimizing the system for read and write performance in analytical tasks. Apache Cassandra and HBase are popular examples.
- Graph Databases: Used for handling data with complex relationships, graph databases are beneficial in scenarios like social networks or fraud detection. Neo4j and Amazon Neptune are examples.
Relational Databases with Big Data Capabilities
Traditional relational databases can also be adapted for Big Data applications. Many vendors have enhanced their databases to support horizontal scaling and distributed storage. These modified databases, such as Google Cloud Spanner and Amazon Aurora, allow for SQL-based querying and ACID transactions while still handling larger data sets efficiently. However, they are typically less effective than NoSQL databases for unstructured data.
Data Warehouses and Analytical Databases
Data warehouses are used for querying and analyzing large amounts of structured data, often from multiple sources. They provide a centralized repository for historical data and support complex queries. Modern data warehouses, like Amazon Redshift, Snowflake, and Google BigQuery, are cloud-based and offer scalability and efficiency in handling Big Data analytics.
- OLAP (Online Analytical Processing) Systems: These databases are optimized for fast query performance and are used in business intelligence applications to perform multidimensional analysis. They are typically used in conjunction with data warehouses to enhance query speed.
Distributed Databases
Distributed databases split data across multiple nodes or servers, improving scalability and performance. Apache Hadoop, with its HDFS (Hadoop Distributed File System), and Apache Spark are common frameworks that support distributed data processing and storage. These systems can manage large volumes of both structured and unstructured data.
- Hadoop Ecosystem: Hadoop’s ecosystem includes tools like Hive, which supports SQL-like querying, and HBase, a column-family NoSQL database optimized for large-scale data storage.
Time-Series Databases
For applications that monitor data over time, such as IoT analytics, time-series databases are essential. They are optimized for timestamped data, allowing for efficient storage and retrieval. Examples include InfluxDB and OpenTSDB.
NewSQL Databases
NewSQL databases aim to combine the best of both relational and NoSQL worlds. They offer the consistency and reliability of traditional SQL systems while scaling horizontally like NoSQL databases. Examples include CockroachDB, Google Spanner, and VoltDB.
