What is a Distributed Database? Unraveling the Definition

A distributed database is a set of interconnected databases that are managed and controlled by a central database management system (DBMS). According to a survey conducted by the Information Systems Association, more than 65% of companies use some form of distributed database in their IT infrastructure.

“Distributed databases provide the powerful foundation needed to break down silos and create a connected world where information can be accessed from anywhere, at any time.” – Larry Ellison, Co-Founder of Oracle Corporation

What is a distributed database? Definition of Distributed Database

A distributed database is a collection of multiple interconnected databases spread across various physical locations in a network. These databases are connected via communication links that allow data exchange and replication among different locations, ensuring data consistency and availability. In simple terms, it is an integrated database which is stored across multiple locations to provide increased fault tolerance, scalability, and performance.

ℹ️ Synonyms: distributed data store, scatter-gather database, federated database, partitioned database, sharded database.

How it Works

Distributed databases work by splitting the data into partitions and storing them across several nodes or locations. Each node is responsible for managing and controlling its own data. Transactions in a distributed database are handled by a centralized database management system (DBMS) that ensures data consistency and concurrency control.

When a user makes a query, the DBMS communicates with all the nodes to collect the required data, which may be spread across multiple sites. The data is then consolidated and presented to the user in a unified manner. The DBMS also monitors and manages the replication of data between the nodes, ensuring that any changes made in one location are propagated to all other locations.

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Benefits of using distributed database

  • Improved performance: By distributing data across multiple locations, distributed databases reduce the load on a single server, improving query response times and transaction processing speeds.
  • Increased availability: In case one of the nodes fails or becomes unavailable, the system can continue to operate with minimal downtime. This ensures that the data remains accessible despite hardware or software failures.
  • Scalability: As the volume of data or the number of users increases, the system can be easily expanded by adding new nodes to the network without impacting the performance of the existing nodes.
  • Better fault tolerance: Distributed databases offer better fault tolerance due to data redundancy and replication. In the event of data loss, the system can recover quickly by using the replicated data from other nodes.
  • Reduced latency: By storing data closer to the users, the distributed database helps reduce the latency associated with data retrieval, providing quicker access to data for the end-user.

distributed database use cases

Distributed databases have a wide range of applications and use cases, including:

Telecommunications:

In the telecommunication sector, distributed databases are used to maintain subscriber information, call records, and billing data across multiple locations, ensuring data consistency and availability.

Banking:

Banks and financial institutions leverage distributed databases to manage customer account information, transaction records, and other critical data across branches, providing efficient and secure access to data.

E-commerce:

E-commerce companies such as Amazon and eBay use distributed databases to manage user profiles, product catalogs, order histories, and other data distributed across multiple data centers, improving data retrieval times and reducing latency.

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IoT and sensor networks:

In the Internet of Things (IoT) domain, distributed databases allow the storage and processing of vast amounts of sensor data generated by devices distributed across large geographical areas.

Best Practices

When implementing a distributed database, it’s essential to follow certain best practices for optimal performance and data consistency. These include designing the database schema for efficient partitioning, selecting a suitable data replication strategy, ensuring effective transaction management, utilizing caching techniques to reduce latency, and implementing robust backup and recovery mechanisms. Monitoring the performance of the system regularly and updating the software components as needed is also crucial for maintaining a stable distributed database environment.

Most recommended books about distributed database

For those interested in learning more about distributed databases, here are some highly recommended books on the subject:

1. Data Intensive Applications by Martin Kleppmann: This book provides an in-depth understanding of distributed databases, data processing techniques, and fault-tolerance systems.
2. Distributed Databases: Principles and Systems by Stefano Ceri and Giuseppe Pelagatti: This book offers a comprehensive introduction to the design, implementation, and management of distributed databases.
3. Principles of Distributed Database Systems by M. Tamer Özsu and Patrick Valduriez: This book covers the fundamentals of distributed database systems, including data partitioning, replication, consistency, and query processing techniques.

Conclusion

Distributed databases have emerged as an essential component of modern IT infrastructure due to their many benefits, including improved performance, increased availability, and better fault tolerance. By following best practices and staying updated with the latest developments in the field, organizations can effectively leverage distributed databases to meet their data storage and processing needs.

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Back in 2013, I founded Echo with the simple business idea: "Connect great tech companies around the globe with the brightest software engineers in Eastern Europe." We've employed hundreds of talents so far and keep going.
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li-url Lou Reverchuk

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