What are the most common database management systems and how are they different?

If you are a data scientist, you probably work with different types of data sources and formats. One of the most common and important data sources is a database, which is a structured collection of data that can be accessed, manipulated, and analyzed using various tools and methods. However, not all databases are the same. There are different types of database management systems (DBMS) that have different features, advantages, and disadvantages. In this article, we will explore some of the most common DBMS and how they differ from each other.

1 Relational DBMS

A relational DBMS is based on the relational model, which organizes data into tables with rows and columns. Each table represents an entity or a concept, and each row represents a record or an instance of that entity. Each column represents an attribute or a property of that entity. Tables can be linked by using primary and foreign keys, which establish relationships between them. Relational DBMS use a structured query language (SQL) to manipulate and query data. Some examples of relational DBMS are MySQL, PostgreSQL, Oracle, and SQL Server.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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Relational DBMS is like the reliable storyteller of databases, weaving narratives with tables and keys. In my experience, the structured nature of SQL makes it the go-to storyteller, simplifying the data plot. Whether it's MySQL or Oracle, they're like seasoned authors, crafting tales of entities and relationships. They've been my trusted companions for handling structured data, creating a database novel that's easy to follow and understand.

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2 Non-relational DBMS

A non-relational DBMS is also known as a NoSQL DBMS, which stands for not only SQL or not SQL. A non-relational DBMS does not follow the relational model and does not use tables to store data. Instead, it uses various data models, such as key-value, document, graph, or columnar, to store and manage data. Each data model has its own way of representing and querying data, and some of them are more suitable for certain types of data than others. For example, key-value DBMS are good for storing simple pairs of keys and values, document DBMS are good for storing semi-structured or unstructured data, graph DBMS are good for storing data with complex relationships, and columnar DBMS are good for storing data with high scalability and performance. Some examples of non-relational DBMS are Redis, MongoDB, Neo4j, and Cassandra.

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3 Hybrid DBMS

A hybrid DBMS is a combination of relational and non-relational DBMS. A hybrid DBMS can store and manage data using both the relational and the non-relational data models, depending on the needs and preferences of the users. A hybrid DBMS can offer the benefits of both types of DBMS, such as the consistency and integrity of relational DBMS and the flexibility and scalability of non-relational DBMS. However, a hybrid DBMS can also pose some challenges, such as the complexity and compatibility of integrating different data models and systems. Some examples of hybrid DBMS are Amazon Aurora, Google Spanner, and IBM Db2.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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Hybrid DBMS is like having the best of both worlds in the database universe – a versatile Swiss army knife. In my journey, I've found them to be the chameleons of data storage, adapting to relational or non-relational needs seamlessly. While the perks are exciting, integrating these models can be a dance of complexity. It's like juggling different tools; challenging, but with Amazon Aurora or Google Spanner, the balance is worth the versatility they bring to the data game

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4 In-memory DBMS

An in-memory DBMS is a type of DBMS that stores and processes data in the main memory or RAM of the computer, rather than on the disk or other external storage devices. An in-memory DBMS can provide faster and more efficient data access and manipulation, as well as lower latency and higher throughput, than a disk-based DBMS. An in-memory DBMS can also support real-time analytics and transactions, as well as complex calculations and operations, on large and volatile data sets. However, an in-memory DBMS can also have some drawbacks, such as the higher cost and lower capacity of memory, as well as the risk of data loss or corruption in case of power failure or system crash. Some examples of in-memory DBMS are SAP HANA, Oracle TimesTen, and Redis.

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5 Distributed DBMS

A distributed DBMS is a type of DBMS that distributes and stores data across multiple nodes or servers, rather than on a single node or server. A distributed DBMS can offer several advantages, such as the scalability and availability of data, the fault tolerance and resilience of the system, the parallelism and concurrency of data processing, and the reduced network traffic and load balancing of the nodes. However, a distributed DBMS can also face some challenges, such as the consistency and synchronization of data, the security and privacy of data, the complexity and overhead of data replication and partitioning, and the trade-off between performance and reliability. Some examples of distributed DBMS are Cassandra, MongoDB, and Google Bigtable.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common database management systems and how are they different?

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1 Relational DBMS

2 Non-relational DBMS

3 Hybrid DBMS

4 In-memory DBMS

5 Distributed DBMS

6 Here’s what else to consider

Data Science

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What are the most common database management systems and how are they different?

1

2

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5

6

1 Relational DBMS

2 Non-relational DBMS

3 Hybrid DBMS

4 In-memory DBMS

5 Distributed DBMS

6 Here’s what else to consider

Data Science

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