Exploring the World of NoSQL Databases


Traditionally, relational database management systems (RDBMS) is the go-to choice. These database, like MySQL and PostgreSQL, has the ability to organize data into tables with predefined schemas, making them ideal for structured data and complex queries. However, with the increasing demand for more flexible and scalable data solutions, Non-Relational databases, also known as NoSQL databases, have gained significant popularity. Examples of NoSQL databases include Amazon DynamoDB, Firestore, MongoDB and CouchDB.

NoSQL databases are designed to handle unstructured data, which is very useful when:

  • Your application requires super-low latency.
  • Your data is unstructured, or you do not have any relational data.
  • You only need to serialize and deserialize data (e.g., JSON, XML, YAML).
  • You need to store a massive amount of data despite some relational database can handle big data.

NoSQL databases are grouped into four main categories, each serving different use cases:
  1. Key-Value Stores: These databases store data as key-value pairs, making them simple and efficient for lookups. Example: Amazon DynamoDB, Redis, and Bigtable by GCP.
  2. Graph Stores: Ideal for applications that involve complex relationships between data points, such as social networks. Example: Neo4j, Amazon Neptune, and JanusGraph.
  3. Column Stores: These databases store data in columns rather than rows, which can improve performance for certain types of queries. Example: Apache Cassandra and HBase.
  4. Document Stores: These databases store data as documents, often in JSON format, providing flexibility for storing complex data structures. Example: CouchDB, MongoDB, and Firestore.

When to Use NoSQL Databases

Through my studies, I've learned that NoSQL databases are particularly useful when your application has one or more of the following characteristics:
  • Super-Low Latency Requirements: NoSQL databases are designed for high performance and can handle rapid data access with minimal delay. However, consistent data structure and relationships between multiple sources can be challenging.
  • Unstructured Data: If your data doesn’t fit neatly into tables and columns, NoSQL databases offer the flexibility to store and manage it effectively. For example, I used MongoDB to ingest data from multiple sources, and this database automatically structured the data, so later on, I could design a relational database that fit the business needs to do reporting grouping and joining data from multiple tabeles. 
  • Serialization and Deserialization Needs: NoSQL databases can easily handle data formats like JSON, XML, and YAML, making them ideal for applications that need to read and write data in these formats.
  • Massive Data Storage: NoSQL databases are built to scale horizontally, allowing them to store and process large volumes of data efficiently. However, I used a PostgreSQL database with a JSON column that facilitated data storage and made data retrieval fast. This approach shows how we could potentially combine NoSQL and SQL capabilities in a single database.

When Not to Use NoSQL Databases

While NoSQL databases offer many advantages, I've also learned that there are scenarios where a traditional relational database is a better fit:
  • Structured Data and Complex Queries: If your data is highly structured and you need to perform complex queries, joins, and transactions, an RDBMS might be more suitable.
  • ACID Transactions: Applications that require strong consistency and transactional integrity (ACID properties) often benefit from the robustness of relational databases.
  • Mature and Proven Technology: For applications that require tried-and-true technology with extensive support, relational databases are often the preferred choice.
  • Standardized Data Models: If your application benefits from predefined schemas and standardized data models, relational databases provide the necessary structure.

Conclusion

NoSQL databases provide a powerful alternative to traditional relational databases, offering flexibility, scalability, and performance benefits for many applications. Whether you're working with unstructured data, require low latency, or need to handle massive amounts of information, exploring the different types of NoSQL databases can help you find the right solution for your needs. However, it’s important to evaluate your specific requirements and choose the database that best aligns with your application's needs. Keep in mind that complex queries might be challenging on NoSQL databases, and there may be times when you need to combine these two different database types.


Resources:
Your Google Cloud database options, explained : https://cloud.google.com/blog/topics/developers-practitioners/your-google-cloud-database-options-explained


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