What are the most common database architectures used in modern software development?

Depending on the specific use case, you can leverage various types of databases: 1. Utilize Relational Databases such as MySQL and PostgreSQL for managing structured data. 2. Opt for NoSQL Databases in scenarios requiring schema-less and scalable solutions. 3. Enhance data access speed with In-memory databases like Redis and Memcached. 4. Manage time-stamped data efficiently using Time-series databases like InfluxDB. 5. Embrace Multi-model databases like ArangoDB, offering support for multiple data models. 6. Achieve scalability with Distributed databases such as Cassandra, distributing data across various locations. 7. Leverage Cloud-native databases like AWS Aurora specifically designed for cloud environments.

The most common database architectures in modern software development include: 1. **Relational Databases (RDBMS):** Organized into tables with predefined relationships. 2. **NoSQL Databases:** Flexible, schema-less databases, suitable for dynamic and evolving data. 3. **In-Memory Databases:** Store data in RAM for faster access. 4. **Columnar Databases:** Optimize storage and retrieval for columns of data. 5. **Graph Databases:** Designed for managing interconnected data using graph structures. 6. **NewSQL Databases:** Combine aspects of traditional relational databases with the scalability of NoSQL databases.

Relational Database Management System (RDBMS): Traditional, table-based, for structured data (e.g., MySQL, PostgreSQL). NoSQL Databases: For unstructured data or flexible schemas, including document-based (MongoDB), key-value (Redis), wide-column (Cassandra), and graph databases (Neo4j). NewSQL Databases: Blend NoSQL flexibility with RDBMS ACID guarantees (e.g., Google Spanner). In-Memory Databases: Fast data processing, storing data in RAM (e.g., Redis). Data Warehouses: For analytical processing of large data sets (e.g., Amazon Redshift) Time Series Databases: Optimized for time-stamped data (e.g., InfluxDB) Graph Databases: Manage and query complex networks (e.g., Neo4j) Cloud Databases: Scalable, cloud-based solutions (e.g., Amazon RDS)

To organize data in proper way and to manage easily, some databases are used. Many databases are used now a days to organize data in proper way. Some are -> Relational database management -> No SQL database -> Mysqli database

It really depends on the use case. If we need structured data then SQL is the way to go. If we will get a lot of unstructured data, we should go with NoSQL. But if strictly answer that question what is more common then it's NoSQL databases because of popularity if MERN stack in newer developments

J'utilise de nombreuses bases de données NoSQL mais jamais en tant que base de données principale. Pour moi, ce sont des bases très adaptées et très performantes pour certains use cases de lecture, mais qui ne conviennent pas pour stocker la donnée métier en premier lieu.

In relational database architecture, data is organized into tables with predefined schemas, where relationships between tables are established using keys. These databases, managed using SQL, provide ACID (Atomicity, Consistency, Isolation, Durability) compliance, ensuring data integrity. Ideal for scenarios with well-defined data structures and complex relationships, relational databases are a go-to choice for applications where consistency and transactional support are critical.

NoSQL databases often communicate with applications via APIs, which can pose a security threat if they are not secure or configured correctly. Additionally, NoSQL databases do not have a fixed schema, which means they rely more on the application layer for data validation. If the application does not validate input correctly, it can result in various security problems.

NewSQL databases, such as CockroachDB and TiDB, distinguish themselves through unique features tailored to specific needs. CockroachDB prioritizes resilience, leveraging a distributed architecture for fault tolerance. On the other hand, TiDB excels in handling hybrid transactional and analytical processing (HTAP) workloads. These vendor-specific optimizations cater to diverse application requirements, providing organizations with the flexibility to choose a NewSQL solution that aligns precisely with their use cases.

NewSQL databases are helpful for applications that require the scalability of NoSQL systems but also need to ensure data consistency and reliability. Examples include financial systems, e-commerce platforms, and any other application that needs to handle high volumes of transactions reliably and in real-time.

Unlike transactional databases, data warehouses prioritize analytical prowess, supporting complex queries and trends analysis. With a focus on integration, scalability, and OLAP capabilities, they provide a centralized hub for strategic decision-making.

Powerful tool for storing large data when updates and deletes are rarely used. Best utilization is to • Makes prediction based upon stored historical data • Visualization of past trends • Generating reports, even the same one presented in a different ways suitable for different purposes • Documents on demand, and in many different formats, from those intended for human readability to more often machine formats as an input to data mining algorithms. Shifting our view on documents, usually seen as 1 on 1 with files, and allowing documents and reports to be rendered as interactive software, putting emphasis on content and context allowing existing and yet to be made tools for presenting them.

Some additional database architecture includes: 1. real-time databases like Firebase Cloud Firestore. 2. In memory databases like Redis, Memcache 3. Graph databases like Neo4j

Data warehouses are useful for business intelligence, historical data analysis, reporting, and decision support. For example, a retail company may use a data warehouse to analyze purchasing trends over time, which can help it make informed decisions about inventory management.

While a data lake excels in managing raw and untouched data, its suitability varies among companies. It may not align with every organization's needs, but proves invaluable for those aiming to analyze extensive and diverse datasets.

A data lake should support the company's business objectives, providing valuable insights that drive decision-making. Ensuring this alignment can be challenging but is vital for the success of the data lake. One way to get around this is to Involve business leaders in the planning and managing of the data lake to ensure it supports business goals.

Everything boils down to your use case and the projected scalability. For a small size project any DB will get the job done. But if you want to a specific use case on a large scale you need to carefully think through. Eg : 1. You need cache - Consider Redis 2. You need to store images/videos - It falls under Blob Storage so Amazon S3 + CDN may be an obvious choice. 3. You need strong ACID properties - Go the traditional route on RDBMS

Relational Databases: SQL-based, structured data (MySQL, PostgreSQL) NoSQL Databases: Schema-less, scalable; types include Document (MongoDB), Key-Value (Redis), Wide-Column (Cassandra), Graph (Neo4j) NewSQL Databases: SQL-like, scalable, high throughput (Google Spanner) In-Memory Databases: Fast access, data stored in RAM (Redis, Memcached) Data Warehouses: Large-scale data analysis, often columnar storage (Amazon Redshift) Time-Series Databases: Optimized for time-stamped data (InfluxDB) Object-Oriented Databases: Data as objects (db4o). Multi-Model Databases: Support multiple data models (ArangoDB) Distributed Databases: Data spread across locations, scalable (Cassandra) Cloud-Native Databases: Designed for cloud environments(AWS Aurora)

Cloud-based databases are built specifically for the cloud, with scalability, availability, and performance in mind. Examples include Amazon DynamoDB, Google Cloud Spanner, and Microsoft Azure Cosmos DB. As the data is stored in the cloud, it can be accessed from anywhere in the world, making it ideal for remote teams or businesses with multiple locations.