1. Time-series Databases Use time-series databases when dealing with data generated over time, such as sensor data, logs, and historical records. Pros: Efficient storage and retrieval of time-stamped data. Optimized for time-series data analysis. Scalability to handle a high volume of data points. Real-time data processing and analytics. Cons: Limited support for complex queries compared to general purpose databases May not be suitable for non-time series data. Use cases: Sensor data storage and analysis. Monitoring and predictive maintenance. Environment monitoring. Financial data analysis. Example Databases: InfluxDB, TimescaleDB, OpenTSDB (integrated analytics or custom functions can be used as well) 2. NoSQL Databases: NoSQL databases are a diverse group of databases designed to store and retrieve data in ways that differ from traditional relational databases. They are often used for handling large volumes of unstructured or semi-structured data and are suitable for various use cases. Pros: Flexible schema design. Scalability to handle large datasets. High availability and fault tolerance. High perfomance, optimized for specific data models Support for semi-structured and unstructured data. Cons: May lack ACID compliance. Complex queries might be slower than in traditional databases. Use Cases: Large-scale data storage. Real-time analytics. Edge computing. Distributed IoT systems. Example Databases: MongoDB, Cassandra, Amazon DynamoDB. 3. SQL Databases: SQL databases, also known as relational databases, are widely used in a variety of applications for their structured data storage and powerful querying capabilities. Pros: ACID compliance for data consistency. Support for complex queries and transactions. Data integrity and security features. Mature and well-established technology. Cons: May have limitations in handling scalability and high write loads. Not ideal for time-series data. Use Cases: IoT applications with strong data consistency requirements. Data warehousing. Regulatory compliance in IoT deployments. Example databases: MariaDB, Oracle Database, MySQL 4. NewSQL Databases: NewSQL databases are a class of relational databases that aim to combine the benefits of traditional SQL databases (ACID compliance, structured data) with the scalability and performance required for modern, distributed, and cloud-based applications. Pros: Combines the scalability of NoSQL with ACID compliance of SQL databases. Distributed architecture for high availability. Suitable for handling IoT data at scale. Cons: Relatively new technology. Limited options compared to NoSQL and SQL databases. Use Cases: High-performance IoT applications. Complex data processing with ACID requirements. IoT data consolidation and analysis. Example Databases: CockroachDB, NuoDB, Google Spanner 5. In-Memory Databases (NoSQL type): In-memory databases are databases that store data primarily in the main memory (RAM) of a computer, rather than on disk. This allows for extremely fast data access and retrieval, making them suitable for real-time and high-performance applications. Pros: Extremely fast data retrieval and processing. Suitable for real-time analytics and decision-making. Better performance through specialization Cons: Limited storage capacity, Ram is more expensive More complex architecture, permanent form of storage still needed Use Cases: Edge AI/ML High-frequency trading. IoT applications requiring low latency. Example databases: Redis, Memcached, Aerospike, TimescaleDB 6. Distributed Databases: Distributed databases are designed to operate across multiple nodes or servers, often in different geographical locations, to provide high availability, fault tolerance, and scalability. Pros: Scalability for large-scale IoT deployments. High availability through distributed architecture. Load balancing and fault tolerance. Cons: Complex setup and configuration. May require special hardware or cloud infrastructure. Use Cases: Large-scale IoT platforms. ( manufacturing control system ) Data consolidation from multiple sources. ( multimedia applications ) Global or multi-region IoT solutions. ( Military’s control system, Hotel chains etc. ) Example databases: Amazon Aurora, HBase, CrateDB, TiDB, YugabyteDB 6. DuckDB-An in-process SQL OLAP database: DuckDB is an open-source, in-memory analytical database management system (DBMS) designed for efficient querying and processing of data for analytical and business intelligence (BI) workloads. Pros: Cons: High Perfomance Paralellism, optimization techniques compatible with multiple platforms community and open source In memory database than can also handle larger datasets by spilling excess data to disk ACID compliance It may not be the ideal choice for centralized enterprise data warehousing due to the complexity and scale of such systems. DuckDB is not optimized for high-concurrency write operations when multiple concurrent processes need to read data from a single writable database, there may be contention and potential performance issues. Use Cases: Processing and storing tabular datasets, e.g., from CSV or Parquet files Interactive data analysis, e.g., join & aggregate multiple large tables Concurrent large changes, to multiple large tables, e.g., appending rows, adding/removing/updating columns Large result set transfer to client Resources: https://www.techtarget.com/iotagenda/tip/How-to-select-the-right-IoT-database-architecture https://www.intuz.com/guide-on-top-iot-databases https://en.wikipedia.org https://www.influxdata.com https://www.geeksforgeeks.org/distributed-database-system https://aws.amazon.com/nosql/#:~:text=NoSQL%20databases%20are%20designed%20for,compose d%20of%20rows%20and%20columns https://duckdb.org/