How to store Performance Measurement data?

When storing performance measurement data from a 5G Radio Access Network (RAN) for AI/ML purposes, you need to consider factors such as data volume, access patterns, data structure, query complexity, and scalability. Different databases have their strengths and weaknesses, so the best choice depends on your specific use case and requirements. Here are some database options to consider:

1. **Time-Series Databases:**
   Time-series databases are designed to handle data points with timestamps. They are well-suited for storing performance metrics collected over time. Examples include:
   - InfluxDB: Optimized for high-frequency time-series data.
   - TimescaleDB: Built on PostgreSQL and designed for efficient time-series data storage and querying.
   - OpenTSDB: Distributed and scalable time-series database.

2. **Columnar Databases:**
   Columnar databases store data in columns rather than rows, making them efficient for analytical queries.
   - Apache Cassandra: Distributed, highly scalable, and suitable for write-intensive workloads.
   - Apache HBase: Built on Hadoop, designed for scalability and fast read/write access to large datasets.

3. **NoSQL Databases:**
   NoSQL databases provide flexible data models and can handle semi-structured or unstructured data.
   - MongoDB: Document-based NoSQL database, good for storing JSON-like documents.
   - Couchbase: Distributed NoSQL database with support for caching and full-text search.

4. **Data Warehouses:**
   Data warehouses are optimized for complex analytical queries and aggregations.
   - Amazon Redshift: Managed data warehouse service by AWS.
   - Google BigQuery: Fully-managed and serverless data warehouse by Google Cloud.
   - Snowflake: Cloud-based data warehousing platform.

5. **Graph Databases:**
   If your data has complex relationships, a graph database might be suitable.
   - Neo4j: Graph database optimized for querying graph data structures.
   - Amazon Neptune: Managed graph database service by AWS.

6. **Distributed File Systems:**
   If you need to store and process large files or raw data, a distributed file system might be appropriate.
   - Hadoop HDFS: Distributed file system optimized for storing and processing large files.
   - Amazon S3: Object storage service suitable for storing large datasets and files.

7. **Hybrid Solutions:**
   Depending on your data requirements, a hybrid approach that combines multiple database types might be useful.

Remember that AI/ML applications often involve complex data transformations, preprocessing, and feature engineering. Consider the capabilities of the database for performing these tasks efficiently. Additionally, ensure that the chosen database can handle scalability as your dataset grows.

Before making a decision, thoroughly evaluate the database options based on your specific use case, data volume, query patterns, available resources, and the skills of your team.