Hello Everyone 
,
Because of GridDB’s robust support for IoT data as well as time-series functionality, we have selected it as our primary databases solution for the project I’m working on, which entails handling and analysing a large volume of time-series data. But as our data grows, I want to be sure we’re maintaining and optimising performance by adhering to best practices.
I would want to ask more detailed questions and seek guidance from more seasoned GridDB users in the following areas:
Data Sharding and Partitioning: Which techniques are the most effective for sharding and splitting data in GridDB? 
Have you discovered any particular patterns or setups that work very well at distributing the workload evenly among the nodes?
Indexing and Query Optimisation: Could you provide advice on how to use GridDB indexes efficiently? What typical mistakes should be avoided while building indexes, and how may queries be optimised to maximise their efficiency?
Memory Management: What were the most important things to keep in mind when handling memory in GridDB? Do you have any recommendations for setup settings or tools for monitoring to control memory utilisation, particularly as the data set grows?
Backup and Recovery: In order to guarantee reliable backup and recovery procedures in GridDB, what are your suggested practices? What programs or tools do you think would be especially helpful for automating such duties?
Real-Time Analytics: Our goal is to analyse our time-series data in real-time. Which techniques are most effective for reducing latency and maximising throughput while ingesting and querying data in real-time in GridDB?
High Availability and Scaling: How have you scaled GridDB clusters to accommodate growing user and data loads? Can you suggest me the best Microsoft certification I can do? If anyone know about it; Which procedures work best in a production setting to provide fault tolerance and high availability?
Thank you in advance.