Can you recommend a warehouse for storing long-term history and trend data?

Yes — for long-term history and trend data, a data warehouse / analytical data lakehouse is usually the right fit rather than an OLTP database.

Good options by common need

1. Snowflake

Best if you want: easy management, strong SQL analytics, good concurrency, and minimal ops.

• Great for storing large historical datasets
• Handles trend analysis well
• Easy to scale and share data
• Good choice if you want a managed warehouse with less tuning

2. Google BigQuery

Best if you want: serverless analytics and you’re already in Google Cloud.

• Very good for append-only historical data
• Excellent for ad hoc trend analysis
• Minimal infrastructure management
• Cost can work well for large analytical workloads if queries are optimized

3. Amazon Redshift

Best if you want: AWS-native warehouse and tighter control.

• Solid for historical reporting and dashboards
• Works well with existing AWS pipelines
• More tuning/ops than Snowflake or BigQuery, but still a common choice

4. Databricks Lakehouse / Delta Lake

Best if you want: both warehousing and data engineering/ML on the same platform.

• Good for raw history plus curated trend tables
• Flexible with structured and semi-structured data
• Strong if you need ETL/ELT, ML, or streaming in addition to reporting

Practical recommendation

If you just want a straightforward warehouse for long-term history and trend analysis, I’d usually recommend:

• Snowflake if you want the easiest managed experience
• BigQuery if you’re on GCP and want serverless analytics
• Redshift if you’re already deep in AWS
• Databricks if your “warehouse” needs overlap heavily with data engineering and ML

What to optimize for

For long-term history/trends, look for:

• Low-cost storage for cold data
• Fast aggregations over time ranges
• Partitioning/clustering on date or event time
• Ability to keep raw + cleaned + aggregated layers
• Support for BI tools and SQL

Simple rule of thumb

• Mostly BI/reporting: Snowflake or BigQuery
• AWS-native stack: Redshift
• Data engineering + ML + warehouse together: Databricks

If you want, I can also recommend one based on your cloud provider, expected data volume, and budget.