I'm building a cloud analytics platform and want low ops overhead; what should I choose?

If your top priority is low ops overhead for a cloud analytics platform, the safest default is:

Choose a fully managed cloud data warehouse

Examples:

• Google BigQuery
• Snowflake
• Amazon Redshift Serverless
• Azure Synapse / Fabric Warehouse (depending on your stack)

These give you:

• minimal infrastructure management
• automatic scaling / elastic compute options
• backups, patching, and failover handled for you
• SQL-first analytics workflow
• good support for BI tools and ELT pipelines

Best choice by common scenario

• Want the least maintenance overall: BigQuery
• Want strong enterprise flexibility and broad ecosystem: Snowflake
• Already deep in AWS: Redshift Serverless
• Already deep in Microsoft stack: Fabric / Synapse

If you also need a lakehouse

If your platform is more about combining data lake + warehouse + ML, consider:

• Databricks Lakehouse
• BigQuery + GCS
• Snowflake + external tables / Iceberg
• Fabric OneLake

This is useful if you need semi-structured data, streaming, or ML workflows, but ops is still lower than self-managed Spark clusters.

What to avoid if low ops is the goal

Avoid building around:

• self-managed Hadoop/Spark clusters
• self-hosted Trino/Presto
• custom Kubernetes-based analytics stacks
• on-prem style MPP systems you must patch/tune yourself

These can be powerful, but they increase operational burden a lot.

Practical recommendation

If you’re starting fresh and want low ops, I’d suggest:

1. Pick a managed warehouse
2. Use ELT tools to load data
3. Add dbt for transformations
4. Connect BI tools like Looker / Power BI / Tableau
5. Only introduce lakehouse or custom compute if you truly need it

If you want, I can recommend a specific stack based on your cloud provider, data volume, streaming needs, and budget.