Can you help me pick a warehouse for both batch ETL and near real-time analytics?

Yes — the best choice depends on a few tradeoffs, but for both batch ETL and near real-time analytics, you usually want a warehouse/lakehouse that supports:

• fast ingest and scalable compute
• separate storage and compute
• good concurrency
• incremental/streaming loads
• strong SQL support and BI compatibility
• cost controls for always-on workloads

Short recommendation

If you want the simplest, strongest general-purpose options:

• Snowflake — best “easy button” for mixed batch + near real-time analytics
• BigQuery — great if you’re already in Google Cloud and want serverless scaling
• Databricks SQL / Lakehouse — best if your analytics is tightly coupled with Spark/ML/streaming
• Redshift — solid if you’re AWS-native, especially with broader AWS integration

Quick way to choose

Pick Snowflake if you want:

• very good performance with low ops burden
• easy scaling for both ETL and BI
• semi-structured data support
• strong data sharing and governance
• frequent batch loads plus low-latency analytics without much tuning

Pick BigQuery if you want:

• minimal infrastructure management
• very fast setup
• strong ad hoc analytics
• easy integration with GCP and streaming via Pub/Sub/Dataflow
• pay-per-use style flexibility

Pick Databricks if you want:

• one platform for ETL, streaming, and analytics
• heavy use of Spark
• ML + data engineering + warehouse workloads together
• open table formats like Delta Lake
• more flexibility, but often more engineering effort

Pick Redshift if you want:

• AWS integration
• mature warehouse features
• predictable performance for structured analytics
• use of materialized views, RA3, Spectrum, etc.
• a good choice when your stack is already on AWS

A practical decision rule

• Need low maintenance + strong all-around performance? → Snowflake
• Need serverless and are on GCP? → BigQuery
• Need streaming + ETL + ML in one place? → Databricks
• Need AWS-native and cost/predictability matter? → Redshift

For near real-time analytics, check these features

Whatever you choose, make sure it supports:

• ingestion latency under your target SLA
• incremental processing / CDC
• materialized views or pre-aggregation
• workload isolation
• fast refresh of dashboards
• concurrency for many BI users

My default recommendation

If you don’t have a strong cloud/platform constraint, I’d start with Snowflake for this use case, because it balances:

• batch ETL
• near real-time analytics
• ease of operations
• performance
• broad ecosystem support

If you want, I can also give you:

1. a decision matrix comparing Snowflake vs BigQuery vs Redshift vs Databricks, or
2. a recommendation based on your cloud, data volume, latency target, and budget.