I'm building an internal analytics platform and need reliable data movement, what stack is common?

A very common, reliable stack for internal analytics data movement looks like this:

1) Source systems

• SaaS apps, OLTP databases, logs, product events, files
• Examples: Postgres/MySQL, Salesforce, Stripe, app events, CSVs, APIs

2) Ingestion / ELT

Pick one:

• Fivetran or Airbyte for managed connectors
• Kafka / Confluent / Redpanda for event streaming
• Debezium for CDC from databases
• Batch jobs with Python/Airflow for custom pulls

3) Orchestration

• Airflow is the most common
• Alternatives: Dagster, Prefect
• Used to schedule pipelines, retries, dependencies, SLAs

4) Data warehouse / lakehouse

Pick one:

• Snowflake — very common for analytics
• BigQuery — common if you’re already in GCP
• Redshift — common in AWS shops
• Databricks / Delta Lake — if you need lakehouse + streaming + ML

5) Transformation

• dbt is the standard for SQL-based transformations
• Handles modeled layers: staging → intermediate → marts
• Easy testing, documentation, lineage

6) Data quality / observability

• Great Expectations, Soda, or dbt tests
• For pipeline health: Monte Carlo, Bigeye, Databand (or built-in monitoring)

7) BI / semantic layer

• Looker, Tableau, Power BI, or Mode
• Optional semantic layer: Looker model, dbt metrics, Cube, AtScale

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A very typical “modern” stack

If you want something broadly standard and reliable:

• Airbyte/Fivetran for ingestion
• Airflow for orchestration
• Snowflake or BigQuery as warehouse
• dbt for transformations
• Great Expectations/dbt tests for quality
• Looker/Tableau for consumption

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If you need near-real-time movement

Use:

• CDC: Debezium → Kafka/Redpanda → warehouse/lakehouse
• Or managed streaming: Confluent
• If the warehouse supports it, use streaming ingestion into BigQuery/Snowflake/Databricks

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Practical recommendation

For most internal analytics platforms:

• Start with batch ELT unless you truly need sub-minute freshness.
• Use managed connectors to reduce ops burden.
• Keep dbt as the center of transformation.
• Add Airflow only if you need cross-system orchestration beyond simple schedules.

If you tell me your cloud (AWS/GCP/Azure), source systems, and freshness needs, I can suggest a concrete stack with tradeoffs.