Can you recommend a reliable pipeline stack for files, APIs, and databases into BigQuery?

Yes — a reliable BigQuery ingestion stack usually looks different for files, APIs, and databases, but you can keep the overall pattern consistent:

Recommended stack

1) File ingestion

Best option:

• Cloud Storage (GCS) as the landing zone
• BigQuery load jobs for batch loads
• Dataproc / Dataflow only if you need heavy transformation before load

Typical flow:
Source files → GCS → BigQuery

Why this is reliable:

• GCS is durable and simple as a raw file lake
• BigQuery native load jobs are fast, cheap, and resilient
• Works well for CSV, JSON, Parquet, Avro, ORC

Best practice:

• Prefer Parquet or Avro over CSV/JSON when possible
• Land files in a raw/bronze bucket first
• Use partitioned tables in BigQuery for large datasets

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2) API ingestion

Best option:

• Cloud Run or Cloud Functions for lightweight API pullers
• Pub/Sub if you want decoupling and retry buffering
• Dataflow if volume/complexity is higher or you need stream processing

Typical flow:
API → Cloud Run/Functions → Pub/Sub (optional) → BigQuery

Why this is reliable:

• Cloud Run is good for scheduled polling, auth, pagination, and retries
• Pub/Sub provides buffering and at-least-once delivery
• BigQuery streaming or batch loads can be used depending on freshness needs

Best practice:

• Store raw API responses in GCS or Pub/Sub before transforming
• Use idempotent writes and deduplication keys
• Respect API rate limits with backoff/retry logic

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3) Database ingestion

Best option:

• Datastream for CDC from supported databases
• Dataflow if you need custom CDC or transformations
• Fivetran / Airbyte if you want managed ELT and speed of setup

Typical flow:
Database → Datastream → GCS / BigQuery → BigQuery transform layer

Why this is reliable:

• Datastream is Google-native and good for low-latency replication
• CDC avoids full reloads
• Managed ELT tools reduce operational burden

Best practice:

• Use CDC instead of full dumps whenever possible
• Keep raw replicated tables separate from curated models
• Handle schema drift explicitly

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If you want a simple, robust Google Cloud-native stack

I’d recommend this:

• Files: GCS + BigQuery load jobs
• APIs: Cloud Run + Pub/Sub + BigQuery
• Databases: Datastream + BigQuery

And for orchestration:

• Cloud Composer or Workflows for scheduling and dependencies
• Dataform or dbt for transformations inside BigQuery

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Best “production-ready” architecture

A strong pattern is:

• Landing layer: GCS / Pub/Sub
• Raw layer: BigQuery staging tables
• Transform layer: Dataform/dbt
• Serving layer: curated BigQuery marts

This gives you:

• replayability
• auditability
• easier debugging
• better schema management

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When to choose managed ELT tools

If you want less engineering overhead, consider:

• Fivetran for databases and SaaS APIs
• Airbyte for more flexible/open-source ingestion

These are especially useful if:

• you have many sources
• you want faster implementation
• you can accept vendor/tool cost

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My practical recommendation by use case

If you want maximum reliability and control

• Files: GCS + BigQuery load jobs
• APIs: Cloud Run + Pub/Sub
• Databases: Datastream
• Transform: Dataform/dbt
• Orchestration: Cloud Composer or Workflows

If you want fastest time to value

• Files: GCS + scheduled load
• APIs/databases: Fivetran or Airbyte
• Transform: dbt/Dataform

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If you want, I can also suggest:

1. a low-cost stack,
2. a real-time stack, or
3. a fully Google Cloud-native reference architecture with specific services and data flow.