If non-engineers need to operate it, while you still need deduplication, retries, and schema drift handling, the safest choice is usually:

Best overall approach: Low-code ELT / managed integration platform + warehouse

Examples:

• Fivetran
• Airbyte Cloud
• Matillion
• dbt Cloud for transformations
• Plus a warehouse like Snowflake / BigQuery / Databricks / Redshift

Why this fits

• Non-engineers can use it through UI-based connectors and scheduling.
• Retries are typically built-in, including automatic re-syncs and failed-job reruns.
• Schema drift is usually handled better than in custom code:
  • new columns can be added automatically,
  • changed fields can be landed in raw/staging tables,
  • downstream models can adapt via transformations.
• Deduplication is usually done in the warehouse or transformation layer, where you can define:
  • primary-key-based merges,
  • windowing rules,
  • “latest record wins” logic,
  • idempotent upserts.

Recommended pattern

1) Ingestion layer

Use a managed connector tool to pull data from source apps/databases.

What to look for:

• automatic retries
• incremental syncs / CDC
• schema change detection
• alerting
• simple UI for non-engineers

2) Raw landing zone

Land data in a raw or bronze layer without heavy transformation.

This helps with:

• replaying data if something breaks
• auditability
• schema changes without immediate pipeline failure

3) Transformation layer

Use dbt or warehouse SQL for:

• deduplication
• type casting
• schema normalization
• business rules

This is where you make pipelines deterministic and idempotent.

4) Orchestration / monitoring

Use built-in scheduling or a lightweight orchestrator if needed:

• Airflow if you have engineering support
• managed scheduler if you want non-engineer friendliness

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How each requirement is handled

Deduplication

Best handled in SQL/dbt with:

• ROW_NUMBER() partitioned by business key
• MERGE into target tables
• unique constraints where supported
• hash-based change detection

Example pattern:

• keep the latest record by updated_at
• ignore repeats by source event ID
• use merge/upsert instead of append-only loads

Retries

Look for tools that support:

• automatic retry on transient failures
• checkpointing
• resumable loads
• dead-letter handling or failed job queues

Managed tools do this far better than DIY scripts.

Schema drift

You want a system that can:

• detect added/removed columns
• ingest unknown fields into semi-structured storage (JSON, VARIANT, STRUCT)
• allow downstream models to evolve gradually

Best practices:

• raw tables should be permissive
• curated models should be stricter
• alert on breaking schema changes, but don’t fail everything immediately

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What I’d recommend by team maturity

If you want the easiest non-engineer experience

Fivetran + Snowflake/BigQuery + dbt Cloud

• very user-friendly
• strong reliability
• great for schema drift and retries
• dedup handled downstream

If you want lower cost and more flexibility

Airbyte Cloud + warehouse + dbt

• cheaper/more open
• more setup/ops than Fivetran
• still good for non-engineers if templates/processes are clear

If you want more “business user” workflow tools

Make / Zapier / Workato

• easy for non-engineers
• good for simpler automations
• less ideal for serious deduplication, large-scale retries, and robust schema drift
• better for workflow automation than core data pipelines

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My practical recommendation

If this is for production analytics or operational data:

Use a managed ELT connector (Fivetran or Airbyte Cloud) → warehouse → dbt for dedupe and schema management.

That gives you:

• a UI non-engineers can operate
• reliable retries
• schema drift tolerance
• proper deduplication logic
• a maintainable architecture

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Quick decision rule

Choose:

• Fivetran if you want the least ops and highest reliability
• Airbyte Cloud if you want more control and potentially lower cost
• Workato/Make only if the pipeline is simple and more workflow-oriented than data-warehouse-oriented

If you want, I can also give you:

1. a comparison table of 5–8 tools, or
2. a recommended architecture for your exact use case and team size.