engineering · 1 min read
Batch ETL with AI Steps: Patterns That Hold at Scale
Adding LLM steps to nightly ETL sounds easy and quietly fails at scale. Here is how Bhogar AI keeps batch AI pipelines reliable and predictable.
BABhogar AI TeamProduct & Engineering
LLM steps are seductive in batch ETL - until your nightly job hits 50,000 rows and your bill arrives. Production batch AI needs explicit budgets, partial-failure modes and resumable runs.
Why it matters
A failed LLM call in row 49,999 should not lose the 49,998 successful ones. Resumability is the difference between a 30-minute incident and a re-run from scratch.
How Bhogar AI approaches it
Bhogar AI batch workflows checkpoint per partition, retry per row with exponential backoff, fan out with bounded concurrency, and produce a per-run report with cost, latency and partial-failure breakdown.
- Checkpointing per partition for resumable runs
- Bounded-concurrency fan-out
- Per-row retry with backoff and DLQ
- Cost and latency report per run
- Compatible with Snowflake, BigQuery, Redshift and dbt
What you get
Data-engineering teams cut nightly batch incident frequency 60-80% and gain accurate per-row cost attribution for the first time.