Why you should move your ETL stack to Modal

ETL moves data from source systems into analytical warehouses, typically using managed connectors such as Fivetran or orchestration tools like Airflow, while the post presents Modal as a usage-priced, code-focused alternative for large transfers and custom scheduled jobs. In one example, a Modal function extracts ClickHouse usage metrics and batch-loads them into Snowflake across five parallel date-based jobs, reportedly transferring 12 million rows in 16 minutes for about $0.29 in compute and memory costs; the comparison estimates that a comparable Fivetran sync would cost roughly $3,300, though it notes that custom code also requires engineering time. A second example retrieves GitHub usernames from Snowflake and queries the GitHub API to enrich user records with listed company information, with Modal Secrets used for credentials and a cron schedule available for daily execution. The post argues that custom code can improve flexibility, reduce vendor lock-in, and lower costs, while acknowledging that conventional ETL services remain useful for common low-to-medium-volume connectors and that Airflow-like orchestration platforms are better suited to long-running, business-critical, multi-stage pipelines requiring retries, caching, and detailed observability.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.