Use Case · Data

Data pipeline on the Software Lifecycle Cloud

Data pipelines on KERNSTACK replace the laptop cron script with declared jobs: scheduled workflows start workers that extract from source systems, validate against rules kept as data, and publish into tables the product and the analysts both read. Each run leaves a record — rows in, rows rejected and why, duration — so the pipeline’s health is a query, not a rumour in a chat channel.

Container WorkersNoSQL TablesApproval WorkflowsProduct Analytics
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The engineering bottleneck with assembled stacks

Analysts cron a laptop script against a production database because it works until it visibly does not: the laptop sleeps through a run, the source schema changes silently, and the published numbers are wrong for a week before anyone’s dashboard looks odd. The script’s author becomes infrastructure, with a bus factor of one and a vacation schedule.

Architecture on the Software Lifecycle Cloud

A workflow on a cron trigger starts the extract worker, which reads sources with credentials held as project secrets, validates each record, and writes good rows to staging tables. A second step promotes validated batches into the published tables and records the run summary. Rejected records land in a failures table with the rule they broke — data quality becomes a queue somebody owns, not a surprise in a chart.

Validation failures are output, not errors

A pipeline that only publishes successes teaches the business to distrust every number slightly. Publishing the failure count and the offending rows beside the result teaches the opposite: consumers see coverage (“98.2% of orders made it; here are the 41 that did not and why”), and the owners of the source data get a precise, recurring fix-list instead of a quarterly reckoning.

  • The run record — started, rows, rejects, duration — is a table row per run, trendable over time
  • Source credentials are project secrets; the departing analyst’s laptop holds nothing
  • Published tables feed product features and analytics from the same declaration

Scale honesty

This pattern moves and validates product-scale data: nightly extracts, partner feeds, operational synchronisation. Warehouse-scale ELT over billions of rows belongs in tools built for it — and can still land its outputs here for the product to serve. Choosing the boundary by volume, in the open, is the engineering judgement the page can help with but not make for you.

FAQ

Frequently asked questions

As workflows with cron triggers declared in stack.json, starting workers that do the extracting. The schedule is a reviewed line in the project, visible to everyone — not a crontab on a machine nobody remembers.

Staging isolates the damage: partial batches stay in staging tables, the run record shows where it stopped, and published tables keep serving the last good batch. A rerun is safe because promotion is a distinct step.

They read the published tables through the same project surfaces the product uses, or scheduled exports into files. The failures table is theirs too — most analyst trust is earned right there.

Connected in the Project Graph

Related capabilities, architectures, and comparisons

Category PillarThe Software Lifecycle CloudWhy people and agents build, deploy, and operate on a single vertically integrated stack.Service · ComputeWorkersContainer workers for long jobsService · DataTablesServerless key-value and document tablesService · OrchestrationWorkflowsDurable workflows with human pausesService · InsightAnalyticsProduct analytics on the same fabricIndustry SolutionE-commerce Cloud SolutionsE-commerce on KERNSTACK owns the parts of commerce that constitute the business: the catalogue and its search, carts and checkout state, the order lifecycle, raw payment events reconciled into provable order states, and fulfilment workflows with human steps for the exceptions. Cards stay with the payment provider; the data that makes the business worth buying moves onto infrastructure the merchant controls, in Europe.Use Case · ProductExperiment assignmentAssignment is a pure function of the subject key and the experiment’s salt — stable across requests, no coordination needed. The function records the assignment row once and serves the variant; outcome handlers were already writing events, which now simply carry the experiment id the assignment returned. Analysis reads assignments and outcomes from neighbouring tables with the same keys.Use Case · AgenciesAgency client deliveryThe studio’s delivery shape is a template stack.json: gateway, functions or workers in the client’s stack languages, tables, buckets, and apps under one target per client, on European infrastructure the studio can name in the pitch. Previews run the same declaration; the client’s approval happens in the bound conversation with a live link. Every client’s meter is separate by construction — the studio sees delivery cost per client without allocation spreadsheets.

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