Stop scheduled dbt runs and manual data quality checks. Trigger pipelines on source data changes. Govern with approval workflows.
What Analytics Engineers Face
Manual dbt schedules, broken pipelines, no data quality checks, and ad-hoc requests from stakeholders with no governance.
Scheduled dbt Runs
dbt runs every hour via cron, even when source data unchanged. Wastes compute, delays fresh data (source updates at 3:15pm, dbt runs at 4:00pm).
Broken Pipelines
Source schema changes (column renamed, type changed). dbt models break, downstream dashboards show errors. Discover issue when stakeholder complains.
No Data Quality Checks
Deploy dbt model with bug (wrong JOIN logic). Bad data flows to production dashboards. Finance uses wrong numbers in board deck—credibility lost.
Ad-Hoc Query Chaos
Business users write their own SQL queries. Each defines revenue differently. Finance, Sales, and Marketing spend time arguing about whose numbers are right.
Manual Approvals
Junior analyst changes production model. No peer review, breaks downstream reports. Need approval workflow before prod deploy—currently email-based.
No Lineage Visibility
Stakeholder asks where a dashboard gets its revenue numbers from. You trace lineage manually through dbt docs and SQL files. It takes 30 minutes and you are still not fully confident in the answer.
How Fastero Transforms Your Workflow
Event-driven dbt execution, built-in data quality checks, and governed self-service analytics.
❌ Before (Manual)
cron schedule → dbt run
Runs even if no source data changed
Wasted compute + stale data
✅ After (Event-Driven)
CDC detects source data change → dbt run
Runs only when data changed
Fresh data + zero waste
❌ Before (Manual)
dbt runs every hour via cron (dbt run --profiles-dir... in Airflow DAG). Runs even when source data unchanged. Wastes compute, 45-minute latency if source updates mid-hour.
✅ After (With Fastero)
CDC detects source table change (new row inserted, updated, deleted). Triggers dbt run shortly after the change. Downstream models refresh only when upstream data changed—less waste, fresher data.
❌ Before (Manual)
Deploy dbt model to prod via git push → CI/CD pipeline. No data quality checks—bugs reach prod. Discover issue when Finance complains revenue numbers wrong.
✅ After (With Fastero)
Save query in Fastero Workbench, click "Run Tests". Fastero runs data quality checks (row count, null checks, schema validation). If tests pass, submit for approval. Peer reviews, then deploy to prod.
❌ Before (Manual)
A business user asks for ARR. Ten analysts write ten different SQL queries. Each defines ARR differently (include trials? exclude churned customers?). You end up in meetings just to reconcile numbers.
✅ After (With Fastero)
Analytics Engineer defines the ARR metric in the Data Catalog (SQL logic plus business description). Business users query a pre-defined metric—everyone uses the same definition and reconciliation meetings largely disappear.
Features Built for Analytics Engineers
SQL IDE, event-driven execution, data quality checks, semantic layer, and approval workflows—no infrastructure management.
SQL Workbench
Browser-based SQL IDE with autocomplete, query history, parameterized queries. Connect to Snowflake, BigQuery, Databricks, PostgreSQL—no local setup.
Event-Driven dbt
Trigger dbt run on source data change (CDC), not just on a cron schedule. Downstream models refresh when upstream changes, instead of on a fixed timer.
Data Quality Checks
Row count tests, null checks, schema validation, custom SQL assertions. Run before deploy. If tests fail, block production deployment—prevent bad data.
Data Catalog
Semantic layer for metrics. Define "ARR", "churn_rate", "DAU" once. Business users query pre-defined metrics—everyone uses the same definitions. Lineage shows data flow.
Approval Workflows
Junior analyst submits query for review. Senior analyst approves or requests changes (threaded comments). Only approved queries deploy to prod—governed self-service.
Lineage Tracking
Click a metric in a dashboard to see upstream lineage (source tables, transformations, dbt models). Trace data flow from raw source to final report and answer where a number comes from in seconds.
What changes with Fastero for your team
Pipelines run in response to data changes instead of only on a fixed schedule.
Data quality checks and approvals catch many issues before they reach production.
A semantic layer gives teams a shared definition for core metrics.
Pipelines run when data actually changes, not just because the clock says so.
Integrates with Your Stack
Connect to data warehouses, orchestrators, and git repos you already use.
Snowflake
Native connector with CDC support
dbt
Trigger dbt Cloud or dbt Core runs
PostgreSQL
CDC via logical replication
Kafka
Consume events, trigger workflows
A Day in the Life with Fastero
Check data quality dashboard
Dashboard shows overnight pipeline runs. All dbt tests passed. Zero errors—no fire drills. Spend morning on new model, not debugging.
Finance asks for new revenue metric
Define "net_new_ARR" in Data Catalog (SQL + description). Finance queries metric via semantic layer—see same number in all reports. No follow-up questions.
Source schema changes (vendor renamed column)
dbt model breaks (missing column). Fastero alert fires: "Model customer_daily failed, column revenue_usd not found". Fix SQL, re-run—downtime 15 minutes, not 4 hours.
Junior analyst submits new model for review
Review query in Workbench (see SQL diff, run tests, check lineage). Suggest JOIN improvement via threaded comment. Analyst fixes, resubmits. Approve—deploys to prod automatically.
Source data updated (daily ETL from CRM)
CDC detects new rows in raw.salesforce_opportunities. Triggers dbt run for downstream models. Finance dashboard refreshes within 2 minutes—no manual refresh button.
Common Questions
How does event-driven dbt work?
Fastero monitors source tables via CDC. When row inserted/updated/deleted, CDC trigger fires. Workflow runs: dbt run --models +downstream_model. Downstream models refresh only when upstream data changed. No cron schedule, zero wasted runs.
Can I still use dbt Cloud?
Yes—Fastero triggers dbt Cloud via API. CDC detects source change → Fastero calls dbt Cloud API (trigger job). Or run dbt Core directly in Fastero (Python notebook with dbt commands).
What data quality checks are supported?
Row count tests (expect > 0 rows), null checks (column should not be null), schema validation (type, column names), custom SQL assertions (revenue > 0, email format valid). Run tests before deploy—block if tests fail.
How do approval workflows work?
An analyst saves a query in Workbench and submits it for review. A senior analyst gets a notification, reviews the SQL (diff, tests, lineage), and then either approves or requests changes with threaded comments. Approved queries can be deployed to production automatically.
Can business users query without SQL?
Yes—the Data Catalog exposes metrics as a semantic layer. Business users can query via NL2SQL or BI tools (Tableau, Looker connecting to the semantic layer). Metrics are defined once and reused in many places.
How is this different from Airflow + dbt?
Airflow: cron-based scheduling (runs even when no data changed). Fastero: event-driven (runs only when data changed). Airflow: Python DAGs. Fastero: SQL-first, no Python required. Airflow: self-hosted infrastructure. Fastero: fully managed, zero ops.
Ready to level up your data pipelines?
Event-driven dbt. Data quality checks. Governed self-service. Stop manual schedules and broken pipelines—start free today.