Data ETL Pipelines

Overview#

A financial crime unit receives transaction data from six different banking systems, each with a different schema, date format, and identifier convention. Before analysts can search across all of it, the data needs to be extracted, normalised to a common model, enriched with entity resolution results, and loaded into PostgreSQL. If any stage fails partway through, the pipeline needs to resume from where it stopped rather than reprocess records it already handled correctly. And this entire process needs to run automatically, every hour, without anyone babysitting it.

Argus ETL Pipelines handle exactly this kind of work. Built on Apache Airflow for orchestration and Apache NiFi for data flow management, the pipeline builder lets data engineers design, schedule, and monitor multi-stage transformation workflows without writing the orchestration infrastructure from scratch. Kafka Streams handles real-time event processing where sub-second latency matters. For intelligence agencies, government registries, healthcare data controllers, and critical infrastructure operators, reliable automated data pipelines are what turns raw incoming data into something analysts can actually use.

Key Features#

• Visual Pipeline Builder: Design complex data flows using a drag-and-drop interface with visual connections, branching logic, and reusable components. Engineers can see the full pipeline structure without reading code.
• High-Performance Processing: Execute pipelines at scale with parallel processing, configurable batch sizes, and resource management. Apache Kafka Streams handles real-time throughput; Apache Airflow manages scheduled batch execution.
• Smart Transformations: Apply built-in transformation operations or write custom scripts for specialised processing logic. Forty-plus transformation types cover the most common data preparation needs.
• Flexible Scheduling: Schedule pipeline execution using cron-based schedules or event-driven triggers to automate recurring data workflows. Apache Airflow manages schedule execution with retry and alerting.
• Real-Time Monitoring: Track pipeline execution progress with live metrics, stage-by-stage status, and detailed performance reporting. Operators can see which stage is running and what the throughput looks like.
• Error Recovery: Handle failures gracefully with automatic retry logic, dead letter queue management, and checkpoint-based resumption for long-running pipelines. A pipeline that fails at stage seven does not reprocess stages one through six.
• Built-In Data Quality: Validate data at pipeline entry and between critical stages to catch quality issues early and prevent bad data from reaching PostgreSQL.
• Pipeline Versioning: Version control pipeline definitions with rollback capabilities so teams can safely iterate on pipeline designs and recover from configuration errors without downtime.
• Modular Design: Break complex pipelines into reusable components that can be shared across teams and composed into larger workflows. A normalisation stage built for one source can be reused by another.
• Idempotent Execution: Design pipelines to be safely rerunnable, so retries or replays do not create duplicate data or inconsistent results.

Use Cases#

• Recurring Data Processing: Automate daily, hourly, or event-driven data transformation workflows that extract data from sources, apply business logic, and load results into the platform. The pipeline runs unattended; engineers are only notified when something needs attention.
• Data Warehouse Population: Build ETL pipelines that extract data from operational systems, transform it into analytics-ready formats, and load it into data warehouses for reporting and analysis.
• Data Migration Workflows: Design multi-stage migration pipelines with validation checkpoints, error handling, and progress monitoring to safely move and transform data between systems.
• Complex Data Enrichment: Chain multiple transformation stages together to cleanse, normalise, enrich, and aggregate data from diverse sources into unified, analysis-ready datasets.
• Operational Data Pipelines: Build real-time or near-real-time pipelines using Kafka Streams that process operational data for dashboards, alerting, and decision support with minimal lag.

Integration#

The ETL Pipelines module integrates with Apache Airflow for orchestration, Apache NiFi for data flow management, and Apache Kafka Streams for real-time processing. Pipeline outputs write to PostgreSQL as the primary data store. The module connects with the platform's data quality validation, schema management, and monitoring infrastructure, and supports connections to external databases, APIs, and file storage through the 153 available connectors.

Open Standards#

• OpenLineage Specification: Pipeline lineage tracking implements the OpenLineage spec (openlineage.io/spec), emitting RunEvent, Job, Run, and Dataset facets for every pipeline execution to produce a queryable lineage DAG.
• GraphQL: All pipeline management, ingestion, dead-letter queue, Kafka Streams, and Apache NiFi operations are exposed through a typed GraphQL API with queries and mutations defined per domain.
• ISO 8601: All timestamps produced or consumed by the pipeline engine are normalised to ISO 8601 format, including transformation coercion of Unix epoch values and varied source date strings.
• Apache Kafka (log-compaction and Streams API): Real-time event processing uses Apache Kafka Streams, with topic metadata synchronised and managed through the platform's Kafka Streams domain.
• POSIX Cron Expression Syntax: Pipeline scheduling uses POSIX cron expressions to specify recurring execution intervals, managed by Apache Airflow's DAG scheduler.
• RFC 5322 (Internet Message Format): Email addresses extracted or ingested during pipeline processing are validated against the RFC 5322 pattern to ensure structural correctness before loading.
• JSON (RFC 8259): Pipeline payloads, lineage event facets, dead-letter queue entries, and schema mapping definitions are serialised and stored as JSON throughout, including JSONB columns in PostgreSQL.
• OAuth 2.0 / JWT (RFC 7519): Access to ingestion queue REST endpoints requires a scoped RS256 service JWT, minted via the platform's OAuth 2.0 client-credentials flow and verified against the JWKS endpoint.

Last Reviewed: 2026-02-05 Last Updated: 2026-04-14