CURRENTETL & Data Pipeline Engineering
ETL and data pipeline engineering, built and maintained by us. Move data into your systems without writing another loader. Current handles ingestion, transformation, and delivery — and tells you the moment something breaks.
// THE ETL PIPELINE: INGEST, TRANSFORM, LOAD
[1] INGEST
Current ingests structured data from Canvass or from your existing internal databases, at whatever volume you are running.
[2] TRANSFORM
Apply transformations, filtering, schema mapping, and validation in flight, before anything reaches its destination.
[3] LOAD
Delivery is tracked end to end, with automated retries, dead-letter queues, and alerting when a load fails.
// WHAT WE BUILD
ETL & ELT DEVELOPMENT
New pipelines built to your schema, or existing ones rebuilt. Batch, incremental, or change data capture — whichever the source actually supports rather than whichever is fashionable.
MIGRATION & MODERNISATION
Moving off legacy ETL, consolidating warehouses, or migrating to a lakehouse. We map dependencies first, cut over in stages, and keep the old path running until the new one is proven.
STREAMING & REAL-TIME
Streaming ingestion where latency genuinely matters, and honest batch where it does not. We help drive to the need versus want with a focus on picking the right balance of speed and cost
// PRODUCTION-READY DATA INFRASTRUCTURE
SUPPORTED DESTINATIONS
Out-of-the-box loading for Snowflake, BigQuery, PostgreSQL, AWS S3, and custom webhooks. If you need a destination we do not list yet, it is usually a short build — ask.
SECURITY & ACCESS
Data is encrypted in transit with TLS 1.2+ and at rest with AES-256. Access is governed by scoped IAM roles, and every read and write is captured in an audit log you can export.
// RELIABILITY IS THE PRODUCT
MONITORING & DATA QUALITY
Validation at every stage, schema drift detection, freshness checks and row-count assertions. When a load fails you get an alert with the failing record, not a silent gap discovered a week later in a dashboard.
ORCHESTRATION
We work in your scheduler rather than imposing ours — Airflow, Databricks Workflows, dbt, or whatever is already running. Handover includes the runbook.
// UNTANGLE YOUR DATA PIPELINE
Send us your current pipeline. We will map where it is losing time and what we would change.
Request an architecture audit