Data Engineer
Configure sources through a UI instead of writing custom ETL. Pipelines auto-retry and self-heal, so on-call incidents drop.
Canvas · Self-Serve Data Intelligence Platform
Your data is arriving. Is it ready to use?
Canvas turns raw, messy source feeds into clean, governed, analysis-ready data, automatically. Ingest from any source, normalise every column, and publish to the Golden Layer in minutes, not months.
canvas · data
Raw
Refined
Trusted
no-code
01
Connect
Plug in REST APIs, databases, file uploads and URL feeds in a single guided form.
02
Validate
Automated quality checks and column normalisation catch problems before they reach analysts.
03
Publish
A 9-step wizard produces governed, analysis-ready datasets on demand.
- 11
- automated quality checks per batch
- 9
- steps to a governed dataset, no SQL
- 4
- source types: API, database, file, URL
- 5
- naming conventions normalised automatically
01 The problem
Why teams can't use their own data
Most data teams spend the bulk of their time moving data, not using it. The result is slow reporting, quality surprises, and decisions made on stale numbers.
The integration tax
Every new source means weeks of bespoke ETL code, manual schema mapping, and fragile pipelines that break on the next API change. Teams spend most of a sprint just moving data.
Quality blind spots
Data lands with no checks. Nulls, duplicates, schema drift and out-of-range values are discovered downstream by users, not by systems.
The analyst bottleneck
Every new report or dataset needs an engineering ticket. Analysts wait, and decisions get made on stale data.
Governance gaps
No audit trail, no approval workflow, credentials in spreadsheets, and a scramble at audit time.
02 Objectives
What we set out to do
Canvas is built around five objectives, each removing a reason your team can't get to clean, governed data on its own.
- 01Eliminate the integration tax: connect a new source and run a first ingestion in under 15 minutes, no engineering ticket.
- 02Automate quality enforcement: 11 built-in checks run on every batch, blocking bad data before it reaches analysts.
- 03Self-service by design: a guided wizard and command palette let business users build governed datasets without SQL.
- 04Governance out of the box: audit log, role-based access, approval workflows and vault-managed credentials, on by default.
- 05Standards at scale: automatic column normalisation across 5 naming conventions, so data lands consistently every time.
03 Who uses it
From engineers to the CDO
Each role gets what it needs: engineers stop firefighting pipelines, analysts stop waiting, stewards get traceability, and leadership gets visibility.
Business Analyst
Build governed datasets through a 9-step wizard. Self-serve, always fresh, no more 4–6 week waits on engineering.
Data Steward / QA
11 automated quality checks per run, schema-drift alerts, and a full audit log with actor, timestamp and payload.
Source System Owner
Register the source once. The platform handles every downstream consumer, schedule and format conversion.
Platform / IT Manager
All secrets in the vault, role-based access per workspace, and immutable audit logs for compliance.
Chief Data Officer / CTO
A real-time view of ingestion health, quality scores and pipeline status, with engineering freed for high-value work.
04 Capabilities
Six capabilities, one platform
From connecting a source to publishing a governed dataset, everything happens in one place, with quality and governance built in.
Connect any source
REST APIs with configurable auth and pagination, native database connectors (PostgreSQL, MySQL, SQL Server, Oracle), drag-and-drop file upload (CSV, JSON, XLSX, Parquet), and authenticated file URLs.
Intelligent ingestion control
Full refresh, incremental and append-only modes; scheduled runs down to the minute; automatic retry with backoff and alerting; and a live run monitor with row counts and throughput.
Quality engine
Eleven automated checks (nulls, uniqueness, duplicates, accepted values, ranges, freshness, schema drift and more) run on every batch before any analyst sees the data.
Automatic column normalisation
Every column is standardised to a consistent format across five engines (SQL Server, Spark, Delta Lake, Parquet, Power BI), with reserved-word and name-collision handling.
Self-service dataset builder
A 9-step wizard publishes governed, analysis-ready datasets from validated sources: define grain, join sources, set business and conflict rules, attach quality policy, then publish, with versioning and rollback.
Enterprise governance & security
Role-based access (five roles), a credential vault, an immutable audit log, approval workflows, isolated multi-workspace boundaries, and corporate single sign-on.
05 The foundation
Three layers, one source of truth
Canvas follows the industry-standard medallion architecture, proven at scale across healthcare, finance and life sciences. Data moves forward only when it's ready.
01
Raw
An exact, immutable copy of every source record as it arrived, timestamped and never overwritten. A perfect audit trail.
02
Bronze
Cleaned, normalised and quality-checked data. Nothing advances to the next layer without passing a quality gate.
03
Golden
Analyst-ready, governed datasets. Joins, business rules and conflict resolution are declared once and applied consistently every run.
Built on Azure, no lock-in
Runs on enterprise Azure infrastructure, with data stored as open Parquet, readable by Power BI, Synapse, Databricks or a simple Python script.
Quality gates, not reports
Checks run inline and block bad data from advancing. Quality is enforced structurally, it can't be bypassed by an impatient analyst.
Secrets never in the database
Credentials live in a managed vault; the database holds only a reference. A database breach exposes no usable secrets.
Scales with your workload
Compute scales to zero when idle and out under load, with storage lifecycle policies for older data, so you pay for what you use.