Topical guide
Enterprise data platform: what good looks like
Data lake, pipelines, governance, and analytics -- the infrastructure layer that determines whether your organization can actually use its data. A practical guide for organizations building or modernizing.
Platform components
What an enterprise data platform actually requires
Six layers, each of which needs to be designed and maintained for the platform to be reliable.
Data ingestion
Batch and streaming ingestion from operational databases, SaaS applications, APIs, IoT devices, and files. Event-driven pipelines that process data in real time and batch jobs that handle high-volume historical loads.
Data lake and warehouse
A structured data lake (S3, ADLS, or GCS) with a warehouse layer (Snowflake, BigQuery, Redshift, or Synapse) that provides governed, queryable access to data across the organization.
Data transformation
dbt for SQL transformation, Spark for large-scale processing, and the orchestration layer (Airflow, Prefect, or Dagster) that manages dependencies and handles failures gracefully.
Data quality and lineage
Automated data quality checks integrated into pipelines. Data lineage tracking that shows where data came from and how it was transformed -- essential for regulated industries.
Data governance
Data catalogues, access control policies, PII classification, and retention management. The governance layer that keeps data compliant with PIPEDA, provincial privacy laws, and sector regulations.
Analytics and visualization
BI tools (Power BI, Tableau, Looker) connected to a governed semantic layer. Dashboards that business users can trust because the data underneath them is reliable and well-defined.
Data maturity
Where is your organization on the data maturity curve?
Most organizations know they need to improve their data infrastructure. The question is what to build next, given where they are now.
Spreadsheets and silos
Data lives in spreadsheets and individual application databases. No central data store. Reports built manually by analysts who each have a different version of the truth.
Next step: Build a basic data warehouse and automate core reporting pipelines.
Basic data warehouse
Central data warehouse exists but pipelines are fragile, documentation is sparse, and data quality is unreliable. Analytics team spends most time on data prep.
Next step: Modernize pipelines, implement data quality checks, and build a data catalogue.
Modern data stack
Cloud data lake and warehouse, dbt for transformation, CI/CD for data pipelines, basic governance. Analytics team can answer new questions quickly.
Next step: Add real-time streaming for time-sensitive use cases and build the feature store for ML.
Data platform
Self-service analytics, governed data products, real-time pipelines, and ML feature store. Data is treated as a product with defined SLAs.
Next step: Extend to AI/ML applications and build the organizational capability to maintain it.
How we help
Data platform design and engineering
Common questions
Data platform -- FAQs
What is an enterprise data platform?
An enterprise data platform is the infrastructure and tooling that collects, processes, stores, governs, and makes accessible the data an organization uses for analytics, reporting, and AI. It is the difference between data scattered across databases and data that is available, reliable, and usable by the people and systems that need it.
What is the modern data stack?
The modern data stack is a set of cloud-native tools for data engineering: a cloud data warehouse (Snowflake, BigQuery, Redshift), data transformation tool (dbt), orchestration (Airflow or Prefect), ingestion connectors (Fivetran, Airbyte), and a BI layer (Looker, Tableau, Power BI).
How do we handle data governance for Canadian privacy requirements?
Data governance for Canadian privacy compliance involves: identifying all personal information in your data platform, classifying it, restricting access based on least privilege, implementing retention and deletion policies, and maintaining audit trails. We integrate governance tooling into the platform from the start.
How long does it take to build an enterprise data platform?
A basic modern data stack with reliable pipelines can be operational in 4-8 weeks. A production-grade platform with data quality, governance, and self-service analytics takes 3-6 months. The timeline is usually dominated by data quality issues, not technical implementation.
Building or modernizing your data platform?
We assess your current data infrastructure, identify the bottlenecks and gaps, and build the platform that your analytics and AI programs actually need to succeed.