Enterprise Data Warehouse Modernisation with Cloud Analytics
Enterprise data warehouses are among the most valuable and most entrenched technology assets in any organisation. Built over decades, they contain the analytical history of the business — customer behaviour patterns, financial trends, operational metrics, and market intelligence. They also represent some of the largest technical debt in the enterprise: rigid schemas designed for yesterday’s questions, monolithic architectures that resist change, and licensing costs that grow year over year without proportional value increases.
The cloud analytics revolution — led by Snowflake, Google BigQuery, Amazon Redshift, and Azure Synapse — offers a fundamentally different model: elastic compute that scales with demand, storage costs that decrease over time, and architecture patterns that separate storage from processing to enable concurrent analytical workloads without contention.
But migrating an enterprise data warehouse is among the most complex and risky technology initiatives an organisation can undertake. The data warehouse is deeply embedded in business processes, regulatory reporting, and executive decision-making. Getting the migration wrong has immediate, visible consequences.
Assessing the Current State
Before selecting a target platform or designing a migration plan, a thorough assessment of the current data warehouse estate is essential.
Workload Analysis: Understand what the data warehouse actually does. This means cataloguing all reports, dashboards, ad hoc queries, ETL processes, and downstream consumers. Many enterprise data warehouses support workloads that no one knows about — scheduled queries, automated extracts, and integration feeds that were created years ago and never documented.
Workload analysis reveals the true scope of migration. An organisation may believe it has a hundred reports, only to discover through query log analysis that it has a thousand distinct queries from three hundred users. Understanding the actual workload prevents migration plans that account for known uses while leaving undocumented uses broken.
Data Quality Assessment: Migration provides an opportunity to address data quality issues that have accumulated over years. Identifying data quality problems before migration prevents them from being faithfully replicated in the new platform. Common issues include inconsistent date formats, duplicate records, orphaned foreign keys, and business logic embedded in ETL processes that no one fully understands.
Dependency Mapping: The data warehouse does not exist in isolation. Upstream systems feed data into it through ETL/ELT pipelines. Downstream systems consume data through queries, extracts, and API integrations. Understanding these dependencies is essential for sequencing the migration and managing the transition period when old and new warehouses may need to operate in parallel.
Cost Baseline: Establish the true cost of the current environment, including hardware, licensing, administration, development, and the opportunity cost of capabilities the current platform cannot provide. This baseline enables meaningful comparison with cloud alternatives and sets expectations for migration ROI.
Cloud Data Warehouse Platform Selection
The three dominant cloud data warehouse platforms each have distinct architectural characteristics:
Snowflake: Separates compute and storage completely, enabling independent scaling of each. Virtual warehouses (compute clusters) can be provisioned on demand, suspended when idle, and sized independently for different workloads. Snowflake’s multi-cluster architecture enables true workload isolation — analytics queries do not compete with ETL processing for resources. The credit-based pricing model requires careful management to avoid cost surprises.
Google BigQuery: A serverless architecture that eliminates infrastructure management entirely. Queries are priced per terabyte scanned (or through flat-rate reservations), and storage is priced per gigabyte. BigQuery’s architecture is optimised for large-scale analytical queries across massive datasets. Its integration with the Google Cloud ecosystem (Dataflow, Pub/Sub, Vertex AI) is compelling for organisations standardised on GCP.
Amazon Redshift: The most traditional architecture of the three, with provisioned clusters that combine compute and storage. Redshift Serverless, introduced in 2021, provides a serverless option, and RA3 instances separate compute from managed storage. Redshift’s strength is deep integration with the AWS ecosystem and compatibility with PostgreSQL-based tooling. For organisations heavily invested in AWS, Redshift provides a natural migration target.
The selection should be driven by workload characteristics, cloud strategy, existing skills, and total cost of ownership rather than feature-by-feature comparison. All three platforms are capable of supporting enterprise analytical workloads. The differentiators are in pricing models, ecosystem integration, and operational characteristics.
The Lakehouse Pattern
The data lakehouse architecture, popularised by Databricks and increasingly adopted across the industry, blurs the traditional boundary between data lakes and data warehouses. Rather than maintaining separate systems for structured analytical data (warehouse) and semi-structured or unstructured data (lake), the lakehouse stores all data in an open format on cloud object storage and provides a query engine that supports both SQL analytics and data science workloads.
Technologies like Delta Lake, Apache Iceberg, and Apache Hudi add ACID transactions, schema enforcement, and time travel capabilities to data stored in object storage formats like Parquet. This enables warehouse-like reliability and governance on data stored at lake-like costs.
For enterprises modernising their data warehouse, the lakehouse pattern is worth evaluating because it addresses several chronic pain points:
Cost efficiency: Storing data in open formats on object storage is dramatically cheaper than storing it in proprietary warehouse formats. A lakehouse architecture can reduce storage costs by eighty to ninety percent compared to traditional data warehouses.
Unified platform: Data scientists, analysts, and engineers can work on the same data without ETL processes to move data between systems. This eliminates the data movement costs and latency that separate warehouse and lake architectures impose.
Open formats: Data stored in Parquet, ORC, or Avro with metadata management from Delta Lake or Iceberg is not locked to any specific query engine. Multiple tools can access the same data, preventing vendor lock-in and enabling best-of-breed tool selection.
Migration Approach
The migration itself should be planned as a phased programme, not a single project.
Phase 1 — Foundation: Establish the cloud data platform infrastructure, set up data ingestion pipelines, and migrate reference data and dimensional models. Build the ELT/ETL pipelines that will feed the new platform and validate data quality between source and target.
Phase 2 — Parallel Operation: Migrate workloads incrementally while maintaining the existing warehouse. Run critical reports against both platforms and validate result consistency. This phase is the most expensive because two platforms operate simultaneously, but it is essential for building confidence.
Phase 3 — Cutover: Migrate remaining workloads, redirect downstream consumers to the new platform, and decommission the legacy warehouse. The cutover should be reversible — the ability to fall back to the legacy platform provides a safety net for issues discovered after migration.
Continuous Improvement: Post-migration, optimise the new platform for cost and performance. Cloud data warehouses require different optimisation techniques than on-premises systems: clustering keys, materialised views, query profiling, and compute scaling policies. The optimisation work is ongoing and should be budgeted as operational expense.
Throughout the migration, data governance must be maintained. Access controls, audit logging, data lineage, and compliance reporting must work on the new platform before workloads are migrated. A cloud data warehouse that delivers faster queries but loses compliance controls is not an improvement.
Data warehouse modernisation is a multi-year programme that touches every part of the analytics value chain. The CTO’s role is to set the strategic direction, secure sustained investment, and ensure that the migration delivers genuine business value rather than simply changing the technology without changing the outcomes. The organisations that succeed treat modernisation as a business transformation enabled by technology, not as a technology project with business implications.