Data Modernization
Modernize the data estate behind your decisions
Build and scale a governed cloud data estate that brings together structured and unstructured data, streaming and batch, with quality, lineage and ownership built in.
What it is
Data modernization moves an organization from scattered, hard-to-trust data to a platform that people and applications can rely on. Depending on the need, that platform may be a data warehouse, a data lake or a lakehouse, with raw, refined and curated (medallion) layers and open table formats such as Delta Lake and Apache Iceberg. Where several teams own data, we also design a data mesh approach, in which domains publish governed data products with clear owners and service levels.
Modernization covers more than storage. It includes extract, load and transform (ELT) and extract, transform and load (ETL) pipelines, change data capture, streaming and event data, orchestration, a metadata and data catalog, lineage, and master data management (MDM) with shared reference data. Work happens in stages, so the business keeps running while the estate changes.
Problems it addresses
- Critical data sits in separate systems with conflicting definitions, so reports disagree.
- Batch jobs run overnight, and decisions rely on stale views.
- Nobody can say where a figure came from, who owns it or whether it can be trusted.
- Customer, product and account records are duplicated across systems with no master record.
- Documents, recordings and sensor or event data stay outside the analytics estate.
- Legacy platforms cost more to run each year and limit what analytics and AI can do.
What we deliver
- A target data architecture covering ingestion, storage, medallion layers, serving and a semantic layer.
- Batch and streaming pipelines using ELT or ETL and change data capture, with orchestration and monitoring.
- Data quality rules, tests and data contracts between producers and consumers.
- A metadata and data catalog with business glossary, lineage and ownership.
- Master data management and reference data for customers, products, accounts and counterparties.
- Data products with documented owners, interfaces and access policies.
- Migration plans with reconciliation against legacy systems and a decommissioning path.
- Runbooks, cost controls and training so your team can operate the platform.
Our method
How we work
We follow our four-step method, and each step ends with a review where you can pause, adjust or stop. See the full method.
Discovery & Strategy
We inventory sources, data flows, quality issues and consumers, and choose the domains to move first by value and risk.
Pilot & Validation
A pilot domain is migrated end to end, with outputs reconciled against the legacy source and quality checks running.
Build & Scale
Further domains follow in waves, using shared patterns, infrastructure as code and automated tests for pipelines.
Optimize & Evolve
We monitor freshness, quality and cost, retire legacy components and extend the catalog and data products.
Technologies
Technologies we commonly work with
These are examples of tools we commonly work with. Choices depend on your existing licenses, skills and workloads, and we compare options openly.
Cloud
The three major clouds we design and build on, shaped around the estate you already run.
- AWSAmazon Web Services: broad infrastructure, data and AI services.
- Microsoft AzureMicrosoft's cloud, closely tied to Microsoft 365, Fabric and Entra ID.
- Google CloudGoogle's cloud, known for BigQuery, Dataplex and Vertex AI.
- Cloud object storageAmazon S3, Azure Data Lake Storage and Google Cloud Storage as lake foundations.
Data platforms
Warehouses, lakes and lakehouses where governed data is stored, modeled and shared.
- DatabricksLakehouse platform for data engineering, SQL analytics and machine learning.
- SnowflakeCloud data platform for warehousing, sharing and analytics.
- Microsoft FabricUnified analytics platform with OneLake, pipelines and Power BI.
- Google BigQueryServerless data warehouse with built-in machine learning.
- Amazon RedshiftCloud data warehouse in the AWS ecosystem.
- Azure Synapse AnalyticsAnalytics service combining warehousing and big data on Azure.
- Delta LakeOpen table format that adds reliability to data lake storage.
- Apache IcebergOpen table format for large analytic tables across engines.
Data engineering
Pipelines that move, transform and orchestrate data in batch, as change data capture and as streams.
- Apache SparkDistributed engine for large-scale batch and stream processing.
- dbtSQL-based transformation, testing and documentation for analytics models.
- Apache AirflowWorkflow orchestration for scheduled and dependent data jobs.
- Apache KafkaDistributed event streaming platform for real-time data.
- Azure Data FactoryManaged integration service for ELT/ETL pipelines on Azure.
- AWS GlueServerless data integration and catalog service on AWS.
- Google DataflowManaged batch and streaming processing based on Apache Beam.
- FivetranManaged connectors that load source data into a warehouse.
- DebeziumOpen source change data capture for operational databases.
- Apache FlinkStateful stream processing for low-latency event pipelines.
Governance and security
Catalogs, lineage, access control and key management that make data and AI auditable.
- Microsoft PurviewData catalog, classification and governance across Microsoft estates.
- Unity CatalogUnified governance for data and AI assets on Databricks.
- CollibraData governance and catalog platform for policies and stewardship.
- AWS Lake FormationFine-grained access control for data lakes on AWS.
- Google DataplexData management and governance across Google Cloud data.
- Azure Key VaultManaged storage for secrets, keys and certificates on Azure.
- AWS KMSManaged encryption keys for services and applications on AWS.
- Microsoft Entra IDIdentity and access management for people and workloads.
- OpenLineageOpen standard for collecting data lineage across tools.
- Great ExpectationsOpen source framework for data quality checks.
These tools are examples of what we commonly work with, listed as text only. We are vendor-neutral and recommend what fits your estate. Product names belong to their owners, and their appearance here does not imply a partnership, endorsement or certification.
Governance and compliance
A data platform concentrates sensitive information, so its controls matter as much as its design. We plan classification of personal and sensitive data, role-based and attribute-based access, encryption and key management, retention and deletion, and lineage that supports audit. Data residency is treated as a design input: where data may be stored and processed can differ across Canada, the United States, the European Union and other jurisdictions, and our designs document it. Privacy laws such as Personal Information Protection and Electronic Documents Act (PIPEDA), General Data Protection Regulation (GDPR) and state privacy laws, and sector rules in financial services, insurance and healthcare, are context for these choices. Your compliance function confirms what applies.
Ways to start
Engagement options
Most clients begin with an assessment or a pilot. Scope, owners and success measures are agreed in writing for each option, and the options can be combined.
Assessment
A current-state review of sources, platforms and quality, with a target architecture and a sequenced migration plan.
Pilot
One domain moved end to end on a new platform, to test the pattern, the cost and the reconciliation approach.
Build
Full delivery of pipelines, models, catalog, quality controls and data products, working alongside your engineers.
Advisory retainer
Senior review of architecture, vendor and cost decisions while your team runs the platform.
This page is general information about ARCHR services. It is not legal, financial or investment advice, and it does not assure compliance with any regulation.
FAQ
Frequently asked questions
Do we need to migrate everything to the cloud?
No. We typically recommend moving the domains that carry the most value or risk first and leaving stable systems in place where there is no clear benefit to moving them.
Should we choose a data warehouse, a data lake or a lakehouse?
It depends on your data types, skills and workloads. A warehouse suits structured reporting, a lake suits raw and unstructured data, and a lakehouse combines both on open table formats. We compare the options against your needs.
Can you handle streaming and unstructured data?
Yes. Platforms can be designed for streaming event data, change data capture, documents and recordings alongside conventional batch data, with governance applied to each.
How do you validate a migration?
By reconciling outputs from the new platform against the legacy systems for an agreed period, with exceptions reviewed by the data owners before cutover.
Let's talk about your data and AI priorities
Tell us what you are trying to solve. We aim to reply within one business day.