Data architecture consulting and engineering on AWS, Azure and Google Cloud
Data Architecture Services
Design the foundation once, stop paying for rework.
Data architecture is the foundation that determines how data is collected, stored, organized, integrated and accessed across an organization. Scattered systems and poorly structured databases slow reporting and make decisions harder. A strong architecture keeps data accurate, accessible, secure and scalable across every platform and workflow.
- 2-day snapshot Your current-state architecture visualized, with next steps, in two days
- AWS Premier Tier Partnered with AWS, Microsoft Azure and Google Cloud
- SOC 2 + ISO 27001 HIPAA, PCI and GDPR zones designed into the architecture, not bolted on
- 1 business day Reply with relevant experience and a first read on your problem
What is data architecture?
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What is data architecture?
Data architecture is the design and organization of data systems, including how data is collected, stored, processed, and accessed across an organization.
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What are data architecture services?
Data architecture services include data modeling, data platform design, data integration, storage solutions, governance frameworks, and cloud data architecture planning.
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What does a data architecture company do?
A data architecture company designs scalable data systems, defines data strategies, ensures data quality, and enables efficient data flow across business applications.
Our core data architecture consulting services
Two fixed-scope entry points, then the design and build they call for. Every engagement ends with a document or a platform your team owns.
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Current-state architecture assessment
Two days with your data team to map sources, pipelines, stores and consumers, find the bottlenecks and the risks, and hand over a Day 1 and Day 2 report your team can act on.
- Two-day snapshot
- Source and pipeline map
- Risk and cost findings
- Written report
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Modernization roadmap
A four-week jump start that turns the snapshot into a target architecture, a lift-and-shift, re-platform or re-architect decision per workload, and a phased, costed plan.
- Four weeks
- Target architecture
- Decision per workload
- Costed roadmap
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Modern data architecture design Scoped from the roadmap.
Lakehouse, streaming and change data capture pipelines, catalog and lineage, designed as code on AWS, Azure or Google Cloud, with Databricks, Snowflake, Redshift or BigQuery where they fit.
- Lakehouse
- Streaming and CDC
- Catalog and lineage
- Infrastructure as code
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Data governance and compliance architecture
Access control, lineage, data quality and retention designed in from the start, so HIPAA, PCI, GDPR and SOC 2 audits become a document review rather than a project.
- Access control
- Data quality
- Retention and lineage
- Audit readiness
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Cloud data migration
Databases, warehouses and lakes moved to AWS, Azure or Google Cloud in fixed-scope phases, run in parallel and reconciled before anything is switched over.
- Assessment in two weeks
- Parallel runs
- Reconciliation
- Zero-downtime cutover
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Data warehouse and lakehouse platforms
Redshift, Snowflake, BigQuery or Databricks designed, built or migrated, with the catalog and the cost controls in place from day one.
- Redshift
- Snowflake
- BigQuery
- Databricks
Need something not listed? Ask a data architect. One business day to a first read.
What we design and build
Data platform architecture
Data platform architecture
The blueprint for your entire data estate: where data lives, how it moves and which teams access what, designed against your workloads, compliance needs and budget rather than a vendor's reference diagram.
What you get
- Target architecture with lineage diagrams
- Security zones mapped to HIPAA, PCI and GDPR
- Cost model per workload before you build
- A phased build roadmap your team can execute
Lakehouse design and build
Lakehouse design and build
Our lakehouse-first method combines affordable object storage with high-performance query engines, Redshift, Snowflake or Databricks, so one platform serves analysts, data scientists and applications alike.
What you get
- Object storage layer with open table formats
- Query engine sized and tuned to your workloads
- Batch and real-time on the same platform
- No more warehouse-versus-lake duplicate stacks
Streaming and CDC pipelines
Streaming and CDC pipelines
Change-data-capture pipelines built with Debezium and Kafka stream every insert, update and delete from operational databases into the platform, so analytics reflects the business as of now, not as of last night.
What you get
- CDC from operational databases without load spikes
- Sub-minute data freshness for dashboards
- Replayable streams for recovery and backfill
- Schema-evolution tooling that survives changes
Data catalog and lineage
Data catalog and lineage
Every table catalogued in Glue or Hive Metastore, every transformation traceable. When a number looks wrong on a dashboard, you trace it to the source in minutes, not in a week of chat archaeology.
What you get
- Central catalog with ownership and definitions
- Column-level lineage across pipelines
- Impact analysis before schema changes
- Self-service discovery for analysts
Governance, security and compliance
Governance, security and compliance
Access patterns, encryption, retention and audit trails designed into the architecture, so HIPAA, PCI and GDPR reviews find a system built for them, not around them.
What you get
- Role- and attribute-based access controls
- Security zones separating regulated data
- Audit-ready logging and retention policies
- Anonymization and masking where required
Infrastructure as code and DataOps
Infrastructure as code and DataOps
The whole platform, storage, compute, pipelines and permissions, lives in version-controlled code with automated tests and blue-green deployments, so changes ship safely and environments never drift.
What you get
- Terraform-managed platform, reviewable in Git
- CI/CD for pipelines with automated testing
- Blue-green cutovers with automated rollback
- Identical dev, staging and production
Three ways to modernize, one assessment decides
Every workload gets one of three treatments. The assessment picks which, not a default habit.
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Lift and shift
Fastest. Data and applications move as they are, no changes: the quickest path to the cloud and often the most expensive to run long term. Right for deadline-driven data centre exits and low-change legacy systems.
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Re-platform
Balanced. Targeted changes before the move, managed-service swaps and compatibility fixes: slower than a lift, cheaper every month after. Right for databases and ETL moving to managed cloud equivalents.
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Re-architect
Biggest payoff. A full redesign for cloud-native performance and scale, the option where the lakehouse pays for itself. Right for analytics platforms, AI workloads and systems at their limits.
How is data architecture implemented?
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How do companies implement data architecture?
Companies implement data architecture by analyzing business requirements, designing data models, selecting technologies, and building scalable data pipelines and storage systems.
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What is the data architecture process?
The process includes data assessment, architecture design, data modeling, technology selection, implementation, and ongoing optimization and governance.
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How long does it take to implement data architecture?
Implementation timelines vary from a few weeks to several months depending on data complexity, system scale, and integration needs.
How an engagement runs
The first two steps are the accelerators below; the rest is scoped from the roadmap they produce.
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2 days
Visualize the current state
- Problem framing and event storming
- C1 view of the systems and integration points
- Clear next steps for the modernization plan
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4 weeks
Modernization roadmap
- User journeys and C1 / C2 diagrams
- Bounded contexts and target-state options
- A draft roadmap ready for action
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Scoped from the roadmap
Design and build as code
- Target architecture in Terraform, reviewable in Git
- CDC pipelines where downtime is not an option
- Lift, re-platform or re-architect per workload
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Per workload
Validate and cut over
- Row counts, checksums and parallel runs
- Blue-green cutover with a rehearsed rollback
- Your team trained to run the platform
Our accelerators
Two fixed-scope entry points. Each ends with a document your team can act on.
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Visualize your current architecture state
2 days
- Day 1: problem framing and event storming
- Day 2: C1 view of your systems and integration points
- Outcome: a starting point for the modernization plan
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Architecture modernization jump start
4 weeks
- Weeks 1-2: discovery, user journeys, C1 / C2 diagrams
- Weeks 3-4: bounded contexts, target-state options, draft roadmap
- Outcome: a visual and strategic view, ready for action
What are the benefits of data architecture?
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Why use data architecture services?
Data architecture services help organizations manage data efficiently, improve data accessibility, and support analytics and decision-making.
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What are the key benefits of data architecture?
Key benefits include better data organization, improved data quality, enhanced scalability, stronger governance, and support for advanced analytics.
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How does data architecture improve business performance?
Data architecture improves performance by enabling faster data access, supporting real-time insights, and improving operational efficiency.
The stack behind the architecture
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Amazon S3
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Snowflake
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Databricks
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BigQuery
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Microsoft Fabric
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Amazon Redshift
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Trino
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ClickHouse
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DuckDB
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Spark
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Kafka
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Debezium
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Apache Flink
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Amazon MSK
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Amazon Kinesis
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dbt
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Airflow
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Airbyte
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AWS Glue
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Unity Catalog
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DataHub
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Great Expectations
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Terraform
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OpenTofu
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Kubernetes
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GitHub Actions
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Grafana
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Docker
FAQ
Do we need a full re-architecture, or can we modernize incrementally?
Almost always incrementally. The assessment classifies each workload as lift and shift, re-platform or re-architect. Most estates end up a mix, with the full redesign reserved for the systems where it pays for itself.
How do you keep mission-critical databases running during migration?
Change-data-capture replication keeps source and target in sync while workloads move. Blue-green deployment patterns and a rehearsed rollback make the final cutover a minutes-long switch with a known way back.
Which cloud should we choose?
It depends on your workloads, compliance profile and existing skills. We work across AWS, Azure and Google Cloud, with the deepest bench on AWS as a Premier Tier partner. The recommendation comes out of the assessment, with reasoning you can challenge.
How does this support AI and ML workloads?
A lakehouse gives models governed, catalogued, fresh data, which is the single biggest predictor of an AI project reaching production. The same platform serves BI dashboards and feature pipelines, so there is no separate ML data stack to maintain.
What does an engagement cost?
The two-day snapshot and the four-week jump start are fixed-scope offers, quoted in the first call. Build phases are estimated from the roadmap, so you see the number per phase before committing to any of them. See the accelerators.
What tools are used in data architecture?
Object storage and a query engine (Amazon S3 with Redshift, Snowflake, Databricks or BigQuery), Kafka and Debezium for streaming and CDC, dbt and Airflow for transformation and orchestration, a catalog such as AWS Glue or DataHub, and Terraform for the platform itself. The set is chosen to fit your estate, not a fixed toolkit.
What is the difference between data architecture and data engineering?
Architecture decides the structure: where data lives, how it moves, who can reach it and which controls apply. Engineering builds and runs that structure: the pipelines, the platform code, the tests. We do both, and the architecture is written down before the first pipeline is built.
What is data governance in data architecture?
The policies and controls that keep data accurate, secure and compliant: ownership and definitions in the catalog, role- and attribute-based access, retention and audit trails. Designed into the architecture, governance becomes a document review at audit time rather than a retrofit.
What are data architecture consulting services?
An assessment of how your data is collected, stored, moved and governed today, a target architecture for where it should go, and the engineering to get there. Ours start with a two-day current-state snapshot and a four-week modernization jump start, both fixed scope and fixed price.
What is modern data architecture?
A design built around a cloud lakehouse or warehouse, streaming and change data capture instead of nightly batches, a catalog with lineage, and governance and infrastructure as code. It separates storage from compute so cost scales with use, and it feeds analytics, machine learning and AI from one governed source.
What does a data architecture consultant deliver?
Documents your team can act on. From the snapshot, a map of sources, pipelines and consumers with the risks ranked. From the jump start, a target architecture, a per-workload modernization decision and a costed roadmap. From a build, the platform as code with runbooks and a handover.
Which clouds and tools do you design for?
AWS first, where we are a Premier Tier partner, plus Azure and Google Cloud. Databricks, Snowflake, Redshift, BigQuery, Kafka, Kinesis, dbt, Airflow and the catalog tools in the stack below, chosen per workload rather than by default.
Question not answered? Ask a data architect. Same-day reply on feasibility questions.
Modern data architecture consulting services
Data architecture consulting services decide how data is collected, stored, moved, governed and served across an organization. Done well, the architecture is the reason analytics, machine learning and AI projects ship on time. Done badly, it is the reason every new report needs a new pipeline. Dedicatted provides data architecture consulting and engineering on AWS, Azure and Google Cloud, from a two-day current-state snapshot to a modern data platform built as code.
What our data architecture consultants do
Our data architecture consultants start with the estate you have. Sources, pipelines, warehouses, lakes and the people who depend on them are mapped in two days, with the bottlenecks, duplicated data and risks ranked. The four-week modernization jump start turns that snapshot into a target architecture and a costed, phased roadmap, with a lift-and-shift, re-platform or re-architect decision for every workload rather than one default for all of them.
Modern data architecture, designed as code
A modern data architecture separates storage from compute, replaces nightly batches with streaming and change data capture where the business needs fresher data, and puts a catalog with lineage over everything so that access, quality and retention are governed in one place. We design on Databricks, Snowflake, Redshift or BigQuery depending on the workload, deliver the platform as infrastructure as code, and hand over runbooks so your team can operate and extend it.
Data architecture solutions that stay compliant
Healthcare, financial services, retail and manufacturing clients bring HIPAA, PCI, GDPR and SOC 2 requirements. We design access control, lineage, data quality and retention into the architecture from the start, so audits become a document review. As an AWS Premier Tier partner with the Data and Analytics competency, we also bring the AWS programs and funding that reduce the cost of a modernization.
Get started with a data architecture consultant
Outline your data platform, warehouse or governance challenge. Our team responds within one business day with relevant experience and initial technical insights.
Thanks, we have it.
Our team replies within one business day, with relevant experience and a first read on your problem.