Data & Analytics services on AWS, Azure and Google Cloud
Data & Analytics Services
The foundation designed once, every pipeline tested before it runs on your data.
Dedicatted designs, builds and migrates the data platforms enterprises run on: architecture, lakehouse platforms on Databricks and the AWS data stack, cloud data migration and the governance that comes with them, delivered by engineers who work with AI and prove every pipeline against a baseline before cutover.
- 2-day snapshot Your current-state architecture visualized, with next steps, in two days
- From $10,000 Fixed-scope migration assessment; the price is set before any migration work
- AWS Premier Tier Partnered with AWS, Microsoft Azure, Google Cloud and Databricks
- SOC 2 + ISO 27001 HIPAA, PCI and GDPR zones designed into the architecture, not bolted on
What are data and analytics services?
What are data and analytics services?
Data and analytics services design, build and run the platforms that collect, store, govern and analyze a company’s data: data architecture, warehouses and lakehouses, pipelines, migration to the cloud, and the reporting and analytics on top.
What do data and analytics services include?
They include current-state assessment, target architecture, lakehouse and warehouse build on Databricks, Redshift or Snowflake, streaming and CDC pipelines, catalog and lineage, governance and compliance zones, and cloud data migration in fixed-scope phases.
What does a data and analytics company do?
A data and analytics company turns scattered systems into one governed platform: it profiles the estate, designs the target model, builds and tests the pipelines against a baseline, migrates the data and trains the team to run it.
Our core data and analytics services
Data Architecture
Design the foundation once: how data is collected, stored, organized, integrated and accessed across the organization, with the target model set by engineers and a roadmap your team keeps.
- Current-state snapshot
- Target architecture
- C1 and C2 diagrams
- Roadmap
Data Warehouse & Lakehouse Platforms
Build the platform on object storage with Redshift, Snowflake or Databricks on top, with streaming and CDC pipelines, a catalog with lineage, and infrastructure as code from day one.
- Databricks
- Redshift and Snowflake
- Streaming and CDC
- Catalog and lineage
- DataOps
Cloud Data Migration
Move warehouses, pipelines, reports and databases to AWS, Azure or Google Cloud in fixed-scope phases, proved on a proof of concept first and reconciled before anything is switched off.
- Assessment from $10,000
- Parallel runs
- Reconciliation reports
- Cutover and training
Governance, Security & Compliance
Design HIPAA, PCI and GDPR zones into the architecture, put access control and lineage in place, and keep the platform audit-ready under the same practices we hold ourselves to.
- Compliance zones
- Access control
- Lineage
- Audit readiness
Need something not listed? Ask a data architect. One business day to a first read.
Our accelerators
Three ways to start. Each has a fixed scope and ends in something your team keeps.
Architecture snapshot
2 days, fixed scope
Your current-state architecture visualized, with next steps, in two days.
- Day 1: problem framing and event storming
- Day 2: C1 view and integration points
- A starting point for the plan
Modernization jump start
4 weeks, fixed scope
Discovery, diagrams, target-state options and a draft roadmap.
- Weeks 1 to 2: discovery and diagrams
- Weeks 3 to 4: target state and roadmap
- A visual and strategic view, ready for action
Data migration assessment
Fixed scope from $10,000
Every object and pipeline inventoried, the migration scoped and priced in phases.
- Inventory of objects and pipelines
- Phases with parallel runs planned
- A price before any work begins
How are data and analytics services delivered?
How do companies use data and analytics services?
Companies use them to consolidate reporting on one platform, to move warehouses and pipelines to the cloud, to build the governed foundation AI needs, and to bring lineage and access control to data that regulators ask about.
What is the data and analytics process?
The process runs snapshot, design, build, migrate: the current state visualized in two days, a target architecture and roadmap in four weeks, pipelines built and tested against a baseline, then a phased migration with parallel runs and reconciliation before cutover.
How long do data projects take?
The architecture snapshot takes two days and the modernization jump start four weeks. Migration phases are scoped after the assessment and typically run in months, with each phase priced before it starts.
How an engagement runs
Engineers use AI at every stage. A person owns every pipeline that ships.
-
Snapshot
The current state, visualized
- Problem framing and event storming
- C1 view of systems and integration points
- The starting point for the plan
-
Design
The target model set by engineers
- Bounded contexts and target-state options
- Compliance zones designed in
- A roadmap with phases and prices
-
Build
Pipelines proved before they run on your data
- Lakehouse or warehouse built as code
- Every pipeline tested against a baseline
- Catalog, lineage and access control in place
-
Migrate and run
Cutover with parallel runs
- Row counts, checksums and reconciliation reports
- Cutover only when the numbers match
- Your team trained to run the platform
What are the benefits of data and analytics services?
Why use data and analytics services?
They put a governed, cloud-native data platform in place without a year of internal build: the architecture set by engineers who have done it, and every pipeline proven against a baseline before it runs on your data.
What are the key benefits of data and analytics services?
One source of truth for reporting, faster and cheaper analytics on cloud storage, compliance zones designed in, lineage that answers auditors, and a foundation ready for machine learning.
How do data and analytics services improve business performance?
By shortening the path from a question to a trusted answer, retiring the systems that slowed reporting, and giving AI and analytics teams data they can rely on.
The stack behind the practice
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Databricks -
Amazon Redshift -
Snowflake -
BigQuery -
Kafka -
Debezium -
Airflow -
dbt -
Amazon S3 -
Apache Iceberg -
Spark -
Trino -
Unity Catalog -
DataHub -
Great Expectations -
QuickSight
FAQ
Which platforms do you build on?
Object storage on AWS, Azure or Google Cloud with Redshift, Snowflake or Databricks on top, chosen for your workloads rather than a default. We are partnered with AWS and Databricks.
Do we have to migrate everything at once?
No. Migration runs in fixed-scope phases, each proved on a proof of concept and priced before it starts, with parallel runs until the numbers reconcile.
How do you keep the data accurate through a migration?
Row counts, checksums and reconciliation reports at every phase, and cutover only when they match. Pipelines are tested against a baseline before they run on your data.
Who owns the platform afterwards?
You do. Infrastructure, pipelines and models are delivered as code into your repositories and accounts, and your team is trained to run them.
Can you handle regulated data?
Yes. HIPAA, PCI and GDPR zones are designed into the architecture, access follows your policies, and our own delivery is audited under SOC 2 and ISO 27001.
Where do we start if we are not ready for a migration?
With the two-day architecture snapshot. It maps the systems your reports come from and gives you a starting point for a plan, with no migration commitment.
How do you use AI in delivery?
Our engineers use AI at every stage: profiling an estate, drafting pipelines, tests and documentation. Every result is verified by an engineer against a baseline before it ships.
Do you offer data warehouse consulting?
Yes. Our data warehouse consulting services cover assessment, design, build or migration on Amazon Redshift, Snowflake, BigQuery and Databricks, in fixed-scope phases with parallel runs before cutover.
Question not answered? Ask a data architect. Same-day reply on feasibility questions.
What are data and analytics services?
Data and analytics services help businesses design, build and migrate the platforms their data runs on: data architecture, scalable lakehouse and warehouse platforms on Databricks, Amazon Redshift and Snowflake, streaming pipelines, data governance and cloud data migration in fixed-scope phases. Dedicatted delivers data and analytics services on AWS, Microsoft Azure and Google Cloud for enterprise clients, with the architecture set by engineers and every pipeline tested against a baseline.
Data architecture, cloud data migration and analytics
Dedicatted helps organizations consolidate reporting on one governed, scalable data platform, move warehouses, pipelines and databases to the cloud, and build the secure data foundation that analytics and AI depend on. Our data engineers and architects combine data architecture, lakehouse engineering, streaming and CDC pipelines, catalog and lineage, governance for HIPAA, PCI and GDPR, and cloud data migration with parallel runs and reconciliation before cutover.
Our data and analytics services include:
- data architecture and a two-day current-state snapshot
- lakehouse and data platform build on Databricks, Redshift, Snowflake and BigQuery
- streaming, CDC and data pipeline engineering
- data catalog, lineage and governance
- cloud data migration and modernization in fixed-scope, priced phases
- analytics and BI on AWS, Azure and Google Cloud
Get started with a data architect
Tell us which systems your reports come from and what slows them down. Our team responds within one business day with relevant experience and a first read.
Thanks, we have it.
Our team replies within one business day, with relevant experience and a first read on your problem.