Data Architecture and DE Consulting: Take Control of Your Data
We design and build data systems that scale—moving you from fragmented data to architectures that execute strategy rather than create bottlenecks. The real advantage is that we've spent 18 years identifying where implementations fail, so we architect around the problems most consultancies discover too late. With 92% client retention across nearly two decades, we've proven we can deliver systems that work long after we leave.
PARTNER
PARTNER
FEATURED IN
01
Data Architecture Design
We develop scalable cloud data infrastructure (Snowflake, Redshift, Databricks, BigQuery) that consolidates all business data into a single environment. Analytics teams move from querying more than five disparate systems to accessing unified data in milliseconds, reducing query execution time by 70–80%. This is a key element of our data architecture and data platform design services.
02
ETL / ELT Pipeline Development
We create automated, fault-tolerant data collection and transformation workflows that combine data from CRM systems, marketing tools, web platforms, and internal databases into a single structured format. Our pipelines operate 24/7 with no downtime, reducing manual data reconciliation time by 95% and eliminating over 40 hours per month spent on data processing.
03
Data Modeling & Schema Optimization
We develop effective analytical data models (with data modeling and governance playing a key role) that enable fast queries, accurate analytics, and seamless integration with business intelligence tools and AI workflows. Optimized schemas reduce data processing costs by 40%, enabling the creation of a self-service analytics platform. Our expertise includes data warehouse architecture and data lake design using advanced schema optimization techniques.
04
API and System Integration
We connect internal and third-party systems—CRM, ERP, custom applications—via well-documented APIs. API-based data integration is crucial. Teams receive a single source of truth, updated in seconds rather than days, enabling real-time decision-making. This is the key to a robust data integration architecture; we can also offer middleware for data synchronization or alternatives to an enterprise service bus.
05
Data Quality & Validation Frameworks
We are implementing automated data quality management, deduplication, and validation pipelines to ensure the reliability of data fed to AI models. Data-driven decisions become 99% more reliable, reducing costly errors related to incorrect data. This also increases reliability by 85%, eliminating manual quality checks that previously took over 20 hours per week. This is part of our ongoing efforts to improve data reliability.
06
Data Governance & Access Control
We implement role-based access control, data and metadata lineage tracking, and a data governance framework that ensures compliance, empowering teams to work with data independently and securely. Organizations achieve GDPR/SOC2 compliance by reducing data access request times from two weeks to 24 hours and providing data access to 10 times more users without the risk of regulatory compliance.
07
Cloud Infrastructure Optimization
We optimize cloud storage, compute costs, and data flow performance while maintaining the scalability and resiliency of data systems. Teams achieve 35–50% cloud cost savings, 99.9% uptime, and the ability to scale from millions to billions of rows without redesigning the infrastructure. We provide specialized cloud architecture services as part of data infrastructure modernization.
08
BI Enablement & Real-Time Reporting
We develop real-time dashboards (Power BI, Looker, Tableau) and key performance indicator (KPI) tracking systems that instantly provide senior executives and managers with valuable information. This includes developing a robust BI data architecture. Executives move from monthly reports to real-time dashboards, enabling 10x faster decision-making and reducing the time to insights from days to seconds.
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Isolated Data Stores and Fragmentation
Inconsistent customer data across different systems makes it difficult to find answers to basic questions, which is a problem for any business.
- A consolidated data architecture is needed to bring all customer information together in one place.
- This approach eliminates the need to spend weeks manually reconciling data between CRM, financial, and marketing platforms.
- A unified view also enables seamless cross-functional analytics, eliminating the need for specialised data transformation layers.
Scalability And Performance Challenges
Data volumes have doubled, leading to slower queries and a sharp increase in cloud computing costs.
- Avoid using excess computing power as a solution; instead, redesign schemas and partitioning to speed up queries.
- To reduce cloud computing costs by 35–50%, optimize storage and computing resources to avoid paying for unused memory.
- Build a reliable foundation that can scale predictably to billions of rows without requiring a complete system overhaul.
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Data Quality Issues
Inaccurate data undermines all subsequent decisions.
- Duplicates, gaps, or schema changes can slip through the net, resulting in meaningless information being displayed on dashboards.
- Validate data as it arrives and block unwanted data before it enters analytics or AI models.
- Automate deduplication and maintain complete visibility so you know exactly where and why something went wrong.
Complex Integration Challenges
Connecting CRM, ERP & API systems may seem simple, but schema mismatches and missing fields can ruin everything.
- Create transformations once and reuse them instead of updating each new source.
- Use connectors that adapt to schema changes without redeployment.
- When something breaks, you can see what broke and why before it appears in your reports.
Cloud Infrastructure Optimization
You're scaling up quickly. Your cloud computing costs are growing faster than your revenue. You're caught between maintaining performance and going bankrupt.
- Stop overpaying for computing resources. Optimize your resources to control the cost curve, rather than letting it control you.
- Ensure resilience without unnecessary losses. Scale predictably from thousands to billions of rows without redesign.
- Maintain 99.9% uptime while reducing costs by 35–50%. Infrastructure that grows with you costs less, not more. This is included in our cloud architecture consulting services.
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Real-Time Reporting and BI Bottlenecks
Executives have to wait for overnight reports, while dashboards display yesterday's data. Pipeline delays slow down the decision-making process.
- Replace batch cycles with streaming pipelines. Fresh KPIs are monitored in seconds.
- There is no need to wait for data. Operations teams can act on current signals rather than outdated ones.
- Create actionable dashboards. Make real-time decisions faster than the competition.
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Data Modelling Difficulties
Incorrect schemas slow the query process. Business logic is scattered across pipelines. Rework is never-ending.
- Design once and execute queries quickly. Star schemas can reduce execution time by 60–75% without requiring constant tuning.
- Schema changes should not disrupt system operation. Create flexible models that can be adjusted to changes in source systems.
- Centralized data models provide analysts with the information they need to write queries, rather than having to struggle with the structure.
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ETL/ELT Pipeline Failures
Data pipeline failures occur due to type errors, key violations, or missing rows. You only become aware of them when analysts complain.
- Build failure detection into the pipeline architecture. Track mistakes during execution.
- Resolve transformation errors immediately. Incorrect data never makes it into reports or models.
- Deploy without downtime. Broken pipelines do not lock up the entire system.
Analytical Data Architecture Across Industries
Organizations use data systems that hide failures until they take them down during production. We stabilize the infrastructure so analytics actually work, costs are curbed, and decision-making becomes more informed. Our data architecture services are designed to overcome the challenge, delivering significant benefits during data modernization.
Enra Group is the UK's leading provider and distributor of specialist property finance.
Data parsing
We helped a law consulting company create a unique instrument to collect and store data from millions of pages from 5 different court sites. The scraped information included PDF, Word, JPG, and other files. The scripts were automated, so the collected files were updated when information changed.
These guys are fully dedicated to their client's success and go the extra mile to ensure things are done right.
All Success Stories
Technologies for Data Architecture Services
Arangodb
Neo4j
Google BigTable
Apache Hive
Scylla
Amazon EMR
Cassandra
AWS Athena
Snowflake
AWS Glue
Cloud Composer
Dynamodb
Amazon Kinesis
On premises
AZURE
AuroraDB
Databricks
Amazon RDS
PostgreSQL
BigQuery
AirFlow
Redshift
Redis
Pyspark
MongoDB
Kafka
Hadoop
GCP
Elasticsearch
AWS
Cloud Architecture Consulting Process
Fix the weakest links in your data supply chain so models stop failing in production with data architecture services.
Discovery and risk mapping
Find what breaks: Interview stakeholders, inventory sources, and rank incidents that threaten models and KPIs.
01
Contract and ingest design
Lock the source surface: Define schema/value contracts, quarantine rules, and partitioning with precise retry semantics in data architecture services.
02
Observability baseline
Make signal visible: Instrument arrival latency, failed-row rates, schema versions, and feature-level distribution metrics.
03
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Feature platform and lineage
Build reproducible surfaces: Materialize versioned features, capture complete lineage, and enable idempotent recompute with snapshots inside data architecture services.
04
Model deployment and retrain controls
Treat retrains as releases: Implement canary retrains, holdouts, gating metrics, and automated rollback for every promotion.
05
Failure modes and runbooks
Prepare for the top incidents: Create with data architecture services one-step recovery paths for late partitions, schema changes, data poisoning, and ETL failure.
06
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Automation and scaling
Reduce human toil: Add circuit breakers, backoff, incremental recompute, and auto-scaling based on observed bottlenecks.
07
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Governance and SLOs
Define operational contracts: Set SLOs for freshness, schema stability, and latency, as well as for access, masking, and audit rules in data architecture services.
08
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Handover and continuous improvement
Close the loop: Deliver dashboards, runbooks, and training, and schedule recurring game days and backlog reviews.
09
Advanced Cloud Architecture Capabilities
Your data infrastructure is usually the thing you built three years ago that nobody fully understands anymore. We help you replace it with something that scales, but more importantly, something your team can maintain without burning out.
Data Is Stored in Different Places
You've got databases in production, warehouses in the cloud, CSVs on someone's laptop, SQL Server running in a closet. When leadership asks a question, nobody agrees on the answer because everyone's looking at different data. We then take all these different versions of the truth and combine them into one single version. It's tedious work. But it's also the only thing that actually matters.
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Cost Usually Wakes People Up
Cloud bills grow faster than actual usage. The maths don't add up because your infrastructure wasn't designed for cloud economics — it was ported from on-premises thinking. We rebuild for consumption-based pricing: you only pay when you run a query. If you don't run them, you don't pay for them. It sounds obvious. But most setups don't work this way.
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Real-Time Analytics
Most companies don't really need real-time analytics; they need hourly or daily ones, which are cheaper and easier. Ask yourself: has the delay between data coming in and your team receiving it ever led to a breakdown in decision-making? If not, then batch processing is perfectly acceptable.
Data quality
The real challenge is reaching agreement on what "good" data should look like. Once that has been achieved, validation reveals irregularities at the source rather than in month-old reports. This approach saves time and highlights organizational issues you have been avoiding.
Operational Efficiency
Automating routine work that people were already doing well is obvious. But the trap is automating the human attention that was actually preventing problems. While you can dynamically deploy cloud infrastructure, a mistake that costs you $100 a month on a small scale will cost you $10,000 a month once you scale up.
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Data Warehouses
Departments created separate systems for two reasons: they didn't trust centralized data, and they had needs that the leading platform couldn't meet. A 'single source of truth' only works if people truly embrace it. This work is structural and political, not technical.
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Questions on Data Architecture
How long does a typical data architecture services project take?
Typical engagements run three to twelve months, depending on scope and scale in data architecture services. We split work into incremental sprints so you get production-safe improvements early. Expect measurable SLO wins within the first two to three sprints.
What are the key cost benefits of modernizing our data infrastructure?
You reduce compute and storage waste through better partitioning and incremental recompute. Faster failure detection lowers incident labor and business losses with data architecture services. Reusable feature surfaces speed model experiments and shorten time to value.
How do you ensure data security during migration?
We use end-to-end encryption in transit and at rest and enforce least-privilege access. Migrations run in isolated staging before any production cutover with data architecture services. Every step includes audit logging and reversible rollbacks.
Can your solutions integrate with our existing technological ecosystem?
We design adapters for your orchestration, storage, and identity systems. We prefer incremental integration to avoid rip-and-replace projects with data architecture services. Deliverables include the connector code and integration tests.
What compliance standards do you support?
We support GDPR, HIPAA, SOC2, and industry-specific audit requirements as needed in data architecture services. Controls include masking, consent flags, and retention policies tied to pipelines. We also help generate documentation for auditors.
How do you handle data from multiple, diverse sources?
We apply strict ingest contracts and per-source quarantine policies in data architecture services. Normalization happens in dedicated staging layers with lineage preserved. Late arrival and schema drift get explicit handling rules.
What level of technical expertise is required from our team?
You need senior owners for data, ML, and infra to make decisions and onboard controls. We handle the deep engineering work and provide runbooks for operators as part of our data architecture services. Minimal day-to-day input is required after initial design approvals.
How do you manage ongoing support and evolution of data systems?
We deliver runbooks, monitoring templates, and transfer ownership through shadowing sessions. Optionally, we offer managed support and quarterly game days to validate responses. Roadmaps include technical debt reduction and periodic drift reviews using data architecture services.
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