Data Engineering on AWS, Azure, Databricks & Snowflake

Technology we work

Our Track Record

20+ Years in Software Development

8+ Years in Data Platforms & Analytics

100+ Successful Projects

50+ Happy Clients

About Our Expertise

We're Microsoft, Databricks & Snowflake Partner with core strengths in AI-driven data engineering & cloud architecture and enterprise web development with ASP.NET Core & C#. Our team has 20+ years of software development experience and 8+ years focused on data platforms, databases, data engineering and AI-powered analytics.

We design and run data lakes/lakehouses, vector databases and metadata-driven ETL/ELT on AWS, Azure, Databricks and Snowflake, covering streaming, governance, performance, cost control and GenAI readiness. In parallel, we build production-grade ASP.NET Core web apps, AI-enabled APIs and microservices (containerized and CI/CD-driven) so your data and applications move in lockstep.

Enterprise Data Solutions

Most organisations do not have a data problem so much as a sprawl problem. Sources multiply, pipelines get added one at a time and nobody owns the whole picture. The result is a platform that technically works but nobody trusts, costs more every quarter and takes weeks to change.

We work the other way round. We start from the questions the business needs answered, design the smallest architecture that answers them reliably and then make it fast and cheap to run. That usually means a lakehouse on Databricks or a warehouse on Snowflake, fed by metadata-driven pipelines, governed centrally and surfaced through a semantic layer your analysts can actually use. Where generative AI is on the roadmap we build the retrieval layer alongside it, so the models answer from governed data rather than a copy nobody is maintaining.

Everything we build is designed to be handed over. You get the architecture documentation, the reasoning behind each decision, the runbooks and the enablement your engineers need to own it. We would rather be kept on because the work is good than because nobody else can operate what we left behind.

What We Build

Databricks Consulting

Medallion lakehouse architecture, Delta Lake table design, Apache Spark optimization and Unity Catalog governance.

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Snowflake Data Engineering

Warehouse and role design, Snowpark pipelines, secure data sharing and credit consumption you can forecast.

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Azure Data Platform

Azure Data Factory, Synapse Analytics, Microsoft Fabric, Azure SQL and governed Power BI semantic models.

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AWS Data Engineering

S3 data lakes in open formats, AWS Glue, Redshift modelling, EMR and Athena with cost and security guardrails.

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ETL & Data Pipelines

Metadata-driven ELT, real-time streaming and orchestration with Dagster and Airflow, with data quality tests built in.

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Web & Application Development

Production ASP.NET Core applications, AI-enabled APIs and containerized microservices delivered through CI/CD.

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AI-Ready Data Infrastructure

Generative AI is only as good as the data underneath it. We build the retrieval layer, the embeddings and the governance that turn a data platform into something an LLM can safely answer from.

RAG Pipelines

Retrieval augmented generation wired into your own content: chunking, embedding, retrieval and evaluation you can measure rather than guess at.

Document chunking and metadata enrichment
Embedding generation and refresh
Hybrid keyword and semantic retrieval
Grounding, citations and evaluation harnesses

Vector Database Integration

Vector storage sitting next to your existing warehouse rather than as a separate system nobody owns.

OpenSearch vector engine and k-NN indexes
pgvector on PostgreSQL and Azure Database
Databricks Vector Search and Snowflake Cortex Search
Index sizing, recall tuning and cost control

LLM Data Preparation

The unglamorous work that decides whether a GenAI project ships: cleaning, deduplicating and permissioning the source data.

Extraction from PDF, SharePoint and ticketing systems
PII detection, redaction and masking
Row and document level access control at retrieval time
Golden datasets for evaluation and regression testing

We work with the managed model services already inside your cloud rather than adding another vendor: Snowflake Cortex AI, AWS Bedrock and Azure OpenAI. That keeps your data inside your own security boundary and keeps inference spend on a bill you already understand.

What Clients Get Out of It

Pipelines That Finish On Time

Long-running Spark and warehouse jobs profiled and rebuilt so the morning reports are ready before the business opens.

Cloud Spend Under Control

Right-sized compute, cluster policies and resource monitors so the monthly bill is predictable and attributable.

Governance That Passes Review

Central catalogues, row and column level security, lineage and audit trails your security team can sign off.

Migrations Without Drama

On-premises to cloud and cloud-to-cloud moves run in parallel with reconciliation before cutover.

Teams That Can Take Over

Documentation, runbooks and enablement so your engineers own the platform after handover.

Direct Access to Engineers

You talk to the people doing the work. No account layer between you and the engineering.

Read Client Testimonials

Frequently Asked Questions

What does Advancing Data Solutions actually do?

We design, build and operate cloud data platforms. That covers data lakes and lakehouses, ETL and ELT pipelines, data warehouses and the analytics layer on top, on AWS, Azure, Databricks and Snowflake. We also build the enterprise web applications and APIs that sit alongside those platforms using ASP.NET Core and C#.

Which cloud platforms do you specialise in?

AWS and Azure for infrastructure, with Databricks, Snowflake and Microsoft Fabric for data platforms. We are a Microsoft, Databricks and Snowflake Partner, and our engineers hold certifications across all four including AWS Data Engineer Associate, Azure Data Engineer Associate, Databricks Data Engineer Associate and SnowPro Core.

Do you work on existing platforms or only new builds?

We handle both. Most of our work focuses on existing platforms rather than greenfield builds. A typical engagement starts with pipelines that run too long, cost too much or fail without anyone noticing. We assess what is there, fix what is breaking and then improve the architecture underneath.

How quickly can you start?

We reply to every enquiry within 24 hours on business days. A free consultation is a 30 to 60 minute call, after which you get a written proposal with recommendations, a timeline and pricing. Most engagements begin within two weeks of that proposal being accepted.

How do you control cloud costs?

Cost work is part of the engineering, not a separate exercise. That means right-sizing compute, tuning the queries and jobs that dominate spend, setting cluster policies and resource monitors and tagging workloads so spend is attributable to a team or product.

How do you handle security and governance?

Governance is designed in rather than added later. That means least-privilege access through Unity Catalog, Snowflake roles or IAM, row and column level security where the data requires it, encryption in transit and at rest, plus lineage and audit trails your security team can review before sign-off.

Can you make our data platform ready for AI and GenAI?

Yes, and that is a data engineering problem before it is a model problem. We build the retrieval layer: documents chunked and embedded on a schedule, vectors in OpenSearch, pgvector, Databricks Vector Search or Snowflake Cortex Search, sensitive fields redacted before indexing and permissions enforced when a document is retrieved. We use the model services already in your cloud such as AWS Bedrock, Azure OpenAI or Snowflake Cortex AI. We also embed alongside your own engineers as often as we deliver a scoped piece of work.

What happens when the engagement ends?

You get documentation, runbooks and enablement for your own engineers. We build platforms your team can operate without us. Several clients keep us on for ongoing support, but that is a choice rather than a dependency we engineer in.

Enterprise Cloud Data Engineering & AI Infrastructure