Services / Data Engineering

Data lakes and lakehouses, engineered for the AI era.

AI is only as good as the data under it. We design and build governed data platforms, and the cloud architecture they run on, across AWS, Azure Databricks, Data Factory and Microsoft Fabric: ingestion, pipelines, quality checks and access control, ready for analytics and AI.

Discuss your platform
  • Amazon S3
  • AWS Glue
  • Lake Formation
  • Delta Lake
  • Unity Catalog
  • DLT
  • DABs
  • Auto Loader
  • ADF
  • SHIR
  • Fabric OneLake
  • Direct Lake
  • Photon
What we build

Four platforms, engineered as one estate.

A · AWS

Data Lakes on AWS

  • S3 data lake design: zones, partitioning, Iceberg or Delta tables, lifecycle policies to keep storage cheap
  • Ingestion with AWS Glue, DMS for change data capture, Kinesis and Amazon MSK for streaming
  • Lake Formation and IAM for fine-grained access, tagging and audit
  • Query and serve with Athena, Redshift and EMR, chosen by workload and cost
  • Orchestration with Step Functions or MWAA (Airflow), deployed with Terraform or CDK
  • Well-Architected reviews by an AWS Certified Solutions Architect
B · AZURE DATABRICKS

Lakehouse Engineering

  • Medallion architecture — Bronze / Silver / Gold on Delta Lake with schema evolution and MERGE-based CDC
  • DLT pipelines with expectations, quarantine tables, and APPLY CHANGES INTO for SCD 1 & 2
  • Structured Streaming — Auto Loader, Kafka / Event Hubs, watermarking, stateful aggregations
  • Unity Catalog — row/column security, dynamic views, ABAC tags, lineage, audit via system tables
  • DABs — jobs, dev → uat → prod promotion, CI/CD via GitHub Actions / Azure DevOps
  • Performance & cost — Photon, Liquid Clustering, Predictive Optimization, cluster policies, chargeback
C · AZURE DATA FACTORY

Orchestration & Ingestion

  • Metadata-driven ingestion — a control table drives source, load type, and target; one pipeline, hundreds of tables
  • Copy patterns: full, incremental watermark, CDC, upsert-to-Delta via staging
  • SHIR for on-prem SQL Server, Oracle, SAP, SFTP — HA node pairs, auto-update rings
  • Connectors daily: SAP CDC, Salesforce, Dynamics 365, ServiceNow, Snowflake, BigQuery, REST/OAuth
  • Managed Identity + Key Vault linked services — no secrets in JSON
  • Git-integrated deployment, Log Analytics + KQL alerting, lineage into Purview
D · MICROSOFT FABRIC

Unified Analytics Platform

  • OneLake architecture — shortcuts to ADLS Gen2 / S3 / Dataverse, no data duplication
  • Lakehouse & Warehouse — Delta-native, T-SQL endpoint, cross-workspace shortcuts
  • Direct Lake mode in Power BI — no import, no DirectQuery latency
  • Real-Time Intelligence — Eventstreams, KQL Databases, Activator alerting
  • Fabric ↔ Databricks interop — UC mirroring to OneLake; we design the boundary by cost, governance, and skill mix
  • Capacity management — F-SKU sizing, autoscale, workload isolation, pause/resume automation
Cloud architecture for data

The platform under the platform.

A lakehouse is only as reliable as the cloud estate it runs in. We design that too:

  • Landing zones for AWS and Azure: accounts or subscriptions per environment, guardrails and policies.
  • Networking: private endpoints and PrivateLink, VNet injection, no public data paths.
  • Identity: IAM roles, managed identities, Lake Formation and Unity Catalog permissions that line up.
  • Cost: tagging, budgets, cluster policies and storage lifecycle rules.
  • Infrastructure as code: Terraform, CDK or Bicep for every resource, promoted dev → uat → prod.
CLOUD ACCOUNT · LANDING ZONEPRIVATE NETWORKGATEWAYCOMPUTEDATAIAMKMSLOGSCOSTINFRASTRUCTURE AS CODE · TERRAFORM / CDK / BICEP
Reference architectures

Patterns we deliver, drawn as pipelines.

Seven reference architectures we design and build. Each one comes with a diagram, infrastructure as code, and a runbook.

01

Enterprise Lakehouse on Databricks + ADF

  1. ADF ingestion
  2. ADLS Gen2 Bronze
  3. DLT Silver/Gold
  4. Unity Catalog
  5. Power BI / DBSQL
02

Fabric-Native Analytics Platform

  1. Sources
  2. Fabric Pipelines
  3. OneLake Lakehouse
  4. SQL Endpoint
  5. Power BI Direct Lake
03

Hybrid Databricks + Fabric

  1. Databricks Gold (UC)
  2. OneLake shortcut
  3. Power BI Direct Lake
04

Real-Time Streaming Pipeline

  1. Event Hubs
  2. Structured Streaming
  3. Delta streaming tables
  4. Alerts (Activator / DBSQL)
05

CDC from On-Prem SQL / Oracle / SAP

  1. SHIR
  2. ADF CDC connector
  3. Bronze Delta
  4. APPLY CHANGES INTO
  5. SCD2 Silver
06

RAG Agent on Enterprise Docs

  1. ADLS
  2. Auto Loader
  3. Chunking
  4. Vector Search
  5. Agent Bricks
  6. Model Serving
07

Cost-Optimized Modern Data Stack

  1. Serverless SQL
  2. Job clusters
  3. Liquid Clustering
  4. Predictive Optimization
  5. Tagged chargeback
Engineering standards

Non-negotiables on every engagement.

These hold on every project, whatever the platform. They are what makes a data platform something your team can run after we leave.

  • 01Every pipeline ships with unit tests (pytest, chispa, dlt.expect) and data quality tests
  • 02All code deployed via DABs / ARM / Terraform — zero UI-clicks in production
  • 03Git-first: feature branches, PRs, code review, protected main, semantic versioning
  • 04CI/CD: lint (ruff, black), test, deploy dev → uat → prod with approvals
  • 05Environment isolation via UC catalogs (dev_*, uat_*, prod_*) and separate ADF factories
  • 06Observability: Log Analytics + DLT event logs + system.access.audit + ops dashboards
  • 07Cost guardrails: cluster policies, budget alerts, system.billing.usage chargeback
  • 08Security baseline: Private Link everywhere, managed identity, Key Vault-backed secret scopes, PII tagging
  • 09Documentation: ADRs, runbooks, data dictionaries auto-generated from Unity Catalog
Migrations

Legacy in, lakehouse out.

SSIS
ADF / Databricks Workflows
On-prem SQL Server / Oracle DW
Lakehouse or Fabric Warehouse
Synapse Dedicated SQL Pool
Fabric Warehouse / Databricks SQL
Synapse Spark
Databricks / Fabric Spark
Hadoop / Cloudera
Databricks or an S3 data lake on AWS
Legacy data marts
Medallion Gold layer
Power BI Import mode
Direct Lake on Fabric
Hive Metastore
Unity Catalog (SYNC + governance uplift)
Technology matrix

The breadth, in one grid.

INGESTION
AWS Glue, AWS DMS, Kinesis, Amazon MSK, ADF, Fabric Data Factory, Auto Loader, Event Hubs, Kafka, Fivetran, Debezium, SAP CDC
STORAGE
Amazon S3, ADLS Gen2, OneLake, Delta Lake, Apache Iceberg, Parquet
PROCESSING
Databricks Spark, DLT, Amazon EMR, Athena, Redshift, Fabric Spark, Databricks SQL, Fabric Warehouse
GOVERNANCE
Unity Catalog, AWS Lake Formation, Microsoft Purview, OneLake Catalog, sensitivity labels
STREAMING
Structured Streaming, Fabric Eventstream, KQL Database, Activator
AI / ML
Agent Bricks, MLflow 3.0, Vector Search, Model Serving, AI Gateway, Feature Store
BI
Power BI (Import / DirectQuery / Direct Lake), Databricks SQL Dashboards, AI/BI Genie
DEVOPS
Terraform, AWS CDK, Databricks Asset Bundles, ARM/Bicep, GitHub Actions, Azure DevOps
SECURITY
IAM, KMS, PrivateLink, Managed Identity, Key Vault, customer-managed keys, VNet injection
OBSERVABILITY
CloudWatch, Log Analytics, KQL, Databricks system tables, DLT event log, Grafana
LANGUAGES
Python, PySpark, Spark SQL, T-SQL, KQL, Scala, Terraform HCL
Packaged offers

Well-bounded starting points.

Scope agreed up front, timelines confirmed after a short discovery call. Every deliverable lands in your repository, not in a slide deck.

TYPICALLY 3 WEEKS

AWS Data Lake Foundation

S3 lake layout, Glue catalog and crawlers, Lake Formation permissions, one ingestion pipeline end to end, Athena access, Terraform repo, CI/CD, cost alarms, runbook.

TYPICALLY 3 WEEKS

Lakehouse Foundation on Databricks

Workspace + Unity Catalog + external locations, cluster policies, DABs repo scaffold, sample DLT pipeline (Bronze→Silver→Gold), GitHub Actions CI/CD, cost + audit dashboards, runbook.

TYPICALLY 3 WEEKS

Fabric Adoption Kickstart

Capacity sizing, workspace + domain design, OneLake structure, one end-to-end pipeline into a Lakehouse, Power BI Direct Lake report, capacity monitoring app.

TYPICALLY 4 WEEKS

ADF Metadata-Driven Ingestion Framework

Control-table-driven pipeline, ~10 source connectors wired, Key Vault + Managed Identity, incremental & CDC support, DevOps CI/CD, Log Analytics alerts.

1 WEEK · FIXED SCOPE

Databricks Health Check & Cost Review

Written report: cluster config, job vs. all-purpose usage, Photon opportunities, storage layout, UC compliance, top 10 cost savings, remediation backlog.

5 WEEKS

Agent Bricks Production Agent

Source ingestion + chunking, Delta-synced Vector Search index, agent with tools, evaluation dataset + LLM-judge scorers, Model Serving endpoint, guardrails, MLflow trace monitoring.

SCOPED PER ESTATE

Synapse / SSIS → Databricks Migration

Inventory, dependency map, wave plan, converted pipelines, parallel-run validation, cutover runbook.

From experience and experiments

Patterns we know first-hand.

Drawn from our engineers’ earlier work and from our own experiments. Shown as technical patterns, without client names or figures.

Change data capture from SAP into a governed Gold layer

SAP CDC connector in ADF → Bronze Delta on ADLS Gen2 → DLT APPLY CHANGES INTO for SCD2 in Silver → aggregated Gold with Liquid Clustering. Watermark drift alerting via Log Analytics and system tables.

Direct Lake reporting without copying data

A Databricks-owned Gold table mirrored to Fabric OneLake through a shortcut, served to Power BI in Direct Lake mode: no import and no duplicate storage.

Cutting SQL warehouse cost without slowing queries

Moving warehouse queries to Serverless, materialised views on Silver, Predictive Optimization and Deletion Vectors, with query latency tracked before and after each change.

A RAG agent grounded on enterprise documents

Auto Loader → parsing → chunking → embeddings → Vector Search Delta Sync index → agent with tool calling → Model Serving, with MLflow traces and LLM-judge scorers for continuous evaluation.

FAQ

Data engineering questions.

Should we build on Databricks or Microsoft Fabric?

It depends on your estate, skills and cost profile. Databricks suits heavy engineering and ML workloads; Fabric suits all-Microsoft estates that centre on Power BI. Many estates use both, with Databricks owning Gold tables and Fabric serving them through OneLake shortcuts. We design that boundary with you.

What is a medallion architecture?

A layered lakehouse design: Bronze holds raw data as it arrived, Silver holds cleaned and conformed data, and Gold holds business-ready tables. Each layer is tested, so problems are caught before they reach reports or AI systems.

Can you migrate us off SSIS, Synapse or Hadoop?

Yes. We inventory what you run, map dependencies, plan migration waves, convert pipelines, run old and new in parallel to validate results, and cut over with a runbook.

Do you set up the cloud environment as well as the pipelines?

Yes. We design the landing zone, networking, identity, security and cost controls on AWS or Azure, and deploy them as code alongside the data platform, so your team inherits a well-architected estate rather than just a set of pipelines.

Want a second opinion on your data platform?

Data lakes, Databricks workspaces, Data Factory pipelines or Fabric capacities: we review what you run and give you a written report on cost, performance, governance and security.

Talk to us