Azure Data

I help teams land, model, and expose data on Azure — Azure SQL and Cosmos for operational stores, Storage Accounts and Data Lake for scale, Microsoft Fabric and Azure Data Factory for integration and analytics, and Power BI for the reports leadership actually acts on.

What I help with

Get every source into Azure without hand-rolling scripts

Azure Data Factory and Fabric pipelines with linked services, integration runtimes, and self-hosted IR — scheduled, monitored, and re-runnable when something fails.

Give analysts and Power BI one place to query everything

Microsoft Fabric Lakehouse and Warehouse on OneLake, with DirectLake semantic models so reports read straight from the lake — no nightly cube refresh, no stale numbers.

Pick the right store for each workload — not SQL for everything

Azure SQL and Managed Instance for relational, Cosmos DB for globally distributed NoSQL, ADLS Gen2 for lake, Fabric Warehouse for analytics — with honest trade-offs written down.

Move on-prem SQL Server to Azure without a hard cutover

Azure SQL Managed Instance or SQL Database via Data Migration Service, with compatibility assessment, right-sizing, and a rollback plan — not a big-bang weekend.

Build Power BI reports users actually trust

Semantic models with row-level security, workspace governance, refresh strategies that hold up, and DirectLake or DirectQuery where it belongs — not just published PBIX files.

Turn OLTP data into a Lakehouse without disrupting production

Fabric Mirroring, Synapse Link, or CDC pipelines that stream operational data into the lake continuously — so analytics never blocks the transactional database.

Stop paying for storage you don’t actually need

ADLS Gen2 lifecycle policies, hot/cool/cold/archive tiering, retention rules, and Storage Account consolidation — with the savings quantified before you commit.

Secure sensitive data end-to-end

Private Endpoints for every data service, managed identities instead of connection strings, TDE and Always Encrypted, row- and column-level security, and Purview for classification and lineage.

Some Azure Data things I work with

Databases: Azure SQL Database, Azure SQL Managed Instance, SQL Server on Azure VMs, Azure Cosmos DB (NoSQL, MongoDB, PostgreSQL, Cassandra, Gremlin, Table), Azure Database for PostgreSQL Flexible Server, Azure Database for MySQL Flexible Server.

Storage & data lakes: Azure Storage Accounts, Blob Storage, Azure Data Lake Storage Gen2, access tiers (hot, cool, cold, archive), lifecycle management, immutable blob storage, SFTP on Blob.

Analytics & lakehouse: Microsoft Fabric (OneLake, Lakehouse, Warehouse, Data Engineering, Data Science, Real-Time Intelligence), Azure Synapse Analytics, Azure Databricks integration, Delta Lake, Parquet.

Integration, ETL & ELT: Azure Data Factory, Fabric Data Factory, pipelines, dataflows, linked services, integration runtimes, self-hosted IR, Fabric Mirroring, Azure Synapse Link, change data capture.

BI & reporting: Power BI (Pro, Premium Per User, Premium capacity, Fabric capacity), semantic models, DirectLake, DirectQuery and Import, row-level security, Power BI Embedded, workspace governance, deployment pipelines.

Governance & security: Microsoft Purview data catalog and lineage, Private Endpoints, managed identity, Entra ID authentication, RBAC, Transparent Data Encryption, Always Encrypted, row- and column-level security.

How I engage

1. Assess

I start with what you have today — sources, sizes, SLAs, and who consumes the data — and where the current pain is: refresh time, cost, reliability, security, or governance.

2. Blueprint

I produce a target-state architecture with the right stores, the right integration patterns, and a phased plan — costs, risks, and dependencies written down so leadership can decide.

3. Build

I deliver the platform in increments: infrastructure as code, pipelines under source control, Power BI in reviewed workspaces, and runbooks your team can actually operate on day two.