Microsoft Fabric · 13 min read · May 2026
Microsoft Fabric Data Engineering in 2026: Architecture Decisions That Actually Matter
By Thinklytics Partners, Microsoft Practice
OneLake, Spark vs T-SQL, Direct Lake mode, Eventstream, pipeline orchestration. The architecture decisions data engineering teams actually have to make once their organization picks Fabric, from inside Thinklytics engagements.
Topics covered
- Microsoft Fabric data engineering
- OneLake architecture
- Direct Lake mode
- Fabric Spark vs T-SQL
- Fabric Eventstream
- Fabric pipeline orchestration
- Fabric Lakehouse vs Warehouse
- Synapse to Fabric migration
Frequently asked questions
What does OneLake actually replace?
OneLake is Fabric's unified storage layer, built on ADLS Gen2 under the hood and addressable via a single tenant-wide namespace. For new builds it replaces the per-workspace ADLS containers, the Synapse dedicated SQL pool storage, and the Power BI Premium dataset cache that teams used to manage separately. The shortcut feature lets a single OneLake path mirror data from S3, ADLS, or Dataverse without copying, which is the main 2026 reason teams pick it over warehouse-native storage.
When should we use Spark vs T-SQL inside Fabric?
Spark wins for unstructured data prep, ML feature engineering, and any code your team already wrote in PySpark for Synapse or Databricks. T-SQL wins for analytical workloads where SQL ergonomics, query optimizer maturity, and dashboard latency dominate. The decision is per workload, not per company. Most production Fabric tenants run both in parallel: Spark notebooks for the messy ingestion and feature layer, T-SQL warehouses for the cleaned analytical layer.
Direct Lake mode vs Import vs DirectQuery, which Power BI mode is right?
Direct Lake is the 2026 default for Fabric-native deployments above 10 GB. It reads Delta files in OneLake without an import step, gets near-Import performance, and side-steps cache refresh complexity. Import still wins for small datasets under 1 GB and for workloads that hit data sources outside OneLake. DirectQuery survives only for actual live-source requirements that your source system can sustain at query rate.
How do we migrate Synapse Spark notebooks to Fabric?
Three steps. Move the notebook code as-is, repoint storage paths from ADLS to OneLake, and rebuild the pipeline orchestration in Fabric Data Factory. Plan 4 to 8 weeks per Synapse workspace including testing. The trap is environment parity: secrets, linked services, and managed identities all have different shapes in Fabric, and notebooks that worked under Synapse-managed identity often fail under Fabric Trusted Workspace Access until that's reconfigured.
What is the right team structure for Fabric data engineering?
For a tenant running 4 to 8 workloads in production: 1 platform owner (capacity, governance, sec), 2 data engineers (pipelines + Lakehouse), 1 analytics engineer (semantic models + Direct Lake), and 1 part-time architect across all four. Below 4 workloads, collapse to 2 to 3 people. Above 12 workloads, split platform ownership from workload ownership and add a SRE-style on-call for capacity events.
Can we use dbt with Fabric?
Yes, via dbt-fabric (the Microsoft-maintained adapter) targeting Fabric Warehouse or Lakehouse SQL endpoints. The pattern works well for cleaned analytical layers where T-SQL is the right engine. dbt does not target Spark Notebooks in Fabric: that layer stays in native PySpark or Fabric notebook code. Most teams that ship dbt with Fabric run dbt for the gold layer and Spark notebooks for bronze and silver.
How does Apache Iceberg fit into Fabric's storage model?
OneLake is Delta-Parquet native, not Iceberg-native. Microsoft has shipped read support for external Iceberg tables via the Fabric mirroring feature in 2026, but write workloads still target Delta. Teams that need true Iceberg portability across Snowflake, Databricks, and Fabric typically standardize on external Iceberg tables and use OneLake as a query surface, accepting the small write overhead.
How does Thinklytics scope a Fabric data engineering engagement?
We start with a 4-week current-state assessment (capacity, workload inventory, OneLake vs ADLS posture, Power BI mode audit), then a phased build sized to the workload count. Most net-new Fabric tenants land at $260,000 to $540,000 for the foundation plus the first two workloads in production. Synapse-to-Fabric migrations scale higher because the existing inventory drives the work. Read the [Microsoft Fabric consulting](/insights/microsoft-fabric-consulting-2026) article for the procurement-side companion to this.