From Ingestion to Insights: Practical Data Engineering & Data Science in Microsoft Fabric

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Microsoft Fabric brings your lake, warehouse, pipelines, notebooks, and reporting together in one workspace. This guide shows how DBAs, developers, and data-warehouse teams can set it up, keep it fast and reliable, and ship both BI and data-science outputs. I’ll cover beginner to advanced moves, how it all ties together, and how to surface results in paginated reports (RDL) or Power BI.

Fabric in one picture

  • OneLake is the single data store. Lakehouse and Warehouse sit on top of Delta tables in OneLake, so Spark, T-SQL, and Power BI all work against the same files. Microsoft Learn+1
  • Data Engineering gives you notebooks, pipelines, and Dataflow Gen2 to ingest and transform. Microsoft Learn+1
  • Data Science adds notebooks with built-in MLflow for experiment tracking and model management. Microsoft Learn+1
  • Direct Lake lets Power BI read Delta tables in OneLake directly, so interactive reports can be very fast without scheduled imports. Microsoft Learn

Set up the core pieces (beginner)

  1. Create a workspace with Fabric capacity or trial.
  2. Add a Lakehouse for files and Delta tables. Use notebooks or pipelines to land data. Microsoft Learn
  3. Add a Warehouse for T-SQL workloads over the lake. It uses a lake-centric engine and reads open formats. Microsoft Learn
  4. Wire ingestion
    • Low code: Dataflow Gen2 and Pipelines.
    • Code first: COPY INTO from external storage, or CTAS with T-SQL. Microsoft Learn+1
  5. Turn on Git integration if you want version control and isolated branches per workspace. Microsoft Learn+1

A simple, end-to-end path

Ingest to the Lakehouse (Data Engineering)

  • Pipeline + Dataflow Gen2 to land and transform source data. Dataflow Gen2 uses Fabric’s SQL compute under the hood and can stage to both Lakehouse and Warehouse. Microsoft Learn
  • Or use a notebook:
# PySpark in a Fabric notebook
df = spark.read.option("header", True).csv("Files/raw/sales_2025_*.csv")
# Bronze -> Delta
df.write.mode("overwrite").format("delta").save("Tables/bronze_sales")

Optimize your Delta tables in Fabric with V-Order and table maintenance to speed reads. Microsoft Learn+2Microsoft Learn+2

Model the Warehouse (DBA & DW)

Create or load tables with T-SQL:

-- Create a table in the Warehouse
CREATE TABLE dbo.Sales (
  OrderDate  date,
  ProductId  int,
  Qty        int,
  Amount     decimal(18,2)
);

-- High-throughput load from external storage
COPY INTO dbo.Sales
FROM 'https://mystorage.blob.core.windows.net/landing/sales/'
WITH (FILE_TYPE='CSV', CREDENTIAL=(IDENTITY='Managed Identity'));

Fabric Warehouse is built for the lake and supports best-practice tuning for ingestion, table management, statistics, and querying. Microsoft Learn+1

Build experiments and models (Data Science)

Use notebooks to explore features and log runs with MLflow:

import mlflow, mlflow.sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
X = spark.table("gold_features").toPandas().drop(columns=["label"])
y = spark.table("gold_features").toPandas()["label"]

with mlflow.start_run():
    model = LogisticRegression(max_iter=200).fit(X, y)
    mlflow.sklearn.log_model(model, "model")
    mlflow.log_metric("train_accuracy", model.score(X, y))

Fabric’s Data Science experience includes built-in MLflow experiments and model management. Microsoft Learn

Serve analytics fast (BI & RDL)

Create a semantic model on Lakehouse or Warehouse and use Direct Lake so Power BI reads Delta in OneLake with in-memory performance. Microsoft Learn

Need paginated outputs like invoices or pick lists? Build RDL in Power BI Report Builder and publish to your Fabric workspace. Free users can publish to My Workspace; Pro or PPU is required for other workspaces and sharing. Microsoft Learn+2Microsoft Learn+2

How each role benefits

Database administrators

  • Performance & reliability: Warehouse over OneLake with result set caching for repeated queries, plus Delta’s ACID guarantees on data files. Microsoft Learn+1
  • Operations: pipelines for scheduled loads; Git-enabled workspaces for controlled release; Purview for lineage and governance across Fabric items. Microsoft Learn+1

Developers

  • One set of truth: same Delta tables power T-SQL, Spark, and BI.
  • Fewer copies: Direct Lake avoids import refresh and reduces ETL fragility. Microsoft Learn
  • Automated delivery: RDL subscriptions in Power BI Service or app distribution as needed. Microsoft Learn

Data-warehouse teams

  • Lake-centric Warehouse with familiar T-SQL and high-throughput COPY INTO for loads. Microsoft Learn+1
  • Performance: follow Fabric Warehouse performance guidelines for ingestion, stats, and query patterns. Microsoft Learn
  • Storage: V-Order and Delta optimization to keep scans fast for both SQL and Spark. Microsoft Learn+1

Data scientists

  • Notebook-first with MLflow experiment tracking and model registry. Microsoft Learn
  • Same lake data for features and scoring. Publish predictions back to Delta or Warehouse for BI and RDL consumption.

Performance checklist (intermediate)

  • Delta hygiene: compact small files, optimize layout, and use V-Order for fast reads in Fabric engines. Microsoft Learn
  • Direct Lake for big interactive BI models. Keep table column types and relationships clean for the semantic model. Microsoft Learn
  • Result set caching on Warehouse and Lakehouse SQL endpoints for recurring queries:
ALTER DATABASE MyWarehouse SET RESULT_SET_CACHING ON;
  • Microsoft Learn
  • Modeling: pre-aggregate “gold” tables in the lake to simplify BI and RDL. Pattern aligns with materialized-view thinking. Microsoft Learn
  • Warehouse tuning: follow Fabric’s guidance for stats, distributions, and ingestion paths. Microsoft Learn

Governance, security, and DevOps (advanced)

  • Use Microsoft Purview with Fabric to track lineage from sources through Lakehouse/Warehouse to BI artifacts and paginated reports. Microsoft Learn+1
  • Enable Git integration for your workspace so branches map to isolated workspaces and you can promote Dev → Test → Prod. Automate with the Git integration APIs if you need. Microsoft Learn+2Microsoft Learn+2

Viewing options for RDLs

  • Power BI Service / Fabric workspaces: publish RDLs with Power BI Report Builder. Share within a workspace or via apps, subject to license and access rules. Microsoft Learn+1
  • My Workspace: publish with a free license for personal testing; sharing requires Pro or PPU. Microsoft Learn

A reference blueprint you can copy

  1. Bronze: land raw files to Lakehouse with Dataflow Gen2 or COPY. Microsoft Learn+1
  2. Silver: clean and join in notebooks; write Delta with V-Order. Microsoft Learn
  3. Gold: model star schema in Warehouse; enforce data quality with T-SQL; enable result set caching. Microsoft Learn
  4. BI: build a Direct Lake semantic model for interactive dashboards. Microsoft Learn
  5. Data Science: experiment with MLflow, write predictions to a Delta table that both Power BI and RDL can read. Microsoft Learn
  6. RDL: publish paginated reports for operational documents and scheduled PDFs. Microsoft Learn
  7. DevOps & Governance: connect the workspace to Git and register items in Purview. Microsoft Learn+1

Summary

Fabric unifies your data lake and warehouse so T-SQL, Spark, BI, and paginated reporting all work off the same Delta tables. Data engineers ingest with pipelines, Dataflow Gen2, notebooks, or COPY. Warehouse teams get a lake-centric engine with performance guidance and result set caching. Data scientists track experiments with MLflow and write predictions straight back to OneLake. Power BI can query with Direct Lake for speed, and RDLs cover pixel-perfect operational reporting. Microsoft Learn+3Microsoft Learn+3Microsoft Learn+3

Final thoughts

Start with one workspace and a small medallion flow. Land data, optimize your Delta tables, and put a simple Direct Lake report on top. Add one paginated report that prints cleanly. When that’s stable, connect Git, set up a Test and Prod workspace, and bring Purview in for lineage. The wins compound because everything reads the same lake.

References

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