Fabric Data Pipelines: Orchestrating ML Feature Engineering at Scale

Microsoft Fabric’s unified data platform simplifies ML feature engineering by eliminating data movement between systems. Here’s how to build feature pipelines that scale to petabytes while maintaining data freshness for real-time ML models.

Building Feature Engineering Pipelines

Fabric notebooks integrate seamlessly with Data Factory pipelines for scheduled feature computation:

# feature_engineering_notebook.py
from pyspark.sql import SparkSession
from pyspark.sql import functions as F
from pyspark.sql.window import Window

spark = SparkSession.builder.getOrCreate()

# Read from Lakehouse
transactions = spark.read.table("bronze.transactions")

# Time-based aggregation features
window_7d = Window.partitionBy("customer_id").orderBy(
    F.col("transaction_date").cast("long")
).rangeBetween(-7 * 86400, 0)

window_30d = Window.partitionBy("customer_id").orderBy(
    F.col("transaction_date").cast("long")
).rangeBetween(-30 * 86400, 0)

customer_features = transactions.select(
    "customer_id",
    "transaction_date",
    F.sum("amount").over(window_7d).alias("spend_7d"),
    F.sum("amount").over(window_30d).alias("spend_30d"),
    F.count("*").over(window_7d).alias("txn_count_7d"),
    F.avg("amount").over(window_30d).alias("avg_txn_30d"),
    F.stddev("amount").over(window_30d).alias("stddev_txn_30d")
).dropDuplicates(["customer_id", "transaction_date"])

# Write to feature store
customer_features.write.mode("overwrite").saveAsTable("gold.customer_features")

Pipeline Orchestration

Configure Data Factory pipelines for incremental feature updates:

{
  "name": "FeatureEngineeringPipeline",
  "properties": {
    "activities": [
      {
        "name": "ComputeDailyFeatures",
        "type": "SynapseNotebook",
        "linkedServiceName": "FabricWorkspace",
        "typeProperties": {
          "notebook": {
            "referenceName": "feature_engineering_notebook",
            "type": "NotebookReference"
          },
          "parameters": {
            "process_date": {
              "value": "@pipeline().TriggerTime",
              "type": "Expression"
            }
          }
        }
      }
    ],
    "triggers": [
      {
        "type": "ScheduleTrigger",
        "recurrence": {"frequency": "Hour", "interval": 1}
      }
    ]
  }
}

Real-Time Feature Serving

For real-time inference, query the feature store directly from your model serving endpoint. Fabric’s query optimization ensures millisecond-level feature retrieval.