DataKnits | MongoDB to Redshift Data Pipeline

Why Move MongoDB Data Into Redshift

MongoDB's flexible document model is great for application data, but BI tools and SQL analysts expect a relational, columnar structure. Loading MongoDB collections into Redshift makes that data queryable alongside the rest of a company's structured warehouse data.

How DataKnits Builds the Pipeline

✓ Connect to MongoDB and select source collections
✓ Flatten nested documents and arrays visually into a relational shape
✓ Map the flattened schema to Redshift tables
✓ Compile to native PySpark for the transform, with a Redshift-native load step
✓ Run as a scheduled batch job or a continuous sync

Compiled Output (Example)

df_mongo = spark.read.format("mongodb")
  .option("collection", "orders").load()
df_flat = df_mongo.select(
  "order_id", "customer.email",
  explode("items").alias("item"))

df_flat.write.format("redshift")
  .option("dbtable", "public.order_items")
  .mode("append").save()

Frequently Asked Questions

How do I build a MongoDB to Redshift data pipeline?

Connect DataKnits to your MongoDB collections as the source, use the visual mapping canvas to flatten nested documents and arrays into a relational structure, point the destination node at Redshift, and DataKnits compiles the transformation and load into native PySpark code.

How does DataKnits handle nested MongoDB documents when loading into a relational warehouse like Redshift?

The visual mapping canvas lets you explicitly flatten nested fields and arrays into separate columns or child tables before the data reaches Redshift, rather than requiring a hand-written flattening script.

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