DataKnits | Oracle Database to Snowflake Migration

Why Move Analytics Off Oracle and Into Snowflake

Oracle licensing and infrastructure costs push many teams to move analytical workloads to a cloud warehouse like Snowflake, while keeping Oracle for the transactional systems that still need it. That migration usually means rebuilding a large set of legacy ETL jobs by hand — the part DataKnits is built to remove.

How DataKnits Builds the Pipeline

✓ Connect to Oracle and introspect schemas and tables
✓ Map source structures to Snowflake tables on the visual canvas
✓ Run as a one-time cutover migration or a phased, ongoing sync
✓ Compile to native SQL push-down or PySpark — no proprietary migration runtime
✓ Reuse the same canvas for other Oracle-sourced pipelines as they come up

Compiled Output (Example)

df_oracle = spark.read.format("jdbc")
  .option("url", "jdbc:oracle:thin:@source-db")
  .option("dbtable", "FINANCE.GL_ENTRIES").load()

df_oracle.write.format("snowflake")
  .options(**sf_options)
  .option("dbtable", "ANALYTICS.GL_ENTRIES")
  .mode("append").save()

Frequently Asked Questions

How do I migrate Oracle Database to Snowflake?

Connect DataKnits to your Oracle instance, select the schemas or tables to migrate, map them visually to Snowflake, and DataKnits compiles the extraction and load into native code — useful both for a one-time migration and for keeping Snowflake current with Oracle on an ongoing basis.

Is this a one-time migration or can it run continuously?

Both are supported. Run the pipeline once for a cutover-style migration, or schedule it (or configure Change Data Capture) to keep Snowflake continuously synchronized with Oracle during a phased migration.

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