In today’s data-driven world, the ability to ingest data from cloud storage into Databricks efficiently can make or break the success of your data pipelines. Databricks provides multiple ingestion methods, each suited for specific workloads, scalability needs, and automation levels.
This guide breaks down three major ingestion options — CREATE TABLE AS (CTAS) + spark.read, COPY INTO, and Auto Loader — helping you choose the right tool for your use case.
1. CREATE TABLE AS (CTAS) + spark.read
Ingestion Type: Batch
Best For: Smaller datasets or one-time ingestion.
Key Highlights:
- Syntax & Interface:
- Python using
spark.read - SQL using
CREATE TABLE AS
- Python using
- Idempotency: ❌ Not idempotent — repeated runs may duplicate data unless manually handled.
- Schema Evolution: Manual or inferred during read.
- Latency: High (reads and processes entire dataset).
- Ease of Use: Simple — minimal setup and straightforward for ad-hoc tasks.
- Summary: Ideal for ad hoc analysis or one-time ingestion. Can be scheduled, but not optimized for incremental loads.
2. COPY INTO
Ingestion Type: Incremental Batch
Best For: Thousands of files in cloud storage, repeatable incremental ingestion.
Key Highlights:
- Syntax & Interface: SQL-based.
- Idempotency: ✅ Yes — can be run repeatedly without re-ingesting existing files.
- Schema Evolution: Supported with options (manual config may be required).
- Latency: Moderate (scheduled execution).
- Ease of Use: Simple, SQL-only — great for automation without heavy coding.
- Summary: Excellent for incremental ingestion jobs and pipelines, particularly for periodic scheduled runs.
3. Auto Loader
Ingestion Type: Incremental (Batch or Streaming)
Best For: Scaling to millions+ of files per hour and billions for backfills.
Key Highlights:
- Syntax & Interface:
- Python using
spark.readStream - SQL with Delta Live Tables (DLT) using
CREATE OR REFRESH STREAMING TABLES
- Python using
- Idempotency: ✅ Yes — designed for continuous ingestion without duplication.
- Schema Evolution: Fully automated — detects and evolves schema on the fly, handles new columns automatically.
- Latency: Low (near real-time) or high (batch), depending on configuration.
- Ease of Use: Intermediate to advanced — requires more setup but offers streaming and batch capabilities.
- Summary: Best choice for real-time streaming, high automation, and large-scale ingestion. Offers maximum scalability and flexibility.
Quick Comparison Table
| Feature | CTAS + spark.read | COPY INTO | Auto Loader |
|---|---|---|---|
| Ingestion Type | Batch | Incremental Batch | Incremental (Batch/Streaming) |
| Best For | Smaller datasets | Thousands of files | Millions+ of files/hour |
| Idempotent | ❌ No | ✅ Yes | ✅ Yes |
| Schema Evolution | Manual/inferred | Supported | Automatic |
| Latency | High | Moderate | Low/High (configurable) |
| Ease of Use | Simple | Simple, SQL-only | Intermediate/Advanced |
| Summary | One-time/ad hoc ingestion | Scheduled incremental jobs | Real-time or large-scale ingestion |
How to Choose the Right Ingestion Method
- Small, quick loads → Use CTAS +
spark.read. - Repeatable scheduled loads → Use COPY INTO.
- Continuous or large-scale ingestion → Use Auto Loader for scalability and automation.
💡 Pro Tip: If your workload needs schema evolution, idempotency, and scalability, Auto Loader is your go-to. For simpler, less frequent tasks, COPY INTO or CTAS might be all you need.