Data Ingestion / Loading

Snowflake Data Loading

Snowflake offers a simple and scalable approach to loading data into its platform. It supports bulk data loading from various sources, including files, cloud storage, and external tables.

Snowflake Data Loading Methods

Snowflake COPY Command: The COPY command is a powerful tool for efficiently loading large volumes of data into Snowflake. It supports various file formats and allows data to be loaded from cloud storage, on-premises storage, or local files.

Snowpipe: Snowpipe is Snowflake's continuous data ingestion service. It allows you to automatically load data into Snowflake tables as soon as new data is added to a staged area in a cloud storage platform like Amazon S3 or Azure Blob Storage. Snowpipe uses event-based triggers to load data in near real-time.

Third-Party ETL Tools: Snowflake integrates with various third-party ETL (Extract, Transform, Load) tools. Popular tools like Apache NiFi, Talend, Informatica, and others can be used to design and execute data integration workflows with Snowflake.

Data Formats and Compression

Snowflake supports multiple data formats, including JSON, Avro, Parquet, ORC, and more. This flexibility allows users to choose the format that best suits their data and use case.

Compression options are also available, helping to optimize storage and reduce data transfer costs.

Concurrency and Multi-Cluster Loading

Snowflake's architecture allows for concurrent data loading, supporting parallel loading of data into tables. This enables efficient utilization of resources and faster data ingestion.

Multi-cluster loading allows you to use multiple Snowflake virtual warehouses to load data concurrently, improving performance and scalability.

Secure Data Ingestion

Snowflake provides features for secure data ingestion, including encryption of data in transit and at rest. It also supports role-based access control to manage permissions for data loading and ingestion processes.

Snowpipe

In simple terms, Snowpipe is a continuous data ingestion service provided by Snowflake that loads files within minutes as soon as they are added to a stage and submitted for ingestion. Therefore, you can load data from files in micro-batches (organizing data into small groups/matches), allowing users to access the data within minutes (very less response time), rather than manually running COPY statements on a schedule to load large batches. By loading the data into micro-batches, Snowpipe makes it easier to analyze it. Snowpipe uses a combination of filenames and file checksums to ensure that only new data is processed.

Advantages of Snowpipe

• By eliminating roadblocks, Snowpipe facilitates real-time analytics.
• It is cost-effective.
• It is simple to use.
• There is no management required.
• It provides flexibility, resilience, and so on.

Snowpipe | Snowflake Documentation

Zero-copy cloning

Snowflake’s Zero Copy Cloning is a feature which provides a quick and easy way to create a copy of any table, schema, or an entire database without incurring any additional costs as the derived copy shares the underlying storage with the original object.

1. Almost no additional storage costs for cloning data
2. No waiting time for cloning data from one environment to another
3. No need for administrative efforts, as cloning is as simple as a Select
4. Since data exists only in one place, it’s easy to maintain
5. Instantly promotes corrected/fixed data to production

Snowflake Zero Copy Cloning - A Comprehensive Guide | Encora

Snowflake Zero Copy Cloning - ThinkETL

Differences

Feature	COPY INTO (Batch Load)	Snowpipe (File-based Auto-ingest)	Snowpipe Streaming (Row-based)
Purpose	Load large volumes of data in batches	Continuously load data from files as they become available	Real-time ingestion of individual rows or small batches of data
Data Source	Files staged in S3, Azure, or GCS	Files staged in S3, Azure, or GCS	Direct API calls from applications, IoT devices, or streaming platforms
Trigger Type	Manual execution of COPY INTO command	Automatic via cloud storage event notifications or REST API calls	API calls to Snowpipe Streaming endpoints
Latency	Minutes to hours	Seconds to a few minutes	Sub-second (real-time)
File Required	Yes	Yes	No
Schema Handling	Fixed schema defined in the COPY INTO command	Fixed schema defined in the COPY INTO command	Flexible schema using VARIANT/JSON or fixed schema defined in the PIPE object
Use Case	Historical data loads, ETL jobs, data migrations	Continuous data ingestion from cloud storage	Real-time analytics, IoT data processing, clickstream data
Monitoring	COPY_HISTORY, LOAD_HISTORY views	LOAD_HISTORY, PIPE_STATUS views	STREAMING_INGEST_HISTORY view
Error Handling	ON_ERROR clause options (CONTINUE, SKIP_FILE)	ON_ERROR clause options (CONTINUE, SKIP_FILE)	Client-side error handling required
Duplicates Handling	Managed via FORCE=FALSE option or staging tables	Managed via file tracking and deduplication logic	Client-side deduplication required
Serverless	No – requires a virtual warehouse	Yes – serverless compute	Yes – serverless compute
Setup Complexity	Easy – straightforward SQL commands	Medium – requires cloud storage setup and event notification configuration	High – requires SDK integration, API key management, and client-side logic
Cost Model	Warehouse compute charges + storage costs	Serverless compute charges based on uncompressed data size	Transparent, throughput-based pricing (credits per uncompressed GB)
Best For	One-time or scheduled batch loads	Continuous ingestion from cloud storage	Real-time streaming analytics and low-latency data processing
Pros	Simple setup; predictable costs; suitable for large data volumes	Automated data loading; serverless; near real-time data availability	True real-time ingestion; flexible schema; no need for file staging
Cons	Not real-time; requires manual intervention; potential for higher latency in large loads	Minor delays due to event notification processing; less control over load timing	Higher complexity; client-side error handling; potential for API rate limits and schema management challenges

Snowpipe Streaming

Snowpipe Streaming is Snowflake’s service for continuous, low-latency loading of streaming data directly into Snowflake. It enables near real-time data ingestion and analysis, allowing high volumes of data from diverse streaming sources to be available for query and analysis within seconds.

Implementations

1. High-performance architecture
   • Uses snowpipe-streaming SDK
   • Throughput-based pricing
   • Uses PIPE objects to manage data flow and lightweight transformations
   • REST API for direct ingestion
   • Server-side schema validation
   • Optimized for high throughput and query efficiency
2. Classic architecture
   • Uses snowflake-ingest-sdk
   • Channels open directly against target tables (no PIPE object)
   • Pricing based on serverless compute and client connections
   • Reliable for existing pipelines

Choosing Implementation

• New projects: High-performance architecture for better scalability, cost predictability, and performance.
• Existing pipelines: Classic architecture continues to operate; migration optional.

Snowpipe Streaming vs Snowpipe

Category	Snowpipe Streaming	Snowpipe
Form of data to load	Rows	Files
Third-party software	Custom Java application code wrapper	None

Note: Snowpipe Streaming complements Snowpipe; it’s ideal when data is streamed as rows rather than written to files.