Let's Talk

Free Cost Analysis

In the world of data analytics, Snowflake has emerged as a dominant player, captivating the hearts and minds of data enthusiasts everywhere. As the popularity of Snowflake continues to soar, one crucial aspect that every data professional must master is the art of data ingestion. After all, to unlock the full potential of Snowflake’s powerful query engine, you need to get your data into the system in the first place.

In this blog post, we’ll dive deep into the various methods available for ingesting data into Snowflake, exploring the pros and cons of each approach, including their cost implications and when to use (or not use) them. Whether you’re a seasoned data engineer or just starting your Snowflake journey, this guide will equip you with the knowledge to choose the right ingestion strategy for your specific needs.

Snowflake Ingestion Methods: Pros, Cons, Cost, and When to Use (or Not Use)

 1. INSERT

• Pros: Simple and straightforward for small datasets, useful for one-time or ad-hoc data loads
• Cons: Not scalable for large datasets, can be slow for high-volume data ingestion, requires manual effort for each data load
• Cost: The least expensive option for small data loads, but becomes cost-prohibitive for large-scale data ingestion due to the manual effort required
• When to Use: The INSERT method is best suited for small, one-off data loads, such as testing or prototyping new data sources.
• When Not to Use: Do not use INSERT for large, regularly occurring data loads, as it is not scalable and can be time-consuming.

 2. COPY Command

• Pros: Efficient for bulk data ingestion, supports a wide range of file formats and compression types, can be automated and scripted
• Cons: Requires more setup and configuration compared to INSERT, needs to handle file location, permissions, and other infrastructure considerations
• Cost: A cost-effective option for regularly ingesting large datasets, as it can be automated and integrated into your data pipeline
• When to Use: The COPY command is the go-to method for regularly ingesting large, structured datasets from various sources, such as CSV files stored in cloud storage buckets.
• When Not to Use: Avoid using the COPY command for one-time or ad-hoc data loads, as the setup and configuration overhead may not be justified.

 3. Snowpipe

• Pros: Fully managed, automatic data ingestion, near real-time data loading, supports a variety of data sources (S3, GCS, Azure Blob Storage)
• Cons: Additional service charges for Snowpipe, requires more setup and configuration compared to COPY command
• Cost: Comes with an additional service charge, but the convenience and near real-time capabilities of Snowpipe may justify the additional cost for certain use cases, such as streaming data or event-driven applications.
• When to Use: Snowpipe is ideal for ingesting data that requires near real-time updates, such as web analytics, sensor data, or financial transactions.
• When Not to Use: Do not use Snowpipe for one-time or ad-hoc data loads, as the additional service charge and setup complexity may not be necessary.

 4. Snowpipe Streaming

• Pros: Enables real-time data ingestion, seamless integration with event-driven architectures, scalable and highly available
• Cons: Snowflake’s streaming API requires a Java client to be written for each streaming data source, such as Amazon Kinesis or change-data-capture streams. This added complexity can make the streaming API less appealing compared to SQL-based ingestion methods for some use cases.
• Cost: Typically more expensive than batch-based ingestion methods, as it requires dedicated compute resources to handle the real-time data flow
• When to Use: Snowpipe Streaming is the perfect solution for ingesting data from real-time event streams, such as Apache Kafka or Azure Event Hubs.
• When Not to Use: Avoid using Snowpipe Streaming for batch-oriented data loads, as the real-time capabilities and associated costs may not be necessary.

 5. External Tables

• Pros: Allows querying data without loading it into Snowflake, useful for exploratory data analysis or as a staging area
• Cons: Slower query performance compared to internal tables, requires additional management of data source and location
• Cost: Generally a cost-effective option, as they don’t require data to be loaded into Snowflake, but the cost savings may be offset by the potentially slower query performance and additional management overhead
• When to Use: External tables are best suited for scenarios where you need to quickly access and analyze data without the overhead of loading it into Snowflake.
• When Not to Use: Do not use external tables for data that requires frequent or complex querying, as the performance may be suboptimal compared to internal Snowflake tables.

 6. Kafka Connect

• Pros: Seamless integration with Kafka for real-time data ingestion, scalable and highly available
• Cons: Requires additional setup and configuration of Kafka infrastructure, may have higher operational overhead compared to other methods
• Cost: Integrating Kafka Connect with Snowflake can incur additional costs for maintaining the Kafka infrastructure, as well as the Snowflake resources used for data ingestion
• When to Use: Kafka Connect is the ideal choice when you need to ingest real-time data streams from Apache Kafka into Snowflake.
• When Not to Use: Avoid using Kafka Connect if you don’t have an existing Kafka infrastructure or if your data ingestion needs can be met by other, simpler Snowflake ingestion methods.

 7. Iceberg

• Pros: Supports table versioning and time travel, efficient for large datasets and open data formats, integrates well with other data processing frameworks
• Cons: Requires additional setup and configuration, may have a steeper learning curve compared to other methods
• Cost: Can be a cost-effective solution for managing large, complex datasets, but the initial setup and configuration costs, as well as the ongoing maintenance, may be higher compared to some other ingestion methods
• When to Use: Iceberg is a great fit for ingesting and managing large, complex datasets, such as those found in data lakes or data warehouses.
• When Not to Use: Do not use Iceberg if your data ingestion needs are relatively simple and can be met by more straightforward Snowflake ingestion methods, as the additional setup and configuration may not be justified. Maintaining an Iceberg environment requires a lot of attention around dealing with metadata operations and file sizing. Consider using Iceberg only if your Data Ingestion cost is taking more than 25% of your overall Snowflake cost (rule of thumb).

3rd Party Tools for Snowflake Data Ingestion

In addition to the native Snowflake ingestion methods, there are also a number of third-party tools and platforms that can be used to ingest data into Snowflake. These tools can be particularly useful in scenarios where you need to transport data from a wide variety of sources or add custom logic during the ingestion process.

When to Use 3rd Party Tools:

• If you have trouble transporting data from your sources and need to write custom connectors or adapters to retrieve the data
• If you require additional data transformation, enrichment, or processing capabilities beyond what Snowflake’s native ingestion methods provide
• If you need to integrate Snowflake with a broader data ecosystem or workflow, and a third-party tool can provide the necessary connectivity and orchestration

Some popular 3rd party tools for Snowflake data ingestion include:

1. Rivery

• Pros: Offers a no-code, cloud-based platform for data integration, supports a wide range of data sources and destinations, including Snowflake
• Cons: Requires a subscription fee, may have limited customization options compared to open-source tools
• Use Case: Ideal for organizations that want a turnkey data integration solution without the need for extensive technical expertise or resources

2. Fivetran

• Pros: Provides pre-built connectors for hundreds of data sources, handles schema changes automatically, and offers a fully managed service
• Cons: Additional service charges on top of Snowflake costs
• Use Case: Ideal for organizations with diverse data sources that need a reliable, low-maintenance solution for getting data into Snowflake

3. Airbyte

• Pros: Open-source, highly customizable, and supports a wide range of data sources and destinations
• Cons: Requires more setup and maintenance compared to fully managed services
• Use Case: Suitable for organizations that require more control over their data pipeline and can allocate resources to manage the Airbyte infrastructure

4. Matillion

• Pros: Offers a visual, low-code/no-code interface for building data pipelines, supports a variety of source systems
• Cons: Can be more expensive compared to other options, may have a steeper learning curve
• Use Case: Beneficial for organizations that prefer a more user-friendly, GUI-based approach to data integration and want to minimize the need for coding

By considering these third-party tools, you can expand the capabilities of your Snowflake data ingestion, especially when dealing with complex or heterogeneous data sources. However, be mindful of the additional costs and management overhead that may be involved when using these solutions.

Conclusion

In this comprehensive blog post, we’ve explored the diverse array of data ingestion methods available in Snowflake. From the simplicity of INSERT to the power of Snowpipe, Iceberg, and a range of third-party tools, each approach offers unique strengths, trade-offs, and considerations around cost, implementation complexity, and suitability for different use cases.

As you step into your Snowflake journey, it’s crucial to take the time to thoroughly understand these ingestion methods and choose the one that best aligns with your specific data needs, infrastructure constraints, and overall budget. While the native Snowflake ingestion options provide a solid foundation, third-party tools can be invaluable when dealing with complex data sources, diverse integration requirements, or the need for additional data transformation and processing capabilities.

Remember, the data ingestion strategy you select will have a significant impact on the efficiency, scalability, and cost-effectiveness of your Snowflake-powered data analytics initiatives.

By carefully evaluating the pros, cons, and use cases of each method, you’ll be well-equipped to make an informed decision and set your Snowflake data ingestion up for success.

Stay tuned for the next blog post in this series, where we’ll dive deeper into cost-saving techniques to optimize your Snowflake data ingestion strategy. Until then, happy data loading!