Optimizing Data Pipeline Performance: Efficient Strategies for Migrating from DynamoDB to Snowflake

As organizations scale, the ability to unlock deep business insights hinges on efficiently moving data from operational systems into powerful analytical platforms. DynamoDB, Amazon’s highly scalable NoSQL database, excels at handling high-velocity transactional workloads. But to perform complex analytics and reporting, many teams choose Snowflake for its scalable, performant data warehousing capabilities.

Migrating data from DynamoDB to Snowflake isn’t just a one-time dump-and-load job — it’s a strategic endeavor that requires balancing DynamoDB’s strict throughput limits, preserving data integrity, and maximizing analytical readiness in Snowflake. In this post, I’ll walk you through practical, incremental strategies to architect a robust data pipeline that optimizes performance and cost without sacrificing reliability.

Why You Can’t Just Dump and Load

A common temptation is to perform a direct export of your entire DynamoDB table using AWS Data Pipeline or even manual scripts, then bulk load into Snowflake via bulk COPY commands from S3. While straightforward, this approach creates several challenges:

Throughput Overruns: DynamoDB enforces provisioned read capacity units (RCUs). Trying to export large tables in one burst often leads to throttling or heavy costs.
Data Staleness: Full dumps run infrequently mean you lose freshness of data in your warehouse.
Resource Waste: Bulk loading massive files repeatedly consumes compute resources unnecessarily.
No Change Capture: Without incremental updates, analytics can lag and inconsistencies may arise.

To avoid these pitfalls, let’s explore incremental, optimized methods.

Step 1: Enable Change Data Capture (CDC) on DynamoDB

To move beyond dumb dumps, start by capturing changes as they happen with DynamoDB Streams:

What are DynamoDB Streams?
Streams capture item-level changes (inserts, updates, deletes) in near real-time.
Why use them?
Instead of periodically scanning entire tables (costly and slow), you can process only changes since the last extraction.

How to enable:

Open your DynamoDB table.
Enable Streams with the NEW_AND_OLD_IMAGES view type.
Set up an AWS Lambda function triggered by stream events.

Step 2: Process Changes Incrementally with AWS Lambda

Lambda allows you to execute scalable functions that augment or transform your data before pushing it downstream.

What Lambda should do:

Deserialize stream records.
Format into analytical-friendly rows.
Batch small sets of changes for efficient writes.
Write batches to an S3 staging area in Parquet or CSV format — efficient for Snowflake ingestion.

Example Lambda function snippet (Node.js):

exports.handler = async (event) => {
  const records = event.Records.map(record => {
    return {
      id: record.dynamodb.Keys.id.S,
      eventType: record.eventName,
      updatedAt: record.dynamodb.ApproximateCreationDateTime,
      ... // reconstruct item attributes as needed
    };
  });

  // Group records and upload to S3 bucket here
};

Keep batch sizes small enough (~1000 rows) to avoid Lambda timeouts but large enough for efficient bulk loads.

Step 3: Stage Data in S3 for Efficient Snowflake Loading

Snowflake ingests data efficiently from cloud storage like Amazon S3.

Why stage in S3?

Decouples extraction from ingestion for better error handling.
Allows compression and columnar formats (Parquet/ORC).
Supports scalable COPY command usage.

Organize files by date partitions:

s3://your-bucket/dynamodb-changes/year=2024/month=06/day=12/

Step 4: Use Snowpipe or Batch COPY Commands for Near Real-Time Loading

Snowpipe provides continuous ingestion by auto-detecting new files in S3 and loading them immediately. This reduces latency between DynamoDB updates and availability in Snowflake analytics.

Basic Snowpipe setup:

Create file format matching your staged data (CSV/Parquet).
Define an external stage pointing at your S3 bucket prefix.
Create a pipe linked with SQL COPY statement targeting your analytics table:

CREATE OR REPLACE PIPE my_pipe AS 
COPY INTO analytics_table 
FROM @my_s3_stage/dynamodb-changes
FILE_FORMAT = my_csv_format;

Configure event notifications or manual triggers for Snowpipe ingestion.

Alternatively, if latency requirements are modest, schedule batch COPY commands at fixed intervals via Airflow or AWS Glue jobs.

Step 5: Maintain Data Integrity and Handle Deletes

Deletions pose a challenge since DynamoDB streams emit delete events — these must be reflected in Snowflake too.

Two strategies:

Soft Deletes: Introduce a deleted_at timestamp column rather than hard deletes; mark records as inactive but keep history.
Merge Operations: Use MERGE statements on incoming staged data based on primary key matching:

MERGE INTO analytics_table AS target
USING staged_changes AS source
ON target.id = source.id
WHEN MATCHED AND source.eventType = 'REMOVE'
THEN DELETE
WHEN MATCHED THEN 
UPDATE SET ...
WHEN NOT MATCHED THEN 
INSERT ...

This ensures your analytical tables remain consistent with the source state.

Putting It All Together: An Example Flow

DynamoDB streams changes → triggers
AWS Lambda processes & batches → uploads files to
S3 staging area → triggers or schedules
Snowpipe / Batch COPY ingests into
Snowflake Analytical Tables

This architecture enables continuous incremental syncs while respecting capacity limits and minimizing latency.

Bonus Tips: Optimize Performance & Cost

Monitor provisioned RCUs on DynamoDB; consider on-demand mode if spikes happen.
Use appropriate file formats (Parquet preferred) and compressions like snappy in S3.
Partition Snowflake tables by time dimensions matching S3 prefixes.
Leverage auto-suspend & auto-resume on Snowflake warehouses to save costs when idle.
Implement monitoring on pipeline health via CloudWatch metrics & alerts.

Conclusion

Migrating from DynamoDB to Snowflake doesn’t have to be painful or inefficient — but it requires moving past “dumb” full-table loads toward an event-driven incremental approach that respects source throughput constraints and plays well with warehouse capabilities. By combining DynamoDB Streams + Lambda + S3 staging + Snowpipe batch/load mechanisms, you can build a performant pipeline delivering fresh insights while optimizing costs and integrity.

Got questions about your migration strategy? Drop a comment below! I’m happy to help troubleshoot real-world scenarios or architect custom solutions tailored for your workload.

Happy analyzing! 🚀

Dynamodb To Snowflake