Optimizing Data Pipeline Performance: DynamoDB to Snowflake Migration Strategies

Moving transactional data from DynamoDB into Snowflake isn’t a naive process of periodic bulk exports. If you’ve ever hit Read Capacity Unit (RCU) limits or struggled with lagging analytics due to stale dumps, you already know: throughput throttling, data staleness, and inconsistent warehouse state are real bottlenecks. Any architecture ignoring these will burn both time and budget.

Below are field-tested strategies for constructing a robust, incremental pipeline between DynamoDB and Snowflake—balancing source API limitations, data fidelity, and analytics timeliness.

Full Table Dump? Trade-Offs Are Steep

Initial instinct: “Export table with AWS Data Pipeline or EMR, upload to S3, use Snowflake’s COPY INTO.” Easy in theory, problematic at scale.

For production pipelines (think >100 GB tables and sub-hour freshness), periodic bulk loads are unsustainable.

Step 1: Capture Change Events with DynamoDB Streams

DynamoDB Streams provide near real-time, immutable ordered logs of item-level changes. This enables CDC (Change Data Capture) without heavy scans.

Stream configuration (recommended):

• ViewType: NEW_AND_OLD_IMAGES — enables both before/after change capture, critical for deletes and schema evolution.
• Stream retention: Default 24 hours; increase if you have downstream lags.

To enable via AWS CLI:

aws dynamodb update-table \
  --table-name user_events \
  --stream-specification StreamEnabled=true,StreamViewType=NEW_AND_OLD_IMAGES

Note: Streams can introduce a cost penalty if your write pattern spikes. Monitor with CloudWatch (metric: StreamRecordsProcessed).

Step 2: Process Stream Records with Lambda

AWS Lambda offers serverless compute to rapidly fan out event ingestion. Each stream shard can invoke a Lambda handler, processing batches of 100–1000 records (limited by resource settings and function timeout).

What goes in the handler?

• Deserializing DynamoDB’s AttributeValue JSON into flat records.
• Formatting for analytics. Usually involves additional normalization: timestamps (ISO8601), explicit type coercion, event metadata.
• Buffering up to 1000 records or 5 MB (whichever hits first).
• Writing compressed Parquet (or gzipped CSV for smaller volumes) to a partitioned S3 path.

Example (Node.js, DynamoDB Streams to Parquet via AWS SDK and parquetjs):

const parquet = require('parquetjs');
...
exports.handler = async (event) => {
  // transform and buffer stream records
  // write Parquet files to S3 partitioned by event timestamp
}

Imperfection: Lambda cold starts can delay file creation under low change volume. Scheduled “finalizer” jobs may be required to flush half-full batches (see: Lambda concurrency quirks).

Step 3: S3 Staging—the Decoupling Layer

All ingestion into Snowflake should stage through S3.

S3 structure matters:
Partition by UTC event date for optimal warehouse pruning and minimal file set per load.

s3://analytics-staging/dynamodb-table=users/year=2024/month=06/day=13/

File format & naming:

Parquet preferred for high-volume, wide tables (users, orders, logs). For legacy interop or ultra-simple models, CSV w/ gzip suffices.

Step 4: Ingest to Snowflake—Snowpipe vs Batch

Snowpipe provides event-driven, low-latency loading directly from S3. Recommended when analytics tolerates <5 minute lag and steady file trickle (dozens per hour). Batch COPY handles bulk or low-frequency ingest if latency can be relaxed.

Snowpipe basics:

• External stage must point to S3 prefix.
• File format must match S3 artifact (define via CREATE FILE FORMAT).
• Pipe ties stage to target table.

CREATE OR REPLACE FILE FORMAT stg_parquet 
  TYPE = PARQUET 
  COMPRESSION = SNAPPY;

CREATE OR REPLACE STAGE user_stage 
 URL='s3://analytics-staging/dynamodb-table=users/';

CREATE OR REPLACE PIPE user_pipe AS
COPY INTO dw_users
FROM @user_stage
FILE_FORMAT = (FORMAT_NAME = 'stg_parquet');

Trigger Snowpipe ingest using S3 bucket notifications (SNS -> SQS) or via REST API if you require programmatic/manual control.

Batch COPY tip:
For multi-GB ingestion, run incremental COPY commands every hour; orchestrate via Airflow or AWS Glue for audit/logging. This also gives you a chance to run supplemental validations prior to load.

Step 5: Handling Deletes and Upserts

DynamoDB Streams include 'REMOVE' events—do not ignore these.

Best practice:

• Soft deletes: Add a deleted_at field. Retain record, mark deleted.
• Idempotent merge: Use Snowflake’s MERGE operation for true upsert (and delete on eventType = 'REMOVE'):

MERGE INTO dw_users AS tgt
USING (SELECT * FROM @user_stage) AS src
ON tgt.id = src.id
WHEN MATCHED AND src.eventType = 'REMOVE' 
  THEN DELETE
WHEN MATCHED 
  THEN UPDATE SET ...
WHEN NOT MATCHED 
  THEN INSERT ...

Known issue: Out-of-order event arrival (rare, but real). To mitigate, track updated_at and only apply records newer than target; guard against race conditions—Snowflake MERGE can help enforce this logic.

End-to-End Flow (Condensed ASCII):

[DynamoDB]
   │
   ├─(Streams)─> [Lambda] ──> [S3 - partitioned Parquet]
                             │
                  [Snowpipe/Scheduled COPY]
                             │
                        [Snowflake Table]

Practical Details

• Monitor DynamoDB RCU usage hourly; switch to On-Demand mode during unpredictable load, then revert.
• Use S3 Lifecycle policies to apply object expiry or move older artifacts to Glacier.
• Snowflake credits add up quickly. Enable AUTO_SUSPEND=60 seconds and AUTO_RESUME=TRUE on all ingestion warehouses.
• CloudWatch alarms on LambdaErrors and S3 bucket notification delivery errors are essential to avoid silent pipeline breakage.

Non-obvious tip:
Test S3 permissions explicitly: Lambda’s role (or Snowpipe’s external stage) should never have delete permissions on the S3 bucket. Accidental deletion of historical loads is nearly impossible to recover.

Final Considerations

Event-driven CDC from DynamoDB to Snowflake minimizes latency and resource burn. S3 acts as a reliable, decoupled hand-off. The main complexities: event order handling, soft deletes, and cost optimization—not bulk load mechanics.

If you must support near real-time analytics, watch for occasional stream lags due to AWS regional events or downstream warehouse scaling issues (seen in us-east-1 during Q4 sales spikes; prepare to alert accordingly).

For further details on version handling, schema drift, or cross-account setups—these edges require additional controls but are not covered here.

If your situation demands tuning for high cardinality partition keys or multi-tenant workloads, standard approaches may need extending. Reach out or review the official CDC patterns in the DynamoDB and Snowflake documentation.