Mastering Data Migration: Efficiently Sync AWS Glue with DynamoDB for Real-Time Analytics

Most think of AWS Glue and DynamoDB as separate tools—what if you could unify their power to eliminate data silos and accelerate your analytics pipeline with less overhead? This guide cuts through the complexity to show exactly how.

In today’s fast-paced business environment, real-time analytics aren't just a competitive advantage—they're a necessity. Seamlessly integrating AWS Glue with DynamoDB empowers organizations to build agile, scalable data pipelines that drive real-time insights and operational efficiency, crucial for dynamic decision-making.

If you’ve wrestled with syncing structured data transformations in AWS Glue with a fast, NoSQL store like DynamoDB, this post is for you. I’ll walk you through the practical how-to approach to efficiently migrate and continuously sync data between these two powerful services, complete with examples.

Why Sync AWS Glue with DynamoDB?

Before diving into the technical steps, let's briefly cover why this integration matters:

AWS Glue is a fully managed ETL (Extract, Transform, Load) service perfect for preparing and cleaning large datasets.
Amazon DynamoDB is a low-latency NoSQL database designed for real-time, highly scalable applications.

By connecting AWS Glue’s heavy-duty ETL capabilities with DynamoDB's ultra-fast key-value store, your organization can:

Automate complex data transformations.
Load only relevant data subsets into DynamoDB.
Enable near real-time analytics by updating live tables.
Reduce manual intervention in pipeline management.

Key Concepts Before Starting

Data Catalog & Crawlers: AWS Glue crawlers automatically catalog your data, maintaining schema info needed for ETL jobs.
Glue ETL Jobs: These jobs read from source data (e.g., S3 buckets), transform it using Apache Spark under the hood, then write it out.
DynamoDB Tables: Schema-less tables that use partition keys (and optional sort keys) to provide highly available read/write capacity.

AWS Glue natively supports several sinks like S3, RDS, Redshift—but writing directly to DynamoDB requires some custom handling, which we’ll cover here.

Step 1: Set Up Your Source and Destination

Data source example: S3 CSV Dataset

Assume customer transactional data stored as CSV files in an S3 bucket:

s3://my-company-data/customer_transactions/
  ├── transactions_2024-01.csv
  ├── transactions_2024-02.csv
  └── ...

Destination: DynamoDB Table

Create a table named CustomerTransactions:

Partition key: TransactionID (String)
Sort key: TransactionDate (String)

Optionally provision throughput or enable on-demand capacity based on workload.

Step 2: Configure Your AWS Glue Data Catalog

Create a crawler pointing to the s3://my-company-data/customer_transactions/ path.
Run the crawler to populate the AWS Glue Data Catalog with a schema for your dataset.
Inspect the table metadata in the console; confirm column names and types match expectations.

Step 3: Author Your AWS Glue ETL Job to Read and Transform Data

Because we want efficient migration and syncing into DynamoDB, let’s use AWS Glue Studio or script in Python (PySpark):

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from awsglue.context import GlueContext
from pyspark.context import SparkContext
import boto3
import json

args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = glueContext.create_job(args['JOB_NAME'])

# Read from Data Catalog table
datasource0 = glueContext.create_dynamic_frame.from_catalog(database="my_database", table_name="customer_transactions")

# Simple transformation example—filter transactions over $1000
filtered_df = datasource0.toDF().filter(datasource0.toDF().amount > 1000)

# Convert back to DynamicFrame for compatibility if needed
filtered_dynamic_frame = DynamicFrame.fromDF(filtered_df, glueContext, "filtered_dynamic_frame")

Step 4: Writing Data into DynamoDB from Glue Job

AWS Glue does not have a direct native connector to write into DynamoDB tables—so there are two common approaches:

Option A: Use boto3 within the Glue Job

You can convert each record into a JSON item and use boto3’s put_item API:

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('CustomerTransactions')

def write_to_dynamodb(row):
    item = {
        'TransactionID': str(row.TransactionID),
        'TransactionDate': str(row.TransactionDate),
        'CustomerID': str(row.CustomerID),
        'Amount': float(row.amount),
        # add more fields as needed
    }
    table.put_item(Item=item)

filtered_df.foreach(write_to_dynamodb)

Note: This approach writes each row synchronously—suitable for small-to-medium datasets but may be slow or costly at scale.

Option B: Write Parquet/JSON files to S3 and Use Lambda or Kinesis as an Intermediate Pipeline

Use Glue job just to process & output refined JSON/Parquet files back into S3.
Trigger an AWS Lambda function on new file upload or batch from Kinesis Streams.
Lambda reads the files and batch-writes them into DynamoDB using BatchWriteItem API.

This decouples transformation from writing and improves scalability but adds architectural complexity.

Step 5: Automating Real-Time Syncing

For truly real-time analytics pipelines:

Setup continuous crawlers or scheduled jobs in AWS Glue that pick incremental datasets.
Push updates quickly using Lambda functions listening on event streams like Kinesis Data Streams or SQS queues containing incoming change records.
Update DynamoDB tables with key updates swiftly without full reloads.

This event-driven design enables near-real-time reflection of changes in your analytical environment.

Extra Tips & Best Practices

BatchWriteItem API: When writing in batches via boto3/Lambda to DynamoDB, always handle retries/exponential backoff since BatchWrite has capacity limits.
Schema Evolution: Since DynamoDB is schemaless aside from keys, ensure your ETL cleanses and conforms important attributes ahead of time.
Monitoring & Logging: Use CloudWatch logs inside your Glue Jobs alongside custom metrics tracking successful writes vs failed attempts.

Conclusion

Syncing AWS Glue with DynamoDB unlocks powerful workflows that combine massive ETL transformation capabilities with low-latency NoSQL storage optimized for real-time use. While there isn’t an out-of-the-box “Glue → Dynamo” connector yet (as of now), by embedding boto3 calls inside your ETL script or architecting event-driven pipelines triggered by processed outputs you can efficiently master this migration challenge.

With these practical steps, you are now equipped to build seamless data pipelines that keep your analytics timely and your decision-making sharp. Give it a try on your next project!

Happy Syncing!
If you have questions or want me to expand on any specific integration example like streaming realtime updates from Kinesis → Lambda → DynamoDB based on Glue processed records just drop a comment below.

Aws Glue To Dynamodb