AWS DocumentDB is a document database that offers SQL querying of large datasets with MongoDB compatibility. Being a document database, it facilitates faster query performance, easy replication support, and a dynamic schema. 

However, it does not support complex joins and stores data in document format, which is less organized than relational databases. You can integrate DocumentDB with relational databases like Amazon Redshift to overcome these limitations. It supports complex joins, columnar storage, and massively parallel processing capabilities for optimized query performance. 

This article explains four methods for data integration from AWS DocumentDB to Redshift for faster and more complex data analytics. 

Why Integrate AWS DocumentDB to Redshift?

You should migrate data from AWS DocumentDB to Redshift for several reasons, such as:

  • AWS DocumentDB is a document database, whereas Redshift is a relational database management system (RDBMS). RDBMS stores the data in a more organized format in the form of rows and columns, whereas document database stores data in the form of documents. Also, a relational database supports complex joins for advanced analytics, which is not supported in document databases. 
  • You can use Redshift to process large volumes of data through its massively parallel processing capabilities, which are not provided by DocumentDB. 
  • Redshift facilitates columnar data storage format, which helps with better data compression and faster query performance. 

AWS DocumentDB Overview

AWS DocumentDB is a managed JSON document database service offered by Amazon Web Services. It is MongoDB compatible and supports the 3.6, 4.0, and 5.0 versions, allowing you to integrate easily with MongoDB drivers and applications. DocumentDB provides scalability up to the petabyte level, which helps you handle massive amounts of data efficiently. Further, it also provides high data security because of its authorization and encryption features. Additionally, DocumentDB integrates with Amazon SageMaker to help you develop applications based on generative artificial intelligence and machine learning functionalities. 

Redshift Overview

Redshift is a cloud-based data warehouse solution of Amazon Web Services (AWS). It is a fully managed service that reduces your responsibility for infrastructure management. Redshift offers high scalability up to the petabyte level and uses massively parallel processing technology to optimize performance. It also stores data in columnar format for efficient data storage. Further, you can use simple SQL commands with the Redshift ML to build, deploy, and execute machine learning models within the data warehouse. This can help you with predictive analysis in fraud detection, risks, and customer churn rates. 

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Methods to Integrate AWS Document to Redshift

Method 1: Using AWS Cloud to Integrate AWS DocumentDB to Redshift

AWS DocumentDB to Redshift: Configuration of AWS Cloud for DocumentDB to Redshift Integration
AWS DocumentDB to Redshift: Configuration of AWS Cloud for DocumentDB to Redshift Integration

You can use AWS Cloud to integrate DocumentDB to Redshift by following the below steps. To do this, first, ensure the deployment of all the CloudFormation resources

Step 1: Connect to the Amazon DocumentDB cluster using AWS Cloud9

  • From the AWS Cloud9 Console, launch the AWS Cloud9 environment DocDBStreaming
  • You should run the following commands to install the essential packages and the Amazon DocumentDB Certificate Authority (CA) certificate key:
# Setting up mongo 4.0 repo

echo -e "[mongodb-org-4.0] \nname=MongoDB Repository\nbaseurl=\ngpgcheck=1 \nenabled=1\ngpgkey=" | sudo tee/etc/yum.repos.d/mongodb-org-4.0.repo

# Installing packages

sudo yum -y update

sudo yum -y install mongodb-org-shell

# Downloading the SSL file and the loader

  • Now, to connect to the Amazon DocumentDB cluster, follow these steps:
    • First, navigate to Secrets Manager Console to access the credentials for your DocumentDB cluster and search for the secret named secretDocDBAdminUser-<randomly generated string>.
    • Navigate to the Amazon DocumentDB Console and select Clusters in the Navigation Pane.
    • Select the cluster transactcluster.
    • From the Connectivity & Security section, copy the Mongo shell command and then add the username and password from the Secrets Manager as mentioned earlier. 
# Connect to the cluster

mongo --ssl --host <cluster endpoint>:27017 --sslCAFile global-bundle.pem  --username <user_name> --password <insertYourPassword>

Step 2: Create the Database and Collection and Enable Change Streams

You can enable the change streams at cluster, database, and collection levels. Here, it is enabled at the collection level. The below mentioned commands can help you to create the database named database1 and the collection named collection1. You can capture  transactions and enable the change streams for this collection:

# Create database 

use database1

# Create Collection 


# Enable change streams for collection

db.adminCommand({modifyChangeStreams: 1,

database: "database1",

collection: "colllection1",

enable: true});

# Insert a record into the new Collection

db.collection1.insertOne({“parameter1” : “value1”, “parameter2” : “value2”, “parameter3” : “value3”});

Step 3: Configure a trigger for the Lambda function

AWS DocumentDB to Redshift: Configuration of Trigger for Lambda Function
AWS DocumentDB to Redshift: Configuration of Trigger for Lambda Function
  • Choose Add and wait for the event source mapping to be created and the state to change to Enabled. This can take a few minutes. 
AWS DocumentDB to Redshift: Configuration of Trigger for Lambda Function
AWS DocumentDB to Redshift: Configuration of Trigger for Lambda Function

Step 4: Set up Amazon Redshift streaming ingestion 

  • From the left navigation pane on the Amazon Redshift Console, choose Query Editor v2.
AWS DocumentDB to Redshift: Configuration of Kinesis Data Stream
AWS DocumentDB to Redshift: Configuration of Kinesis Data Stream
  • Choose the workgroup Serverless:work-group-streamingblog in the left pane in the query editor, and toggle to use the database user name and password.
  • Then, navigate to the secrets manager and use the username and password for the secret named secretRedshiftAdminUser-<randomly generated string>.
  • Run the following command to create an external schema. This will map the data from Kinesis Data Streams to a Redshift object:
IAM_ROLE default;
  • You can run the following command to create the materialized view. It is set to auto-refresh and will be refreshed whenever data arrives in the stream.
json_parse(kinesis_data) as payload
FROM database1analysis.docdbstream
WHERE CAN_JSON_PARSE(kinesis_data);
  • You can also create a view on top of the materialized view of database1_extract for near-real-time analysis of the collection1. This approach allows you to integrate data from multiple sources easily. 
  • Then, you can query the near-real-time view to validate the transformation of raw data into columnar data. Use the following query to examine the structured data resulting after the transformation process:
Select * from "public"."database1_extract_view"
AWS DocumentDB to Redshift: Loading Data to Redshift
AWS DocumentDB to Redshift: Loading Data to Redshift

Limitations of Using AWS Cloud to Integrate AWS DocumentDB to Redshift

There are certain limitations of using AWS Cloud in order to load AWS DocumentDB to Redshift, such as:

  • Inefficiency of Lambda Function: AWS Lambda cannot efficiently handle functions that require excessive processing, creating computational constraints. 
  • Limited Scalability: Kinesis has scalability constraints, which limit data streaming and data loading to Redshift.

Method 2: Using Pymongo to Integrate AWS DocumentDB to Redshift

Pymongo is a local Python driver for MongoDB, and DocumentDB is MongoDB-compatible. You can use the following steps to integrate AWS DocumentDB to Redshift using Pymongo. 

Step 1: Connect to DocumentDB 

  • To connect to DocumentDB, first install Pymongo in your Python notebook using the following command:

pip install pymongo

  • If you already have a connection with MongoDB, you just need to specify the host, port, username, and password and use the following command to establish a connection with DocumentDB.
import pymongo
import os
CURRENT_DIR = os.path.dirname(os.path.abspath(_ _ file _ _))
congif = {
'host': '<host>',
'post': 3000,
'username': <name-of-documentdb-user>
'password': <password-ofdocument-user>
'database': <name-of-database-we-want-to-connect>
connection_params = {
'host': config['host'],
'port': int(confid['port']),
'username': config.get('user',None),
'password': config.get('password', None),
'authSource': config['database'],
'ssl': True,
'ssl_ca_certs': os.path.join(*[CURRENT_DIR,'certs','rds-combined-ca-bundle-pem']),
'ssl_match_hostname': False,
'replicaset': congig.get('replica_set',None),
'readPreference': 'secondaryPreferred'
if not verify mode and use_ssl:
connection_params['ssl_cert_reqs'] = ssl.CERT_NONE
client = pymongo.MongoClient(**connection_params)

Step 2: Extract Data from AWS DocumentDB

  • To extract data from any DocumentDB database, you first need to connect to it in the DocumentDB cluster. For instance, you might want to extract data from the college database for student collection.
database name = 'college'
db = client[database name]
  • After connecting to the database, you need to connect to the collection from which you want the data (student in this case). 
collection_name = 'student'
collection = db[collection_name]
  • You can use the following code to get data from the collection. 
with collection.find(sort=[('id',pymongo.ASCENDING)])as cursor:
for row in cursor:

Step 3: Define a Schema 

  • Define a schema with keys you want to load to Redshift from DocumentDB. 
schema = {
for row in data:
for key in row:
if ket not in schema['columns'] #if key is not in schema columns add it to delete_keys
for key in delete_keys: #remove all not required keys
del row[key]
  • For real-time data capturing, you can use the change stream method to get the data into Redshift. When you add, update, or delete a document in the DocumentDB cluster database, you are notified immediately. However, this method requires you to keep a constant eye on your database.
change_stream =
#above will return you full document whenever some event occurs

with change_stream as cursor:
data = []
while cursor.alive:
change = cursor.try_next() #listing for other events
if change is not None:
operation_type = change.get('operationType')
#Get the full document from event
record_row =  change.ger('fullDocument')
data += record_row
#process the data we have recieved till now
for row in data:
detele_keys = []
for key in row:
if key not in schema['columns'] #if key is not in schema column add it to delete keys
for key in delete_keys: # remove all not required keys
del row [key]

You can then load this data to Redshift using psycopg2 to successfully integrate AWS DocumentDB to Redshift. 

Limitations of Using Pymongo to Integrate AWS DocumentDB to Redshift

Pymongo is a native Python driver for MongoDB and possesses the limitations of MongoDB. Some of the disadvantages of using Pymongo to integrate AWS DocumentDB to Redshift are:

  • Limited Joins: Joining documents in MongoDB can be difficult, and it also fails to support joins in relational databases. This can be a significant drawback while using Pymongo for AWS DocumentDB to Redshift integration. 
  • High Storage Memory: It has high storage memory because of data duplication and lack of support for joins. This leads to an increase in data redundancy as extra data storage consumes unnecessary space in the memory.

Method 3: Using CSV File to Load Data from AWS DocumentDB to Redshift

Step 1: Transfer AWS DcoumentDB Data to a CSV File

You can use the mongoexport tool to transfer AWS DocumentDB data to a CSV file in your native system with the help of the following code:

mongoexport --ssl \

    --host="" \

    --collection=sample-collection \

    --db=sample-database \

    --out=sample-output-file \

    --username=sample-user \

    --password=abc0123 \

    --sslCAFile global-bundle.pem

Step 2: Loading Data from a CSV File to S3 Bucket

Follow the steps below to upload a CSV file to the Amazon S3 bucket.

Create a Bucket
  • Sign in to the AWS Management Console and go to the Amazon S3 Console.
  • Choose Create bucket and the AWS region where you want to create it. 
  •  Enter a unique bucket name in the Create Bucket dialog box’s Bucket Name option.
  • Choose the suggested defaults for the rest of the options and then choose Create bucket.
  • On successfully creating an Amazon S3 bucket, the Console displays your empty bucket in the Buckets panel.
Create a Folder
  • Choose the name of the new bucket.
  • Click on the Create Folder button.
  • Name the new folder as load.
Upload the CSV Files to the New Amazon S3 Bucket
  • Choose the name of the data folder.
  • In the Upload wizard, choose Add Files. Follow the Amazon S3 Console instructions to upload all of your CSV files. 
  • Choose Upload.

Step 3: Export Data from S3 Bucket to Redshift

After transferring the CSV file to the S3 bucket, you should use the COPY command to export the data in it to Redshift using the following code:

COPY table_name

FROM 'path_to_csv_in_s3'




Limitations of Using CSV File to Load Data from AWS DocumentDB to Redshift

  • Limited Functionalities of CSV: CSV files only support basic data types such as numbers or dates, not complex data types like nested objects or images. They also do not provide data security mechanisms like access control or encryption, which can expose your data to potential cyber threats. 
  • Limitations of S3 bucket: The S3 bucket is owned by the AWS account in which it was created, and its ownership is not transferable to other accounts. Also, you cannot change the bucket’s name and the AWS region after setting it up. This can create discrepancies in seamless data integration, especially for new AWS users. 

Method 4: Using Hevo Data to Integrate AWS DocumentDB to Redshift

Hevo Data is a no-code ELT platform that provides real-time data integration and offers a cost-effective way to automate your data pipelining workflow. With over 150 source connectors, you can integrate your data into multiple platforms, conduct advanced analysis on your data, and produce useful insights.

Here are some of the most important features provided by Hevo Data:

  • Data Transformation: Hevo Data allows you to transform your data for analysis with a simple Python-based drag-and-drop data transformation technique.
  • Automated Schema Mapping: Hevo Data automatically arranges the destination schema to match the incoming data. It also lets you choose between Full and Incremental Mapping.
  • Incremental Data Load: It ensures proper bandwidth utilization at both the source and the destination by allowing real-time data transfer of the modified data.

Step 1: Configuration of Amazon DocumentDB as Source


Follow the below steps to configure Amazon DocumentDB as a source in Hevo:

  • Click PIPELINES in the Navigation Bar.
  • Click + CREATE in the Pipelines List View.
  • From the Select Source Type page, select Amazon DocumentDB.
  • In the Configure your Amazon DocumentDB Source page, specify the following:
AWS DocumentDB to Redshift: Configure Source Settings
AWS DocumentDB to Redshift: Configure Source Settings

For more information on the configuration of Amazon DocumnetDB as a source, refer to the Hevo documentation

Step 2: Configuration of Amazon Redshift as Destination


You should follow the steps below to configure the Redshift as a destination in Hevo:

  • From the Navigation Bar, click DESTINATIONS.
  • Click + CREATE in the Destinations List View.
  • Select Amazon Redshift on the Add Destination page.
  • On the Configure your Amazon Redshift Destination page, specify the following details:
AWS DocumentDB to Redshift: Configure Destination Settings
AWS DocumentDB to Redshift: Configure Destination Settings

For more information on the configuration of Redshift as a destination, refer to the Hevo documentation

Use Cases of AWS DocumentDB to Redshift Integration

There are several use cases of integrating AWS DocumentDB with Redshift, including:

  • Data Warehousing: You can use Redshift as a central repository to store data from multiple sources in a consistent format and retrieve it for reporting and analytics purposes. 
  • Real-time Analytics: Redshift uses massively parallel processing technology to speed up data processing. Thus, it can be used in your organization to perform real-time data analytics for better customer service.
  • Business Intelligence: Redshift integrates with BI tools like Power BI, Tableau, and Looker to create interactive dashboards and reports. You can connect your BI tools directly to Amazon Redshift via JDBC or ODBC drivers to visualize your data for ease of understanding and better decision-making. 


This blog provides you with an overview of AWS DocumentDB and Redshift Integration. It gives a detailed explanation of four methods of how to transfer data from DocumentDB to Redshift. While you can use any of these methods for AWS DocumentDB to Redshift data integration, using an automated platform such as Hevo is beneficial. The platform offers a zero code configuration, a vast library of connectors, and a simple interface to set up data pipelines.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.

Share your experience of AWS DocumentDB to Redshift integration in the comments section below!


  1. Where can you use Amazon DocumentDB?

The DocumentDB has a document model and can be used with dynamic datasets that require ad-hoc querying, indexing, and aggregations. It is used extensively for content management, personalization, catalogs, mobile and web applications, IoT, and profile management.

  1. What are Amazon Redshift Clusters?

Clusters are groups of nodes, a collection of computing resources in Amazon Redshift. Each cluster has one or more databases and runs its own Redshift engine. 

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