MongoDB to Redshift ETL: 2 Easy Methods

Migrating data from MongoDB to Amazon Redshift is not just a technical task. It is a strategic decision that determines how easily your team can analyze data, scale reporting, and trust insights going forward.

At first glance, the move may seem complex. MongoDB’s flexible, schema-less structure does not naturally align with Redshift’s columnar, structured design. Query logic changes. Transformations are required. Without a clear plan, migrations can quickly become fragile or expensive to maintain.

Yet the payoff is significant. Amazon Redshift is built for analytics at scale. Its columnar storage and fast query performance make it a powerful foundation for teams that need consistent, high-performance insights from growing data volumes.

In this guide, we examine two reliable methods for moving data. Whether you prefer manual scripts for full control or want a tool like Hevo to automate the process, there is an approach to suit every need.

Understanding MongoDB to Redshift Integration

MongoDB focuses on operational flexibility, while Redshift is built for analytics at scale. Integrating them allows you to keep application workloads fast while still enabling deep data analysis.

What is MongoDB?

MongoDB is a leading NoSQL database program that distinguishes itself through its document-oriented architecture. Written in C++, MongoDB stores data in JSON-like documents with optional schemas and provides exceptional flexibility for modern application development.

Features of MongoDB:

• Flexible data model: JSON-like documents accommodate evolving data structures without rigid schema constraints.
• Horizontal scalability: Built-in sharding capabilities enable seamless scaling across distributed systems.
• High availability: Automatic failover through replica sets ensures continuous operation.
• Rich query language: Support for ad-hoc queries, indexing, and aggregation frameworks.

MongoDB excels at handling large volumes of structured and unstructured data. Its feature set addresses diverse requirements, including data integration, load balancing, ad-hoc queries, sharding, and indexing. MongoDB use cases can help you identify when this database is the optimal choice for your application.

MongoDB supports all major operating systems (Linux, macOS, and Windows) and provides drivers for popular programming languages, including C, C++, Go, Node.js, Python, and PHP. If you work extensively with MongoDB data, you may explore MongoDB ETL tools to streamline data workflows significantly.

What is Amazon Redshift?

Amazon Redshift is a fully managed, petabyte-scale cloud data warehouse designed for high-performance analysis of large datasets. It enables companies to store and query massive amounts of data across accessible clusters using parallel processing capabilities.

Features of Amazon Redshift

• Columnar storage: Data is stored in columnar format, dramatically reducing I/O operations and optimizing analytical query performance.
• Massively parallel processing (MPP): Distributes query execution across multiple nodes, enabling complex queries on vast datasets.
• Result caching: Frequently accessed query results are cached, significantly reducing runtime for repeated queries.
• Fully managed: Amazon handles infrastructure maintenance, backups, configuration, and security automatically.

Amazon Redshift’s architecture divides clusters into slices, enabling more granular dataset analysis. Its multi-layered design processes multiple queries simultaneously, minimizing wait times and maximizing throughput. Organizations looking to maximize their Redshift investment should familiarize themselves with Redshift best practices to explore the platform’s full potential.

2 Methods to Migrate Data from MongoDB to Redshift

When migrating data from MongoDB to Redshift, you can choose from two primary approaches. Each method has its own advantages, depending on your technical requirements, team resources, and data complexity.

Aspect	Hevo (Automated ELT)	Custom Scripts (Manual ETL)
Best For	Scalable, production-ready MongoDB to Redshift pipelines	One-time or highly custom migrations
Setup & Maintenance	No-code, fully managed	Engineering-heavy, manual upkeep
CDC & Sync	Built-in, near real-time updates	Batch-based, custom logic required
Schema Handling	Automatic schema evolution	Manual schema fixes and ALTERs
Operational Effort	Minimal ongoing effort	High maintenance overhead
Time to Insights	Fast, analytics-ready data	Slower due to preprocessing

Method 1: Using Hevo to Move MongoDB to Amazon Redshift

Check out our 1-minute demo below to see the seamless integration in action!

Hevo provides a fully managed, no-code data pipeline platform that simplifies MongoDB to Redshift migration. This approach is ideal for teams looking to automate their data integration pipeline without writing extensive code.

This method suits when you need low-latency sync, automatic schema handling, and minimal operational overhead.

Step 1: Configure MongoDB as the Source in Hevo

Create a new pipeline in Hevo and select MongoDB as the source connector.

MongoDB Deployment Configuration

Hevo supports the following MongoDB deployment types:

• MongoDB Atlas (recommended for CDC)
• Self-managed Replica Sets
• Standalone instances (full load only, no CDC)

Incremental sync requires Replica Set or Atlas. Standalone MongoDB does not support Change Streams.

Connection Details
Provide one of the following:

• Standard connection parameters:
  Host, Port, Database Name, Username, Password
  
• MongoDB Connection URI (recommended for Atlas):
  Includes authentication source, replica set name, and SSL parameters
  

Security & Network Configuration

• Enable TLS/SSL for encrypted data transfer
  
• For private deployments, configure one of the following:
  
  • IP Whitelisting (Hevo static IPs)
  • VPC Peering (AWS-to-AWS setups)
  • SSH Tunnel (on-prem or restricted networks)

Step 2: Collection Selection and Primary Key Strategy

Collection Sync Configuration

• Select specific collections instead of full databases to reduce load.
• Hevo performs an initial snapshot followed by incremental sync (if enabled).

Primary Key Mapping

• Define a primary key per collection for upserts in Redshift.
• By default:
  
  • MongoDB _id → Redshift primary key
    
• Composite keys are not supported directly; use transformations if required.

Step 3: Configure Load Mode and Change Data Capture

Select the appropriate load mode:

• Full Load for one-time migrations or historical backfills.
• Incremental Load (CDC) to capture INSERT, UPDATE, and DELETE events using MongoDB Change Streams.

Ensure oplog or change stream retention exceeds the longest expected pipeline downtime to avoid data gaps.

Step 4: Handle Nested Data and Transformations

MongoDB documents often contain nested fields and arrays. Hevo supports two ingestion approaches:

• Flattening for BI-friendly tabular schemas (may increase row count)
• Redshift SUPER type for storing nested JSON and querying with PartiQL

Optional transformations include null handling, field casting, array parsing, derived columns, and timezone normalization.

Step 5: Configure Amazon Redshift as the Destination

Provide Redshift connection details, including cluster endpoint, database, credentials, and target schema.

Configure S3 staging with:

• An S3 bucket.
• An IAM role attached to Redshift with s3:GetObject access.

Hevo uses S3 staging and Redshift COPY commands for high-throughput loading.

Step 6: Validate, Monitor, and Operate at Scale

Start the pipeline and monitor snapshot progress, CDC lag, and error logs. Validate data in Redshift using row counts, sample records, and key uniqueness checks.

Hevo manages ongoing operations:

• Automatic schema evolution for new fields.
• Append or upsert write modes.
• Soft or hard delete handling.
• Alerts for schema drift, failures, and lag.

Security best practices include credential rotation, restricted IAM access, PII masking, and audit logs.

If you’re evaluating other data warehouse options, Hevo offers seamless Google BigQuery integration with a similar no-code setup and automatic schema management.

For more information on how you can use the Hevo connectors for MongoDB to Redshift ETL, check out:

• Redshift Destination Connector
• MongoDB Source Connector

Method 2: Using Custom Scripts to Move Data from MongoDB to Redshift

For teams requiring full control over the migration process or handling one-time data transfers, custom scripts provide a flexible alternative. This manual approach uses MongoDB’s native export tools, Amazon S3 for staging, and Redshift’s COPY command for data loading.

Step 1: Export MongoDB Data

Use the mongoexport command-line tool to extract data from your MongoDB collection. You can export in either CSV or JSON format, depending on your data structure and Redshift loading requirements.

Export as CSV:

bash

mongoexport --db=db_name --collection=collection_name --type=csv --out=outputfile.csv --fields=field1,field2,field3

Export as JSON:

bash

mongoexport --db=db_name --collection=collection_name --out=outputfile.json

Step 2: Upload the File to Amazon S3

Before loading data into Redshift, stage the exported file in an S3 bucket:

• Design a Redshift table schema that matches your exported file structure (MongoDB’s flexible schema requires careful mapping to Redshift’s relational structure)
• Upload the file to S3 using the AWS CLI:

bash

aws s3 cp outputfile.csv s3://your-bucket-name/outputfile.csv

Step 3: Create the Table in Redshift

Connect to your Redshift cluster and create a table matching your data structure:

sql

CREATE TABLE users (

  id INT,

  name VARCHAR(100),

  email VARCHAR(200),

  created_at TIMESTAMP

);

Step 4: Load Data from S3 to Redshift

Use Redshift’s COPY command to efficiently load data from S3:

For CSV files:

sql

COPY users

FROM 's3://your-bucket-name/outputfile.csv'

IAM_ROLE 'arn:aws:iam::<aws-account-id>:role/<role-name>'

FORMAT AS CSV IGNOREHEADER 1;

For JSON files:

sql

COPY users

FROM 's3://your-bucket-name/outputfile.json'

IAM_ROLE 'arn:aws:iam::<aws-account-id>:role/<role-name>'

FORMAT AS JSON 'auto';

At this point, your MongoDB data is successfully loaded into Amazon Redshift.

Limitations of Using Custom Scripts to Move Data from MongoDB to Redshift

Here is a list of limitations of using the manual method of moving data from MongoDB to Redshift:

• No fixed schema to work with upfront: 

Unlike a relational database, a MongoDB collection doesn’t have a predefined schema. Hence, it is impossible to look at a collection and create a compatible table in Redshift upfront.

• Constant schema drift and maintenance overhead: 

Fields can be added, removed, or modified in MongoDB at any time. Each schema change requires manual updates in Redshift using ALTER statements, increasing operational effort and the risk of pipeline failures.

• Data type and length inconsistencies: 

The same field in MongoDB can store values of different data types across documents, such as numbers and strings. MongoDB also does not enforce string length limits. In Redshift, these differences require manual casting, column resizing, and frequent data validation.

• Complex handling of nested documents and arrays: 

A document can have nested objects and arrays with a dynamic structure. The most complex of MongoDB ETL problems is handling nested objects and arrays.

When evaluating data warehouse costs, it’s useful to compare Google BigQuery pricing models against Redshift to determine the most cost-effective solution for your workload.

Why Perform MongoDB to Redshift ETL?

Migrating MongoDB data to Amazon Redshift bridges the gap between operational databases and analytical data warehouses.

Key Benefit	Why MongoDB Alone Falls Short	How Redshift Helps
Enable advanced analytics	MongoDB is built for transactions, not complex analytical queries across large datasets.	Columnar storage and a powerful SQL engine make large-scale analytics fast and efficient.
Centralize data for business intelligence	Data stays siloed in operational systems like CRM, marketing, and finance tools.	Redshift combines data from multiple sources to create a single source of truth.
Simplify schema management	Flexible schemas make reporting and dashboard design inconsistent and harder to maintain.	Structured tables provide consistent schemas that work smoothly with BI tools.
Cost-effective analytics at scale	Analytical queries on production clusters can affect application performance and increase costs.	Analytics run separately from operations, improving stability and keeping costs predictable.
Support near real-time pipelines	Operational databases are not designed for continuous analytical syncing and long-term history.	Near real-time pipelines enable faster insights, preserve history, and support data governance.

Teams managing multiple data warehouses can apply similar BigQuery ETL strategies for a consistent pipeline architecture.

Common Challenges When Migrating from MongoDB to Redshift

Migrating from MongoDB to Amazon Redshift presents some challenges due to fundamental differences in data storage and management.

1. Schema differences 

MongoDB’s flexible, schema-less structure contrasts with Redshift’s requirement for well-defined schemas. MongoDB documents can have varying fields, nested objects, and dynamic additions, while Redshift needs structured data.

Solution: Normalize MongoDB data before importing. Use BigQuery ETL tools like Hevo or custom scripts to flatten JSON structures. Understanding Google BigQuery data types helps plan appropriate type mappings. Analyze collections to identify all fields and create a normalized Redshift schema.

2. Data type mismatches 

MongoDB types like ObjectId, embedded arrays, timestamps, and binary data lack direct Redshift equivalents.

Solution: Implement type mapping during ETL. Platforms like Hevo handle automatic conversion. For custom implementations, use Talend or Apache NiFi. Document all type conversions for troubleshooting.

3. Performance issues with large datasets 

Direct insertion of millions of rows causes slow transfers, timeouts, and system overload.

Solution: Use staged migration: export to Amazon S3 in CSV/Parquet format, then use Redshift’s COPY command for parallel processing. For very large datasets, partition exports, run during off-peak hours, and implement incremental loads.

4. Inconsistent or missing data 

MongoDB’s flexibility allows missing fields, null values, and inconsistent naming, which Redshift’s strict schema rejects.

Solution: Profile data to identify inconsistencies, fill missing values with defaults, standardize field naming, and create data quality rules. Establish a framework that monitors MongoDB data consistency. Learning about Google BigQuery data warehouse architecture provides insights for Redshift implementations.

From Operational Data to Analytics-Ready Insights

In this blog, we looked at two ways to move data from MongoDB to Amazon Redshift. You can build custom ETL scripts or use a managed platform like Hevo. Both approaches work, but the effort and scalability are very different.

Hevo is a strong choice when teams want to avoid ongoing maintenance. It removes the need to manage scripts, retries, and schema changes. Fault-tolerant pipelines keep data flowing even when source systems change.

Hevo also goes beyond MongoDB. It supports data ingestion from 150+ sources, including databases, cloud applications, and SDKs. This makes it easier to bring all your data into Redshift from one place.

Once the data is in Redshift, you can build joins, aggregates, and materialized views to improve query performance. Teams familiar with BigQuery analysis will find similar optimization techniques apply to Redshift workloads. With Hevo’s Workflows, you can also model data and define dependencies using a simple drag-and-drop interface.

If you want to move MongoDB data to Redshift without handling infrastructure or scripts, Hevo offers a simpler path. You can start with a free trial and see how easily your data pipelines scale as needs grow.

Related reads:

• Redshift functions
• Creating a table in Redshift

FAQs