MongoDB Atlas Integrations for CDKTF are now Generally Available

Darshana Paithankar and Zuhair Ahmed
February 28, 2023

Automation - helping to ensure repeatable, consistent, and reliable infrastructure deployments
Version Control - check in IaC code into GitHub, BitBucket, or GitLab for improved team collaboration and higher code quality
Security - create clear audit trails of each infrastructure modification
Disaster Recovery - IaC scripts can be used to quickly recreate infrastructure in the event of availability zone or region outages
Cost Savings - prevent overprovisioning and waste of cloud resources
Improved Compliance - easier to enforce organizational policies and standards

Today we are doubling down on this commitment and announcing MongoDB Atlas integrations with CDKTF (Cloud Development Kit for Terraform). These new integrations are built on top of the Atlas Admin API and allow users to automate infrastructure deployments by making it easy to provision, manage, and control Atlas infrastructure as code in the cloud without first having to create in HCL or YAML configuration scripts.

CDKTF abstracts away the low-level details of cloud infrastructure, making it easier for developers to define and manage their infrastructure natively in their programming language of choice. Under the hood, CDKTF is converted into Terraform config files on your behalf. This helps to simplify the deployment process and eliminates context switching.

MongoDB Atlas & HashiCorp Terraform:

MongoDB began this journey with our partners at HashiCorp when we launched the HashiCorp Terraform MongoDB Atlas Provider in 2019. We then have since grown to 10M+ downloads over all time and our provider is the number one provider in the database category.

Today we are delighted to support all CDKTF supported languages including JavaScript, TypeScript, Python, Java , Go, and .NET. In addition, with CDKTF users are free to deploy their MongoDB Atlas resources to AWS, Azure and Google Cloud enabling true multi-cloud deployments.

Learn how to get started via this quick demo.

Start building today!

MongDB Atlas CDKTF integrations are free and open source licensed under Mozilla Public License 2.0. Users only pay for underlying Atlas resources created and can get started with Atlas always free tier (M0 clusters).

Getting started today is faster than ever with MongoDB Atlas and CDK for HashiCorp Terraform. We can’t wait to see what you will build next with this powerful combination!

Learn more about MongoDB Atlas and CDK for Hashicorp Terraform

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MongoDB Atlas Integrations for AWS CloudFormation and CDK are now Generally Available

Infrastructure as Code (IaC) tools allows developers to manage and provision infrastructure resources through code, rather than through manual configuration. IaC have empowered developers to apply similar best practices from software development to application instructure deployments. This includes: Automation - helping to ensure repeatable, consistent, and reliable infrastructure deployments Version Control - check in IaC code into GitHub, BitBucket, AWS CodeCommit, or GitLab for improved team collaboration and higher code quality Security - create clear audit trails of each infrastructure modification Disaster Recovery - IaC scripts can be used to quickly recreate infrastructure in the event of availability zone or region outages Cost Savings - prevent overprovisioning and waste of cloud resources Improved Compliance - easier to enforce organizational policies and standards Today we are doubling down on this commitment and announcing MongoDB Atlas integrations with AWS CloudFormation and Cloud Development Kit (CDK). AWS CloudFormation allows customers to define and provision infrastructure resources using JSON or YAML templates. CloudFormation provides a simple way to manage infrastructure as code and automate the deployment of resources. AWS Cloud Development Kit (CDK) is an open-source software development framework that allows customers to define cloud infrastructure in code and provision it through AWS CloudFormation. It supports multiple programming languages and allows customers to use high-level abstractions to define infrastructure resources. These new integrations are built on top of the Atlas Admin API and allow users to automate infrastructure deployments by making it easy to provision, manage, and control Atlas Infrastructure as Code in the cloud. MongoDB Atlas & AWS CloudFormation: To meet developers where they are, we now have multiple ways to get started with MongoDB Atlas using AWS Infrastructure as Code. Each of these allow users to provision, manage, and control Atlas infrastructure as code on AWS: Option 1: AWS CloudFormation Customers can begin their journey using Atlas resources directly from the AWS CloudFormation Public Registry . We currently have 33 Atlas Resources and will continue adding more. Examples of available Atlas resources today include: Dedicated Clusters, Serverless Instances, AWS PrivateLink , Cloud Backups, and Encryption at Rest using Customer Key Management. In addition, we have published these resources to 22 (and counting) AWS Regions where MongoDB Atlas is supported today. Learn how to get started via this quick demo . Option 2: AWS CDK After its launch in 2019 as an open source project, AWS CDK has gained immense popularity among the developer community with over a thousand external contributors and more than 1.3 million weekly downloads. AWS CDK abstracts away the low-level details of cloud infrastructure, making it easier for developers to define and manage their infrastructure natively in their programming language of choice. This helps to simplify the deployment process and eliminates context switching. Under the hood, AWS CDK synthesizes CloudFormation templates on your behalf which is then deployed to AWS accounts. In AWS CDK, L1 (Level 1) and L2 (Level 2) constructs refer to two different levels of abstraction for defining infrastructure resources: L1 constructs are lower-level abstractions that provide a one-to-one mapping to AWS CloudFormation resources. They are essentially AWS CloudFormation resources wrapped in code, making them easier to use in a programming context. L2 constructs are higher-level abstractions that provide a more user-friendly and intuitive way to define AWS infrastructure. They are built on top of L1 constructs and provide a simpler and more declarative API for defining resources. Today we announce MongoDB Atlas availability for AWS CDK in JavaScript and TypeScript, with plans for Python, Java, Go, and .NET support coming later in 2023. Now customers can easily deploy and manage all available Atlas resources by vending AWS CDK applications with prebuilt L1 Constructs. We also have a growing number of L2 and L3 CDK Constructs available. These include Constructs to help users to quickly deploy the core resources they need to get started with MongoDB Atlas on AWS in just a few lines JavaScript or TypeScript (see awscdk-resources-mongodbatlas to learn more). Users can also optionally select to add more advanced networking configurations such as VPC peering and AWS PrivateLink. Option 3: AWS Partner Solutions (previously AWS Quick Starts) Instead of manually pulling together multiple Atlas CloudFormation resources, AWS Partner Solutions gives customers access to pre-built CloudFormation templates for both general and specific use cases with MongoDB Atlas. By using AWS Partner Solution templates, customers can save time and effort compared to architecting their deployments from scratch. These were jointly created and incorporate best practices from MongoDB Atlas and AWS. Go to the AWS Partner Solutions Portal to get started. Start building today! These MongDB Atlas integrations with AWS CloudFormation are free and open source licensed under Apache License 2.0 . Users only pay for underlying Atlas resources created and can get started with Atlas always free tier ( M0 clusters ). Getting started today is faster than ever with MongoDB Atlas and AWS CloudFormation. We can’t wait to see what you will build next with this powerful combination! Learn more about MongoDB Atlas integrations with AWS CloudFormation

February 28, 2023

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Leveraging BigQuery JSON for Optimized MongoDB Dataflow Pipelines

We're delighted to introduce a major enhancement to our Google Cloud Dataflow templates for MongoDB Atlas. By enabling direct support for JSON data types, users can now seamlessly integrate their MongoDB Atlas data into BigQuery, eliminating the need for complex data transformations. This streamlined approach not only saves users time and resources, but it also empowers customers to unlock the full potential of their data through advanced data analytics and machine learning. Figure 1: JSON feature for user options on Dataflow Templates Limitations without JSON support Traditionally, Dataflow pipelines designed to handle MongoDB Atlas data often necessitate the transformation of data into JSON strings or flattening complex structures to a single level of nesting before loading into BigQuery. Although this approach is viable, it can result in several drawbacks: Increased latency: The multiple data conversions required can lead to increased latency and can significantly slow down the overall pipeline execution time. Higher operational costs: The extra data transformations and storage requirements associated with this approach can lead to increased operational costs. Reduced query performance: Flattening complex document structures in JSON String format can impact query performance and make it difficult to analyze nested data. So, what’s new? BigQuery's Native JSON format addresses these challenges by enabling users to directly load nested JSON data from MongoDB Atlas into BigQuery without any intermediate conversions. This approach offers numerous benefits: Reduced operating costs: By eliminating the need for additional data transformations, users can significantly reduce operational expenses, including those associated with infrastructure, storage, and compute resources. Enhanced query performance: BigQuery's optimized storage and query engine is designed to efficiently process data in Native JSON format, resulting in significantly faster query execution times and improved overall query performance. Improved data flexibility: users can easily query and analyze complex data structures, including nested and hierarchical data, without the need for time-consuming and error-prone flattening or normalization processes. A significant advantage of this pipeline lies in its ability to directly leverage BigQuery's powerful JSON functions on the MongoDB data loaded into BigQuery. This eliminates the need for a complex and time-consuming data transformation process. The JSON data within BigQuery can be queried and analyzed using standard BQML queries. Whether you prefer a streamlined cloud-based approach or a hands-on, customizable solution, the Dataflow pipeline can be deployed either through the Google Cloud console or by running the code from the github repository . Enabling data-driven decision-making To summarize, Google’s Dataflow template provides a flexible solution for transferring data from MongoDB to BigQuery. It can process entire collections or capture incremental changes using MongoDB's Change Stream functionality. The pipeline's output format can be customized to suit your specific needs. Whether you prefer a raw JSON representation or a flattened schema with individual fields, you can easily configure it through the userOption parameter. Additionally, data transformation can be performed during template execution using User-Defined Functions (UDFs). By adopting BigQuery Native JSON format in your Dataflow pipelines, you can significantly enhance the efficiency, performance, and cost-effectiveness of your data processing workflows. This powerful combination empowers you to extract valuable insights from your data and make data-driven decisions. Follow the Google Documentation to learn how to set up the Dataflow templates for MongoDB Atlas and BigQuery. Get started with MongoDB Atlas on Google Marketplace . Learn more about MongoDB Atlas on Google Cloud on our product page .

December 17, 2024