Sold by: DBLab by Postgres.AIÂ
Deployed on AWS
DBLab Standard Edition (DBLab SE) delivers fast, cost-effective database cloning, enabling agile teams to create instant, full-size production clones for development, testing, and staging, all managed like code, integrated into CI/CD pipelines, and optimized with transparent pricing.
Overview
DBLab Standard Edition (DBLab SE) delivers blazing-fast database cloning and database branching capabilities, empowering agile teams to create instant, full-size clones of production databases for building robust development, testing, QA, and staging environments. With a self-service UI, team members can quickly spin up virtual test data environments, managed like code, supporting version control and seamless sharing across teams. These environments enable efficient testing of migrations, SQL query optimization, troubleshooting, and deployment of staging applications with production-grade data. Integrated into the CI/CD pipeline, DBLab SE accelerates development, integration, bug resolution, and testing while minimizing storage overhead.
Instant cloning is enabled by ZFS's copy-on-write (CoW) feature and containers, with processes optimized for Postgres-specific requirements.
An example of a small team of 3-5 engineers and a 1 TiB database:
- r7i.2xlarge (8 vCPUs, 64 GiB RAM) and 3 TiB disk space
- total monthly cost $962.77 (compute: $386.32, DBLab SE $330.69, storage $245.76
- serves 3 snapshots, unlimited user snapshots and branches, 50 clones, 5-10 of which are simultaneously used
Highlights
- Eliminate downtime and accelerate development with instant production database clones, providing data availability that aligns with the demands of a fast-paced release cycle. Empower your CI/CD pipeline with powerful testing capabilities, enabling teams to rapidly recreate data states for debugging and problem-solving.
- Optimize SQL queries risk-free on database clones, guided by Joe Bot, your virtual DBA assistant.
- Give every developer their own full-size database clone without the hassle or high costs, thanks to virtual copies that stay synchronized with production environments, minimizing cloud storage and compute expenses.
Details
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Delivery method
Delivery option
Database Lab Engine (DLE) for PostgreSQL
Latest version
Operating system
Ubuntu 20.04
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
m7i.12xlarge | $2.177 |
r7i.16xlarge | $3.81 |
m7i.xlarge | $0.181 |
r7i.large | $0.12 |
r7i.8xlarge | $1.91 |
r7i.48xlarge | $11.43 |
m7i.16xlarge | $2.903 |
m7i.48xlarge | $8.709 |
m7i.24xlarge | $4.354 |
m7i.2xlarge | $0.362 |
Vendor refund policy
We do not currently support refunds, but you can cancel at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Database Lab Engine (DLE) for PostgreSQL
DLE configuration will be done inside DLE UI, once all AWS resources are already provisioned. For provisioning, you need to:
- Choose AWS region
- Choose EC2 instance type (the more CPU power and RAM you have, the more thin clones can be running with good performance, e.g. m5.2xlarge with its 8 vCPUs / 32 GiB is good to run up to 30 thin clones, but more vCPUs might be needed of more than 5-8 people or CI pipelines are going to work with DLE in parallel)
- Specify the database size (this will define the size of the EBS volume used)
- Optionally, configure SSL
CloudFormation Template (CFT)
AWS CloudFormation templates are JSON or YAML-formatted text files that simplify provisioning and management on AWS. The templates describe the service or application architecture you want to deploy, and AWS CloudFormation uses those templates to provision and configure the required services (such as Amazon EC2 instances or Amazon RDS DB instances). The deployed application and associated resources are called a "stack."
Version release notes
This is release of DBLab SE 4.0.0 for AWS Marketplace. Release notes: https://github.com/postgres-ai/database-lab-engine/releasesÂ
Additional details
Usage instructions
To access API and UI, read instructions in the "Outputs" section once deployment is complete.
Resources
Vendor resources
Support
Vendor support
- Asynchronous troubleshooting help (please allow us 24 hours to respond during business days)
- If your production database is on a managed Postgres service (such as RDS) and database size exceeds 2 TiB, please reach out to us: consulting@postgres.aiÂ
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By DBLab by Postgres.AI
By Delphix Corp.
By Nimesa Technology
AI generated from product descriptions
Database Cloning Technology
Utilizes ZFS copy-on-write (CoW) feature and containerization for instant database cloning with Postgres-specific optimizations
Virtual Environment Management
Provides self-service UI for creating and managing virtual test data environments with version control support
CI/CD Pipeline Integration
Seamlessly integrates with continuous integration and deployment workflows for accelerated development and testing processes
Snapshot Management
Supports multiple snapshots, unlimited user snapshots, and database branches for comprehensive data state management
Database Performance Optimization
Enables risk-free SQL query optimization through isolated database clone environments
Data Virtualization
Rapidly provision relational, NoSQL, and cloud-native data for development and testing environments
Multi-Source Data Support
Supports diverse data sources including AWS Aurora, AWS RDS, Oracle, SQL Server, PostgreSQL, SAP, and NoSQL databases
Data Masking and Compliance
Automatically find, mask, and govern sensitive information to meet regulatory requirements like GDPR and CCPA
Data Synchronization
Create virtual data copies that remain synchronized with production data across cloud, hybrid, and on-premises environments
Data State Management
Enable versioning, bookmarking, refreshing, and branching of datasets from any point in time
Backup Automation
Cloud-native backup and disaster recovery solution with policy-based snapshot integration for enterprise applications and databases
Database Protection
Native integration with database frameworks like VSS for consistent backups, point-in-time recovery, and log management across multiple database systems
Application Data Mesh
Patent-pending technology enabling intelligent database cataloging, rapid restoration, and cloning of databases or specific files with minimal downtime
Multi-Region Recovery
Capability to perform instant restore and cloning of EC2 and database instances across primary and disaster recovery regions/accounts
Snapshot Management
Space-efficient point-in-time volume and instance copying with comprehensive protection status tracking across global regions
Standard contract
No
No
Customer reviews
Denis
Good and useful product
Reviewed on Dec 06, 2022
Review from a verified AWS customer
We have been using Database Lab Engine at Boost Sport AI for more than two years and it helped us a lot in dealing with data and the development process.
There are two most common usage scenarios for us:
- Having a fast clone for the data scientist or developer to be able to play with data (including necessary data modification) without having to wait for the long process of database cloning. Using Database Lab we can have a new clone in seconds, without having to wait and not needing to have much storage for each of the clones.
- A developer can have a database he/she can use for his development backend instance without having to make a clone on his machine or elsewhere, and he can quickly update this database instance to match the current production data.