Unify customer journeys and automate marketing data extraction across 100+ channels with zero-code ETL pipelines, identity resolution, and unsampled GA insights.
engraph
Build end-to-end ETL pipelines and dbt models from natural language requests to streamline data engineering workflows for rapidly growing data teams.
Starting at
$85/user/month
About engraph
Engraph acts as an automated data engineer, enabling teams to build complete ETL pipelines using natural language commands. By bridging the gap between data sources, warehouses, and modeling, the platform automates the extraction, transformation, and loading processes that typically require extensive manual coding. It integrates with industry-standard infrastructure like Snowflake, dbt, and Fivetran to ensure that the pipelines generated are robust and compatible with existing modern data stacks. The primary goal is to turn a plain text request into a functional data pipeline in approximately ten minutes, significantly reducing the engineering overhead for data-driven organizations. The tool's functionality extends beyond simple data movement; it generates reusable dbt models to help manage and scale transformations across an organization. With access to over 340 integrations, users can connect a wide variety of data tools and sources effortlessly, including popular databases, SaaS platforms, and cloud storage. The platform also includes collaboration features such as granular access control and user roles, allowing teams to manage their data infrastructure collectively while maintaining security. The interface is designed to be intuitive, catering to both seasoned engineers who want to speed up repetitive tasks and teams looking to augment their existing data capabilities without hiring more staff. Engraph is particularly well-suited for startups and enterprise data teams that need to scale their data operations quickly without linearly increasing their headcount. For organizations with strict security requirements, the platform is developing on-premise deployment options for its language models to keep data within the company's own infrastructure. By automating the boilerplate of data engineering, it allows professionals to focus on higher-level architecture and analysis rather than the manual maintenance of connectors and transformation scripts. This automated data engineer approach helps maintain data quality while drastically shortening the development cycle for new data products. What differentiates engraph from standard ETL tools is its heavy reliance on a natural language interface for end-to-end pipeline creation. While many tools offer pre-built connectors, engraph automates the logic behind the transformation stage by generating the necessary dbt models and documentation automatically. This holistic approach, combined with a roadmap that includes real-time monitoring and data quality validation, positions it as a comprehensive solution for modern data stack automation. Users benefit from a system that not only moves data but also understands the intent behind the transformation, making it a powerful ally for any data-centric business.
engraph pros & cons
Pros
- Converts plain text requests into functional data pipelines in approximately 10 minutes.
- Integrates with over 340 different data tools and sources out of the box.
- Automates the creation of reusable dbt models to standardize data transformations.
- Powered by industry-standard infrastructure including Snowflake, Fivetran, and dbt.
- Provides high-tier support with response times as fast as one hour for enterprise clients.
Cons
- Pipeline monitoring and data quality features are currently in the 'coming soon' stage.
- The Self-serve plan is strictly limited to 10 dbt models per user.
- On-premise deployment is not yet available for high-security environments.
- Basic plan users may experience wait times of up to 48 hours for support responses.
engraph use cases
- Data engineering teams can automate the creation of boilerplate ETL pipelines and dbt models to focus on complex architecture.
- Growing startups can use the fully managed warehouse option to establish a data stack without hiring a full-time data engineer immediately.
- Analytics managers can use the natural language interface to quickly extract and model new data sources for immediate business insights.
engraph features
- built-in data quality validation (coming soon)
- real-time pipeline monitoring (coming soon)
- managed data warehouse options
- granular team access control
- 340+ data source integrations
- reusable dbt model generation
- end-to-end pipeline automation
- natural language etl creation
engraph pricing
Is engraph free? No, engraph doesn't offer a free plan; pricing starts at $85 / user/month.
Self-serve
$85 / user/month
- Self-serve onboarding
- Bring your own warehouse
- Up to 10 dbt models
- You manage deployments
- 48-hour support response time
Startup
$170 / user/month
- Hands-off onboarding
- Fully managed warehouse
- Up to 50 dbt models
- Automated deployments
- 24-hour support response time
Enterprise
Price varies
- Hands-off onboarding
- Tailored warehouse solutions
- Unlimited dbt models and projects
- Tailored automated deployments
- 1-hour, dedicated support response time
engraph FAQs
What is engraph?
Engraph is an automated data engineering platform that builds end-to-end ETL pipelines from natural language requests. It manages the entire process from connecting data sources to data warehouse configuration and transformation modeling.
How does engraph work?
Users submit data requirements via an intuitive natural language interface. The system then automatically creates, manages, and executes the necessary ETL pipelines and transformations, often in as little as 10 minutes.
Does engraph support dbt?
Yes, the platform generates reusable dbt models to facilitate data transformation management. Depending on your plan, you can manage between 10 and an unlimited number of dbt models and projects.
Can I bring my own data warehouse?
Yes, the Self-serve plan is specifically designed for teams that want to bring their own infrastructure. Alternatively, the Startup and Enterprise plans offer fully managed or tailored warehouse solutions.
How many data tools can engraph connect to?
The platform features over 340 integrations, allowing it to connect with a vast array of data tools and sources in your existing stack effortlessly.
Does the tool offer on-premise deployment?
On-premise deployment is a scheduled feature listed as coming soon. It will allow organizations to deploy the language model locally to ensure maximum data security and privacy.
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