MotherDuck Integrations

PuppyGraph empowers you to seamlessly query one or multiple data stores as a unified graph model. Graph databases are expensive, take months to set up, and need a dedicated team. Traditional graph databases can take hours to run multi-hop queries and struggle beyond 100GB of data. A separate graph database complicates your architecture with brittle ETLs and inflates your total cost of ownership (TCO). Connect to any data source anywhere. Cross-cloud and cross-region graph analytics. No complex ETLs or data replication is required. PuppyGraph enables you to query your data as a graph by directly connecting to your data warehouses and lakes. This eliminates the need to build and maintain time-consuming ETL pipelines needed with a traditional graph database setup. No more waiting for data and failed ETL processes. PuppyGraph eradicates graph scalability issues by separating computation and storage.

Orb is built with flexibility at its core. Our unique architecture separates how your product works with how you price, so you can evolve easily. Orb accelerates engineering velocity so you can launch on time. Now your team can focus on your core product, instead of on billing. Orb is your single source of truth for all your billing needs. Our powerful workflows finally bring together your finance and eng teams. Orb is for businesses that need to stay flexible in their pricing. With our unique model, you can bill on seats, consumption, and everything in between. Experiment with different value metrics and provide custom-negotiated discounts and add-ons. From PLG to SLG, Orb has you covered. Orb allows you to approach revenue in a more modern way. Our architecture decouples your product logic from your billing logic so you can try new pricing and optimize your revenue. Connect product usage ‍directly to billing.

Streamkap is a streaming data platform that makes streaming as easy as batch. Stream data from database (change data capturee) or event sources to your favorite database, data warehouse or data lake. Streamkap can be deployed as a SaaS or in a bring your own cloud (BYOC) deployment.

nao is an AI-powered data IDE designed specifically for data teams, combining a code editor with native integration to your data warehouse so you can write, test, and maintain data-centric code with full context. It supports warehouses such as Postgres, Snowflake, BigQuery, Databricks, DuckDB, Motherduck, Athena, and Redshift. Once connected, nao replaces a traditional data-warehouse console by offering schema-aware SQL auto-completion, data previews, SQL worksheets, and the ability to switch easily between multiple warehouses. The core of nao is its AI agent, which has full awareness of your actual data schema, tables, columns, metadata, and your codebase or data-stack context. It can generate SQL queries or full data-transformation models (e.g., for dbt workflows), refactor code, add or update documentation, run data-quality checks and data-diff tests, and even surface insights or run exploratory analytics, all while respecting data structure and quality constraints.

Pylar is a secure data-access layer that sits between AI agents and your databases, enabling agents to safely interact with structured data without giving them direct database access. It connects to one or more data sources (like BigQuery, Snowflake, PostgreSQL, business apps such as HubSpot or Google Sheets). Pylar can create governed SQL views using its built-in SQL IDE; those views define exactly which tables, columns, and rows agents are allowed to access. It lets you build “MCP tools” (either by writing natural-language prompts or manual configuration) that wrap SQL queries into standardized, safe operations. Agents can access data through a single MCP endpoint, compatible with multiple agent builders like custom AI assistants, no-code automation tools, or integrations (e.g. Zapier, n8n, LangGraph, VS Code, etc.).

Compass is an AI-powered, Slack-native data assistant that turns plain English questions into instant answers, summaries, charts, and insights powered by your actual warehouse data, so teams can make data-driven decisions without waiting on BI backlogs or building dashboards first. It connects directly to major data warehouses (Snowflake, BigQuery, Redshift, Postgres, AWS Athena, Databricks, and more), learns your schema and context, and generates governed, SQL-backed responses and visualizations in the tools your team already uses, all while keeping your data where it lives and under your control. Compass builds organizational context over time so answers become more accurate and relevant, supports collaboration through Slack threads, can schedule recurring analysis, and provides a shared repository of definitions and insights that help reduce analytical silos and reliance on specialized SQL users.

Processing and storing tabular datasets, e.g. from CSV or Parquet files. Large result set transfer to client. Large client/server installations for centralized enterprise data warehousing. Writing to a single database from multiple concurrent processes. DuckDB is a relational database management system (RDBMS). That means it is a system for managing data stored in relations. A relation is essentially a mathematical term for a table. Each table is a named collection of rows. Each row of a given table has the same set of named columns, and each column is of a specific data type. Tables themselves are stored inside schemas, and a collection of schemas constitutes the entire database that you can access.

Kestra is an open-source, event-driven orchestrator that simplifies data operations and improves collaboration between engineers and business users. By bringing Infrastructure as Code best practices to data pipelines, Kestra allows you to build reliable workflows and manage them with confidence. Thanks to the declarative YAML interface for defining orchestration logic, everyone who benefits from analytics can participate in the data pipeline creation process. The UI automatically adjusts the YAML definition any time you make changes to a workflow from the UI or via an API call. Therefore, the orchestration logic is defined declaratively in code, even if some workflow components are modified in other ways.

Paradime is an AI-powered analytics platform designed to enhance data operations by accelerating dbt pipelines, reducing data warehouse costs by over 20%, and boosting analytics ROI. Its smart IDE streamlines dbt development, potentially saving up to 83% of coding time, while the CI/CD features expedite pipeline delivery, reducing the need for additional platform engineers. The Radar component optimizes data operations, providing automatic cost savings and efficiency improvements. Paradime integrates seamlessly with various applications, offering over 50 integrations to support comprehensive analytics workflows. It is enterprise-ready, providing secure, flexible, and scalable solutions for large-scale data operations. GDPR and CCPA compliant, with appropriate technical and organizational measures in place to protect your information. Weekly vulnerability testing and yearly penetration testing to ensure infrastructure systems are always up to date.