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Astrodata

Data and analytics consultancy focused on leadership, process improvement, implementation, and advanced analytics

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Brooklyn Data Co

Connecting great people with emerging technologies to deliver business value.

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Bytecode

CorrDyn is a data-driven technology consultancy that enables scalable and affordable growth.

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CorrDyn

Supporting your data journey from strategy to value.

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Dataroots

Xebia is a pioneering Software Engineering and IT consultancy company, transforming and executing at the intersection of Domain and Technology to create digital leaders for our people, clients, partners, and communities.

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Xebia

Airbyte is an open-source data integration platform used by over 40,000 engineers, featuring a large catalog of 300+ pre-built connectors. It supports both structured and unstructured data sources and allows users to easily build or edit connectors to suit their needs. Airbyte offers various deployment options, including cloud-managed and self-managed solutions, and integrates with tools like Python through PyAirbyte for enhanced data engineering practices. For integration specifics with DuckDB and MotherDuck, you'd likely utilize one of Airbyte's connectors or leverage the platform's API or Python library to connect and sync data as needed. More detailed information and guidance can be found on [Airbyte's official website](https://airbyte.com/).

## Airbyte and MotherDuck

Airbyte integrates with MotherDuck by facilitating the movement and consolidation of data into DuckDB and the MotherDuck cloud service. This partnership enables easy deployment and replication of data from any source to both open-source DuckDB and MotherDuck's cloud-based platform. Specifically, Airbyte offers a data connector for DuckDB as well as for the MotherDuck cloud service, simplifying the process of integrating data across different sources and destinations. This collaboration is aimed at enhancing productivity and providing users with the flexibility to scale their data analysis needs, combining both local and cloud-based analysis capabilities. Furthermore, this integration supports high performance query execution and universal compatibility, including operation in the browser, making it a powerful solution for organizations looking to leverage open-source technologies for data-driven outcomes

For technical implementation, when using the DuckDB destination with Airbyte, the destination can write data to the MotherDuck service or a local file on the host running Airbyte. To specify a MotherDuck-hosted database as the destination, users need to provide their database URI with the `md:` prefix in the destination_path configuration option. Authentication with MotherDuck is handled through a service credential provided as the `motherduck_api_key` configuration option. This integration allows for full refresh sync and incremental append sync, facilitating efficient data management and analysis.

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Airbyte

Apache Airflow is a platform designed for programmatically authoring, scheduling, and monitoring workflows. It's built with scalability, dynamism, extensibility, and elegance in mind, allowing users to define workflows in Python, which offers dynamic pipeline generation. The platform features a user-friendly interface for monitoring and managing workflows and robust integrations with various third-party services, emphasizing its utility in a wide range of data engineering tasks. Although the specific integration details with DuckDB and MotherDuck were not directly available on the Apache Airflow website, integrating Airflow with these or similar services generally involves using custom or available operators to manage data flows and processing tasks within Airflow's pipelines.

For more detailed information, please visit the [Apache Airflow website](https://airflow.apache.org/).

## Airflow and MotherDuck

Apache Airflow can integrate with MotherDuck by utilizing custom Python operators that leverage the DuckDB Python API for executing SQL queries. This integration involves specifying the MotherDuck database using a connection string that includes authentication details, allowing Airflow to schedule and manage workflows that interact with MotherDuck for data processing tasks.

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AirFlow

Ascend.io offers a data pipeline automation platform, enabling easy data ingestion, transformation, and orchestration. It supports over 250 connectors, automates data lineage, optimizes pipeline execution, and allows for complex data transformations using SQL and Python. Ascend.io streamlines data workflows, ensuring efficient and scalable data processing for analytics and machine learning. For detailed features, visit [Ascend.io](https://ascend.io/).

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Ascend

Astronomer is a data engineering platform designed to simplify and streamline the process of building, deploying, and scaling data pipelines in the cloud. It provides data engineers and developers with a robust and flexible platform to manage data workflows and derive insights from their data assets.

## Key Features

- **Managed Apache Airflow**: Astronomer offers a fully managed Apache Airflow platform, providing users with a scalable and reliable environment for orchestrating data workflows. Users can leverage Airflow's rich ecosystem of connectors, operators, and integrations to build complex data pipelines with ease.

- **Deployment Flexibility**: Astronomer supports flexible deployment options, allowing users to deploy their data pipelines on any cloud provider or on-premises infrastructure. This provides flexibility and control over where data processing tasks are executed, catering to diverse deployment needs.

- **Monitoring and Alerting**: The platform includes built-in monitoring and alerting capabilities to track the performance and health of data pipelines. Users can set up custom alerts to notify them of pipeline failures or performance anomalies, ensuring timely resolution and minimizing downtime.

- **Collaborative Development**: Astronomer facilitates collaborative development by providing features for version control, code sharing, and collaboration within teams. Users can work together to develop, test, and deploy data pipelines in a collaborative and efficient manner.

- **Scalability and Performance**: Astronomer is designed for scalability and performance, allowing users to handle large volumes of data and complex workflows with ease. The platform automatically scales resources based on workload demands, ensuring optimal performance and cost efficiency.

- **Security and Compliance**: Astronomer prioritizes security and compliance, implementing best practices for data encryption, access controls, and compliance with industry standards. This helps organizations maintain data integrity and protect sensitive information across their data pipelines.

## Benefits

- **Streamlined Data Pipeline Development**: Astronomer simplifies the development and management of data pipelines, allowing users to focus on data logic rather than infrastructure management. This accelerates time-to-insight and enables teams to deliver value faster.

- **Increased Productivity and Efficiency**: By providing a managed platform for data pipeline development and deployment, Astronomer reduces operational overhead and eliminates the need for managing infrastructure. This enables data engineers to be more productive and focus on innovation.

- **Scalability and Elasticity**: Astronomer's scalable architecture ensures seamless scalability to handle growing data volumes and processing demands. Users can scale resources up or down based on workload requirements, without worrying about infrastructure provisioning or management.

- **Reliability and Resilience**: Astronomer's managed platform ensures high availability and reliability of data pipelines, with built-in redundancy and failover mechanisms. This minimizes downtime and ensures uninterrupted data processing, even in the event of infrastructure failures.

- **Comprehensive Monitoring and Alerting**: Astronomer provides comprehensive monitoring and alerting capabilities to track the performance and health of data pipelines in real-time. Users can proactively identify and address issues, minimizing downtime and ensuring data reliability.

- **Cost Optimization**: By providing a managed platform for data pipeline development and deployment, Astronomer helps organizations optimize costs by eliminating the need for over-provisioning or underutilization of resources. Users can scale resources dynamically based on actual workload demands, optimizing cost efficiency.

Astronomer empowers data engineers and developers to build, deploy, and scale data pipelines with ease, enabling organizations to derive insights and drive value from their data assets. With its comprehensive features and benefits, Astronomer is a valuable platform for organizations seeking to harness the power of their data.

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Astronomer

Census is a modern data infrastructure platform designed to simplify the process of building, managing, and scaling data pipelines. It provides data engineers and developers with a powerful set of tools and services to streamline data integration, transformation, and analysis, empowering organizations to derive insights and make data-driven decisions.

## Key Features

- **Unified Data Platform**: Census offers a unified platform for managing data pipelines, providing a centralized environment for data ingestion, transformation, and analysis. Users can easily orchestrate complex data workflows and integrate data from diverse sources without the need for multiple tools or systems.

- **Code-Free Data Integration**: Census simplifies data integration with its code-free interface, allowing users to visually design data pipelines using a drag-and-drop interface. This enables data engineers and developers to build complex data workflows without writing code, accelerating time-to-insight.

- **Connectivity**: Census provides a wide range of connectors for popular data sources and destinations, including databases, cloud services, APIs, and more. These connectors streamline the process of integrating data from external systems and enable seamless data movement across the organization.

- **Data Transformation**: Census includes powerful data transformation capabilities, allowing users to clean, enrich, and transform data as it flows through the pipeline. Users can apply a variety of transformations, such as filtering, aggregating, and joining, to prepare data for analysis and consumption.

- **Real-time Data Processing**: Census supports real-time data processing, enabling users to ingest, process, and analyze streaming data in near-real-time. This allows organizations to derive insights from live data and react quickly to changing business conditions, gaining a competitive edge.

- **Scalability and Reliability**: Census is built for scalability and reliability, with a distributed architecture that ensures seamless scalability to handle large volumes of data and processing demands. The platform automatically scales resources based on workload demands, ensuring optimal performance and reliability.

## Benefits

- **Streamlined Data Pipeline Development**: Census simplifies the development and management of data pipelines, allowing users to focus on data logic rather than infrastructure management. This accelerates time-to-insight and enables teams to deliver value faster.

- **Increased Productivity**: With its code-free interface and comprehensive set of tools, Census enables data engineers and developers to be more productive and efficient. Users can quickly design, deploy, and manage data pipelines without writing code, reducing development time and effort.

- **Real-time Insights**: By supporting real-time data processing, Census enables organizations to derive insights from streaming data and make data-driven decisions in near-real-time. This empowers businesses to react quickly to changing market conditions and customer needs, gaining a competitive edge.

- **Cost Efficiency**: Census helps organizations optimize costs by eliminating the need for over-provisioning or underutilization of resources. The platform automatically scales resources based on workload demands, ensuring cost efficiency and optimal resource utilization.

- **Data Quality and Governance**: Census includes features for ensuring data quality and governance, such as data validation, lineage tracking, and access controls. This helps organizations maintain data integrity, compliance, and trustworthiness across their data pipelines.

- **Collaboration and Visibility**: Census provides a collaborative environment for data teams to work together on data pipelines and share insights. Users can easily collaborate on data projects, share datasets, and visualize data using built-in tools and integrations.

Census empowers organizations to build, manage, and scale data pipelines with ease, enabling them to derive insights and make data-driven decisions. With its comprehensive features and benefits, Census is a valuable platform for organizations seeking to harness the power of their data.

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Census

CloudQuery is an open-source ELT framework that empowers data engineers and developers to integrate, transform, and visualize data from any source to any destination. It's designed to handle a wide range of data integration and analysis tasks, making it a versatile tool for modern data stacks.

### Key Features

- **Extensible through Plugins**: Offers the flexibility to write custom plugins in multiple languages (Go, Python, Javascript, or Java), using the CloudQuery SDK powered by Apache Arrow.
- **High Performance**: Optimized for speed, CloudQuery facilitates the quick synchronization of large datasets.
- **Open Source**: Encourages contributions to its ecosystem, fostering innovation and collaboration among developers.
- **Versatile Data Integration**: Enables extraction, transformation, and loading of data from cloud APIs into databases, data lakes, or streaming platforms.
- **Simple Configuration**: Simplifies setup and synchronization tasks with a straightforward YAML configuration.

### Benefits

- **Unified Data Analysis**: By facilitating integration with technologies like DuckDB, CloudQuery enables seamless data exploration and analysis.
- **Scalability**: Its stateless plugin architecture can be scaled horizontally on any platform, meeting the growing data demands of organizations.
- **Elimination of Data Silos**: Enhances collaboration between different organizational teams by unifying data.
- **Ease of Use**: Its user-friendly nature, highlighted by an effortless setup process, makes it accessible for data engineers and developers at all skill levels.

For data engineers and developers looking to explore CloudQuery's capabilities, especially its integration with DuckDB and MotherDuck, diving into the official [CloudQuery documentation](https://docs.cloudquery.io/) and exploring the [CloudQuery blog](https://www.cloudquery.io/blog) provide comprehensive guidance on deployment strategies, plugin development, and real-world applications.

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CloudQuery

Cube is an API-first semantic layer designed to streamline data access and management across applications, serving as a critical tool for data engineers and developers. It enables efficient data model creation, access control management, caching, and data exposure through various APIs, facilitating seamless data integration and analysis.

### Key Features for Integration with DuckDB and MotherDuck

- **Universal Semantic Layer**: Serves as a bridge connecting data silos and ensuring metric consistency across applications.
- **Extensive Data Modeling Capabilities**: Supports dynamic data models with customization through Jinja, Python, or JavaScript.
- **Advanced Caching Mechanisms**: Offers robust caching options to enhance query performance and reduce load times.
- **Comprehensive API Support**: Provides REST, GraphQL, and SQL APIs for versatile data interactions.
- **Configurable Data Sources and Visualization Tools Integration**: Facilitates integration with a wide array of data sources, including DuckDB and MotherDuck, and visualization tools for enhanced data exploration.

### Benefits

- **Enhanced Data Consistency and Accessibility**: Cube's role as a semantic layer ensures data consistency and ease of access across different applications, which is especially beneficial for those working with DuckDB and MotherDuck.
- **Streamlined App Development**: By offering a unified data management approach, Cube reduces development time, allowing developers to focus on creating features instead of managing data inconsistencies.
- **Improved Performance**: Cube's caching mechanisms optimize data retrieval operations, ensuring faster query execution and a smoother user experience.
- **Flexible Data Integration**: Its support for multiple data sources and integration with visualization tools allows for easy adaptation to existing data stacks.
- **Developer-Friendly Platform**: Cube's emphasis on a code-first approach, coupled with extensive documentation and API support, makes it an attractive option for developers looking to enhance their data applications.

For those interested in leveraging Cube for integration with DuckDB and MotherDuck, exploring the [Cube documentation](https://cube.dev/docs) provides a comprehensive guide on configuration, data modeling, API usage, and more, ensuring a smooth integration process with your data stack.

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Cube

Dagster is a comprehensive data pipeline orchestration tool designed for data engineers and developers, focusing on streamlining the creation, maintenance, and execution of complex data workflows. Its cloud-native architecture ensures seamless operation across various environments, from development through to production. For an in-depth understanding of Dagster's capabilities and architecture, visiting the [Dagster official website](https://dagster.io/) provides valuable insights.

### Key Features

- **Software-Defined Assets**: Centers around sophisticated management of data assets such as tables, datasets, and machine learning models.
- **Integrated Lineage and Observability**: Provides insights into data lineage and operational metrics.
- **Declarative Programming Model**: Enhances the readability and maintainability of data workflows.
- **First-Class Testing Support**: Facilitates robust pipeline construction through an emphasis on testing.
- **Comprehensive Development Lifecycle Support**: Supports all stages of the data development lifecycle, including local development, testing, staging, and production.

### Benefits

- **Enhanced Data Asset Management**: Introduces order and observability to the data platform, ensuring data assets are accurate and up-to-date.
- **Improved Debugging and Error Handling**: Makes it easier to identify, debug, and resolve issues by linking computations directly to the production of data assets.
- **Streamlined Collaboration**: Facilitates collaboration between engineers and data stakeholders with a unified view of data pipelines and assets.
- **Simplification of Complex Data Workflows**: Enables data teams to manage complex workflows with greater efficiency.
- **Adoption of Best Practices**: Standardizes on software-defined assets to encourage best practices across all data teams.

For those looking to dive deeper into Dagster's core concepts and effectively implement them in their data platforms, the [Dagster documentation](https://docs.dagster.io/getting-started) offers tutorials, quickstart guides, and courses through Dagster University. These resources are tailored to help users at all levels of expertise learn how to utilize Dagster for their data orchestration needs effectively.

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Dagster

Workspace is an AI-enabled data notebook to discover and share insights effortlessly.

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Datacamp Workspace

Datafold offers a suite of tools designed to enhance the reliability and speed of data engineering workflows, focusing on the automation of data quality testing. Its integration with DuckDB and MotherDuck could significantly streamline the data validation and transformation process, ensuring high data quality and stakeholder trust. For more detailed insights into Datafold's capabilities and integration options, visiting [Datafold's official website](https://www.datafold.com) is recommended.

### Key Features

- **Data Diffing Capabilities**: Allows auditing of specific row and value changes, facilitating the validation of transformation logic changes.
- **Migration Parity and Replication Pipeline Validation**: Enables quick identification of discrepancies between legacy and new transformations or databases.
- **CI/CD Integration**: Supports adding automation and governance to the data quality testing process, ensuring every pull request undergoes rigorous testing.
- **Scalability**: Datafold is built to handle large-scale data environments, supporting millions of tables, billions of columns, and trillions of records.
- **Compliance and Security**: Offers a secure and compliant testing environment, adhering to standards like HIPAA, GDPR, and SOC 2 Type II.

### Benefits

- **Enhanced Development Efficiency**: By automating the testing of data transformations, Datafold reduces the time required for data validation, allowing teams to focus on development.
- **Increased Data Quality and Reliability**: Automated testing and data diffing ensure high data accuracy, significantly reducing the risk of data quality regressions.
- **Stakeholder Trust**: Datafold's reporting tools enable sharing of impact analysis, data diff reports, and column-level lineage with stakeholders, building confidence in the data transformation process.
- **Integration with Modern Data Stacks**: Designed to fit seamlessly into existing and future data workflows, Datafold enhances the capabilities of data engineering tools like dbt and integrates well with data orchestration tools.
- **Improved Code Review Process**: The platform makes SQL code reviews straightforward by eliminating the need for custom scripts and manual audit spreadsheets, streamlining the review process.

Datafold's approach to integrating automated testing into data engineering workflows offers a significant advantage for teams working with DuckDB and MotherDuck. Automating data quality checks and embracing a proactive testing methodology can transform the development lifecycle, resulting in faster releases and higher quality data products.

Discover more about how Datafold can accelerate your data team's workflow and integrate with your DuckDB and MotherDuck setup by exploring the wealth of resources available on [Datafold's documentation and blog](https://www.datafold.com/blog).

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Datafold

DataGrip is a database IDE that is tailored to suit the specific needs of professional SQL developers.

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Datagrip

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Dataherald

dbt is a popular open-source tool for data modeling in data warehouses. It allows data engineers and analysts to transform raw data in their warehouses into structured datasets for analysis and reporting.

### Key Features
- **Version Control**: dbt projects can be version controlled using Git, allowing for collaboration and tracking changes over time.
- **Modularization**: Models in dbt are modular and reusable, promoting best practices in data modeling and reducing duplication of code.
- **Testing**: dbt allows users to write tests for their models to ensure data accuracy and consistency.
- **Documentation**: Documentation is generated automatically based on the code, providing insight into the purpose and structure of each model.
- **Workflow Orchestration**: dbt supports workflow orchestration tools like Airflow and dbt Cloud, enabling automated deployment and scheduling of data transformations.

### Benefits of Integrating with dbt
- **Efficient Data Modeling**: dbt simplifies the process of data modeling, allowing data engineers and analysts to focus on business logic rather than low-level SQL coding.
- **Collaborative Development**: With version control and modularization, teams can collaborate effectively on data modeling projects, ensuring consistency and reliability in data transformations.
- **Data Quality Assurance**: The testing feature in dbt enables users to verify the accuracy and integrity of their data, reducing the risk of errors in downstream analyses.
- **Automated Documentation**: dbt automatically generates documentation for data models, making it easier for users to understand and maintain their data pipelines.
- **Scalability and Flexibility**: dbt is scalable and can handle large datasets, making it suitable for organizations of all sizes. It also supports various data warehouses, including Snowflake, BigQuery, and Redshift, providing flexibility in choice of infrastructure.

### Integration with DuckDB and MotherDuck
- **DuckDB Integration**: dbt can be integrated with [DuckDB](https://duckdb.org/), an in-process analytical database, for efficient data analysis and exploration. By leveraging dbt's data modeling capabilities, users can transform and structure their raw data in data warehouses before loading it into DuckDB for further analysis.
- **MotherDuck Compatibility**: dbt is compatible with [MotherDuck](https://www.cloudquery.io/), a cloud-native analytics platform, enabling seamless integration of dbt's data modeling outputs into MotherDuck for comprehensive analytics and reporting. This integration allows organizations to leverage the strengths of both dbt and MotherDuck to derive valuable insights from their data and drive better decision-making processes.

In conclusion, dbt is a powerful tool for data engineers and analysts seeking to streamline their data modeling processes and ensure data accuracy and consistency in their analytics workflows. By integrating dbt with DuckDB and MotherDuck, users can enhance their data analysis capabilities and derive valuable insights from their data with ease.

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dlt integrates with tools like DuckDB for local development and MotherDuck for cloud deployment, streamlining the transition from testing to production. It emphasizes simplicity in building data pipelines, offering verified sources, schema inference for data loading, and compatibility with analytics tools. By leveraging dlt, users can effortlessly move from DuckDB's local environment to MotherDuck's scalable cloud infrastructure, facilitating a smooth development workflow across different stages of the data pipeline. For more details and example code, visit [dlthub](https://dlthub.com/).

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Estuary is an open-source platform tailored to simplify the development and deployment of data applications, offering a variety of tools and features for data engineers and developers.

### Key Features
- **Data Pipeline Orchestration**: Estuary enables users to orchestrate complex data pipelines effortlessly, providing scheduling, monitoring, and management functionalities.
- **Modular Architecture**: With its modular architecture, Estuary allows users to compose data pipelines using reusable components and services, promoting collaboration and code reuse.
- **Integration with Data Sources**: Estuary seamlessly integrates with various data sources like databases, data lakes, streaming platforms, and cloud services, facilitating data ingestion and processing.
- **Scalability and Fault Tolerance**: Designed for scalability and fault tolerance, Estuary ensures that users can handle large volumes of data while maintaining system reliability.
- **Monitoring and Logging**: Estuary offers monitoring and logging capabilities, empowering users to track the performance and health of data pipelines in real-time and address issues effectively.

### Benefits of Integrating with Estuary
- **Simplified Data Pipeline Development**: Estuary streamlines the development of data pipelines with a user-friendly interface and pre-built components, reducing the time and effort required for deployment.
- **Enhanced Scalability and Reliability**: With its scalability and fault tolerance features, Estuary enables the creation of robust and scalable data pipelines capable of handling large data volumes.
- **Flexibility and Customization**: Estuary's modular architecture allows users to customize and extend the platform according to their specific needs, providing tailored solutions for data integration and processing.
- **Centralized Monitoring and Management**: The monitoring and logging capabilities of Estuary offer visibility into data pipeline performance, enabling users to identify and address issues promptly.
- **Integration with External Systems**: Estuary seamlessly integrates with external systems and services, allowing users to leverage existing infrastructure and tools in their data workflows.

### Integration with DuckDB and MotherDuck
- **DuckDB Integration**: Estuary integrates with [DuckDB](https://duckdb.org/), facilitating efficient data analysis and exploration in data applications. Users can preprocess and analyze data before loading it into DuckDB for further analysis and reporting.
- **MotherDuck Compatibility**: Estuary is compatible with [MotherDuck](https://www.cloudquery.io/), enabling seamless integration of data application outputs into MotherDuck for analytics and reporting. This integration allows organizations to derive insights from their data applications and drive decision-making processes effectively.

In conclusion, Estuary offers a versatile platform for data engineers and developers, providing a range of features to simplify the development and deployment of data applications. Integrating Estuary with DuckDB and MotherDuck enhances data analysis capabilities and enables users to derive valuable insights from their data with ease.

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Estuary

Evidence is a lightweight framework for building data apps. It's open source and free to get started.

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Evidence

Expanso runs jobs exactly where your data resides, utilizing local compute, and minimizing data throughput.

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Expanso

From startups to the Fortune 500 — for analytics or operations — Fivetran is the trusted platform that extracts, loads and transforms the world's data.

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Fivetran

Use AI to rapidly build scalable data products that give end users exactly what they need — from trustworthy and tested data to an intuitive no-code UI.

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GoodData

Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs.

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Google CoLab

BI that will make you love data again. Define metrics on your data warehouse and let your whole team search, explore and find insights that drive your business.

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Hashboard

Queries, notebooks, reports, data apps, and AI — all in the world’s leading collaborative data workspace.

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Lightweight stream processing platform written in Rust and extended by WASM for unmatched speed and flexibility.

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InfinyOn

Kestra is an open-source, event-driven orchestration platform that solves these challenges by simplifying orchestration, unifying disparate tools, improving alignment between engineers and business users, and bringing structure to business-critical operations so that you can build reliable workflows and manage them with confidence.

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Kestra

LangChain is a framework designed to simplify the creation of applications using large language models.

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LangChain

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

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LlamaIndex

Open-source data pipeline tool for transforming and integrating data. The modern replacement for Airflow.

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Mage

Say goodbye to writing, maintaining, and scaling your own API integrations with our declarative code-first data integration engine.

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Meltano

Fast analytics with the friendly UX and integrated tooling to let your company explore data on their own.

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Metabase

Get up and running with large language models, locally.

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Ollama

Explore, visualize, and model data your way with Omni. From spreadsheets to SQL—in a single platform.

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Omni

The dbt™ native workspace for analytics teams.

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Paradime

Open, cost-effective business intelligence for modern teams

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Preset

PuppyGraph empowers you to seamlessly query one or multiple data stores as a unified graph model.

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PuppyGraph

Build fast operational dashboards that your team will actually use.

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Rill Data

About Secoda:
Secoda consolidates multiple tools into a single data management platform to simplify your data catalog, lineage, governance, monitoring, and observability processes.
Secoda creates a single source of truth for an organization’s data by connecting to all data sources, models, pipelines, databases, warehouses, and visualization tools. Regardless of technical ability, it is the easiest way for any data or business stakeholder to turn their insights into action.

Benefits:
Data catalog and discovery: Easily search across all columns, tables, dashboards, events, and metrics. AI powered search lets you ask any question to your data and get a contextual answer, fast. Get answers to questions like “Can I drop customers_id without impacting other data?”

Enterprise data governance at scale: Control who has access to what data assets internally and externally with granular role-based access controls.
Automate workflows and documentation: Perform bulk updates, tag PII data, manage tech debt, build custom integrations, identify the least used resources, and more. Eliminate manual error and have total trust in your knowledge repository. You can also generate documentation and queries from your metadata.
Full lineage across your entire data infrastructure: Identify the impact to upstream or downstream assets and immediately notify stakeholders before changes are made. Bring data quality tests into lineage so you can stay ahead of any data issues.

Built in data quality tools: Secoda connects data quality, observability, and discovery in one platform. Get visibility into pipeline metadata such as cost, query volume, and popularity. Monitor and optimize the health of your pipelines, processes, and data infrastructure as a whole.

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Secoda

Move your data at superhuman speeds with the power of your code and low-code combined.

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Shipyard

Running your EL tasks from the CLI has never been simpler. Whether ingesting CSV or JSON files, transferring data between databases, or exporting a custom SQL query to a Parquet file — Sling is the solution that empowers you to achieve it effortlessly.

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Sling Data

Embed Soda data quality tests in the data stack and workflows your teams already use. Test data at multiple points in your CI/CD and data pipelines to prevent downstream data quality issues, ensuring that your colleagues have data products they can trust.

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Soda

SQLMesh is an open source data transformation framework that brings the best practices of DevOps to data teams. It enables data scientists, analysts, and engineers to efficiently run and deploy data transformations written in SQL or Python.

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SQLMesh

Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.

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Streamlit

Superset is fast, lightweight, intuitive, and loaded with options that make it easy for users of all skill sets to explore and visualize their data, from simple line charts to highly detailed geospatial charts.

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Superset

Tableau is a visual analytics platform transforming the way we use data to solve problems—empowering people and organizations to make the most of their data.

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Tableau

The fastest way to get actionable insights from your database just by asking questions.

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Vanna

Tap into BI that answers your data questions in seconds, eliminating your reliance on a time-strapped analytics team.

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Technology Partners

Consulting Partners