About ReOrc

What is ReOrc?

ReOrc is a unified data platform that delivers seamless analytics and high-quality data at scale. The Reorc platform empowers all stages of data lifecycle, allowing data practitioners to build and maintain robust transformation pipelines.

Key modules

As an all-in-one solution for data needs, ReOrc contains the following key modules:

Data ingestion: ReOrc offers a wide range of built-in connectors to effortlessly load data from sources into a common destination. You can also create your own connectors to customize ingestion scheme.
Data modeling: ReOrc supports data transformation through SQL and Python models, leveraging the best practices of data modeling with version control and lineage tracking to ensure transparency and consistency.
Semantic modeling: ReOrc semantic layer enables organization to create a unified and business-friendly representation of data, such as key metrics and their relationships. End-users can then query from semantic models to make decision based on the data.
Data orchestration: Build end-to-end pipelines that cover the entire data life cycle, including ingestion, transformation, semantics, with out-of-the-box operators and intuitive visual graphs.
Quality control: ReOrc is built with quality control in mind, that's why tests and quality checks are applied to all types of assets to make sure your data meet predefined standards.

While embracing the modern data stack's innovation, ReOrc also helps data practioners solve key challenges in modern data systems:

Tangled pipelines: With data of high volume and complexity, comes the need for intricate ETL processes, models, and tools. Managing these disparate components is challenging without a centralized control system.
Insufficient governance: Control is scattered across multiple tools and users, making it difficult to enforce consistent, secure, and compliant data practices.
Fragmented observability: Without a unified view of your data flow, cataloging assets, tracking lineage, monitoring quality, and troubleshooting issues are significantly hindered.
Siloed knowledge: Knowledge silos are inevitable when teams and individuals become more specialized in isolated tools. This cripples collaboration and cross-functional communication, leading to missed opportunities and less effective data solutions.

NextSet up and deployment

Last updated 15 days ago

Key modules

As an all-in-one solution for data needs, ReOrc contains the following key modules:

Data ingestion: ReOrc offers a wide range of built-in connectors to effortlessly load data from sources into a common destination. You can also create your own connectors to customize ingestion scheme.

Data modeling: ReOrc supports data transformation through SQL and Python models, leveraging the best practices of data modeling with version control and lineage tracking to ensure transparency and consistency.

Semantic modeling: ReOrc semantic layer enables organization to create a unified and business-friendly representation of data, such as key metrics and their relationships. End-users can then query from semantic models to make decision based on the data.

Data orchestration: Build end-to-end pipelines that cover the entire data life cycle, including ingestion, transformation, semantics, with out-of-the-box operators and intuitive visual graphs.

Quality control: ReOrc is built with quality control in mind, that's why tests and quality checks are applied to all types of assets to make sure your data meet predefined standards.

While embracing the modern data stack's innovation, ReOrc also helps data practioners solve key challenges in modern data systems:

Tangled pipelines: With data of high volume and complexity, comes the need for intricate ETL processes, models, and tools. Managing these disparate components is challenging without a centralized control system.

Insufficient governance: Control is scattered across multiple tools and users, making it difficult to enforce consistent, secure, and compliant data practices.

Fragmented observability: Without a unified view of your data flow, cataloging assets, tracking lineage, monitoring quality, and troubleshooting issues are significantly hindered.

Siloed knowledge: Knowledge silos are inevitable when teams and individuals become more specialized in isolated tools. This cripples collaboration and cross-functional communication, leading to missed opportunities and less effective data solutions.