Data orchestration
made easy

Orchestrate data pipelines intuitively. Eliminate infrastructure and code overhead to scale your ELT data pipelines faster.

Automate data pipelines, make your data work for you

Setup workflows in minutes

Accelerate your workflow development via no-code orchestration

Reference ingestion or transformation steps from any pipeline

Scale data operations

Avoid risks or wasted time on infrastructure work with serverless data pipelines

Chain pipelines together and stop complex time-based scheduling

Manage with ease

Gain full transparency into every step of your workflows

Unify fragmented data tools in one place

Deliver advanced business logic

Deliver advanced business logic

  • Apply smart conditional logic with dynamic variables and loops
  • Insert push-down SQL, Python scripts or both in a single workflow
  • Trigger any REST API to Reverse ETL insights into operational systems

Control your activities and costs

  • Define containers to run steps in parallel and modularize your workflow
  • Easily monitor workflows execution health and address problems
  • Manage costs with detailed consumption data across all workflows operation

Pre-built data workflow templates

  • Enjoy analytics-ready data models with predefined kits for a variety of sources
  • Reduce dev time with unified workflow templates for ingestion, transformation and orchestration
  • Integrate transformations executed via dbt, Databricks notebook or other within the workflow
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Daniel Rimon,

Head of Data Engineering, at Resident Brands.

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"Rivery has the most flexibility that I saw. You can really build the data process as if you built it yourself from scratch, but without the time consuming part. You can do it in a few minutes instead of a few days, but exactly like you want."

How it works

1

Select your workflow steps - SQL, Python, reference other pipeline or action.

2

Define the work order with conditions, loops and containers.

3

Schedule your workflows or trigger via API.

See it in action – data workflow setup

FAQs

What is data orchestration?

Data orchestration is the process of designing, implementing, and managing workflows and data pipelines to ensure seamless integration, transformation, and analysis of data from multiple sources. It involves organizing and coordinating data-related tasks to optimize data processing and enhance overall data management efficiency.

Why is data orchestration important?

Data orchestration is crucial for businesses because it helps streamline complex data workflows, improve data quality, enhance collaboration among teams, and enables data-driven decision-making. It ensures that data is transformed, enriched, and delivered to the right people and systems at the right time, enhancing the overall operational efficiency of an organization.

What are the key components of data orchestration?

The key components of data orchestration include data integration, data transformation, workflow automation, data monitoring, error handling, and data security. These elements work together to ensure that data flows smoothly through the entire data lifecycle.

How does data orchestration differ from traditional data integration?

Data orchestration goes beyond traditional data integration by not only focusing on connecting various data sources but also managing the entire data workflow, from data ingestion to data delivery. It involves complex data transformations, scheduling, and monitoring, providing a more holistic approach to data management compared to traditional integration methods.

What types of data can be orchestrated?

Data orchestration can handle various types of data, including structured data (such as databases and spreadsheets), semi-structured data (like JSON and XML files), and unstructured data (such as text documents and multimedia files). It can also manage real-time streaming data from sources like IoT devices and social media platforms.

How does data orchestration enhance data quality and accuracy?

Data orchestration ensures that data is cleansed, validated, and transformed according to predefined rules and standards. By automating these processes, it reduces the chances of human error, enhances data consistency, and improves overall data quality and accuracy.

Can data orchestration be used for real-time data processing?

Yes, data orchestration can be used for real-time data processing. It allows organizations to create workflows that can process and analyze streaming data in real-time, enabling businesses to make immediate decisions based on up-to-the-minute information.

Is data orchestration secure?

Yes, data orchestration solutions often incorporate robust security measures to protect data during transit and at rest. These measures include encryption, access controls, authentication mechanisms, and auditing capabilities, ensuring that sensitive information remains secure throughout the orchestration process.

Simplify and scale the orchestration of your ELT pipelines

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