Daniel Buchuk
MAR 23, 2022
5 min read
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The highly anticipated Python integration is here. For data engineers using Rivery, this was one of the most requested features, especially among advanced data teams that need programming-based solutions for complex data analysis and workflows. 

While we’re officially announcing this new capability today, we have already been working with amazing design partners and customers to test and optimize it. In addition to improving our product, we have gained a deeper understanding of what our customers want, need – and which are some of the most important use cases that by adding Python to the mix, helps users take their data processes to the next level.

With most data platforms choosing “no-code,” why Python ETL tools?

Within the realm of tools and platforms to build data pipelines and workflows, it can be said that the overall industry trend gravitates towards simple, easy-to-use, no-code solutions that can fit most use cases and needs. In fact, Rivery is no different and we take pride in offering the ecosystem to “plug-and-play” when it comes to connecting, transforming, and orchestrating data. However, working with the world’s most advanced data teams means that addressing most use cases isn’t enough. 

Teams who work on edge cases that need powerful customization are welcoming this new launch with open arms. Especially for companies that need complex data analysis for machine learning, this new integration is essential. Ultimately, we want to offer Rivery users the best of both worlds. Data teams and analysts can manage and orchestrate their data without the need from dev and engineering teams – which covers 80-90% of Rivery users’ needs. For the additional 10-20%, by adding Python as a native source/target in Rivery’s ETL workflows, data teams can now handle any of their complex data pipeline needs.

Python for ETL in Rivery

In essence, we have simplified the process of leveraging Python. With our new interaction, we’ve reduced the process to 3 steps on Rivery. 

First, just pull the data directly into your DataFrame. Secondly, execute your python script using the data you just pulled into your DataFrame. Finally, simply push the transformed data into your data warehouse. 

This new feature is available to all users of Rivery to facilitate custom connectivity, complex transformations, AI/machine learning, and data enrichment.



Want to see the Python for ETL in action?

If you want to see what Rivery’s new Python integration looks like, don’t miss this 10-minute video introduction. With help from one of our super-talented solutions engineers, Ujjwal “JJ” Tamhankar, you can quickly understand what the capability looks like in action. In addition, we briefly discuss how data teams can fully harness the power of Python, as well as some of the key use cases for which data engineers are now using this highly requested feature.

Want to learn more? Check out our on-demand Python webinar, where you can discover new Python use cases and see how your organization can leverage Python to create optimal data workflows.

See how you can run custom Python code directly within a no-code ETL platform and easily get your data into (or out of) Python without the need to write any connectivity code. Watch today!

Simple Solutions for Complex Data Pipelines

Rivery's SaaS ELT platform provides a unified solution for data pipelines, workflow orchestration, and data operations. Some of Rivery's features and capabilities:
  • Completely Automated SaaS Platform: Get setup and start connecting data in the Rivery platform in just a few minutes with little to no maintenance required.
  • 200+ Native Connectors: Instantly connect to applications, databases, file storage options, and data warehouses with our fully-managed and always up-to-date connectors, including BigQuery, Redshift, Shopify, Snowflake, Amazon S3, Firebolt, Databricks, Salesforce, MySQL, PostgreSQL, and Rest API to name just a few.
  • Python Support: Have a data source that requires custom code? With Rivery’s native Python support, you can pull data from any system, no matter how complex the need.
  • 1-Click Data Apps: With Rivery Kits, deploy complete, production-level workflow templates in minutes with data models, pipelines, transformations, table schemas, and orchestration logic already defined for you based on best practices.
  • Data Development Lifecycle Support: Separate walled-off environments for each stage of your development, from dev and staging to production, making it easier to move fast without breaking things. Get version control, API, & CLI included.
  • Solution-Led Support: Consistently rated the best support by G2, receive engineering-led assistance from Rivery to facilitate all your data needs.

Minimize the firefighting.
Maximize ROI on pipelines.

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