Rivery, a leading data management company, announced the integration of Python as a native source or target for any data workflow, empowering data analysts and engineers with fully customizable and complex data workflows. This industry-first solution, available to all Rivery customers, means that Python DataFrames can now be used as a native source or target in their ETL/ ELT workflows, eliminating the need for writing any “plumbing” code in Python.
In the past, organizations looking to build an end-to-end data stack had to choose between ease of use in a “no-code” platform, or complex solutions built for engineers that support advanced use cases and customization. While Rivery’s no-code solution addressed most of the challenges data analysts and BI teams face, adding Python solves advanced needs including custom connectivity, complex transformations, AI/machine learning, and data enrichment.
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One of Rivery’s Partners in the data space is LEIT DATA, a specialist data and analytics consultancy with a focus on providing DataOps solutions. Recently, LEIT DATA delivered a platform for one of its media clients using Rivery’s new Python integration. Chris Tabb, Co-Founder of LEIT DATA, said: “Having the ability to embed Python as part of a data pipeline using Rivery’s Python integration was a game changer for our client. It enabled us to deliver the customer solution in less time and with less cost than any other alternatives we have used in the past. The ability to manage all the business logic of your end-to-end data pipeline within the Rivery platform allows you to get the best agility from your data engineering team.”
Aviv Noy, CTO of Rivery said: “Python frees data engineers to customize their data workflows as far as their imagination can go.The addition of Python integration empowers Rivery users with full customization and data enrichment with machine learning and complex transformation.”
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