Over the years, businesses have increasingly turned to Snowflake AI Data Cloud for various use cases beyond just data analytics and business intelligence. From data engineering and machine learning to real-time data processing, Snowflake has become a central hub for organizations seeking to unify and leverage their data at scale. However, one consistent challenge customers face is efficiently integrating and moving data between on-premises systems, cloud environments, and other data sources. With so many different ways to get data into Snowflake—from traditional ETL tools to APIs, batch processing, and streaming data—it can quickly become overwhelming to choose the right approach.
This complexity often leads to the need for additional tooling. Many of the traditional methods for data integration are cumbersome, lack flexibility, or aren’t well-suited for the scale of data that modern enterprises need to manage. As a result, businesses frequently experience delays in proving the value of their Snowflake use cases. Integrating the right data, transforming it, and preparing it for analysis can take longer than expected, making it harder to demonstrate tangible business outcomes.
To address these challenges, Snowflake recently announced its acquisition of Datavolo to streamline and simplify data integration into its platform. Datavolo addresses these common pain points by providing a seamless, flexible, and scalable solution for moving data into Snowflake from both on-premises and cloud-based systems. By automating key integration tasks and supporting complex data workflows, Datavolo helps businesses accelerate their time to value, reduce integration complexities, and ultimately prove the impact of their Snowflake deployments faster.
What is it?
Datavolo is the solution that tackles these common challenges head-on. It is an application and infrastructure platform designed to move data efficiently between systems. With Datavolo, organizations can seamlessly integrate data into Snowflake, regardless of whether it originates from on-premises systems, cloud environments, or even hybrid infrastructures.
Datavolo is more than just an ETL tool—it provides functionality for Reverse ETL as well, enabling organizations to push data from Snowflake into other systems. It is a robust platform that supports Enterprise Service Bus (ESB) workloads, with Snowflake as the main transformation engine.
Datavolo’s power comes from its flexibility and open framework. Developers can build new flows, processors, or process groups, bringing structured and unstructured data to Snowflake. Its agent-based data replication ensures that it works with both on-prem and cloud-hosted source systems, providing a fault-tolerant, scalable solution for data integration.
Furthermore, Datavolo provides a graphical UI that simplifies defining data pipelines. These pipelines can be templatized and reused for common ingestion patterns, which significantly accelerates setting up data pipelines across different use cases.
Core Component: Apache NiFi
At the heart of Datavolo lies Apache NiFi, an open-source data integration tool that simplifies the process of data routing, transformation, and mediation. NiFi’s power lies in its ability to handle complex data flows and large data sets, making it the perfect tool to form the backbone of Datavolo’s data processing capabilities.
Apache NiFi enables Datavolo to offer flexible, reusable, and scalable data pipelines, allowing organizations to seamlessly integrate various data sources and destinations. With NiFi’s robust user interface, teams can build workflows that handle complex data operations with minimal effort, significantly reducing the time spent on manual processes.
Datavolo’s multimodal pipelines allow data engineers to build workflows that seamlessly integrate and process both data (structured and unstructured), providing businesses with a holistic view of their data landscape (Figure 1).
phData Has Deep Expertise And is Excited About This Addition
At phData, we have deep experience with Apache NiFi, the core component of Datavolo, and we’re excited about this addition to the Snowflake ecosystem. In our Hadoop era, we extensively leveraged Apache NiFi to integrate large ERP systems and centralize business-critical data. Our team has been working with Apache NiFi for over a decade, and we’ve built a wealth of expertise in utilizing it to support various data integration and data flow use cases.
Our team includes members who have actively contributed to the Apache NiFi project, helping to improve the platform and stay ahead of the latest developments in the open-source community. This close involvement with Apache NiFi has allowed us to develop and refine best practices for designing, deploying, and maintaining Apache NiFi-based data pipelines.
We’ve gained significant experience in supporting, monitoring, and maintaining these data pipelines once they’re in production. Our Elastic Operations (EO) team has developed automated solutions for provisioning the necessary infrastructure and software that runs Apache NiFi, ensuring that customers’ data flows are reliable and scalable. Implementing these automation solutions, we help organizations achieve a smooth, seamless integration process while keeping their data operations stable and scalable.
As a result, phData is uniquely positioned to help businesses maximize the value of their Snowflake deployment by leveraging Datavolo and our deep expertise with Apache NiFi.
Why We’re Excited: Value for Customers
We believe that Snowflake’s acquisition of Datavolo brings significant benefits to customers. Here’s why:
Rapid Prototyping and Proving Business Value with Minimal Barriers to Entry: Datavolo enables businesses to quickly prototype and demonstrate the value of their Snowflake use cases without requiring significant upfront engineering effort. It’s easier to bring data in with Datavolo and test the use case, proving its value before committing to more expensive tools like Fivetran. This can lead to wasted resources if the data doesn’t provide the expected return.
Confident and Timely Access to Business-Critical Data: With Datavolo, organizations can ensure their data is accessible and available when needed, giving them the ability to make data-driven decisions in real-time.
Develop Templates and Reusable Data Pipelines: Datavolo allows customers to build reusable data pipelines to accelerate time to value and simplify future data integrations.
For example, when a customer needs to:
Bring ERP or on-prem data sources into Snowflake; Datavolo provides a straightforward solution for data integration, making the transition to the cloud seamless.
Perform simple data transformations and cleansing before ingestion; Datavolo automates these tasks, ensuring clean and transformed data enters Snowflake.
Build a platform to support structured and unstructured data pipelines, enabling Agentic AI applications to leverage Snowflake’s processing power for advanced data use cases.
Implement a Snowflake First approach to data management and integration, ensuring that Snowflake is at the core of your data ecosystem while minimizing complexity.
We believe Datavolo is a great solution for many use cases, but it may require some additional architecture and engineering support for more complex scenarios, especially if you don’t have dedicated data engineers on your team. Unlike some plug-and-play CDC tools like Fivetran, Datavolo offers flexibility that can be tailored to your specific needs with extra setup.
Use Case for Key Industries
1. Healthcare: Leveraging Datavolo for AI and ML in Healthcare
Healthcare generates vast amounts of unstructured data, including medical images, clinical notes, and doctor-patient conversations. Analyzing this data is critical for improving diagnostics and patient care.
Challenge: Unstructured data, like images and text, is often siloed, making it hard to process and analyze in real-time.
Solution: Datavolo integrates this unstructured data into Snowflake, enabling AI/ML models to:
Analyze medical images for early disease detection.
Use NLP to extract insights from clinical notes.
Transcribe audio for actionable data from doctor-patient conversations.
Outcome: By processing both structured and unstructured data, healthcare providers can:
Improve diagnostics and personalized treatments.
Predict patient outcomes and make smarter decisions.
Datavolo empowers healthcare organizations to unlock AI-driven insights, enhancing patient care and efficiency.
2. Quick Serve Restaurants (QSRs): Tracking Inventory and Sales with Snowflake
To stay competitive, QSRs need to track inventory, sales, and customer behavior across multiple locations in real time. However, managing this data can be challenging due to disconnected systems.
Challenge: QSR chains often use various POS systems, inventory software, and customer databases, making it hard to get a unified view of operations.
Solution: Datavolo integrates data from all QSR locations into Snowflake, consolidating sales, inventory, and customer feedback. This enables real-time analysis of trends and performance across locations.
Outcome: QSRs can optimize inventory management, reduce waste, and improve sales forecasting. Corporate teams gain insights to enhance marketing, pricing, and supply chain strategies.
Closing
Are you looking to improve your Snowflake ingestion pipeline? Want to understand how Datavolo’s acquisition by Snowflake can enhance your data operations? phData is here to help.
With over a decade of experience working with Apache NiFi and Snowflake, we can guide you through the best practices for building scalable, automated data workflows. Contact our sales team today to discuss your data ingestion needs, and let’s explore how we can optimize your data architecture for better performance, lower costs, and faster insights.
