The modern data stack is defined by its ability to handle large datasets, support complex analytical workflows, and scale effortlessly as data and business needs grow. It must integrate seamlessly across data technologies in the stack to execute various workflows—all while maintaining a strong focus on performance and governance.
Two key technologies that have become foundational for this type of architecture are the Snowflake AI Data Cloud and Dataiku. With its decoupled compute and storage resources, Snowflake is a cloud-native data platform optimized to scale with the business. Dataiku is an advanced analytics and machine learning platform designed to democratize data science and foster collaboration across technical and non-technical teams.
Snowflake excels in efficient data storage and governance, while Dataiku provides the tooling to operationalize advanced analytics and machine learning models. Together they create a powerful, flexible, and scalable foundation for modern data applications.
This blog will explore why the combination of these two platforms is ideal for enterprises looking to build resilient and future-proof data stacks that are flexible for data and business professionals alike.
While phData has experience with all components of the modern stack, today we’ll focus on data science and how a modern cloud warehouse like Snowflake supports data science workloads in Dataiku. Additionally, we will demonstrate a complete machine learning pipeline showing why Dataiku and Snowflake are cornerstones of a modern platform.
Snowflake and Data Warehousing
Companies find success with Snowflake by storing all their data in a centralized platform to model data, build data applications, and procure analytics reporting to understand and improve their business. Snowflake’s cloud-agnosticism, separation of storage and compute resources, and ability to handle semi-structured data have exemplified Snowflake as the best-in-class cloud data warehousing solution. With all this packaged into a well-governed platform, Snowflake continues to set the standard for data warehousing and beyond.
Snowflake supports data sharing and collaboration across organizations without the need for complex data pipelines. Since solidifying itself as the clear leader in the cloud data warehousing space, Snowflake has grown its feature offering to include the handling of unstructured data, Snowpark Container Services to deploy and scale data applications, and a full suite of Generative AI tools on Snowflake Cortex.
Snowflake serves as a powerful conduit to best-in-class technology companies enabling seamless integration and collaboration across a rich ecosystem of data solutions like Dataiku.
By providing a single, unified platform for data storage, management, and analysis, Snowflake connects organizations to leading software vendors specializing in analytics, machine learning, data visualization, and more. Its cloud-native architecture, combined with robust data-sharing capabilities, allows businesses to easily leverage cutting-edge tools from partners like Dataiku, fostering innovation and driving more insightful, data-driven outcomes.
Dataiku and Data Science
Over the past two years, we’ve seen tremendous developments in the fields of artificial intelligence, data science, and machine learning. With data software pushing the boundaries of what’s possible in order to answer business questions and alleviate operational bottlenecks, data-driven companies are curious how they can go “beyond the dashboard” to find the answers they are looking for.
By providing an integrated environment for data preparation, machine learning, and collaborative analytics, Dataiku empowers teams to harness the full potential of their data without requiring extensive technical expertise.
One of the standout features of Dataiku is its focus on collaboration. The platform allows data scientists, analysts, and business stakeholders to work together seamlessly. With tools designed for both coders and non-coders, Dataiku bridges the gap between technical and non-technical team members, promoting a culture of data-driven decision-making across the organization. This collaborative approach not only accelerates project timelines but also ensures that insights are aligned with business goals, fostering a more agile response to market demands.
Moreover, Dataiku simplifies the complexities of data science operations through its end-to-end capabilities. From data ingestion and cleaning to model deployment and monitoring, the platform streamlines each phase of the data science workflow.
Automated features, such as visual data preparation and pre-built machine learning models, reduce the time and effort required to build and deploy predictive analytics. As a result, companies can focus more on deriving value from their data and less on managing the intricacies of the underlying technology.
In essence, Dataiku is transforming how organizations approach data science by making it more collaborative, accessible, and efficient. By lowering the barriers to entry and providing robust tools for managing data projects, Dataiku enables companies to leverage their data assets effectively, driving innovation and competitive advantage in an increasingly data-centric world.
Data Science Use Cases with Dataiku
Much of the above is hypothetically great, but what does data science on Dataiku look like when applied to a real business use case? Let’s jump into a few data science and analytics outputs using a real-life example that takes you from data transformation to predictive modeling and generative AI.
Let’s say your company makes cars. Here are some simplified usage patterns where we feel Dataiku can help:
Data Preparation
Dataiku offers robust data preparation capabilities that streamline the entire process of transforming raw data into actionable insights. Its intuitive visual interface allows users to clean, blend, and enrich datasets through a variety of tools, including data wrangling, filtering, and transformation functions.
For our use case, we are using Dataiku to clean CRM data for downstream initiatives:
Reaggregate, Reformat, and Join Data
Dataiku simplifies the data prep process through Visual Recipes that require no code, yet deliver the flexibility to blend, transform, and clean data. Users can easily join datasets, remove duplicates, handle missing values, and create custom variables, laying a strong foundation for accurate model training. This accessible approach to data transformation ensures that teams can work cohesively on data prep tasks without needing extensive programming skills.
With our cleaned data from step one, we can now join our vehicle sensor measurements with warranty claim data to explore any correlations using data science.
From Data to Predictions Using Visual ML
Dataiku’s automated feature engineering tools further accelerate the preparation process by automatically generating features based on the dataset’s content. This capability can reveal hidden patterns and optimize data for improved model performance. Combined with the visual data prep interface, this allows users to seamlessly add derived variables without leaving the platform, significantly reducing the time to valuable insights.
Through its intuitive visual ML interface, Dataiku empowers users to build and compare machine learning models with ease. Dataiku automatically suggests algorithms, and users can compare a variety of models—such as random forests, XGBoost, or logistic regression—via a straightforward, visual comparison interface.
Dataiku strongly emphasizes model interpretability, which is crucial for building trust in AI solutions. Within the platform, users can visualize feature importance and generate partial dependence plots, providing insights into how specific features impact predictions. For regulated industries, Dataiku also supports model explainability techniques, making it easier to meet compliance requirements while promoting transparency.
Furthering our use case with these capabilities from Dataiku, we can predict car battery failures based on vehicle sensors using machine learning within Dataiku. To ensure that we are utilizing a strong-performing model, Dataiku has auto-generated model performance metrics for us to analyze before putting our model into production.
Deploying to a Generative AI Interface
Dataiku offers a smooth path to deploying upstream machine learning workloads. With just a few clicks, users can move models into production, deploying them as APIs or batch processes or utilizing Dataiku Answers, a packaged, scalable web application democratizing enterprise-ready LLM chat and RAG usage across processes and teams. This deployment pipeline is integrated directly into the Dataiku platform, ensuring that data science teams can efficiently transition from model training to actionable outcomes and utilization.
For our hypothetical car company, we will use Dataiku’s Answers application to create a personalized customer service chatbot that can pull data from warranty contracts, car spec manuals, and customer history to respond to inquiries.
The Dataiku Snowflake Link
You’re probably thinking, “These are some interesting use cases, but what does this have to do with Snowflake?”
Beyond Snowflake being the strong, highly-governed, and secure foundational data platform – the Dataiku usage patterns we just described can all leverage Snowflake data, compute, and AI services behind the scenes, including:
For data cleaning, joins, and aggregations, Dataiku auto-generates SQL queries and can push them down to a Snowflake warehouse.
For machine learning, Dataiku can take a trained ML model and generate a Snowflake UDF for batch scoring or build a container and deploy it to Snowpark Container Services for real-time scoring.
For GenAI applications, Dataiku can send prompts to Snowflake and utilize LLMs hosted on Snowflake Cortex.
The Dataiku application serves as a low-code control plane that doesn’t consume much computational resources. Its power lies in its ability to orchestrate workloads on the highly scalable and performant Snowflake Data Cloud using its storage and compute resources.
This integration enables users to utilize Snowflake’s processing power directly within Dataiku, ensuring a streamlined and efficient path from data storage to advanced analytics and AI-driven insights. Together, Snowflake and Dataiku empower organizations to build sophisticated, data-driven solutions quickly and at scale.
Dataiku and Snowflake: A Good Combo?
Yes! Organizations looking for a modern, cloud-native data warehouse and data science platform should strongly consider Dataiku and Snowflake to bring the AI/ML pipelines into production. Their collective usability, performance, flexibility, and tight integrations make this stack greater than the sum of its parts.
Continue your journey by exploring our blog on the top use cases of Snowflake and Dataiku
