When it comes to code development for analytics and data science, there are many tools, methods, and practices that data teams can choose to follow. With several paths for development available to one team, such as open source frameworks like Python and proprietary languages like SAS, collaboration can become a challenge. Often you have a trade-off — you can use an analytics platform to help with this collaboration problem, but that platform may lock you into specific languages and engines, restrict you from accessing the latest open source techniques, or silo coders away from working together with business stakeholders.
Dataiku is a platform designed to offer collaboration without that tradeoff. The platform can meet these needs by offering a range of features that empower teams, streamline workflows, and ensure transparency throughout the analytics process. Below, we summarize and group the key features of Dataiku that make it an invaluable tool for modern analytics.
1. Enhanced Collaboration & Reuse:
We’ve talked about some of Dataiku’s modern analytics features in previous blog posts, but Dataiku has features specifically for coders that make collaboration a breeze.
- Feature Stores and Data Collections: Dataiku allows users to create and manage data collections in the Data Catalog and Feature Store, which are groups of curated datasets that can be shared across projects. This promotes reuse, consistency, and ensures that everyone is working with the most accurate and up-to-date data, reducing time spent on data wrangling, the risk of getting different answers to the same question and the cost associated with duplicating data.
- Streamlined Collaboration With Git: Dataiku integrates with Git, allowing you to version control your code and projects without leaving the platform. You can create branches, commit changes, and merge code, all within Dataiku’s intuitive interface. Teams can collaborate more effectively, knowing that they are working with the latest version of the code and that changes are tracked and documented.
- Easy Reuse of Projects, Code Samples, Plugins and More: You can easily create reusable code “recipes” that team members can duplicate within and across projects. Code samples can also be used to accelerate the development of these recipes, while Python plugins let you package frequently used code as GUI tools for less technical team members.
Add trusted datasets to the data catalog for easy reuse.
2. Flexibility for a Diverse Workforce and Diverse Workloads:
Dataiku is designed to break down barriers between coders and non-coders such as some analysts or business stakeholders, as well as give teams the flexibility to work in the way that they prefer.
- Support for Multiple Coding Languages (and Freedom of No Code): Dataiku's support for Python, R, SQL and multiple flavors of Spark makes it a versatile tool for a wide range of users, from data scientists to analysts and engineers. This flexibility allows teams to work together on the same project using the tools they are most comfortable with, without compatibility issues. Dataiku also offers completely visual, no code tools, in the same interface for those that want to speed up coding processes (i.e., using Visual ML to tackle basic ML use cases) or simply not code at all. Dataiku even offers a built-in AI Code Assistants that help you automatically generate code with simple prompts, and AI Explain which can help summarize project functionality in easy to read text.
- Access the Latest Open Source Libraries and Frameworks: Python’s ecosystem is vast, and libraries like Pandas, scikit-learn, TensorFlow, and Matplotlib offer powerful tools for data analysis and machine learning (ML). Dataiku allows you to create flexible code environments so you can integrate these libraries directly into your workflows. If teams leverage their own home-grown libraries, users can leverage Dataiku’s Libraries to import them from Git, or build them directly in Dataiku. This means that users can now tap into the latest advancements in open source ML and data visualization, as well as their own libraries, without leaving the Dataiku environment.
- Pushdown Execution for Scale: For maximum performance and efficiency on large workloads, Dataiku utilizes a pushdown architecture that allows organizations to take advantage of existing, elastic, and highly scalable computing systems including SQL databases, Spark, Kubernetes, and more. Dataiku provides a fully managed Kubernetes solution that is compatible with all of the major cloud container services to handle large and dynamic loads created by big data analysis, machine learning, and Generative AI in a cost-effective way. This means coders can spend less time planning and connecting to the infrastructure needed to scale their jobs, and more time creating innovative solutions.
Dataiku’s built-in features like Jupyter notebooks allow coders to work how they prefer.
3. Data-Driven Decision Making With Transparency and Oversight:
All of this is not done in a black box, but in such a way that teams can easily test hypotheses and understand the impact of models prior to deployment.
- Model Interpretability
Dataiku provides tools for model interpretability — including visual ways to inspect model fairness, bias, subpopulation analysis, and more — ensuring that users can understand how models are making predictions. This transparency is crucial for building trust in the models and ensuring that they align with business objectives, especially when dealing with advanced ML techniques. - Model Documentation
Automatically generated, customizable documentation for models and pipelines helps teams retain critical project context for reproducibility and compliance purposes while simultaneously reducing the burden of manual documentation. This is especially important in regulatory industries where extensive model documentation is required, but can take months. - Model Monitoring
Dataiku includes robust model monitoring capabilities that track key performance metrics like data drift and alert users to any potential issues. You can even automate the retraining of models if drift is detected within thresholds you set. By proactively managing models, organizations can avoid costly mistakes and ensure their analytics efforts remain effective. - Human-in-the-Loop Governance Workflows
Dataiku emphasizes the importance of human oversight in the analytics process. The platform's human-in-the-loop governance workflows allow organizations to establish checks and balances at critical points in the project lifecycle. This ensures that models are not only accurate but also ethical and aligned with regulatory requirements.
Dataiku Is Your Tool for Coding in Modern Analytics
Dataiku's comprehensive set of features make it an essential platform for modern analytics and AI. By enhancing collaboration, providing flexibility for a diverse workforce, and ensuring transparency and oversight in data-driven decision-making, Dataiku empowers organizations to unlock new levels of agility, innovation, and trust in their analytics efforts.
Want to learn more about how Dataiku empowers modern analytics? Check out this infographic to learn more.