The Future of Analytics: Exploring Advanced Features in Dataiku

Dataiku Product, Featured Lauren Anderson

With the explosion of tools in the AI space over the past couple of years, many analytics and IT teams are struggling to take advantage of new technology to empower the business, while ensuring that they’re consolidating their tech stacks and technical complexity. This presents a huge challenge for many organizations that use legacy data tools in advanced analytics. An upcoming survey from Dataiku of 200 IT leaders found that nearly 44% don’t believe that their current data tools fit with analytics and AI needs. They need a platform that addresses current advanced analytics needs but is both open and flexible enough to meet the current wave of GenAI (and whatever might come next). 

Dataiku — the Universal AI Platform — is just that and offers a suite of advanced analytics features that can enhance your data workflows and improve analytical outcomes. In this blog, we’ll dive into some of the standout advanced analytics features within Dataiku that can help teams meet the challenges of today and tomorrow. 

Can I Connect to All the Data I Need? 

With Dataiku, data is always accessible and you have full flexibility to manage your tech stack as needed. From simple flat files to SQL cloud databases and everything in between, Dataiku excels in integrating data from a myriad of sources. This versatility allows users to import datasets from various sources seamlessly, ensuring that all relevant data is available for analysis. The ability to connect to different data environments facilitates the consolidation of disparate datasets, providing a unified view of data across the organization. 

Trusted datasets can then be published in a data catalog so that they can be easily discovered and shared across the organization to encourage reuse with control (not to mention reduce the burden on IT teams to provide data access). 

dataiku catalog

Easily search for and discover trusted datasets with the data catalog.

Once data is connected, regardless of file type you can work with each dataset in the same way, using everything from no to full code. You can also convert flows from existing analytics tools (for example, from SAS or Alteryx), and convert them into Dataiku with ease. 

Dataiku offers unparalleled flexibility in choosing the computational engine and infrastructure. Whether you are working on-premises, in the cloud, or in a hybrid environment, Dataiku supports various engines such as Apache Spark, SQL databases, and in-memory processing. This flexibility ensures that you can leverage the most suitable infrastructure for your specific data processing needs, optimizing performance and cost.

Open and Tech-Agnostic: Code and Work as You Like

One of the key strengths of Dataiku is its technology-agnostic approach, which allows users to seamlessly integrate and leverage a variety of open-source frameworks and tools. This flexibility ensures that data professionals can work with their preferred technologies, such as Python, R, and SQL, without being constrained by the platform. Dataiku supports popular machine learning (ML) libraries like TensorFlow, scikit-learn, and PyTorch, as well as big data processing frameworks such as Apache Spark and Hadoop. 

By enabling the use of these open-source tools, Dataiku provides an adaptable environment that can accommodate evolving data science practices and integrate with existing workflows. This openness not only fosters innovation but also ensures that you can leverage the latest advancements in the data science field while maintaining compatibility with your existing technology stack.

jupyter notebook in dataiku

Jupyter notebooks are natively supported in Dataiku so you can code however you prefer. 

When it comes to working with code, Dataiku’s SQL query capabilities are designed to enhance efficiency and flexibility in data handling. Users can write SQL queries that create views instead of tables, which is particularly useful for managing large datasets without unnecessary data duplication. This approach not only conserves storage but also allows for dynamic querying of the latest data, ensuring that analyses are always based on the most current information. 

This flexibility ensures that both visual and code-first users can work efficiently within the same platform. Jupyter notebooks are natively supported, providing a familiar environment for data scientists to experiment with data and models.

With APIs and plugins, you can easily both connect to existing tools and export models, data, and more across the business. Any data or models created in Dataiku remain yours, and there are no restrictions on your ability to share, export, or import models from different sources. 

What About Exploratory Data Analysis (EDA)?

When it comes to exploratory data analysis, Dataiku offers several options. Built-in statistical charting means that you can easily generate analysis at just the click of a button and entry of a few parameters. Or, if preferred, you can do it all in code. 

automatic analysis in dataiku

You can even let Dataiku automatically generate the analysis that best fits your data. 

Dataiku allows for sampling and pre-filtering of datasets, which is critical for performing exploratory data analysis on manageable subsets of the data before moving into production. This feature helps in identifying trends and anomalies without the need to process the entire dataset initially.

Dataiku showcases metrics within the columns of datasets, providing immediate insights into data quality and statistical properties. These embedded metrics help in monitoring data consistency and identifying outliers. Data quality rules can also be used to validate data quality in workflows, allowing for more adaptive and responsive data processing.

One of Dataiku’s strengths is its ability to preview datasets directly from the flow. This feature allows users to inspect data at various stages of the workflow, ensuring that transformations and calculations are performed correctly. By providing a real-time glimpse into the data, Dataiku helps identify potential issues early in the data preparation process, saving time and effort.

Faster, Automated Data Preparation 

Dataiku’s automation capabilities are embodied in its scenarios feature. Scenarios allow users to automate repetitive tasks, schedule workflows, and trigger actions based on specific conditions. This level of automation ensures that data processes run smoothly and efficiently, freeing up time for data professionals to focus on more strategic tasks. Scenarios also enhance reliability by reducing the likelihood of human error in routine operations.

data quality scenarios in dataiku

Automatically rebuild datasets, retrain models, get alerts for data quality checks, and more with Scenarios.

Dataiku’s visual recipes also speed up the process of data preparation. Using no code, you can quickly generate common data transformations such as joins, splits, windows, and more. Dataiku’s prepare recipe, a favorite among users, utilizes a point-and-click, spreadsheet-like language with over 100+ built-in transformations that you can easily search for and select in a simple drop-down menu. 

dataiku visual recipes

Visual recipes in Dataiku require no code to set up.

Collaboration in a Central Workbench 

One of Dataiku’s core values is the idea that the process of building and creating data products should be accessible to everyone, and so within Dataiku everyone from business SMEs to data engineers can use the same tool to create shared understanding. The heart of Dataiku is the Dataiku Flow, a visual representation of the entire data pipeline. The flow illustrates how data moves through various stages of transformation, from raw inputs to final outputs. This visual map provides a comprehensive overview of the workflow, making it easier to manage and understand complex data processes. It also aids in troubleshooting and optimizing the data pipeline.

AI explain in dataiku

Using AI Explain, you can even use GenAI to explain exactly what’s occurring in the flow, and automatically add it to the project description.

AutoML and Predictive Modeling 

Dataiku is a powerhouse for predictive modeling, providing a comprehensive suite of tools that streamline the entire process from data preparation to model deployment. The platform supports a wide array of ML algorithms, ranging from classical techniques like linear regression and decision trees to advanced methods such as gradient boosting and neural networks. Dataiku's user-friendly interface allows users to build and train models with ease, incorporating features like automated machine learning (AutoML) to simplify model selection and hyperparameter tuning.

what if analysis dataiku

Use explainability features, like What if? Analysis, to better understand the importance of features and how changes to features impact your results. 

Dataiku also offers extensive model evaluation metrics and visualization tools to ensure that users can accurately assess model performance. Once models are built, they can be seamlessly deployed into production, with capabilities for real-time scoring and integration with operational systems. This end-to-end support for predictive modeling enables data teams to generate accurate forecasts and actionable insights, driving informed decision-making and strategic planning across the organization.

Of course, with everything, if you prefer to do this all in code you can. It’s all up to you!

Specialized Use Cases 

Dataiku empowers users to harness advanced analytical techniques in an accessible and user-friendly manner, making sophisticated data science approachable for a wide range of skill levels. For instance, time series analysis — a complex method essential for forecasting and trend analysis — can be easily implemented using Dataiku's intuitive interface and pre-built components, without requiring extensive coding expertise. 

Governance and Control (to Keep Your IT Team Happy!)

Dataiku excels in supporting data governance, providing robust tools that help IT departments maintain control over data usage and compliance. The platform offers comprehensive governance features such as role-based access control, ensuring that only authorized personnel can access sensitive data and perform specific actions within the system. Detailed audit trails and version control enhance transparency, allowing IT to track changes and monitor data lineage effectively. 

Dataiku's documentation capabilities allow for thorough annotation of data processes, which is crucial for meeting regulatory requirements and conducting audits. These features collectively ensure that data usage adheres to organizational policies and industry regulations, helping IT departments to mitigate risks, maintain data integrity, and uphold security standards across all data science and analytics activities.

random forest in dataiku

Easily export model documentation, models, train and test sets, or compare models with a click. 

Conclusion

Dataiku’s advanced analytics features offer a comprehensive toolkit for data professionals looking to elevate their analytical capabilities. From data integration and preparation to predictive modeling, collaboration, and real-time scoring, Dataiku provides a flexible and scalable platform that supports the entire data science lifecycle. By leveraging these features, data teams can dive into modern analytics with a platform that will support them now and in the future. 

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