Dataiku — Aamir P

I found this tool very interesting and thought of sharing it with you all. I learnt this from Dataiku Academy. You can check out the academy for such free courses on this tool.

Dataiku is an advanced data science platform designed to help organizations build, manage, and deploy AI and machine learning (ML) models at scale. It provides tools for data engineers, data scientists, and business analysts to collaborate on data projects, all within one unified platform.

The systematic approach followed here is that AI is considered to be a common tool, bringing people together, individual talent, and powerful technology that meets people’s imagination and exceptional projects.

In the field of ETL

Dataiku is a powerful tool in ETL, and it also makes users see the visual flow-based interface to build complex data workflows without needing extensive coding knowledge. If custom scripts are required languages like Python, R, SQL, etc. can be used.

Dataiku Launchpad

The Dataiku Launchpad is the central hub for managing Dataiku Cloud access, used exclusively by Dataiku Cloud users. Once logged in, users manage their spaces, which are independent environments running specific versions of Dataiku software. Spaces can be updated as new versions are released and users perform tasks like project creation or job execution within each space.

A space may include several optional add-ons, such as:

• Solutions: Prebuilt accelerators for business use cases.
• Extensions: Additional nodes or integrations (e.g., Spark, Git, R).
• Plugins: Packages extending platform functionality.
• Connections: Data sources and storage.
• Code Environments: Custom Python or R package lists.

Each space also includes tools for monitoring activity, such as the Audit Trail (tracking user access) and Usage & Monitoring (real-time visualizations of tasks and resources). Administrators can invite users, manage profiles and permissions, and adjust space settings.

Once familiar with the Launchpad, users can move on to the Design Node, where most of the AI lifecycle tasks in Dataiku begin.

Data Integration

In the stream of data integration, Dataiku can integrate with databases like SQL/Oracle, Big Data like Hadoop/Spark and Cloud services like AWS/GCP/Azure. The interesting part is this tool can be used in work collaboration with data scientists, engineers, and analysts.

Workflow

If you see the streamlined path to production AI workflow, it will be like this:-

Design -> Deployer->Automation(Scheduling of data pipelines, monitoring)

Design -> Deployer-> APIs(Deployment of scalable, realtime endpoints)

Machine Learning and AI

The code is balanced as it can be reused. Dataiku offers AutoML tools to help users quickly build machine learning models without deep expertise in AI. This allows users to train, evaluate, and tune models with minimal effort. It also offers a wide range of pre-built algorithms for classification, regression, clustering, and time series forecasting. Advanced users can build custom models in Python, R, or other programming languages, giving them full control over algorithms, features, and model performance.

Analytics

Dataiku allows users to create charts, dashboards, and reports with an intuitive drag-and-drop interface. It offers tools to explore datasets, compute statistical analyses, and generate insights from raw data. For more technical users, Dataiku provides code notebooks (Python, R, etc.) that integrate directly with data pipelines.

Deployment

Dataiku provides tools to deploy machine learning models into production environments seamlessly. This includes API deployment for real-time scoring or batch scoring. Dataiku is scalable for both small and large data projects, supporting distributed computing environments like Spark and Kubernetes for larger operations. Once deployed, models can be monitored for performance, drift, and impact, ensuring continuous improvement and avoiding degradation over time.

Use Cases

• Customer Analytics: Understand customer behaviour and create personalized experiences through data-driven insights.
• Fraud Detection: Detect fraud in financial transactions, insurance claims, etc., by building machine learning models to flag suspicious activities.
• Predictive Maintenance: Analyze equipment data to predict and prevent failures, improving operational efficiency.
• Supply Chain Optimization: Optimize logistics, demand forecasting, and inventory management through advanced data analytics.

Dataiku allows users to automate data pipelines, so recurring workflows can run on a scheduled basis, reducing manual effort. It supports integrations with cloud services and can scale with distributed computing resources to handle massive datasets.

How do you create a project?

1. Click on the Design Node on your homepage. Click on the new project tab-> DSS Tutorials->Core Designer-> Create your first project.
2. G + F is a shortcut to see the flow.
3. In flow, click Dataset>Upload files.
4. The tab opens, select and upload the file.
5. Configure button will give a preview.
6. In the Schema tab, click on Infer Types from Data and then confirm to guess the correct storage types.
7. If result is ok, hit the create button or use Ctrl+S.
8. Dataiku will show just a sample of a dataset when you work interactively. By default 10K records of the dataset.
9. Give compute row count next to your sample shown number at the top. Compute will show the complete number of records.
10. To change sample settings click on the left-side sample button.
11. Green colour means storage type and blue indicates meaning.
12. The data quality bar shows green/red which indicates if rows or columns are valid.
13. NOK means Not OK or missing values. Missing values will be in gray colour.
14. Analyse window is used to analyze the content. If you want to analyze the whole data, click the respective button.
15. The charts tab is used to explore the dataset in visuals.

What is the difference between Dataiku and Power BI?

Power BI is primarily a data visualization and business intelligence tool, while Dataiku is focused on the end-to-end data science process, from data preparation and cleaning to model development and deployment.

Power BI is great for business users and visual reporting, whereas Dataiku is more suited for data scientists, engineers, and analysts who are involved in machine learning, data engineering, and predictive analytics projects.

Dataiku empowers teams to work collaboratively on data projects, build machine learning models, etc.

Visual Recipes

Visual Recipes in Dataiku are predefined building blocks that allow you to create data transformation and machine learning workflows without writing code. These recipes help streamline various data operations, making it easy for users to manipulate and prepare data, build models, and generate insights.

Prepare Recipe

• Function: Allows you to clean, standardize, and prepare data.
• Use Cases: Remove duplicates, handle missing values, filter data, perform string manipulations, or apply mathematical formulas.
• Visualization: It’s like a step-by-step editor where you apply operations to columns in a dataset.

Join Recipe

• Function: Used to combine two or more datasets based on a common key or column.
• Use Cases: Merging customer information from different sources, combining sales and product data.
• Visualization: You’ll see a Venn diagram-like interface to manage inner joins, outer joins, left joins, or right joins.

Group Recipe

• Function: Aggregates your data by grouping on one or more columns.
• Use Cases: Summarizing sales data by region, calculating average customer ratings, and counting orders per product.
• Visualization: It provides options to define how columns are grouped and what aggregation functions (sum, count, mean) are applied.

Filter Recipe

• Function: Filters rows from a dataset based on conditions.
• Use Cases: Extracting rows with values greater than a threshold, filtering out erroneous or incomplete data.
• Visualization: A rule-based interface where you specify conditions to keep or remove rows.

Stack Recipe

• Function: Vertically stacks datasets by appending rows from multiple datasets with the same structure.
• Use Cases: Combining datasets from multiple regions or periods into one dataset.
• Visualization: A visual guide to ensure that datasets have matching columns before stacking.

Window Recipe

• Function: Used to apply window functions, such as running totals, moving averages, or rank calculations, over a partition of your dataset.
• Use Cases: Calculating sales trends over time, ranking customers by sales amount.
• Visualization: A step-by-step configuration where you define windowing rules and the calculation applied.

Sync Recipe

• Function: Used to synchronize data between datasets, especially when moving data across different storage types.
• Use Cases: Moving data between a database and a file system, synchronizing a local dataset with cloud storage.
• Visualization: Simple visual interface for selecting input and output formats.

Split Recipe

• Function: Splits a dataset into multiple parts based on a given condition.
• Use Cases: Dividing a dataset into training and test sets, separating rows based on geographical location.
• Visualization: You can visually define rules for splitting the dataset.

Pivot Recipe

• Function: Reshapes your data by pivoting it from a long format to a wide format.
• Use Cases: Converting sales data from a transaction-based format to a summary by month or region.
• Visualization: Offers options for grouping and pivoting columns, with aggregation functions to summarize data.

Unpivot Recipe

• Function: Converts wide datasets into a long format.
• Use Cases: Converting summary data back into a transaction-based format for easier analysis.
• Visualization: A guided interface for selecting which columns to unpivot.

Sample/Resample Recipe

• Function: Samples or resamples datasets to reduce the number of rows or adjust for specific criteria.
• Use Cases: Creating a smaller sample dataset for testing, and resampling data for time-series analysis.
• Visualization: A simple interface to define the sampling rate or resampling method.

Recipe for Machine Learning

• Function: Allows you to build machine learning models visually by selecting algorithms and configuring model parameters.
• Use Cases: Creating predictive models for regression, classification, or clustering tasks.
• Visualization: Visual interface for selecting features, and algorithms, and evaluating model performance through built-in metrics.

Key Benefits of Visual Recipes in Dataiku:

• No-Code Interface: Enables non-technical users to perform complex data transformations and analysis without writing code.
• Reproducibility: Each visual recipe creates a documented step in the data pipeline, making it easy to track and reproduce transformations.
• Modularity: Recipes are reusable and can be connected to form complex workflows, making it easy to iterate on data pipelines.
• Collaboration: Multiple team members can collaborate on building workflows using visual recipes, enhancing productivity and knowledge sharing.

Collaboration

Collaboration in Dataiku is a core feature that allows teams to work together seamlessly on data science projects. Dataiku is designed to foster collaboration between data scientists, analysts, engineers, and business users, making it easier for diverse teams to contribute to the entire data lifecycle.

Tags

Tags are a universal property that allows you to organise your work by categorising these Dataiku objects. This can be set at different levels and helps to keep the work organised. You use this in scenarios where you need models, notebooks, web apps, etc. and to perform tasks like regression, analytics, etc.

Shared Projects

• Project-Based Environment: Dataiku organizes work around projects, where team members can collaborate on data preparation, analysis, and modelling. All project resources, such as datasets, models, and workflows, are stored in one place, making it easy to track progress and share insights.
• Roles and Permissions: You can assign specific roles and permissions to different team members (e.g., reader, editor, or admin), allowing control over who can modify or access different parts of the project.

Collaboration on Datasets and Workflows

• Shared Datasets: Multiple users can collaborate on shared datasets, allowing analysts to clean and transform data while data scientists use the same data to build models.
• Versioning: Dataiku tracks changes made to datasets and workflows, allowing team members to see who made changes and when. This ensures that collaborative work can be traced and understood.

Discussion and Documentation

• Comments and Notes: Dataiku allows users to leave comments on datasets, recipes, models, and dashboards. This feature is useful for providing context, explaining changes, or asking questions directly within the platform.
• Wiki for Documentation: Each project in Dataiku comes with a built-in wiki where teams can document methodologies, assumptions, and goals. This is useful for aligning on business objectives and technical solutions.

Real-Time Collaboration

• Multiple Users Working Simultaneously: Dataiku allows numerous users to work on the same project in real-time. For example, while one team member cleans data, another can build a machine learning model, and a third can set up dashboards, all within the same project.
• Notifications: Dataiku provides notifications when changes are made to datasets, models, or workflows. This ensures that team members are informed of progress or updates in real-time.

Sharing Work and Insights

• Dashboards: Dataiku has built-in tools for creating and sharing interactive dashboards, which can be used to present findings and insights. These dashboards can be shared with both technical and non-technical stakeholders.
• Sharing Models and Code: Dataiku allows for easy sharing of machine learning models, scripts, and code snippets. This makes it possible for data scientists to collaborate on model development, share reusable code, and integrate best practices.

Version Control and Git Integration

• Version Control: Dataiku supports version control for projects, code, and workflows. Team members can work on different branches of a project, track changes, and merge them back into the main branch, similar to how software development teams use Git.
• Git Integration: For advanced users, Dataiku integrates with Git, allowing for sophisticated version control, collaboration on code, and reproducibility.

Scenario Automation

• Automation Workflows: Dataiku allows users to automate workflows (called scenarios) for data preparation, analysis, and machine learning tasks. Teams can set up automated processes, so manual work is reduced, and results are produced consistently and collaboratively.

Collaboration with External Tools

• API Integration: Dataiku integrates with various external tools and platforms, including cloud storage, databases, and analytics platforms. This allows teams to collaborate across different tools seamlessly.
• Exporting Results: You can export data, models, and insights to external platforms for further analysis, presentation, or business use, facilitating collaboration beyond the immediate team.

Collaboration with Non-Technical Users

• Visual Recipes: Dataiku provides visual recipes that allow non-technical users to prepare, transform, and analyze data without writing code. This democratizes data work and enables more team members to contribute to data projects.
• Data Apps: Dataiku allows users to create interactive data applications that can be used by non-technical stakeholders to interact with models, visualizations, or data workflows.

Benefits of Collaboration in Dataiku:

1. Cross-Disciplinary Teams: Collaboration in Dataiku brings together data engineers, data scientists, business analysts, and executives, fostering a data-driven culture where everyone contributes.
2. Transparency: The platform allows team members to see what others are working on, what changes have been made, and the project’s overall status.
3. Efficiency: By centralizing all aspects of a data project, teams avoid siloed workflows and can work more efficiently.
4. Reproducibility: Collaborative workflows in Dataiku are version-controlled and documented, ensuring that any project can be reproduced or passed on to other teams.

So, that’s it for the day! Hope you found the article useful.

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