Learn How to Display Data From Hudi Tables to your Frontend with Flask and Daft (NO SPARK NEEDED)
In today's fast-paced digital landscape, businesses are constantly seeking ways to streamline their data pipelines and enhance their user experiences. One way to achieve this is by leveraging Apache Hudi, a powerful data management framework that enables incremental data processing and streamlines data ingestion into Apache Hadoop.
In this blog post, we'll explore how you can harness the power of Apache Hudi to seamlessly display data from your backend to your frontend using Flask and Daft. The best part? No Spark expertise required!
What is Apache Hudi?
Apache Hudi, short for Hadoop Upserts Deletes and Incrementals, is an open-source data management framework designed to simplify incremental data processing and data pipeline development on Apache Hadoop. It provides efficient mechanisms for handling insert, update, and delete operations on large datasets, making it ideal for use cases such as data warehousing, stream processing, and real-time analytics.
Setting Up Your Environment
Before diving into the implementation, let's ensure that our environment is properly configured. We'll need Python, Flask, and Daft installed, along with Apache Hudi and its dependencies. Additionally, we'll create a sample customer table to work with.
Generate Sample Customer table
Building the Flask Backend
Now that our data is stored in an Apache Hudi table, let's create a Flask backend to serve this data to our frontend. We'll define routes for the home page and data filtering, along with functions to fetch and filter data from the Hudi table.
Designing the Frontend with Daft
To visualize our customer data, we'll design a simple frontend using Daft. We'll create an HTML template with a form for filtering data by state and a table to display the results. Bootstrap CSS will be used for styling to ensure a clean and responsive layout.
Bringing It All Together
With both the backend and frontend components in place, we're ready to bring our application to life. By running our Flask app, we can access the frontend interface, filter customer data based on state, and dynamically update the table display—all powered by Apache Hudi.
Run Python File app.py
Output:
Also you can try Filter Function try searching for records where state = 'NY'
Full Code
NOTE: Beyond just displaying raw data, Apache Hudi opens the door to a myriad of possibilities for enhancing your frontend with analytics charts, graphs, and more. By leveraging simple lookup tables and combining them with your frontend skills, you can create truly remarkable applications that deliver actionable insights to your users in real-time.
This blog has provided you with a solid foundation to get started on your journey with Apache Hudi. With the techniques outlined here, you have the power to build dynamic, data-driven applications that not only display information but also analyze and visualize it in meaningful ways.
Conclusion
In this tutorial, we've demonstrated how to integrate Apache Hudi into your Flask application using Daft to display data on the frontend. By leveraging Apache Hudi's capabilities for incremental data processing and streamlining data access, you can build efficient and responsive applications that provide real-time insights to your users.
Ready to supercharge your frontend with Apache Hudi? Give it a try and unlock the full potential of your data-driven applications!