by mayas1212 | 2025-11-28 07:28:42+00:00
As Thanksgiving approached, Harvest Analytics was gearing up for its biggest sales event of the year. With customers eager to secure the best deals, the team knew they needed real-time visibility into their sales data to drive informed decisions. But they faced a critical challenge: how could they efficiently query streaming data from Kafka while still getting instant access to key metrics?

The Challenge

Harvest Analytics relied heavily on a lakehouse architecture, using kafka_fdw to pull streaming data into their Apache Cloudberry database. However, queries on the external data were often sluggish, hampering their ability to respond quickly during peak sales periods. The team needed a solution that could deliver fast, auto-refreshing access to this critical information.

The Discovery of Dynamic Tables

One day, while discussing their challenges over coffee, Chief Data Officer Lisa recalled a powerful new feature in Cloudberry: Dynamic Tables. These auto-refreshing materialized views could pull data from base tables, external tables, and even other dynamic tables, automatically optimizing query performance. Excited by the potential, Lisa gathered her team to explore how Dynamic Tables could revolutionize their data access. With the ability to automatically rewrite user SQL queries to utilize these dynamic tables, the team saw a glimmer of hope. They were particularly drawn to the declarative programming aspect of Dynamic Tables, allowing them to define their pipeline outcomes using straightforward SQL without worrying about the intricacies of the steps involved.

The Implementation With a sense of urgency, the Harvest Analytics team quickly set up Dynamic Tables to ingest their Kafka data. They configured the tables to refresh every minute, ensuring that the latest sales data was always available for analysis. The key advantage was how these dynamic tables seamlessly integrated with their existing infrastructure, effectively bridging the gap between external lakehouse data and internal analytics.

The Results

As Thanksgiving Day arrived, the transformation was evident:

1. Real-Time Insights: Thanks to Dynamic Tables, the team could now perform continuous queries on their Kafka data, aggregating sales metrics every minute. They could visualize total sales in real-time, empowering them to adjust marketing strategies on the fly.

2. Automatic SQL Rewriting: When team members queried external data, Cloudberry automatically recognized SQL and rewrote it to utilize the Dynamic Tables. This meant that users could focus on their analysis without worrying about the underlying complexity.

3. Speed and Efficiency: The performance boost was staggering. Queries that once took minutes now returned results in seconds, allowing Harvest Analytics to react swiftly to customer behavior and maximize sales opportunities.

4. Seamless Integration: The implementation of Dynamic Tables was smooth and required minimal changes to their existing workflows. The team could continue using their familiar tools while benefiting from the advanced capabilities of Cloudberry.

A Call to Action

If your organization is grappling with the same challenges as Harvest Analytics, it’s time to unlock the potential of Dynamic Tables in Apache Cloudberry. Experience the benefits of auto-refreshing materialized views, seamless SQL rewriting, and lightning-fast queries on lakehouse data.

Join the growing community of Cloudberry users who are transforming their data strategies and driving success in their businesses. Don’t let external data slow you down—embrace Dynamic Tables and watch your insights flourish this Thanksgiving and beyond!