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Apache Flink SQL: Capability Reference Guide

Purpose and Scope

The objective of this document is to provide a standardized technical framework for leveraging Apache Flink SQL within the Kafka-as-a-Service (KaaS) ecosystem. It serves as an introductory guide for users to understand how to process, transform, and analyze streaming data in real-time. By providing validated code templates, this guide ensures that business areas can implement stream processing logic consistently, accurately, and with minimal development overhead.

Event-processing patterns

This document outlines the core event-processing patterns supported by the KaaS platform. It is intended for Developers, Data Engineers, and Architects who need to implement:

* Data Cleaning: Techniques for deduplication and noise reduction. * Operational Logic: Filtering and transforming data streams based on business criteria. * Data Enrichment: Merging transient event data with persistent master data. * Stream Analytics: Performing time-based or key-based aggregations. * Egress Operations: Routing processed data to downstream topics or alert systems.

Deduplication

Description: Eliminating repeated or redundant records for the same unique identifier or event window.

* Typical Scenario: Duplicate signals originating from edge devices (e.g., PLCs), hardware retries, or upstream pipeline replays. * Flink SQL Template:

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SELECT *
FROM (
  SELECT *,
         ROW_NUMBER() OVER (
           PARTITION BY [Field_1] 
           ORDER BY [Event_Timestamp] DESC
         ) AS rn
  FROM [Source_Stream]
)
WHERE rn = 1;

Confluent Reference: Deduplication

Threshold Filtering

Description: Selecting and isolating only the specific records that violate predefined operational limits or business rules.

* Typical Scenario: Real-time monitoring for anomalies such as excessive [Field_4], sudden spikes in [Field_5], or abnormal [Field_6] readings that require immediate attention. * Flink SQL Template:

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SELECT *
FROM [Source_Stream]
WHERE [Field_4] > [Threshold_Value];

Confluent Reference: Overview

Transformation

Description: Creating derived, calculated, or standardized values from raw incoming data.

* Typical Scenario: Performing unit conversions, calculating key performance indicators (KPIs), or reformatting data structures for downstream compatibility. * Flink SQL Template:

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SELECT [Field_1],
       [Field_2],
       [Field_4],
       ([Field_4] * [Multiplier]) + [Constant] AS [Calculated_Field]
FROM [Source_Stream];

Confluent Reference: Overview

Data Enrichment

Description: Combining live event streams with static or slowly-changing reference (master) data to provide critical business context.

* Typical Scenario: Mapping a raw [Field_2] to a specific [Field_7] or [Field_8] for enhanced reporting. * Flink SQL Template:

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SELECT e.[Field_1],
       e.[Field_2],
       m.[Field_7],
       m.[Field_8]
FROM [Source_Stream] e
JOIN [Reference_Table] m
ON e.[Field_2] = m.[Field_2];

Confluent Reference: Joins

Aggregation

Description: Summarizing data over a specific key or time window to identify trends and high-level metrics.

* Typical Scenario: Calculating total output volume or determining the average [Field_4] per [Field_2] over a specific interval. * Flink SQL Template:

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SELECT [Field_2],
       COUNT(*) AS total_events,
       AVG([Field_4]) AS [Average_Field]
FROM [Source_Stream]
GROUP BY [Field_2];

Confluent Reference: Group Aggregation

Action Output (Egress)

Description: Directing the results of a Flink query into a target system or a new Kafka topic to trigger downstream workflows.

* Typical Scenario: Routing critical data to an [Alert_Stream] when values exceed a specific safety limit. * Flink SQL Template:

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INSERT INTO [Target_Stream]
SELECT *
FROM [Source_Stream]
WHERE [Field_4] > [Threshold_Value];

Confluent Reference: Insert Statements

Technical Resources

For a comprehensive list of all supported Flink SQL operations within the Confluent Cloud environment, please refer to the official documentation:

Confluent Cloud Flink Documentation: Reference Guide