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Real-Time Streaming Toolkit

$69

Production streaming patterns for Structured Streaming and Delta Live Tables with Kafka/Event Hub integration and monitoring dashboards.

📁 15 files🏷 v1.0.0
Production-ready
✓ Instant download✓ Lifetime updates✓ MIT licensed✓ MIT license✓ Secure checkout (Stripe)

📋 What's Inside 15 files

  • ✓ README.md—
  • ✓ manifest.json—
  • ✓ structured-streaming/kafka_source.py—
  • ✓ structured-streaming/event_hub_source.py—
  • ✓ structured-streaming/auto_loader_streaming.py—
  • ✓ structured-streaming/deduplication.py—
  • ✓ dlt/streaming_medallion.py—
  • ✓ dlt/cdc_processing.py—
  • ✓ dlt/expectations_library.py—
  • ✓ monitoring/streaming_dashboard.sql—
  • ✓ monitoring/streaming_alerts.sql—
  • ✓ config/autoscaling_config.md—
  • ✓ config/checkpoint_management.md—
  • ✓ guides/performance_tuning.md—
  • ✓ guides/failure_recovery.md—

📁 File Structure 15 files

real-time-streaming-toolkit/
├── README.md
├── manifest.json
├── structured-streaming/
│ ├── kafka_source.py
│ ├── event_hub_source.py
│ ├── auto_loader_streaming.py
│ ├── deduplication.py
├── dlt/
│ ├── streaming_medallion.py
│ ├── cdc_processing.py
│ ├── expectations_library.py
├── monitoring/
│ ├── streaming_dashboard.sql
│ ├── streaming_alerts.sql
├── config/
│ ├── autoscaling_config.md
│ ├── checkpoint_management.md
├── guides/
│ ├── performance_tuning.md
│ ├── failure_recovery.md

📖 Documentation Preview README excerpt

Real-Time Streaming Toolkit

Product ID: real-time-streaming-toolkit

Version: 1.0.0

Price: $69

Category: Data Engineering

Author: [Datanest Digital](https://datanest.dev)

---

Overview

The Real-Time Streaming Toolkit is a production-grade collection of PySpark notebooks, Delta Live Tables pipelines, monitoring dashboards, and operational guides for building robust real-time data pipelines on Databricks. Every component has been battle-tested against high-throughput workloads and designed for exactly-once processing guarantees.

Whether you are ingesting from Kafka, Azure Event Hubs, or cloud object storage via Auto Loader, this toolkit gives you a proven starting point that eliminates weeks of trial-and-error engineering.

What's Included

Structured Streaming Notebooks
FileDescription
`structured-streaming/kafka_source.py`Kafka source with schema registry integration, checkpoint management, and consumer group orchestration
`structured-streaming/event_hub_source.py`Azure Event Hub source with native checkpoint store, partition-aware processing, and backpressure handling
`structured-streaming/auto_loader_streaming.py`Auto Loader (cloudFiles) patterns for file-based streaming from S3, ADLS, and GCS
`structured-streaming/deduplication.py`Exactly-once processing strategies including watermark-based deduplication, idempotent writes, and state management
Delta Live Tables Pipelines
FileDescription
`dlt/streaming_medallion.py`Full medallion architecture (Bronze/Silver/Gold) as a streaming DLT pipeline
`dlt/cdc_processing.py`Change Data Capture processing with APPLY CHANGES INTO for SCD Type 1 and Type 2
`dlt/expectations_library.py`Reusable DLT data quality expectations library with severity levels and alerting hooks
Monitoring & Alerting
FileDescription
`monitoring/streaming_dashboard.sql`SQL dashboard queries for streaming lag, throughput, error rates, and checkpoint health
`monitoring/streaming_alerts.sql`Alert queries for detecting pipeline failures, excessive lag, and data quality regressions
Configuration Guides
FileDescription
`config/autoscaling_config.md`Auto-scaling configuration for streaming clusters with recommended instance types and scaling policies
`config/checkpoint_management.md`Checkpoint repair, migration, and disaster recovery procedures
Operational Guides
FileDescription
`guides/performance_tuning.md`Trigger intervals, partition sizing, shuffle optimization, and state store tuning
`guides/failure_recovery.md`Failure recovery playbook covering checkpoint corruption, schema evolution, and cluster failures

Requirements

  • Databricks Runtime 13.3 LTS or later (14.x+ recommended)
  • Unity Catalog enabled workspace (recommended)

    • ... preview truncated, see full README in product download.

    📄 Code Sample .py preview

    structured-streaming/kafka_source.py# Databricks notebook source # MAGIC %md # MAGIC # Kafka Source — Structured Streaming # MAGIC # MAGIC Production-grade Kafka consumer with Confluent Schema Registry integration, # MAGIC checkpoint management, consumer group orchestration, and dead-letter handling. # MAGIC # MAGIC **Datanest Digital** — https://datanest.dev # MAGIC # MAGIC --- # COMMAND ---------- # MAGIC %md # MAGIC ## Configuration Widgets # COMMAND ---------- dbutils.widgets.text("kafka_brokers", "broker1:9092,broker2:9092,broker3:9092", "Kafka Bootstrap Servers") dbutils.widgets.text("kafka_topic", "events.raw", "Kafka Topic") dbutils.widgets.text("consumer_group", "databricks-streaming-consumer", "Consumer Group ID") dbutils.widgets.text("schema_registry_url", "https://schema-registry:8081", "Schema Registry URL") dbutils.widgets.text("checkpoint_path", "/mnt/checkpoints/kafka_source", "Checkpoint Location") dbutils.widgets.text("target_catalog", "analytics", "Target Catalog") dbutils.widgets.text("target_schema", "bronze", "Target Schema") dbutils.widgets.text("target_table", "raw_events", "Target Table") dbutils.widgets.dropdown("starting_offsets", "latest", ["latest", "earliest"], "Starting Offsets") dbutils.widgets.text("max_offsets_per_trigger", "100000", "Max Offsets Per Trigger") dbutils.widgets.text("trigger_interval", "10 seconds", "Trigger Interval") dbutils.widgets.dropdown("environment", "dev", ["dev", "staging", "prod"], "Environment") # COMMAND ---------- # MAGIC %md # MAGIC ## Imports and Setup # COMMAND ---------- from pyspark.sql import SparkSession, DataFrame from pyspark.sql.functions import (

    📅 Changelog

    v1.0.0 — Initial release.

    Purchases include lifetime updates. Check the product page for the latest version.

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