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Apache Flink

Master Real-Time Data Processing with Apache Flink – Build Scalable Stream Processing Applications from Scratch
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Course Duration: 10 Hours
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Apache Flink is a powerful framework and distributed processing engine for stateful computations over unbounded and bounded data streams. This hands-on course is designed to guide data engineers, developers, and system architects through the core principles of Flink and how to use it for real-time data analytics and complex event processing.
 
Whether you’re transitioning into stream processing or looking to upgrade your skills in real-time analytics, this course helps you gain the practical knowledge needed to build scalable and fault-tolerant stream processing pipelines using Apache Flink.
 
You’ll begin by understanding Flink’s architecture, programming model, and deployment modes. Through project-driven learning, you’ll implement end-to-end streaming jobs, integrate with message brokers like Kafka, work with time windows and event-time processing, and handle fault tolerance and stateful computations. You’ll also explore deploying Flink jobs to production environments such as Kubernetes or Flink’s native cluster managers.

What You Will Gain
 
By the end of the course, you will have built real-world applications such as:
  • A real-time dashboard aggregating metrics from IoT devices.

  • A fraud detection system processing financial transactions on the fly.

  • A log monitoring pipeline with alerts for system anomalies.

These projects demonstrate your ability to work with large-scale, low-latency data pipelines using Apache Flink.
 
You’ll also learn to:
  • Design and implement event-time and processing-time windowed aggregations.

  • Manage Flink job state and checkpoints for fault tolerance.

  • Integrate Flink with Kafka, Elasticsearch, and JDBC.

  • Deploy, monitor, and scale Flink jobs in production.


Who This Course Is For
 
This course is perfect for:
  • Data engineers and developers moving into stream processing.

  • Software architects designing real-time data systems.

  • Engineers familiar with batch processing tools like Spark who want to learn Flink.

  • Professionals working with Kafka, RabbitMQ, or message brokers.

  • Anyone aiming to build event-driven microservices or analytics dashboards.


How to Use This Course Effectively
  1. Start Sequentially: Build a strong foundation by following modules in order.

  2. Code Along: Hands-on implementation is key to mastering Flink.

  3. Experiment: Tweak parameters, use different state backends, and explore Flink SQL.

  4. Document Learnings: Keep notes and bookmark configurations and troubleshooting tips.

  5. Explore Flink’s Ecosystem: Use connectors and libraries to expand your workflow.

  6. Deploy Early: Try deploying to local clusters, then move to Kubernetes or AWS EMR.

  7. Rebuild Projects: Repetition will deepen your understanding.


Why Learn Apache Flink?
 
Apache Flink is one of the most in-demand tools for real-time data processing. Used by companies like Alibaba, Netflix, and Uber, it excels at handling complex event processing, streaming analytics, and real-time machine learning. By learning Flink, you’ll equip yourself with essential skills for the future of data engineering.

Course/Topic 1 - Coming Soon

  • The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.

    • 01:20
Course Objectives Back to Top

By the end of this course, you will be able to:

  • Understand the core architecture of Apache Flink.

  • Build and manage real-time streaming jobs with Flink’s DataStream API.

  • Implement windowing strategies for event-time and processing-time analytics.

  • Use Flink with Kafka, JDBC, and Elasticsearch for end-to-end data pipelines.

  • Handle checkpointing, savepoints, and stateful processing.

  • Deploy Flink jobs on local clusters, YARN, or Kubernetes.

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Stream Processing

  • Batch vs Stream Processing

  • Why Flink? Use Cases

Module 2: Flink Architecture and Environment Setup

  • Job Manager and Task Manager

  • Setting Up Flink Locally

Module 3: DataStream API Fundamentals

  • Source, Transformations, and Sinks

  • Stateless vs Stateful Operators

Module 4: Event Time and Watermarks

  • Time Characteristics

  • Watermark Strategies

Module 5: Windowed Operations

  • Tumbling, Sliding, and Session Windows

  • Window Aggregations

Module 6: Managing State and Fault Tolerance

  • State Backends

  • Checkpointing and Savepoints

Module 7: Flink SQL and Table API

  • Writing SQL Queries

  • Joining Streams and Tables

Module 8: Integration with Kafka and JDBC

  • Reading/Writing to Kafka

  • Sink to JDBC, Elasticsearch

Module 9: Projects and Real-World Use Cases

  • IoT Metrics Dashboard

  • Fraud Detection

  • Log Monitoring System

Module 10: Deployment and Monitoring

  • Flink on YARN/Kubernetes

  • Metrics and Logging

  • Best Practices for Production

Module 11: Flink Interview Questions & Answers

  • Key Concepts

  • Troubleshooting Tips

  • Design Patterns

Certification Back to Top

Upon successful completion, learners will receive a Uplatz Certificate of Completion in Apache Flink. This certification proves your ability to implement, manage, and deploy real-time data pipelines and is a valuable credential for roles like Streaming Data Engineer, Big Data Architect, or Real-Time Systems Developer.

Career & Jobs Back to Top

With real-time data becoming the standard, Apache Flink skills are in high demand. This course prepares you for roles such as:

  • Streaming Data Engineer

  • Data Pipeline Developer

  • Real-Time Analytics Specialist

  • Big Data Developer

  • Software Engineer (Stream Processing)

You’ll find opportunities across tech companies, finance, IoT, e-commerce, and logistics.

Interview Questions Back to Top
  1. What is Apache Flink and how does it compare with Apache Spark Streaming?
    Answer: Apache Flink is a distributed stream processing engine designed for high-throughput, low-latency data processing on unbounded (streaming) and bounded (batch) data. Unlike Spark Streaming, which operates in micro-batches, Flink provides true stream processing with event-at-a-time handling. This allows for lower latency and better support for stateful computations and event-time processing.

  2. How does Flink ensure fault tolerance in streaming jobs?
    Answer: Flink achieves fault tolerance using a mechanism called checkpointing. During checkpoints, the state of the job is saved to durable storage (like HDFS or S3). In case of failure, Flink can recover from the latest successful checkpoint. Additionally, Flink supports savepoints for manually triggered state backups and exactly-once or at-least-once delivery guarantees.

  3. What is the difference between processing time and event time in Flink?
    Answer:

    • Processing time is the time when data is processed on the machine running Flink.

    • Event time is the time embedded in the data itself (e.g., timestamp of an event).
      Flink supports both modes but encourages using event time for more accurate and deterministic results in time-based operations like windowing.

  4. Explain the role of watermarks in Apache Flink.
    Answer: Watermarks are a key concept in event-time processing. They act as a progress indicator for event time and help Flink handle out-of-order data. A watermark tells Flink that no more events with timestamps earlier than the watermark should arrive. This enables the engine to trigger window computations correctly even when data arrives late.

  5. How are stateful operations handled in Flink?
    Answer: Flink allows operators to maintain state during stream processing. This state is fault-tolerant and can be either keyed state (specific to a key in a keyed stream) or operator state (shared across parallel operator instances). Flink manages this state using backends like RocksDB or HeapStateBackend and persists it during checkpoints.

  6. What are the key components of Flink’s architecture?
    Answer:

    • JobManager: Manages job coordination, scheduling, and checkpointing.

    • TaskManager: Executes tasks (operators) and manages local state and buffers.

    • Dispatcher and ResourceManager: Used in Flink’s newer deployment modes (e.g., Kubernetes/YARN) for handling job submissions and resource allocation.

  7. How do you deploy and monitor Flink jobs in production?
    Answer: Flink jobs can be deployed in standalone, YARN, Kubernetes, or cloud-native environments. Monitoring is done via the Flink Web UI, logs, metrics (integrated with Prometheus or Grafana), and alerts. Job restarts, checkpoints, and performance can be managed through the UI or REST API.

  8. What are Flink’s windowing strategies and when would you use each?
    Answer: Flink offers several types of windows:

    • Tumbling Windows: Fixed-size, non-overlapping intervals.

    • Sliding Windows: Fixed-size, overlapping windows that slide at a configured interval.

    • Session Windows: Dynamic, based on gaps in event time.
      Use tumbling for periodic summaries, sliding for rolling averages, and session windows for user activity tracking with inactivity gaps.

  9. How can you integrate Flink with Kafka and JDBC?
    Answer: Flink provides connectors for Kafka and JDBC:

    • Kafka: Used as both source and sink for real-time data streams. FlinkKafkaConsumer and FlinkKafkaProducer classes manage this integration.

    • JDBC: Sink connector writes processed results to relational databases. Flink handles connection pooling, batching, and retries.

  10. What’s the difference between the DataStream API and Table API in Flink?
    Answer:

    • DataStream API offers low-level, programmatic control over stream transformations and is ideal for complex logic.

    • Table API (along with Flink SQL) is a declarative API used to express queries on streaming/batch data similar to SQL.
      Table API simplifies development for analytics-heavy workloads, while DataStream API is more flexible and customizable for engineering tasks.

Course Quiz Back to Top
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Q1. What are the payment options?
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b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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