7-Day Kafka Intermediate Guide for Data Engineering

-

 


Beginner to Intermediate Level (Enough for Starting Stage)

Objective

This guide is designed for:

  • Data Engineers

  • PySpark Developers

  • ETL Developers

  • Streaming Beginners

Goal:

  • Understand Kafka architecture

  • Learn real-time streaming basics

  • Build producer-consumer understanding

  • Understand enterprise streaming flow

  • Prepare for beginner/intermediate Kafka interviews

Daily Time Commitment:

  • 2 to 3 Hours

  • 7 Days Total

Learning Strategy:

  • 30% Theory

  • 70% Hands-On Practice

SECTION 1 – WHAT IS KAFKA

Apache Kafka is a distributed event streaming platform.

Purpose:

  • Real-time data streaming

  • Message processing

  • Event-driven architecture

  • High-throughput data pipelines

WHY KAFKA IS USED

Problems Kafka Solves:

  • Real-time ingestion

  • Decoupled systems

  • High-volume event processing

  • Streaming analytics

  • Fault-tolerant messaging

REAL-TIME EXAMPLES

Kafka Use Cases:

  • Banking transactions

  • Website clickstreams

  • IoT devices

  • Fraud detection

  • Call center streaming

  • Log processing

  • Real-time dashboards

SECTION 2 – KAFKA ARCHITECTURE

Topics:

  • Producers

  • Consumers

  • Topics

  • Partitions

  • Brokers

  • Offsets

  • Consumer Groups

  • ZooKeeper (basic understanding)

PRODUCER

Purpose:
Send messages to Kafka.

Examples:

  • Application logs

  • API events

  • Transactions

CONSUMER

Purpose:
Read messages from Kafka.

Examples:

  • Spark streaming

  • ETL pipelines

  • Monitoring systems

TOPICS

Purpose:
Logical channel for messages.

Examples:

  • sales_topic

  • employee_topic

  • transaction_topic

PARTITIONS

Critical Topic.

Purpose:
Parallel processing.

Understand:

  • Scalability

  • Ordering within partition

  • Distributed processing

BROKERS

Purpose:
Kafka servers storing messages.

OFFSETS

Purpose:
Track message position.

Critical for:

  • Recovery

  • Reprocessing

  • Fault tolerance

CONSUMER GROUPS

Critical Interview Topic.

Purpose:
Parallel message consumption.

Understand:

  • Load balancing

  • Partition assignment

  • Scaling consumers

SECTION 3 – KAFKA MESSAGE FLOW

Producer
↓
Topic
↓
Partitions
↓
Broker
↓
Consumer Group
↓
Consumers

SECTION 4 – INSTALLATION AND SETUP

Learn:

  • Kafka local setup

  • Kafka commands

  • Topics creation

  • Producer commands

  • Consumer commands

Practice:

  • Create topic

  • Send messages

  • Consume messages

IMPORTANT COMMANDS

Practice:

  • Create topic

  • List topics

  • Delete topic

  • Start producer

  • Start consumer

  • Describe topic

SECTION 5 – PYTHON + KAFKA

Topics:

  • kafka-python

  • Producer implementation

  • Consumer implementation

PRODUCER IMPLEMENTATION

Practice:

  • Send JSON messages

  • Send CSV records

  • Send transaction events

CONSUMER IMPLEMENTATION

Practice:

  • Read messages

  • Deserialize JSON

  • Process records

  • Write logs

SECTION 6 – PYSPARK + KAFKA

Topics:

  • Structured Streaming

  • Kafka integration

  • Read stream

  • Write stream

REAL-TIME STREAMING FLOW

Kafka
↓
PySpark Streaming
↓
Transformations
↓
Delta Lake
↓
Power BI

PRACTICE

  • Read Kafka stream

  • Parse JSON events

  • Aggregate streaming data

  • Write streaming output

SECTION 7 – MESSAGE SERIALIZATION

Topics:

  • JSON

  • Avro (basic understanding)

Purpose:
Efficient data exchange.

SECTION 8 – KAFKA RETENTION

Topics:

  • Retention policies

  • Data persistence

  • Replay capability

RETENTION

Purpose:
Store messages for configured duration.

Benefits:

  • Replay events

  • Recovery

  • Auditing

SECTION 9 – FAULT TOLERANCE

Topics:

  • Replication

  • Leader and follower

  • Broker failures

REPLICATION

Purpose:
High availability.

Understand:

  • Replication factor

  • Failover

SECTION 10 – REAL-TIME PROJECTS

PROJECT 1 – SALES EVENT STREAMING

Requirements:

  • Send sales events

  • Consume using Python

  • Store processed records

Concepts Used:

  • Producer

  • Consumer

  • JSON

PROJECT 2 – CALL CENTER STREAMING

Requirements:

  • Stream call events

  • Detect SLA violations

  • Generate alerts

Concepts Used:

  • Kafka topics

  • Streaming processing

  • Aggregations

PROJECT 3 – WEBSITE CLICKSTREAM ANALYTICS

Requirements:

  • Stream user clicks

  • Track page visits

  • Generate metrics

Concepts Used:

  • Producer

  • Consumer groups

  • Real-time processing

SECTION 11 – KAFKA INTERVIEW QUESTIONS

BASIC QUESTIONS

  1. What is Kafka?

  2. Why Kafka is used?

  3. What is a topic?

  4. What is a partition?

  5. What is an offset?

  6. What is a producer?

  7. What is a consumer?

  8. What is a broker?

  9. What is a consumer group?

  10. Why Kafka is fast?

INTERMEDIATE QUESTIONS

  1. Explain Kafka architecture.

  2. Explain partitioning.

  3. Explain replication.

  4. Explain retention.

  5. Explain fault tolerance.

  6. Explain ordering guarantees.

  7. Explain consumer groups.

  8. Explain Kafka with Spark.

  9. Explain streaming pipelines.

  10. Explain real-time processing flow.

SECTION 12 – 7-DAY EXECUTION PLAN

Day 1

  • Kafka basics

  • Architecture

  • Producers and consumers

Day 2

  • Topics

  • Partitions

  • Offsets

  • Consumer groups

Day 3

  • Kafka setup

  • Kafka commands

  • Message publishing

Day 4

  • Python producer

  • Python consumer

Day 5

  • PySpark streaming with Kafka

Day 6

  • Fault tolerance

  • Replication

  • Retention

Day 7

  • Mini streaming project

  • Mock interview

REAL-TIME BEST PRACTICES

Always Follow:

  • Use proper partitioning

  • Avoid huge messages

  • Use consumer groups efficiently

  • Monitor lag

  • Use replication

  • Handle retries properly

  • Use schema management

WHAT IS ENOUGH FOR STARTING STAGE

For beginner/intermediate Data Engineering roles, focus mainly on:

  • Kafka architecture

  • Producers and consumers

  • Topics and partitions

  • Consumer groups

  • Offsets

  • Basic streaming pipelines

  • Kafka + PySpark integration

  • Real-time use cases

You do NOT need deep Kafka administration initially.

FINAL INTERVIEW EXPECTATIONS

At beginner/intermediate level, interviewers expect:

  • Basic architecture understanding

  • Real-time streaming understanding

  • Kafka + Spark integration knowledge

  • Producer-consumer concepts

  • Event-driven thinking

They usually do NOT expect:

  • Deep cluster administration

  • Advanced tuning

  • Multi-region replication expertise

  • Kafka internals

END OF DOCUMENT

Comments

Post a Comment

Popular posts from this blog

SCD TYPE 2 – INTERVIEW QUESTIONS + MERGE CODE

-
 Below is a COMPLETE, interview-ready guide to SCD Type 2 using MERGE , covering ALL POSSIBLE QUESTIONS (theory + edge cases) AND production-grade SQL code . This is curated for senior data engineer / Azure / Databricks / SQL Server interviews. 🔹 SCD TYPE 2 – INTERVIEW QUESTIONS + MERGE CODE 1️⃣ What is SCD Type 2? Answer: SCD Type 2 maintains full historical changes by: Expiring old records Inserting a new row for every change Typical columns: effective_start_date effective_end_date is_current version (optional) 2️⃣ SCD Type 2 Table Design (Interview MUST) 🎯 Dimension Table CREATE TABLE dim_customer ( customer_sk INT IDENTITY ( 1 , 1 ), customer_id INT , name VARCHAR ( 100 ), city VARCHAR ( 100 ), effective_start_date DATE , effective_end_date DATE , is_current CHAR ( 1 ) ); 🎯 Staging Table CREATE TABLE stg_customer ( customer_id INT , name VARCHAR ( 100 ), city VARCHAR ( 100 ), load_date ...
Read more

TIME-SERIES SQL

-
  🔥 TIME-SERIES SQL (MSSQL SERVER ONLY) 📌 Sample Table (Used in All Queries) CREATE TABLE transactions ( user_id INT , txn_time DATETIME, amount INT ); 🟢 SIMPLE LEVEL 1️⃣ Running Total (Cumulative Sum) SELECT user_id, txn_time, amount, SUM(amount) OVER ( PARTITION BY user_id ORDER BY txn_time ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ) AS running_total FROM transactions; 2️⃣ Row-Based Moving Average (Last 3 Records) SELECT user_id, txn_time, amount, AVG(amount) OVER ( PARTITION BY user_id ORDER BY txn_time ROWS BETWEEN 2 PRECEDING AND CURRENT ROW ) AS moving_avg_3 FROM transactions; 3️⃣ Time Difference Between Events SELECT user_id, txn_time, LAG(txn_time) OVER ( PARTITION BY user_id ORDER BY txn_time ) AS prev_time, DATEDIFF( MINUTE , LAG(txn_time) OVER (PARTITION BY user_id ORDER ...
Read more

TIME-BASED SQL QUERIES

-
  ⏱️ TIME-BASED SQL QUERIES (CODING + LOGIC) 1️⃣ Find Users with N Transactions in X Minutes 🔥 MOST ASKED QUESTION Table: transactions(user_id, txn_id, amount, txn_time) ✅ Requirement Find users with ≥ 3 transactions in 10 minutes SELECT DISTINCT t1.user_id FROM transactions t1 JOIN transactions t2 ON t1.user_id = t2.user_id AND t2.txn_time BETWEEN t1.txn_time AND DATEADD( MINUTE , 10 , t1.txn_time) GROUP BY t1.user_id, t1.txn_time HAVING COUNT ( * ) >= 3 ; 🗣 Explain “This uses a rolling time window using a self-join. Very common in fraud detection.” 2️⃣ Consecutive Days / Events 🔥 Login, sales, activity streak questions Table: logins(user_id, login_date) ✅ Find users with 3 consecutive login days WITH cte AS ( SELECT user_id, login_date, DATEADD( DAY , - ROW_NUMBER() OVER ( PARTITION BY user_id ORDER BY login_date ), login_date ) grp FR...
Read more