Course Content
Prerequisites for a Data Engineering
Preparing for a Data Engineering boot-camp can enhance your experience and success. Here are the core prerequisites:
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Preparing for a Data Engineering boot-camp
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Roadmap to become Data Engineer
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How is Data Engineer different from other Roles
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The Difference
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Data engineer and Data architect same or different???
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Data Ingestion, Storage & Processing
Introduction to Data Engineering Overview of Data Engineering in modern architectures. Data lifecycle and pipelines. Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming). Activity: Discuss a real-world data pipeline use case.
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Overview of Data Engineering in modern architectures.
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Data lifecycle and pipelines
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Key technologies and trends (e.g., ETL, ELT, Batch Processing, Streaming).
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Activity: Discuss a real-world data pipeline use case.
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Assignment 1
Data Ingestion Techniques
Understanding structured, semi-structured, and unstructured data. Batch ingestion: Using Apache Sqoop, Talend. Streaming ingestion: Using Apache Kafka.
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Understanding structured, semi-structured, and unstructured data.
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Batch vs Stream
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Batch ingestion: Using Apache Sqoop, Talend.
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Streaming ingestion: Using Apache Kafka.
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Activity: Set up a simple data ingestion pipeline using Kafka.
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Data Storage Solutions
Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra). Cloud-based data storage (AWS S3, Azure Blob Storage). Choosing the right storage based on use cases.
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Relational databases (e.g., MySQL, PostgreSQL) vs. NoSQL databases (e.g., MongoDB, Cassandra).
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Cloud-based data storage (AWS S3, Azure Blob Storage).
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Choosing the right storage based on use cases.
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Activity: Hands-On Setup for Storing Ingested Data Using AWS S3 or Azure Blob Storage
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Batch Processing with Apache Spark
Understanding Spark architecture. Loading and transforming data using Spark. Difference between RDDs, DataFrames, and Datasets. Activity: Run a sample batch processing job using Spark on a dataset.
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Understanding Spark Architecture
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Loading and Transforming Data Using Apache Spark
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Difference between RDDs, DataFrames, and Datasets.
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Activity: Run a sample batch processing job using Spark on a dataset.
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Data Transformation, Orchestration & Monitoring
Data Transformation & ETL Tools Understanding ETL vs ELT. Using ETL tools: Talend, Apache Nifi, or Airflow. Data cleansing and transformation concepts. Activity: Create a data pipeline with Talend/Airflow for a simple ETL process.
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Understanding ETL vs ELT.
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Using ETL tools: Talend, Apache Nifi, or Airflow.
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Data cleansing and transformation concepts.
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Create a data pipeline with Talend/Airflow for a simple ETL process.
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Data Orchestration
Introduction to orchestration tools: Apache Airflow, AWS Step Functions. Creating workflows to manage complex pipelines. Managing dependencies and retries in workflows.
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Data orchestration process and benifits
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Data Engineering
About Lesson

Streaming ingestion involves continuously collecting and processing data in real time as it is generated. Apache Kafka is one of the most widely used platforms for streaming ingestion due to its robustness, scalability, and fault tolerance. Below, we explore how to use Apache Kafka for streaming ingestion, including its architecture, features, and a practical example.

Overview of Apache Kafka

Apache Kafka is a distributed event streaming platform capable of handling trillions of events a day. It is designed to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.

Key Features of Apache Kafka

  1. Scalability: Kafka can easily scale horizontally by adding more brokers to a cluster.
  2. Durability: Data is replicated across multiple brokers, ensuring no data loss.
  3. High Throughput: Kafka can handle large volumes of data with high throughput, making it suitable for big data applications.
  4. Real-Time Processing: Kafka streams can process data in real time, allowing immediate insights.
  5. Decoupling of Producers and Consumers: Kafka allows producers and consumers to operate independently, facilitating flexible data architectures.

Kafka Architecture

  • Producers: Applications that publish data (messages) to Kafka topics.
  • Topics: Categories to which records are published. Each topic is split into partitions for scalability.
  • Partitions: Each topic can have multiple partitions that allow parallel processing and scalability.
  • Brokers: Kafka servers that store and serve data. Each broker can handle multiple partitions.
  • Consumers: Applications that read data from Kafka topics. They can be part of a consumer group, enabling load balancing.

Setting Up Streaming Ingestion with Apache Kafka

Here’s a step-by-step guide to setting up a simple streaming ingestion pipeline using Apache Kafka.

1. Install Apache Kafka

You can install Kafka on your local machine or use a cloud-based solution like Confluent Cloud. Here’s a brief installation guide for a local setup:

  • Download Kafka from the Apache Kafka website.
  • Extract the files and navigate to the Kafka directory in your terminal.

2. Start Zookeeper and Kafka Broker

Kafka requires Zookeeper to manage its cluster. Start Zookeeper and then the Kafka broker.

bash
# Start Zookeeper
bin/zookeeper-server-start.sh config/zookeeper.properties

# Start Kafka broker
bin/kafka-server-start.sh config/server.properties

3. Create a Kafka Topic

Create a topic to which your data will be published. For example, create a topic named my-topic.

bash
bin/kafka-topics.sh --create --topic my-topic --bootstrap-server localhost:9092 --partitions 3 --replication-factor 1

4. Produce Data to the Topic

You can produce messages to the Kafka topic using a console producer.

bash
bin/kafka-console-producer.sh --topic my-topic --bootstrap-server localhost:9092

You can start typing messages in the console, and each line will be sent as a separate message to the topic.

5. Consume Data from the Topic

To read the messages from the Kafka topic, you can use a console consumer.

bash
bin/kafka-console-consumer.sh --topic my-topic --from-beginning --bootstrap-server localhost:9092

This command will display all messages produced to my-topic from the beginning.

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