ETL and Data Pipelines with Shell, Airflow and Kafka

Go to Course: https://www.coursera.org/learn/etl-and-data-pipelines-shell-airflow-kafka

Introduction

### Course Review: ETL and Data Pipelines with Shell, Airflow, and Kafka #### Overview In the burgeoning field of data engineering, understanding how to effectively convert raw data into analytics-ready formats is pivotal. The Coursera course, **ETL and Data Pipelines with Shell, Airflow, and Kafka**, provides learners with a comprehensive exploration of two core methodologies: the Extract, Transform, Load (ETL) process and the Extract, Load, Transform (ELT) process. This course is particularly relevant for those looking to enhance their skills in data management and processing, as it covers key tools and techniques essential for developing robust data pipelines. #### Course Structure The course is well-structured, with a syllabus that effectively breaks down complex topics into manageable modules: 1. **Data Processing Techniques**: This module introduces learners to the fundamental differences between ETL and ELT processes, emphasizing their relevance in varying data environments like data warehouses versus data lakes. The discussions around data extraction technologies—such as database querying, web scraping, and APIs—provide a solid foundation for understanding how raw data is accessed and prepared for analysis. 2. **ETL & Data Pipelines: Tools and Techniques**: In this section, the course dives into the practical aspects of building ETL pipelines using Bash scripts and cron for scheduling. The distinction between batch and streaming pipelines is a highlight, with ample insights into performance metrics such as latency and throughput, which are crucial for real-time data processing. 3. **Building Data Pipelines using Airflow**: Apache Airflow is introduced as a robust tool for pipeline orchestration. The module covers the advantages of representing workflows as Directed Acyclic Graphs (DAGs), enhancing the maintainability and clarity of data extraction processes. The rich user interface of Airflow and its logging features are valuable skills that learners will appreciate. 4. **Building Streaming Pipelines using Kafka**: This module covers the essential aspects of event streaming using Apache Kafka. It outlines the various components of Kafka and their roles in building event-driven architectures, which are becoming increasingly essential for modern data applications. 5. **Final Assignment**: The course culminates in hands-on labs where learners can apply their knowledge by creating ETL data pipelines with Airflow and streaming data pipelines with Kafka. This practical experience is invaluable, as it allows students to engage with real-world scenarios, fostering a deeper understanding of the subject matter. #### Recommendations **Who Should Take This Course?** This course is ideal for data professionals, aspiring data engineers, and anyone interested in data management and analytics. If you have a basic understanding of data theories but want to bridge the gap between theory and practical application, this course is a great choice. **What Will You Gain?** Participants will come away with a robust skill set in both ETL and ELT frameworks, proficiency in powerful tools like Apache Airflow and Kafka, and hands-on experience that will enhance their employability in the data engineering job market. **Conclusion** The **ETL and Data Pipelines with Shell, Airflow, and Kafka** course on Coursera is an excellent investment for anyone looking to deepen their expertise in data processing. With a balanced mix of theory and practical application, it equips learners with the necessary tools to design and implement efficient data pipelines. I wholeheartedly recommend it for anyone serious about becoming proficient in data engineering. **Enroll today and elevate your data skills to the next level!**

Syllabus

Data Processing Techniques

ETL or Extract, Transform, and Load processes are used for cases where flexibility, speed, and scalability of data are important. You will explore some key differences been similar processes, ETL and ELT, which include the place of transformation, flexibility, Big Data support, and time-to-insight. You will learn that there is an increasing demand for access to raw data that drives the evolution from ETL to ELT. Data extraction involves advanced technologies including database querying, web scraping, and APIs. You will also learn that data transformation is about formatting data to suit the application and that data is loaded in batches or streamed continuously.

Extract, transform and load (ETL) pipelines are created with Bash scripts that can be run on a schedule using cron. Data pipelines move data from one place, or form, to another. Data pipeline processes include scheduling or triggering, monitoring, maintenance, and optimization. Furthermore, Batch pipelines extract and operate on batches of data. Whereas streaming data pipelines ingest data packets one-by-one in rapid succession. In this module, you will learn that streaming pipelines apply when the most current data is needed. You will explore that parallelization and I/O buffers help mitigate bottlenecks. You will also learn how to describe data pipeline performance in terms of latency and throughput.

The key advantage of Apache Airflow's approach to representing data pipelines as DAGs is that they are expressed as code, which makes your data pipelines more maintainable, testable, and collaborative. Tasks, the nodes in a DAG, are created by implementing Airflow's built-in operators.​ In this module, you will learn about Apache Airflow having a rich UI that simplifies working with data pipelines. You will explore how to visualize your DAG in graph or tree mode. You will also learn about the key components of a DAG definition file, and you will learn that Airflow logs are saved into local file systems and then sent to cloud storage, search engines, and log analyzers.

Apache Kafka is a very popular open source event streaming pipeline. An event is a type of data that describes the entity’s observable state updates over time. Popular Kafka service providers include Confluent Cloud, IBM Event Stream, and Amazon MSK. Additionally, Kafka Streams API is a client library supporting you with data processing in event streaming pipelines. In this module, you will learn that the core components of Kafka are brokers, topics, partitions, replications, producers, and consumers. You will explore two special types of processors in the Kafka Stream API stream-processing topology: The source processor and the sink processor. You will also learn about building event streaming pipelines using Kafka.

In this final assignment module, you will apply your newly gained knowledge to explore two very exciting hands-on labs. “Creating ETL Data Pipelines using Apache Airflow” and “Creating Streaming Data Pipelines using Kafka”. You will explore building these ETL pipelines using real-world scenarios. You will extract, transform, and load data into a CSV file. You will also create a topic named “toll” in Apache Kafka, download and customize a streaming data consumer, as well as verifying that streaming data has been collected in the database table.