Essential Tools You Should Know

As data engineers, we rely on a myriad of software tools to process, store, and manage data effectively. In this chapter, we will explore the essential tools every data engineer should know. These tools will empower you to harness the power of the cloud, handle data ingestion and processing, perform distributed computations, and schedule tasks with efficiency and precision. By the end of this chapter, you’ll have a strong understanding of the key tools in data engineering, along with the knowledge of where and how to apply them effectively in your data pipeline.

In this chapter, we will cover the following topics:

• Understanding cloud technologies
• Mastering scheduling tools

Understanding cloud technologies

Cloud technologies provide the fundamental framework for a wide range of data engineering tasks in today’s data-driven world. Cloud platforms provide the scalability, reliability, and flexibility that modern enterprises require, from data collection to processing and analytics. This section gives a brief introduction to cloud computing, outlines the main products and services offered by top cloud providers such as AWS, Azure, and Google Cloud, and goes into detail about key cloud services that are critical to data engineering. Additionally, you’ll discover how to assess cloud solutions according to the most important factors for your data engineering projects, including cost-effectiveness, scalability, and dependability. Understanding the fundamentals of cloud computing will help you make informed decisions in real-world data engineering scenarios and prepare you for the inevitable cloud-related interview questions.

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Mastering scheduling tools

Coordinating these elements into a smooth, automated workflow is crucial after you’ve set up your data engineering environment with the right ingestion, processing, and storage tools. Scheduling tools are useful in this situation. These tools control how jobs and workflows are carried out, making sure that things get done in the right order, at the right time, and in the right circumstances. This section will walk you through the features, use cases, and comparative analysis of some of the most widely used scheduling tools, including Luigi, Cron Jobs, and Apache Airflow. Equipped with this understanding, you will be capable of efficiently designing and overseeing intricate data pipelines—a capability that is not only essential for job interviews but also highly valuable in practical settings.

Importance of workflow orchestration

Beyond just carrying out tasks at predetermined times, scheduling serves other purposes as well. It entails...

Summary

Well done on learning about the key resources that each and every data engineer should be aware of! Cloud applications, data ingestion, processing, storage tools, distributed computation frameworks, and task scheduling solutions were all covered in this chapter. You’ve given yourself a strong toolkit to tackle a variety of data engineering challenges by becoming acquainted with these tools.

Recall that becoming an expert with these tools is only the start of your career as a data engineer. Your ability to adjust to the constantly changing data landscape will depend on your continued exploration of and adherence to emerging technologies and tools. Take advantage of these tools’ opportunities to advance your data engineering abilities.

In the next chapter, we will explore the world of continuous integration/continuous development (CI/CD).