Cracking the Data Engineering Interview E-Book

Kedeisha Bryan

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 284

Veröffentlichungsjahr: 2023

Beschreibung

Preparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey.
The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you’ll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you’ll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions.
By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.

Leseprobe

Cracking the Data Engineering Interview

Land your dream job with the help of resume-building tips, over 100 mock questions, and a unique portfolio

Kedeisha Bryan

Taamir Ransome

BIRMINGHAM—MUMBAI

Cracking the Data Engineering Interview

Copyright © 2023 Packt Publishing

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To my father, Owen Bryan Sr, who has been a rock in my corner in all my endeavors. And always reminding me of my talents when I can’t see them myself.

– Kedeisha Bryan

Thanks

-Taamir Ransome

Contributors

About the authors

Kedeisha Bryan is a data professional with experience in data analytics, science, and engineering. She has prior experience combining both Six Sigma and analytics to provide data solutions that have impacted policy changes and leadership decisions. She is fluent in tools such as SQL, Python, and Tableau.

She is the founder and leader at the Data in Motion Academy, providing personalized skill development, resources, and training at scale to aspiring data professionals across the globe. Her other works include another Packt book in the works and an SQL course for LinkedIn Learning.

Taamir Ransome is a Data Scientist and Software Engineer. He has experience in building machine learning and artificial intelligence solutions for the US Army. He is also the founder of the Vet Dev Institute, where he currently provides cloud-based data solutions for clients. He holds a master’s degree in Analytics from Western Governors University.

About the reviewers

Hakeem Lawrence is a highly skilled Power BI analyst with a deep passion for data-driven insights. He has mastered the art of transforming complex datasets into compelling visual narratives. However, his expertise extends beyond Power BI; he is also a proficient Python developer, adept at leveraging its data manipulation and analysis libraries. His analytical prowess and coding finesse have enabled him to create end-to-end data solutions that empower organizations to make informed decisions. He is also a technical reviewer for Kedeisha Bryan’s second book, Becoming a Data Analyst.

Sanghamitra Bhattacharjee is a Data Engineering Leader at Meta and was previously Director of Machine Learning Platforms at NatWest. She has led global transformation initiatives in the Data and Analytics domain over the last 20 years. Her notable work includes contributions to mobile analytics, personalization, real-time user reach, and NLP products.

She is extremely passionate about diversity and inclusion at work and is a core member of Grace Hopper Celebrations, India. She has organized conferences and meet-ups and she has been a speaker at several international and national conferences, including NASSCOM GCC Conclave, Microstrategy World, and Agile India. She was also awarded a patent for her work on delivering contextual ads for search engines.

Abhishek Mittal is a Data Engineering & Analytics professional with over 10 years of experience in business intelligence and data warehousing space. He delivers exceptional value to his customers by designing high-quality solutions and leading their successful implementations. His work entails architecting solutions for complex data problems for various clients across various business domains, managing technical scope and client expectations, and managing implementations of the solution. He is a Microsoft Azure, Power BI, Power Platform, and Snowflake-certified professional and works as a Principal Architect with Nagarro. He is also a Microsoft Certified Trainer and is deeply passionate about continuous learning and exploring new skills.

Table of Contents

Preface

Part 1: Landing Your First Data Engineering Job

1

The Roles and Responsibilities of a Data Engineer

Roles and responsibilities of a data engineer

Responsibilities

An overview of the data engineering tech stack

Summary

2

Must-Have Data Engineering Portfolio Projects

Technical requirements

Must-have skillsets to showcase in your portfolio

Ability to ingest various data sources

Data storage

Data processing

Cloud technology

Portfolio data engineering project

Scenario

Summary

3

Building Your Data Engineering Brand on LinkedIn

Optimizing your LinkedIn profile

Your profile picture

Your banner

Header

Crafting your About Me section

Initial writing exercise

Developing your brand

Posting content

Building your network

Sending cold messages

Summary

4

Preparing for Behavioral Interviews

Identifying six main types of behavioral questions to expect

Assessing cultural fit during an interview

Utilizing the STARR method when answering questions

Example interview question #1

Example interview question #2

Example interview question #3

Example interview question #4

Example interview question #5

Reviewing the most asked interview questions

Summary

Part 2: Essentials for Data Engineers Part I

5

Essential Python for Data Engineers

Must-know foundational Python skills

SKILL 1 – understand Python’s basic syntax and data structures

SKILL 2 – understand how to use conditional statements, loops, and functions

SKILL 3 – be familiar with standard built-in functions and modules in Python

SKILL 4 – understand how to work with file I/O in Python

SKILL 5 – functional programming

Must-know advanced Python skills

SKILL 1 – understand the concepts of OOP and how to apply them in Python

SKILL 2 – know how to work with advanced data structures in Python, such as dictionaries and sets

SKILL 3 – be familiar with Python’s built-in data manipulation and analysis libraries, such as NumPy and pandas

SKILL 4 – understand how to work with regular expressions in Python

SKILL 5 – recursion

Technical interview questions

Python interview questions

Data engineering interview questions

General technical concept questions

Summary

6

Unit Testing

Fundamentals of unit testing

Importance of unit testing

Unit testing frameworks in Python

Process of unit testing

Must-know intermediate unit testing skills

Parameterized tests

Performance and stress testing

Various scenario testing techniques

Unit testing interview questions

Summary

7

Database Fundamentals

Must-know foundational database concepts

Relational databases

NoSQL databases

OLTP versus OLAP databases

Normalization

Must-know advanced database concepts

Constraints

ACID properties

CAP theorem

Triggers

Technical interview questions

Summary

8

Essential SQL for Data Engineers

Must-know foundational SQL concepts

Must-know advanced SQL concepts

Technical interview questions

Summary

Part 3: Essentials for Data Engineers Part II

9

Database Design and Optimization

Understanding database design essentials

Indexing

Data partitioning

Performance metrics

Designing for scalability

Mastering data modeling concepts

Technical interview questions

Summary

10

Data Processing and ETL

Fundamental concepts

The life cycle of an ETL job

Practical application of data processing and ETL

Designing an ETL pipeline

Implementing an ETL pipeline

Optimizing an ETL pipeline

Preparing for technical interviews

Summary

11

Data Pipeline Design for Data Engineers

Data pipeline foundations

Types of data pipelines

Key components of a data pipeline

Steps to design your data pipeline

Technical interview questions

Summary

12

Data Warehouses and Data Lakes

Exploring data warehouse essentials for data engineers

Architecture

Schemas

Examining data lake essentials for data engineers

Data lake architecture

Data governance and security

Data security

Technical interview questions

Summary

Part 4: Essentials for Data Engineers Part III

13

Essential Tools You Should Know

Understanding cloud technologies

Major cloud providers

Core cloud services for data engineering

Identifying ingestion, processing, and storage tools

Data storage tools

Mastering scheduling tools

Importance of workflow orchestration

Apache Airflow

Summary

14

Continuous Integration/Continuous Development (CI/CD) for Data Engineers

Understanding essential automation concepts

Test automation

Deployment automation

Monitoring

Mastering Git and version control

Git architecture and workflow

Branching and merging

Collaboration and code reviews

Understanding data quality monitoring

Data quality metrics

Setting up alerts and notifications

Pipeline catch-up and recovery

Implementing CD

Deployment pipelines

Infrastructure as code

Technical interview questions

Summary

15

Data Security and Privacy

Understanding data access control

Access levels and permissions

Authentication versus authorization

RBAC

Implementing ACLs

Mastering anonymization

Masking personal identifiers

Applying encryption methods

Encryption basics

SSL and TLS

Foundations of maintenance and system updates

Regular updates and version control

Summary

16

Additional Interview Questions

Index

Other Books You May Enjoy

Preface

Within the domain of data, a distinct group of experts known as data engineers are devoted to ensuring that data is not merely accumulated, but rather refined, dependable, and prepared for analysis. Due to the emergence of big data technologies and the development of data-driven decision-making, the significance of this position has increased substantially, rendering data engineering one of the most desirable careers in the technology sector. However, the trajectory toward becoming a prosperous data engineer remains obscure for many.

Cracking the Data Engineering Interview serves as a printed mentor. Providing ambitious data engineers with the necessary information, tactics, and self-assurance to enter this ever-changing industry. The organization of this book facilitates your progression in comprehending the domain of data engineering, attaining proficiency in its fundamental principles, and equipping yourself to confront the intricacies of its interviews.

Part 1 of this book delves into the functions and obligations of a data engineer and offers advice on establishing a favorable impression before the interview. This includes strategies, such as presenting portfolio projects and enhancing one’s LinkedIn profile. Parts 2 and 3 are devoted to the technical fundamentals, guaranteeing that you will possess a comprehensive understanding of the essential competencies and domains of knowledge, ranging from the intricacies of data warehouses and data lakes to Python programming. In Part 4, an examination is conducted of the essential tools and methodologies that are critical in the contemporary data engineering domain. Additionally, a curated compilation of interview inquiries is provided for review.

Who this book is for

If you are an aspiring Data Engineer looking for a guide on how to land, prepare, and excel in data engineering interviews, then this book is for you.

You should already understand and should have been exposed to fundamentals of Data Engineering such as data modeling, cloud warehouses, programming (python & SQL), building data pipelines, scheduling your workflows (Airflow), and APIs.

What this book covers

Chapter 1, The Roles and Responsibilities of a Data Engineer, explores the complex array of responsibilities that comprise the core of a data engineer’s role. This chapter unifies the daily responsibilities, long-term projects, and collaborative obligations associated with the title, thereby offering a comprehensive perspective of the profession.

Chapter 2, Must-Have Data Engineering Portfolio Projects, this chapter helps you dive deep into a selection of key projects that can showcase your prowess in data engineering, offering potential employers tangible proof of your capabilities.

Chapter 3, Building Your Data Engineering Brand on LinkedIn, this chapter shows you how to make the most of LinkedIn to show off your accomplishments, skills, and goals in the field of data engineering.

Chapter 4, Preparing for Behavioral Interviews, Along with technical skills, the most important thing is that you can fit in with your team and the company’s culture. There are tips in this chapter on how to do well in behavioral interviews so that you can talk about your strengths and values clearly.

Chapter 5, Essential Python for Data Engineers, Python is still an important tool for data engineers. This chapter will help you learn about the Python ideas, libraries, and patterns that every data engineer needs to know.

Chapter 6, Unit Testing, In data engineering, quality assurance is a must. This chapter will teach you the basics of unit testing to make sure that your data processing scripts and pipelines are reliable and strong.

Chapter 7, Database Fundamentals, At the heart of data engineering lies the database. In this chapter you will acquaint yourself with the foundational concepts, types, and operations of databases, establishing a solid base for advanced topics.

Chapter 8, Essential SQL for Data Engineers, SQL is the standard language for working with data. This chapter will help you learn the ins and outs of SQL queries, optimizations, and best practices so that getting and changing data is easy.

Chapter 9, Database Design and Optimization, It’s both an art and a science to make databases work well. This chapter will teach you about advanced design principles and optimization methods to make sure your databases are quick, scalable, and reliable.

Chapter 10, Data Processing and ETL, Turn raw data into insights that can be used. In this chapter we will learn about the tools, techniques, and best practices of data processing in this chapter, which is about the Extract, Transform, Load (ETL) process.

Chapter 11, Data Pipeline Design for Data Engineers, A data-driven organization needs to be able to easily move data from one place to another. In this chapter you will learn about the architecture, design, and upkeep of data pipelines to make sure that data moves quickly and reliably.

Chapter 12, Data Warehouses and Data Lakes, Explore the huge world of ways to store data. This chapter teaches you the differences between data warehouses and data lakes, as well as their uses and architectures, to be ready for the challenges of modern data.

Chapter 13, Essential Tools You Should Know About, It’s important to have the right tool. In this chapter you will learn how to use the most important tools in the data engineering ecosystem, from importing data to managing it and keeping an eye on it.

Chapter 14, Continuous Integration/Continuous Development for Data Engineers, Being flexible is important in a world where data is always changing. In data engineering and in this chapter, you will learn how to use CI/CD to make sure that data pipelines and processes are always up-to-date and running at their best.

Chapter 15, Data Security and Privacy, It’s important to be responsible when you have a lot of data. This chapter will teach you about the important issues of data security and privacy, and get to know the best ways to protect your data assets and the tools you can use to do so.

Chapter 16, Additional Interview Questions, Getting ready is half the battle won. This chapter comprises of carefully chosen set of interview questions that cover a wide range of topics, from technical to situational. This way, you’ll be ready for any surprise that comes your way.

To get the most out of this book

You will need to have a basic understanding of Microsoft Azure.

Software/hardware covered in the book

Operating system requirements

Microsoft Azure

Windows, macOS, or Linux

Amazon Web Services

Windows, macOS, or Linux

Python

Windows, macOS, or Linux

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Cracking-Data-Engineering-Interview-Guide. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

from scrape import *import pandas as pd from sqlalchemy import create_engine import psycopg2

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “You can get your connection string from your Connect tab and fix it into the format shown previously.”

Tips or important notes

Appear like this.

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Part 1: Landing Your First Data Engineering Job

In this part, we will focus on the different types of data engineers and how to best present yourself in your job hunt.

This part has the following chapters:

Chapter 1, The Roles and Responsibilities of a Data EngineerChapter 2, Must-Have Data Engineering Portfolio ProjectsChapter 3, Building Your Data Engineering Brand on LinkedInChapter 4, Preparing for Behavioral Interviews

1

The Roles and Responsibilities of a Data Engineer

Gaining proficiency in data engineering requires you to grasp the subtleties of the field and become proficient in key technologies. The duties and responsibilities of a data engineer and the technology stack you should be familiar with are all explained in this chapter, which acts as your guide.

Data engineers are tasked with a broad range of duties because their work forms the foundation of an organization’s data ecosystem. These duties include ensuring data security and quality as well as designing scalable data pipelines. The first step to succeeding in your interviews and landing a job involves being aware of what is expected of you in this role.

In this chapter, we will cover the following topics:

Roles and responsibilities of a data engineerAn overview of the data engineering tech stack

Roles and responsibilities of a data engineer

Data engineers are responsible for the design and maintenance of an organization’s data infrastructure. In contrast to data scientists and data analysts, who focus on deriving insights from data and translating them into actionable business strategies, data engineers ensure that data is clean, reliable, and easily accessible.

Responsibilities

You will wear multiple hats as a data engineer, juggling various tasks crucial to the success of data-driven initiatives within an organization. Your responsibilities range from the technical complexities of data architecture to the interpersonal skills necessary for effective collaboration. Next, we explore the key responsibilities that define the role of a data engineer, giving you an understanding of what will be expected of you as a data engineer:

Data modeling and architecture: The responsibility of a data engineer is to design data management systems. This entails designing the structure of databases, determining how data will be stored, accessed, and integrated across multiple sources, and implementing the design. Data engineers account for both the current and potential future data needs of an organization, ensuring scalability and efficiency.Extract, Transform, Load (ETL): Data extraction from various sources, including structured databases and unstructured sources such as weblogs. Transforming this data into a usable form that may include enrichment, cleaning, and aggregations. Loading the transformed data into a data store.Data quality and governance: It is essential to ensure the accuracy, consistency, and security of data. Data engineers conduct quality checks to identify and rectify any data inconsistencies or errors. In addition, they play a crucial role in maintaining data privacy and compliance with applicable regulations, ensuring that data is reliable and legally sound.Collaboration with data scientists, analysts, and other stakeholders: Data engineers collaborate with data scientists to ensure they have the appropriate datasets and tools to conduct their analyses. In addition, they work with business analysts, product managers, and other stakeholders to comprehend their data requirements and deliver accordingly. Understanding the requirements of these stakeholders is essential to ensuring that the data infrastructure is both relevant and valuable.

In conclusion, the data engineer’s role is multifaceted and bridges the gap between raw data sources and actionable business insights. Their work serves as the basis for data-driven decisions, playing a crucial role in the modern data ecosystem.

An overview of the data engineering tech stack

Mastering the appropriate set of tools and technologies is crucial for career success in the constantly evolving field of data engineering. At the core are programming languages such as Python, which is prized for its readability and rich ecosystem of data-centric libraries. Java is widely recognized for its robustness and scalability, particularly in enterprise environments. Scala, which is frequently employed alongside Apache Spark, offers functional programming capabilities and excels at real-time data processing tasks.

SQL databases such as Oracle, MySQL, and Microsoft SQL Server are examples of on-premise storage solutions for structured data. They provide querying capabilities and are a standard component of transactional applications. NoSQL databases, such as MongoDB, Cassandra, and Redis, offer the required scalability and flexibility for unstructured or semi-structured data. In addition, data lakes such as Amazon Simple Storage Service (Amazon S3) and Azure Data Lake Storage (ADLS) are popular cloud storage solutions.

Data processing frameworks are also an essential component of the technology stack. Apache Spark distinguishes itself as a fast, in-memory data processing engine with development APIs, which makes it ideal for big data tasks. Hadoop is a dependable option for batch processing large datasets and is frequently combined with other tools such as Hive and Pig. Apache Airflow satisfies this need with its programmatic scheduling and graphical interface for pipeline monitoring, which is a critical aspect of workflow orchestration.

In conclusion, a data engineer’s tech stack is a well-curated collection of tools and technologies designed to address various data engineering aspects. Mastery of these elements not only makes you more effective in your role but also increases your marketability to potential employers.

Summary

In this chapter, we have discussed the fundamental elements that comprise the role and responsibilities of a data engineer, as well as the technology stack that supports these functions. From programming languages such as Python and Java to data storage solutions and processing frameworks, the toolkit of a data engineer is diverse and integral to their daily tasks. As you prepare for interviews or take the next steps in your career, a thorough understanding of these elements will not only make you more effective in your role but will also make you more appealing to potential employers.

As we move on to the next chapter, we will focus on an additional crucial aspect of your data engineering journey: portfolio projects. Understanding the theory and mastering the tools are essential, but it is your ability to apply what you’ve learned in real-world situations that will truly set you apart. In the next chapter, Must-Have Data Engineering Portfolio Projects, we’ll examine the types of projects that can help you demonstrate your skills, reinforce your understanding, and provide future employers with concrete evidence of your capabilities.

2

Must-Have Data Engineering Portfolio Projects

Getting through a data engineering interview requires more than just knowing the fundamentals. Although having a solid theoretical foundation is important, employers are increasingly seeking candidates who can start working right away. This entails building a portfolio of completed projects that show off the depth and breadth of your abilities in practical settings. In this chapter, we will walk you through the fundamental skill sets that a data engineering portfolio should include and demonstrate, with an example project, where you build an entire data pipeline for a sports analytics scenario.

With a well-designed portfolio, employees can see that you are not just knowledgeable about different concepts but also skilled at putting them to use. By the end of this chapter, you’ll have a clear plan for creating projects that stand out from the competition and impress hiring managers and recruiters.

In this chapter, we’re going to cover the following topics:

Must-have skillsets to showcase in your portfolioPortfolio data engineering project

Technical requirements

You can find all the code needed for the sports analytics pipeline at https://github.com/PacktPublishing/Cracking-Data-Engineering-Interview-Guide/tree/main/Chapter-2.

Must-have skillsets to showcase in your portfolio

In the rapidly evolving field of data engineering, having a wide and comprehensive skill set is not only advantageous but also essential. As you get ready for your next professional step, you need to make sure your portfolio showcases your abilities in different areas of data engineering.

This section will act as your resource for key competencies that your data engineering portfolio must highlight. There are a lot of different skills you can add to a project, but we will focus on some fundamentals. The following figure shows the different phases of a data pipeline. Each project does not need to have every single element, but your whole portfolio should cover multiple ones:

Figure 2.1 – Basic phases of the ETL process

These fundamental abilities demonstrated in your portfolio will make you an attractive candidate to potential employers, regardless of your experience level.

Ability to ingest various data sources

The ability to consistently ingest data from multiple sources is one of the most fundamental tasks in data engineering applications. Data can originate from various platforms and come in a variety of formats. These can include flat files, streaming services, databases, and APIs. Your portfolio needs to show that you can handle this diversity. In this section, we’ll look at how to ingest data from various sources, talk about potential problems, and walk you through best practices:

Local files: This includes CSV, Excel spreadsheets, and TXT files. These are files that are normally locally available and are the simplest formats to deal with. However, on the job, you will be most likely dealing with more complex data sources. Websites such as Kaggle, the Google Dataset search engine, data.gov, and the UCI Machine Learning Repository are a few of the various sources for readily available datasets in spreadsheet form.Web page data: You can use this to build web scrapers that pull data from a web page. For Python users, BeautifulSoup, Selenium, Requests, and Urllib are a few libraries you can use to harvest data within HTML. Not all web pages allow for web scraping.Application programming interfaces (APIs): APIs allow you to extract live data from applications and websites such as Twitter or https://www.basketball-reference.com/. Unlike a web scraper, you can query or select the subsets of data that you would like from an API. These APIs may come with documentation that provides instructions on how to write the code to utilize the API.JavaScript Object Notation (JSON) files: When extracting data from an API or dealing with nested data in a database, you will encounter JSON files. Be sure you have practiced the ability to handle JSON data.

For any data engineer, ingesting data from multiple sources is an essential skill. Showcasing your proficiency in managing various data sources will make a great impression on potential employers. Being aware of best practices will help you stand out from the competition. These include handling errors, validating data, and being efficient.

Data storage

Once you have ingested data for your project, you should showcase your data storage skills. Whether you’re dealing with structured data in relational databases or unstructured data in a data lake, your choice of storage solutions has a significant impact on accessibility, scalability, and performance. Relational databases such as PostgreSQL and MySQL are frequently chosen for structured data because of their ACID properties: Atomicity, Consistency, Isolation, and Durability. These databases provide the required robustness for transactional systems, enabling complex querying capabilities. In contrast, NoSQL databases such as MongoDB and Cassandra are gaining popularity due to their ability to scale horizontally and accommodate semi-structured or unstructured data, making them ideal for managing large volumes of data that do not neatly fit into tabular structures:

Relational SQL databases: You can store your various structured data sources in a local relational database such as PostgreSQL, MySQL, or SQLite so that it can be queried for later use. Alternatively, you can use cloud databases by using services such as AWS or Azure. You can also create a data model using either the star or transactional method.

The following diagram depicts the star schema:

Figure 2.2 – Example visual of the star schema

NoSQL databases: All your unstructured data sources (Internet of Things (IoT), images, emails, and so on) should be stored in NoSQL databases such as MongoDB.Storage architecture: Practice staging your data in separate zones based on transformation levels:Raw and unprocessedCleaned and transformedCurated views for dashboarding and reporting

Your portfolio will stand out if you can show that you are capable of managing a variety of data sources, including flat files and APIs. Be sure to highlight certain best practices, including error handling, data validation, and efficiency.

Data processing

Once data has been ingested and stored, the focus shifts to data processing. This is where we transform raw data into a usable form for future analysis. At its core, data processing consists of a series of operations designed to cleanse, transform, and enrich data in preparation for analysis or other business applications. Traditional Extract, Transform, Load (ETL) procedures are being supplemented or replaced by Extract, Load, Transform (ELT) procedures, especially when dealing with cloud-based storage solutions.

In data processing, data quality and integrity are also evaluated. Missing values are handled, outliers are examined, and data types are cast appropriately to ensure that the data is reliable and ready for analytics. Stream processing tools such as Kafka and AWS Kinesis are better suited for real-time data flows, enabling immediate analytics and decision-making.

Here are some aspects of the data processing portion that you want to highlight in your projects:

Programming skills: Write clean and reproducible code. You should be comfortable with both object-oriented and functional programming. For Python users, the PEP-8 standard is a great guide.Converting data types: You should be able to convert your data types as necessary to allow for optimized memory and easier-to-use formats.Handling missing values: Apply necessary strategies to handle missing data.Removing duplicate values: Ensure all duplicate values are removed.Error handling and debugging: To create reproducibility, implement blocks of code to handle anticipated errors and bugs.Joining data: Combine and merge different data sources.Data validation and quality checks: Implement blocks of code to ensure processed data matches the source of truth.

Once a data pipeline has been built, you can use a tool such as Apache Airflow to orchestrate and schedule tasks automatically. This will be particularly useful for projects that use datasets that are refreshed periodically (daily, weekly, and so on).

Cloud technology

Since cloud services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer flexibility and scalability, they have become essential components of contemporary data engineering. Large data processing and storing can now be accomplished by enterprises at a fraction of the cost and effort opposed to bulky hardware and data centers. The goal of this section is to provide you with an overview of the different cloud-based solutions and how the data engineering ecosystem benefits from them. Practical experience with cloud technologies not only increases your adaptability but also helps you stay up to date with new trends and industry best practices.

Among the top cloud service providers is AWS. The following are some important services for data engineering:

S3: Raw or processed data can be stored using S3, a simple storage serviceGlue: An entirely managed ETL solutionRedshift: A solution for data warehousingKinesis: Data streaming in real time

GCP provides a range of cloud computing services that are powered by the same internal infrastructure that Google uses for its consumer goods:

Cloud Storage: AWS S3-like object storage solutionDataflow: Processing of data in batches and streamsBigQuery: A highly scalable, serverless data warehouse

Azure from Microsoft offers a variety of services designed to meet different needs in data engineering:

Blob Storage: Scalable object storage for unstructured dataData Factory: A service for data integration and ETLAzure SQL Data Warehouse: A fully managed data warehouse with performance enhancementsEvent Hubs: Ingestion of data in real time