How to Learn Data Engineering

𝐘𝐨𝐮 𝐃𝐎𝐍'𝐓 𝐧𝐞𝐞𝐝 50 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠. You need 7 books. That's it. Most beginners jump straight into tools — Spark, Kafka, Airflow — without understanding how data systems actually work. Then they wonder why nothing connects. These 7 books fix that 👇 📘 Fundamentals of Data Engineering — Joe Reis & Matt Housley → Read this FIRST. Gives you the full picture before you touch any tool. 📘 Designing Data-Intensive Applications — Martin Kleppmann → The bible of distributed systems. Explains WHY systems fail at scale. 📘 Streaming Systems — Tyler Akidau → Makes Kafka, Flink, and Spark Streaming actually make sense. 📘 The Data Warehouse Toolkit — Ralph Kimball → Old but gold. Dimensional modeling that every DE should know. 📘 Data Engineering with Python — Paul Crickard → Theory to code. Build real pipelines with Python + Airflow. 📘 Data Pipelines Pocket Reference — James Densmore → Quick reference for pipeline patterns. Keep it on your desk. 📘 Designing Cloud Data Platforms — Zburivsky & Partner → Cloud architecture decisions explained clearly. Here's the order I'd recommend: 1 → Fundamentals of Data Engineering (understand the system) 2 → DDIA (understand how systems break) 3 → Data Warehouse Toolkit (understand modeling) 4 → Streaming Systems (understand real-time) 5 → Data Engineering with Python (start building) 6 → Data Pipelines Pocket Reference (quick patterns) 7 → Designing Cloud Data Platforms (cloud architecture) Reading builds intuition. Practice builds skills. You need both. I wrote a detailed breakdown of each book — what it teaches, what it won't help with, and when to read it. You can read it below ⬇️ Save this for later. Share it with someone starting out. ---- Follow Darshil Parmar for more data engineering content.

I am a Senior Data Engineer at Amazon with more than 12 years of experience. Here is the #1 advice I give to anyone wanting to break into Data Engineering in 2025, from my own journey and what I see in the industry. [1] don’t wait for the “perfect” data engineering job, start from wherever you are ○ Most people think you need to land a pure data engineering role to start your journey. That’s not true. ○ My own path began as an analyst. I got thrown into ETL tools, enterprise data warehouses, and learned by building real pipelines for real business problems. ○ Don’t wait for someone to hand you a data engineering title. Start using data skills in your current job, even if it’s not your official title. ○ Look for chances to automate reports, build small data flows, or help your team answer questions with data. It all counts. ○ The best way to learn is by working with real, messy, business-critical data. [2] learn on the job ○ Formal education helped me, but most of my actual skills came from building things at work. ○ Theory is helpful, but the day-to-day challenges, managing ambiguity, scaling systems, and handling constant change, only show up in projects. ○ Don’t get stuck in “prep” mode. The sooner you start solving actual problems, the faster you’ll grow. ○ Every job switch, every new project, every failed deployment teaches you something you can’t learn from a book. [3] say yes to tough projects and new tech ○ I didn’t plan my path. I said yes to working with new tools: Teradata, Informatica, then Spark and Scala when the opportunity came. ○ When my manager asked me to build distributed pipelines, I had just 15 days to figure it out. I jumped in, learned fast, and shipped to production. ○ Every tough assignment (even when I doubted myself) became the thing that leveled up my skills and confidence. ○ Don’t shy away from new frameworks or bigger challenges. Dive in and figure it out along the way. [4] use AI, don’t fear it ○ The role is changing. AI is now a core part of our workflows, writing docs, reviewing code, speeding up development, and democratizing access to data. ○ I use AI for everything from formatting documents to writing better emails and even debugging. ○ Stay current by using AI in your day-to-day work. Experiment, adjust, and keep learning as the tools improve. ○ AI is not a threat, it’s a tool that lets you move faster and focus on the logic and architecture that matter most. [5] don’t focus on brands, focus on learning and growth ○ My best learning came from hands-on work, not company logos. ○ Take the job that gets you closest to working with data, even if it’s not your dream company. ○ The experience you gain will open better doors as you go. ○ Career progress in data engineering is practical: build, learn, adapt, and the opportunities will follow.

Most people think becoming a Data Engineer is about learning tools. It’s not. Tools change every few years. The real skill is learning how data moves through systems. Let’s simplify the roadmap 👇 1️⃣ Learn SQL first Not “basic SQL.” Real SQL: → joins → window functions → aggregations → query optimization SQL is the language of data engineering. Master it. 2️⃣ Learn Python Not for LeetCode. For: → automation → APIs → pipelines → data processing Most beginner engineers underestimate how much scripting matters. 3️⃣ Understand databases deeply Learn: → OLTP vs OLAP → indexing → partitioning → normalization → warehousing concepts Strong data engineers think in systems, not spreadsheets. 4️⃣ Learn one cloud platform Choose one: → AWS → Azure → GCP You don’t need all three. Focus on: → storage → compute → IAM → orchestration → monitoring Cloud is where modern data engineering lives. 5️⃣ Build pipelines This is where real learning happens. Create projects like: → API → warehouse pipeline → streaming dashboard → ETL/ELT workflows → batch processing systems Projects teach architecture better than tutorials. 6️⃣ Learn the modern stack gradually Examples: → Airflow → Spark → dbt → Kafka → Snowflake → Databricks Don’t try to learn everything at once. Depth beats checklist learning. The mistake most beginners make 👇 They collect certifications… …without building anything. But companies hire engineers who can solve problems, not just pass exams. Simple roadmap: SQL → Python → Databases → Cloud → Pipelines → Scale That order matters. The future Data Engineer is not just a pipeline builder. They’re a designer of reliable data systems. Curious If you could restart your journey today, what would you learn differently first?

🚀 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗜𝘀 𝗮 𝗝𝗼𝘂𝗿𝗻𝗲𝘆, 𝗡𝗼𝘁 𝗮 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁 Every successful Data Engineer starts with strong fundamentals. While tools like Spark, Kafka, and Airflow are powerful, mastering the basics first makes learning advanced technologies much easier. 𝗔 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 ✅ 𝗣𝘆𝘁𝗵𝗼𝗻 & 𝗦𝗰𝗿𝗶𝗽𝘁𝗶𝗻𝗴 – Automate tasks and build data pipelines. ✅ 𝗟𝗶𝗻𝘂𝘅 & 𝗦𝗵𝗲𝗹𝗹 𝗦𝗰𝗿𝗶𝗽𝘁𝗶𝗻𝗴 – Work efficiently in server environments. ✅ 𝗦𝗤𝗟 & 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 – Master querying and database optimization. ✅ 𝗗𝗼𝗰𝗸𝗲𝗿 & 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 – Build and deploy containerized applications. ✅ 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 (𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗚𝗖𝗣) – Learn core cloud data services. ✅ 𝗡𝗼𝗦𝗤𝗟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 – Understand scalable, non-relational data storage. ✅ 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – Design efficient database schemas. ✅ 𝗘𝗧𝗟 & 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 – Build scalable data pipelines. ✅ 𝗠𝗲𝘀𝘀𝗮𝗴𝗲 𝗤𝘂𝗲𝘂𝗲𝘀 – Learn Kafka, RabbitMQ, and event-driven systems. ✅ 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – Process real-time data with Spark or Flink. ✅ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 – Automate pipelines with Airflow. ✅ 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴 – Build analytics-ready data platforms. ✅ 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 & 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲𝘀 – Work with Delta Lake, Iceberg, and Hudi. ✅ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 – Manage infrastructure using Terraform or CloudFormation. ✅ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 – Validate, test, and monitor data pipelines. ✅ 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 – Ensure secure, compliant, and discoverable data. ✅ 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 – Architect scalable and reliable data platforms. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 💡 Build a strong foundation before learning advanced tools. 💡 Reinforce every concept with hands-on projects. 💡 Prioritize understanding over memorization. 💡 Keep learning as data technologies continue to evolve. Whether you're just starting out or advancing your career, following a structured roadmap will help you become a confident and effective Data Engineer. 📌 Which stage of your Data Engineering journey are you currently on? Share it in the comments! 📩 Interested in building a career in 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 or advancing your existing skills? 𝗙𝗲𝗲𝗹 𝗳𝗿𝗲𝗲 𝘁𝗼 𝗗𝗠 me for guidance on learning roadmaps, project ideas, interview preparation, cloud technologies, and career growth strategies. What technology or skill has been most valuable in your Data Engineering journey?

Dear Jobseekers, if you're trying to break into Data Engineering in 2025, here's a simple and easy roadmap you can follow: 1/ Phase: Master SQL Fundamentals   - What to Cover:     — Aggregations with `GROUP BY`     — Joins (INNER, LEFT, FULL OUTER)     — Window functions for analytics     — Common Table Expressions (CTEs)   ↬Key Milestone: Become fluent in SQL, capable of writing queries to manipulate and analyze data effectively—essential for almost all data engineering tasks. 2/ Phase: Dive into Data Modeling   - What to Cover:     — Data Normalization & 3rd Normal Form     — Fact, Dimension, and Aggregate Tables     — Efficient table designs: cumulative, MAP, ARRAY, STRUCT   ↬Key Milestone: Understand how to design data models that reduce redundancy, improve access, and make data easier to maintain—crucial for scalable data warehouses. 3/ Phase: Get Comfortable with Python   - What to Cover:     — Basics: loops, conditionals, functions     — Libraries: Pandas, NumPy, scikit-learn, Great Expectations   ↬Key Milestone: Develop strong Python skills to automate data processes and carry out custom data manipulation, which is vital for engineering complex data pipelines. 4/ Phase: Prioritize Data Quality   - What to Cover:     — Writing reliable data checks     — Write-Audit-Publish pattern in pipelines   ↬Key Milestone: Learn to build resilient data systems by embedding quality checks, ensuring data consistency and reliability, which reduces debugging later on. 5/ Phase: Understand Distributed Computing   - What to Cover:     — MapReduce principles and their evolution     — Concepts: Partitioning, Data Skew, Spilling to disk   ↬Key Milestone: Grasp the core ideas behind distributed systems that allow for large-scale data processing, preparing you to work with today’s advanced big data tools. 6/ Phase: Learn Job Orchestration Tools   - What to Cover:     — CRON Jobs for basic scheduling     — Orchestration Tools like Apache Airflow or Prefect   ↬Key Milestone: Master the basics of scheduling and orchestration, which helps ensure your data flows run smoothly, efficiently, and without manual intervention. 7/ Phase: Apply Distributed Compute Techniques   - What to Cover:     — Distributed Cloud Tools: Snowflake, BigQuery     — On-Premise Options: Spark with S3   ↬Key Milestone: Experiment with distributed computing platforms to process data efficiently, giving you practical skills to work on real-world big data projects. 8/ Phase: Prepare for Interviews   - What to Cover:     — Practice data engineering problems     — Mock projects covering data loading, transformation, quality checks, and orchestration   ↬Key Milestone: Solidify your learning by applying it to projects, demonstrating your end-to-end skills in data engineering—ensuring you're ready for technical interviews. -- P.S: Data Engineers in my network, drop some wisdom in the comments and let's help everyone!

If I had to start Data Engineering from zero again, this is the exact roadmap I would follow. To master Data Engineering, the order matters more than most beginners realize. I see many people start with SQL one week, Python the next, then suddenly jump into Kafka, cloud, Airflow, or some random tool they saw online. That is where the confusion starts. Not because they are bad learners, but because no one showed them how these pieces fit together in a real data pipeline. So instead of learning everything randomly, here is the roadmap I would follow today, with free resources: 1. SQL Start with joins, CTEs, aggregations, window functions, subqueries, and query logic. Free resource: SQL Full Course by freeCodeCamp https://lnkd.in/gMDj8EGh Practice: https://sqlbolt.com 2. Python Learn enough Python to clean files, work with APIs, automate tasks, and handle data. Free resource: Python Full Course by freeCodeCamp https://lnkd.in/gzzE5DVr Practice: https://lnkd.in/gitsS4cU 3. Databases Understand tables, keys, indexes, normalization, transactions, and PostgreSQL basics. Free resource: PostgreSQL Tutorial https://lnkd.in/gvMrzS63 Channel: https://lnkd.in/gt6ThdAa 4. Data Engineering Basics Learn how data moves from sources into storage, warehouses, and pipelines. Free resource: Data Engineering Zoomcamp https://lnkd.in/grVZS2Px Channel: https://lnkd.in/gxadfN3u 5. Data Warehousing Learn facts, dimensions, star schema, partitioning, clustering, and data modeling. Free resource: https://lnkd.in/gnd2M88v 6. dbt Learn SQL transformations, testing, documentation, lineage, and analytics engineering workflows. Free resource: https://lnkd.in/gPQj636c Channel: https://lnkd.in/g5nRCK4s 7. Airflow Learn DAGs, scheduling, dependencies, retries, and pipeline orchestration. Free resource: Airflow 101 by Astronomer https://lnkd.in/grBHPkSm Channel: https://lnkd.in/gAVJt5vq 8. Kafka Learn events, topics, producers, consumers, and streaming data basics. Free resource: Kafka 101 by Confluent https://lnkd.in/gnZWsnyN Channel: https://lnkd.in/gy9BrHzj 9. Projects Build an API pipeline, batch ETL pipeline, dbt model, Airflow DAG, warehouse model, and Kafka mini-project. Because courses teach concepts. But projects create proof. And in Data Engineering, proof beats certificates.

𝐖𝐡𝐞𝐫𝐞 𝐀𝐫𝐞 𝐘𝐨𝐮 𝐨𝐧 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 : 𝐚𝐧𝐝 𝐖𝐡𝐚𝐭'𝐬 𝐘𝐨𝐮𝐫 𝐍𝐞𝐱𝐭 𝐒𝐭𝐚𝐠𝐞? "How do I become a data engineer?" gets answered with 30-tool lists that overwhelm everyone. 𝟖 𝐬𝐭𝐚𝐠𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐨𝐫𝐝𝐞𝐫 𝐭𝐡𝐞𝐲 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐛𝐮𝐢𝐥𝐝 𝐨𝐧 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫. 1. Learn Programming Basics: Start with Python. Variables, functions, loops, OOP, APIs, JSON handling. Most data engineers write Python every day for years. Skipping this stage just delays the pain. 2. Master SQL: SELECT, WHERE, JOINS, GROUP BY, window functions, CTEs, subqueries. Learn MySQL, PostgreSQL, or MongoDB. SQL hasn't been disrupted in 40 years and won't be for another 40. Window functions and CTEs are where senior-level skill lives. 3. Learn Data Warehousing: ETL vs ELT, data pipelines, star and snowflake schema, batch processing. Tools: Redshift, BigQuery, Snowflake. Modeling decisions here echo through every downstream report for years. 4. Learn Big Data Technologies: Distributed systems, parallel processing, streaming, real-time analytics. Technologies: Spark, Hadoop, Kafka. The mental model shift: data no longer fits on one machine. Once you internalize partitioning and parallelism, modern data systems start making sense. 5. Learn Data Pipelines: Workflow scheduling, automation, monitoring, error handling. Tools: Airflow, dbt, Prefect. This is where data engineering becomes engineering. Pipelines need observability, retries, and tests like any production system. 6. Learn Cloud Platforms: Cloud storage, data lakes, serverless processing, security basics. Pick AWS, Azure, or Google Cloud. Pick one and go deep. Multi-cloud comes later. 7. Learn DevOps: Version control, CI/CD, containerization, deployment automation. Tools: Git, Docker, Kubernetes. The line between data engineer and platform engineer keeps blurring. DataOps is now part of the job. 8. Build Real Projects: ETL pipeline, real-time streaming, sales data warehouse, data lake. One project per stage demonstrates you can actually do the work. A real pipeline on GitHub beats any certification. 𝐇𝐨𝐰 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐮𝐬𝐞 𝐭𝐡𝐢𝐬 𝐫𝐨𝐚𝐝𝐦𝐚𝐩? • Don't skip stages. Jumping from "I know SQL" to "I want Kafka" rarely works. • Master one tool per stage, then move on. Deep skill in one beats surface familiarity with five. • Strong SQL + Python + Airflow + one cloud platform is enough to start applying for junior roles. • Projects matter more than certifications. Hiring managers want to see what you've built. Data engineering is a stack, not a single skill. The engineers who advance fastest deeply understand these 8 stages and can connect them. 𝐖𝐡𝐢𝐜𝐡 𝐬𝐭𝐚𝐠𝐞 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐢𝐧 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰? ♻️ Repost this to help your network get started ➕ Follow Jaswindder Kummar for more #DataEngineering #Python #CareerGrowth

For those looking to start a career in data engineering or eyeing a career shift, here's a roadmap to essential areas of focus: 𝗗𝗮𝘁𝗮 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Learn both full and incremental data extraction methods. - 𝗗𝗮𝘁𝗮 𝗟𝗼𝗮𝗱𝗶𝗻𝗴: - 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: Master the techniques of insert-only, insert-update, and comprehensive insert-update-delete operations. - 𝗙𝗶𝗹𝗲𝘀: Understand how to replace files or append data within a folder. 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 - 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀: Acquire skills in manipulating CSV and Parquet file data with tools like Pandas and Polars. - 𝗦𝗤𝗟: Enhance your ability to transform data within PostgreSQL databases using SQL. This includes executing complex aggregations with window functions, breaking down transformation logic with Common Table Expressions (CTEs), and applying transformations in open-source databases such as PostgreSQL. 𝗗𝗮𝘁𝗮 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Develop the ability to create a Directed Acyclic Graph (DAG) using Python. - Gain expertise in generating logs for monitoring code execution and incorporate logging into databases like PostgreSQL. Learn to trigger alerts for failed runs. - Familiarize yourself with scheduling Python DAGs using cron expressions. 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗞𝗻𝗼𝘄-𝗛𝗼𝘄 - Become proficient in using GIT for code versioning. - Learn to deploy an ETL pipeline (comprising extraction, loading, transformation, and orchestration) to cloud services like AWS. - Understand how to dockerize an application for streamlined deployment to cloud platforms such as AWS Elastic Container Service. 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗮𝗻𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Begin your learning journey here : https://lnkd.in/e5BxAwEu Mastering these foundational elements will equip you with the understanding and skills necessary to adapt to modern data engineering tools (aka the modern data stack) more effortlessly. Congratulations, you're now well-prepared to start interviewing for data engineer positions! While there are undoubtedly more advanced topics to explore such as data modeling , the courses and key areas highlighted above will give you a solid starting point for interviews.

I've received a lot of DMs asking roadmap, where to start, what to learn first, how to get confidence and so on. No worry I got your back. Here's the best way to learn about #dataengineering. Let me share my approach with you. 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 B𝐮𝐢𝐥𝐝𝐢𝐧𝐠 F𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧  #SQL is the backbone of data engineering as it is used for querying and managing data in relational databases and most important to ace in Data Field. Focus on mastering the basics such as SELECT, JOIN, GROUP BY, and aggregate functions. Additionally, learn advanced concepts like indexing, query optimization, and window functions. Set a deadline for yourself to become proficient in SQL, and practice regularly using platforms like #HackerRank, #LeetCode, #DataLemur, or real-world datasets. #Python is easy to learn and it's essential for data engineering tasks such as data manipulation, automation, & integration with other tools. Aim to understand the core syntax, data structures, and libraries like #Pandas, #NumPy. While deep knowledge of data structures and algorithms isn't necessary, having a moderate understanding will be beneficial. Focus on writing clean and efficient code. #ApacheSpark is a powerful tool for processing large datasets efficiently. Mastery on it, Understand its internal architecture, including concepts like RDDs, DataFrames, & the execution model. Learn how Spark handles big data through transformations and actions. Explore the Spark ecosystem and practice by building simple ETL pipelines. Familiarize yourself with PySpark to leverage Python’s simplicity in Spark applications. Practice it on local or on #Databricks platform In addition to these, learning cloud platforms is essential. Whether you choose #AWS, #Azure, or #GCP, mastering one will make it easier to learn the others. Start by developing a basic foundation in cloud concepts, then focus on services relevant to data engineering, such as data storage, data pipelines, compute services. Don't try to learn everything at once; select the services you need & build from there. 𝐑𝐞𝐬𝐭 a𝐥𝐥 L𝐞𝐚𝐫𝐧 𝐛𝐲 B𝐮𝐢𝐥𝐝𝐢𝐧𝐠 P𝐫𝐨𝐣𝐞𝐜𝐭𝐬. Finally, start doing projects. Begin with basic projects and gradually move to more complex ones. Apply the knowledge you’ve gained in SQL, Python, PySpark, and cloud services. As you gain confidence, tackle more complex projects that incorporate various data engineering techniques such as, #hadoop, #normalization, #denormalization, #datamodeling, .... and tools such as #git, #airflow, #docker, #dbt, #snowflake ... Document your projects thoroughly to showcase your skills, upload on #linkedln, #Github make visibility. Image Credit: Educative 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫, 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐢𝐬 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐫𝐚𝐭𝐡𝐞𝐫 𝐭𝐡𝐚𝐧 𝐣𝐮𝐬𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. 𝐍𝐨𝐭𝐞: Resource Lists with link given below in the comment. If you find it helpful, like the post & drop a comment saying 'helpful' Stay Active Nishant Kumar 🤝