Data Engineering Skills Development Challenge

Since I started mentoring on topmate.io, I met quite a few folks who hope to transition from data analysts or data scientists to data engineering. The most asked question is: "How can I get more DE hands-on experience?" --- Here is my recommendation: 👉🏼 Try to automate & optimize your current DA/DS workflow! No matter what companies/teams you are at, there will always be some degree of overlapping work between DE and DA/DS, it shouldn't be hard to get access to DE infra and toolings. Some examples of DE work you could explore: 🛠 Automate your ad-hoc queries/scripts with a data orchestrator 🛠 Add data quality checks in your data workflows 🛠 Evaluate the performance and optimize your code (e.g. a slow join) 🛠 Version control your code with Git 🛠 Make your code as reusable as possible 🛠 Add unit and/or integration tests to your code 🛠 Streamline deployment and testing with tools like Jenkins (aka CICD) 🛠 [Bonus] Learn data modeling by observing how core tables are designed and thinking about why they are efficient or inefficient. The last step: showcase these aspects of DE work on your resume! --- This list above isn't an exhaustive list of data engineering responsibilities but is what I believe is feasible for DA/DS to experiment with while still benefiting their current roles. Anything else you would add? #dataengineering #data #softwareengineering #datascience #dataanalytics #career

According to a 2021 report by DICE, the role of data engineer is the fastest-growing job title, witnessing a 50% annual growth in 2022. Data engineering is a rapidly growing field with endless opportunities for growth and development. But how do you get started? As a data engineer, you're integral to every operation that requires data. However, the path to becoming a successful data engineer is complex. It's a role that carries significant weight and responsibility. To guide you through the process and help you avoid common pitfalls, let’s walk through the essential steps. First, you need to understand the basics. If you're transitioning from another field, resources like Harvard’s CS50 series on YouTube are useful for grasping computer science fundamentals. 👉 https://lnkd.in/e9QEaVdi Then, focus on programming languages. 🔷 Python is recommended for its simplicity and relevance in data engineering. 🔷 SQL is non-negotiable for database interaction. 🔷 Linux commands are necessary for the remote systems you’ll likely work with. Next, you need to build a strong foundation in data warehousing and data processing. 🔷 Learn about tools such as Snowflake or BigQuery, and frameworks like Apache Spark for batch processing and Apache Kafka for real-time processing. 🔷 Cloud platforms like AWS, Azure, or GCP are also essential, as is understanding workflow management tools like Apache Airflow. As you advance, deepen your knowledge in security, networking, and deployment. 🔷 Explore the Modern Data Stack (MDS), tools like DBT (Data Build Tool) 🔷 Familiarize yourself with Docker or Kubernetes, and study materials like “Designing Data-Intensive Applications.” Data engineering requires continuous learning and adaptation, but for those willing to navigate its complexities, it offers a stable and impactful profession. How did you start your journey in data engineering? Share below! 💬 #dataengineer #webdevelopment #webdesign Credit: Brij kishore Pandey I talk about the latest in WordPress, SEO, Web Design, and Growth. Follow me for weekly updates!

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.