Here's how you can gain hands-on experience in Data Engineering.

If you want to learn SQL, here is my tip, play The SQL Murder Mystery, it is a very fun quick game that covers the basics of SQL. Going to more serious stuff. If you are in a company, learn SQL working on projects, the best way hands down. But if you are not, I would suggest learning SQL from YouTube, you don't need to pay for SQL courses. And using DuckDB as a playground. It runs fast, locally, and it is easy to install. Or you can go with the good old Postgres, but it will require a bit more effort to install. Finally, DBeaver is great for visualizing your database. It is all free!

To gain hands-on experience in Data Engineering: 1. Projects: Work on real-world projects—build ETL pipelines, design databases, and optimize data workflows. 2. Online Courses: Enroll in data engineering courses (e.g., Coursera, edX) to learn tools like Apache Spark, SQL, and cloud platforms. 3. Hackathons: Participate in data-related hackathons to solve practical problems. 4. Open Source: Contribute to open-source data engineering projects. 5. Internships: Seek internships or freelance opportunities. Remember, practice and curiosity drive expertise! ??

SQL is powerful for querying large datasets to get dataset with business value. Analysts and data scientists use SQL to explore datasets, find trends, and generate reports. (1) You can get developer version of Oracle and MS SQL Server both. Just restore any sample database and start learning basic queries. (2) Start with filter (WHERE) conditions (3) Learn GROUP BY and learn aggragion functions (4) Learn JOIN (5) Learn Procedures with and without parameters (6) Learn Functions Once you complete all these then go with exercises available online. Good Luck to all who are reading this.

To gain hands-on experience in Data Engineering, start by learning SQL, as it is essential for managing and querying relational databases. Practice writing and optimizing queries using free online resources, tutorials, and interactive platforms like LeetCode or HackerRank. Next, get familiar with database management systems like MySQL, PostgreSQL, and SQL Server. Work on projects involving data extraction, transformation, and loading (ETL) processes. Use tools like Apache Hadoop, Spark, and Kafka. Additionally, explore cloud platforms like AWS, Google Cloud, or Azure, focusing on their data engineering services. Contribute to open-source projects and build a portfolio showcasing your skills.

Working on real-world projects such as building ETL pipelines, designing data warehouses, and developing data integration solutions... Use tools like Apache Hadoop, Spark, and Kafka, and learn SQL, Python, and cloud platforms like AWS or Google Cloud. Participate in online courses, hackathons, and contribute to open-source projects. Internships or entry-level positions in data-focused companies also provide valuable experience. Regularly practicing with datasets on platforms like Kaggle can help enhance your skills

Leverage Databricks Community Edition to practice building ETL pipelines. It offers a free platform to write and test code, integrate with data sources, and utilize Spark for large-scale data processing. This hands-on experience will familiarize you with industry-standard tools and techniques.

Understanding the end-to-end high-level picture is key here. Only then we can dive into a certain segment of data processing and gain experience on those services/tools/programming language. Understanding the problem instead of trying to solve them at all costs. It's a long road but the one that brings you to your destination packed with experience you gained from either failing or succeeding along the way.

To gain hands-on experience in Data Engineering, start by building data pipelines. Begin with a simple ETL (Extract, Transform, Load) pipeline. Extract data from a source like an API or database, transform it using tools like Python or SQL, and load it into a data warehouse such as AWS Redshift or Google BigQuery. Use platforms like Apache Airflow to automate and orchestrate your pipelines. Experiment with different data sources and destinations, practice error handling, and optimize for performance. Participate in open-source projects, and leverage cloud services for scalable solutions. This practical experience will strengthen your skills and understanding of Data Engineering.

To gain hands-on experience in Data Engineering, start with learning the basics of databases, SQL, and programming languages like Python or Java. Work with Big Data technologies such as Hadoop, Spark, and Kafka. Build projects that involve data collection, transformation, and storage using ETL (Extract, Transform, Load) processes. Use cloud platforms like AWS, Google Cloud, or Azure to handle data pipelines and storage. Participate in online courses, join data engineering communities, and contribute to open-source projects. Practical experience through internships or freelance projects can also be invaluable.

To learn Data Modeling basics, Learn cardinalities and try to understand the real meaning of data relationship. Learn normal forms (NF1, NF2, NF3). Learn data warehousing models. Once master basics, see how you can be helpful to Database Engineers to fetch data effectively and with less of struggles. Learn about performance tuning. If you have a prior knowledge of performance tuning your data model will follow along well with it.

To gain hands-on experience in Data Engineering and master Data Modeling, start by learning the basics of SQL and database management systems. Work on projects that involve designing, implementing, and optimizing databases. Use ER diagrams to visualize and plan data structures. Practice with real-world datasets and tools like PostgreSQL, MySQL, and NoSQL databases such as MongoDB. Participate in open-source projects or internships to apply your skills in a professional setting. Additionally, familiarize yourself with data warehousing concepts and ETL processes. Online courses and certifications can also provide structured learning and practical exercises.

Utilizing cloud services is essential in data engineering, offering a broad range of tools and platforms. Start by exploring cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, and Azure Synapse Analytics. Learn to set up data storage, compute resources, and analytics services to streamline data operations. Experiment with different cloud providers to understand their unique features and services, such as GCP’s machine learning tools and Azure’s enterprise solutions. This hands-on experience is invaluable as many companies move their data operations to the cloud, enhancing your skills and making you more competitive in the job market.

To gain hands-on experience in Data Engineering using cloud services, start by exploring platforms like AWS, Azure, and Google Cloud. Begin with their free tiers to access essential tools and services. Focus on learning and implementing data storage solutions such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. Get acquainted with data processing tools like AWS Glue, Google Cloud Dataflow, and Azure Data Factory. Practice building data pipelines, handling ETL processes, and using managed databases like Amazon RDS, Google BigQuery, and Azure SQL Database. Additionally, explore data analytics tools like AWS Redshift and Google BigQuery for insights and reporting.

- ??? Engage in real-world projects or contribute to open-source initiatives to apply your Data Engineering skills. - ?? Identify and solve problems that interest you, such as building recommendation systems or analyzing public datasets. - ?? Document your work, challenges, and solutions to create a portfolio that showcases your expertise. - ?? Share your projects on platforms like GitHub to increase visibility and attract potential employers. - ?? Collaborate with other data professionals to gain diverse insights and enhance your learning experience.

It all starts with curiosity. For instance, a client has some current architecture in the cloud. What are the biggest spender services in the last 6 months, overall? Key encryption service - ok let's see what that does and what impacts greater costs on that service? Significant amounts of reads, let's estimate how much of the reads occur in this environment? A lot! So some blob storage queries may be inefficient or excessive...but you get the picture, questions guide you further into the challenge and eventually out of it - creating new opportunities and projects along the way.

Gaining hands-on experience in Data Engineering requires engaging in practical projects. Start with learning fundamental concepts like ETL (Extract, Transform, Load) processes, data warehousing, and database management. Use tools like Apache Spark, Hadoop, and SQL. Build projects such as creating a data pipeline to collect and process data from APIs, or designing a data warehouse for a business scenario. Contribute to open-source projects on GitHub, participate in hackathons, and collaborate with others. Regularly document your work and share it on platforms like LinkedIn or a personal blog to showcase your skills and progress.

I would reposition this title as 'engage in something that grabs your interest and ideally is fun' - find a side project or use case where you can apply your data engineering skills. In my case, when SQL Server Integration Services was released, I decided to see how I could use it to process my vast photo library and perform various maniuplations, files, folders, formats. Lots of fun and a great way to get hands on. Also look for volunteering opportunities - everyone needs a data engineer - many public sector orgs deal with vast quanities of files (PDF, Excel, databases) - there are always opportunities to apply your skills. Finally, I would recommend engaging with others through internal or external communities - great way to learn

Participate in Hackathons and Competitions. These events provide opportunities to work on real-world problems and gain practical experience.

Contribute to open-source data engineering projects. It’s a great way to collaborate with others, learn from experienced developers, and build your portfolio.

Another great way to gain hands on experience is to apply for internships/ entry level roles in data engineering this will keep you immersed in real-world projects, allowing you to apply theoretical knowledge, work with experienced professionals, and build a strong foundation for a successful career in data engineering.