Roadmap for becoming a proficient Databricks Data Engineer in 2024

[Rishabh-Pandey]

Creating a roadmap for becoming a proficient Databricks Data Engineer in 2024 involves several key steps and milestones. Below is a detailed roadmap that includes the necessary skills, tools, and knowledge areas to focus on:

Q1: Foundation and Basics

1. Introduction to Databricks:

   • Understand what Databricks is and its place in the data ecosystem.
   • Familiarize yourself with the Databricks interface and workspace.

2. Basic Data Engineering Concepts:

   • Learn the basics of data engineering.
   • Understand ETL (Extract, Transform, Load) processes.

3. Programming Skills:

   • Python: Focus on basics to advanced Python.
   • SQL: Master SQL for querying databases.

4. Introduction to Apache Spark:

   • Learn the basics of Apache Spark.
   • Understand Spark architecture and components.

Q2: Intermediate Skills and Databricks Certification

1. Databricks Platform:

   • Deep dive into the Databricks platform.
   • Understand Databricks notebooks, clusters, and jobs.

2. Spark with Databricks:

   • Learn how to run Spark jobs on Databricks.
   • Explore RDDs, DataFrames, and Spark SQL.

3. Data Ingestion and Storage:

   • Learn to ingest data from various sources (e.g., databases, CSV, JSON).
   • Understand Databricks Delta Lake for data storage.

4. Databricks Certification:

   • Prepare for and obtain the Databricks Certified Associate Developer for Apache Spark certification.

Q3: Advanced Data Engineering and Specialization

1. Advanced Spark Techniques:

   • Learn advanced Spark concepts like optimization, partitioning, and tuning.
   • Explore Spark MLlib for machine learning tasks.

2. Data Engineering on Databricks:

   • Understand advanced data engineering workflows on Databricks.
   • Learn about Databricks Pipelines and scheduling ETL jobs.

3. Delta Lake:

   • Deep dive into Delta Lake features such as ACID transactions, schema enforcement, and time travel.
   • Implement and optimize Delta Lake in your projects.

4. Data Governance and Security:

   • Learn about data governance best practices.
   • Understand how to implement data security and compliance using Unity Catalog in Databricks.

Q4: Real-World Projects and Continuous Learning

1. Real-World Projects:

   • Apply your skills to real-world data engineering projects.
   • Work on end-to-end ETL pipelines, data warehousing solutions, and data streaming applications.

2. Advanced Databricks Features:

   • Explore Databricks features like Databricks SQL, Photon, and AutoML.
   • Understand how to use Databricks for data science and analytics.

3. Community and Continuous Learning:

   • Join Databricks community groups and forums.
   • Attend webinars, conferences, and meetups.
   • Keep up with the latest trends and updates in the Databricks ecosystem.

4. Further Certifications:

   • Aim for advanced certifications like Databricks Certified Professional Data Engineer.

[Elham]

Thanks for clarifying the path to becoming a Data Engineer.😎

I really appreciate it.

[Rishabh_Tiwari]

Thank you for sharing this @Rishabh-Pandey I am sure it will help community members.

Thanks,

Rishabh