Edit on July 26, 2024:
Databricks recently changed their offerings and the structure of the Academy. Now,
I struggle to find the resources that focus on Spark. I think the new best place to
start is the free videos and self-paced material in the
Data Engineer Learning Plan (Public)
or, if you're all new to Spark and Databricks, the
Fundamentals of the Databricks Lakehouse Platform.
In the rapidly evolving landscape of big data processing, Apache Spark continues to be a
dominant force, empowering organizations to derive insights from massive datasets with
remarkable speed and efficiency. Today in 2024, the demand for professionals
proficient in Spark is higher than ever, making it a valuable skill to add to your
repertoire.
When it comes to learning Spark effectively, there are more than enough
useful resources out there. In this short post, I describe my recommendations
to juniors in my teams who want to learn Spark.
Why Apache Spark?
Before delving into the learning journey of Apache Spark, let's briefly revisit why Apache Spark remains indispensable in the realm of big data analytics:
- Scalability: Spark is designed to scale horizontally across clusters of machines to ensure it can handle even petabytes of data.
- Speed: Spark's in-memory computation capabilities enable processing at speed, significantly outperforming more traditional systems.
- Ease of Use: With its user-friendly APIs in Python, Scala, and R, Spark is accessible for a wide range of developers and data scientists.
- Versatility: Spark's unified analytics engine supports all kinds of workloads and transformations including UDFs, batch processing, near-real-time streaming, machine learning, and graph processing.
In sum, I have not yet heard of a computational task that involved too much data or too complex transformations for Spark to handle.
Learning Apache Spark
When I started learning Spark back in 2019, the most recommended resource was O'Reilly's book
'Spark: The Definitive Guide' by Bill Chambers and Matei Zaharia. Comprehensive but long, it
was quite tedious for me as a beginner to tackle all 600 pages. Fortunately for today's beginners,
many other resources are now available. In my opinion, the Databricks Academy stands out,
offering comprehensive training through its free video courses and practices.
Databricks Academy
The Databricks Academy is a service by Databricks — the company founded by the
creators of Apache Spark — that provides top-notch training resources tailored for
learning and mastering Spark. Here's why it's the go-to resource for anyone looking to
improve on their Spark skills:
- Comprehensive Curriculum: Databricks Academy offers a comprehensive curriculum
covering all aspects of Apache Spark, from the fundamentals to advanced topics
like optimization techniques, handling data skew, and machine learning.
- Expert-led Video Courses: The heart of Databricks Academy's training lies in
its expert-led video courses. These courses are designed and taught by seasoned
professionals who bring real-world experience to the table and are skilled in
communicating with learners. There are free ones to start with and paid ones for more
advanced topics.
- Hands-on Labs: The best way to learn is practice. Most learning modules offer
the opportunity to apply theoretical knowledge in a practical setting, using the Databricks platform.
- Self-paced Learning: Flexibility is key, especially for busy professionals.
Databricks Academy's self-paced learning model allows learners to progress through
the courses at their own speed, fitting learning around their schedule. This is
crucial for me since it's so much more effort to free up a day for a live, instructor-led course.
- Certification Programs: For those seeking formal recognition of their Spark proficiency,
Databricks Academy offers certification programs that validate your skills and expertise in Apache Spark.
Embarking on Your Learning Journey
Here's my suggested roadmap to get you started:
- Begin with the Basics: Start with Databricks Academy's foundational
courses to grasp the core concepts of Spark, including RDDs (Resilient Distributed Datasets), DataFrame API, and Spark SQL.
The module to kick-off training should be 'Apache Spark Programming with Databricks'.
- Dive Deeper: Progress to intermediate and advanced courses to explore topics like Spark Streaming, the Databricks platform, and performance optimization techniques.
- Hands-on Practice: Make the most of Databricks Academy's hands-on labs to reinforce your understanding and gain practical experience working with Spark.
- Specialize: Depending on your interests and career goals, consider specializing in specific Spark components such as MLflow for machine learning or Spark Structured Streaming for real-time data processing.
I highly recommend the Databricks Specialist Sessions to gain a deep understanding of these topics.
- Certification: Once you feel confident in your Spark skills, consider pursuing certification through Databricks Academy to formalize your expertise and enhance your professional credibility.
Conclusion
In the era of big data, proficiency in Apache Spark is a valuable asset that can open doors to exciting career opportunities.
With Databricks Academy's expert-led video courses, hands-on labs, and certification programs, mastering Spark has never been more accessible.
So, seize the opportunity to elevate your skills and stay ahead in the fast-paced world of big data analytics. Happy learning!