Apache spark proficient

[SamAWS]

What is the best way to be proficient in Apache spark?

[Kaniz_Fatma · Accepted solution]

Hi @SamAWS , 

To become proficient in Apache Spark™, significantly leveraging the resources available in the Databricks Community, follow these steps:

1. Getting Started with Databricks Community:

   • Sign up for a Databricks Community account if you haven't already.
   • Explore the Databricks platform interface to get familiar with its layout and features.

2. Access Learning Resources:

   • Databricks provides extensive documentation and tutorials that cover a wide range of topics related to Apache Spark™.
   • Utilize Databricks’ "Workspace" to access notebooks and learning materials. Start with the provided examples and tutorials.

3. Interactive Notebooks:

   • Databricks notebooks are a powerful way to learn and experiment with Spark.
   • Begin by running simple code snippets to understand the basics of Spark's DataFrame API and RDDs (Resilient Distributed Datasets).

4. Online Courses and Certifications:

   • Databricks offers online courses and certifications that can significantly accelerate your learning process.
   • Take courses on Spark essentials, data engineering, machine learning with Spark, and more.

5. Collaborate and Learn from the Community:

   • Databricks Community has a vibrant forum where you can ask questions, share insights, and learn from other users.
   • Engage with the community to solve problems and discuss best practices.

6. Hands-On Projects:

   • Apply what you've learned by working on real-world projects. Databricks allow you to create and manage clusters to process data.

7. Performance Optimization:

   • Dive into Spark's performance optimization techniques. Databricks provides tools for monitoring and profiling your Spark jobs.

8. Advanced Topics:

   • Once you're comfortable with the basics, explore more advanced topics like structured streaming, graph processing, and deep learning with Spark.

9. Stay Updated:

   • Follow Databricks’ official blog and release notes to stay up-to-date with the latest features and enhancements.

10. Networking:

    • Attend Databricks webinars, conferences, and meetups to connect with other data professionals and learn from their experiences.

Remember, becoming proficient in Apache Spark™ takes time and practice. The Databricks Community offers an excellent platform to learn, experiment, and grow your Spark skills. Good luck on your learning journey!

[SamAWS]

Thank you so much for answering my question. 
Based on your experience. Should I use Scala or Python for data engineering?

[SamAWS]

Thank you for the quick response.

[Kaniz_Fatma]

Hi @SamAWS, The choice between Scala and Python for data engineering depends on various factors, such as the specific use case, the team's expertise, and the nature of the tasks.

Here's a comparison of both languages based on the provided information:

1. **Scala**:
  - Scala is native to the Apache Spark™ ecosystem, which Databricks is built upon. This means Scala might offer better performance and access to the latest features of Spark API (https://docs.databricks.com/getting-started/dataframes-scala.html)).
  - Scala can be used with the Databricks SDK and provides a comprehensive development environment with tools like IntelliJ IDEA ([Docs: sdk-java](https://docs.databricks.com/dev-tools/sdk-java.html)).
  - However, there are some limitations with Scala, such as Scala UDFs not being included in specific previews and not being supported for encrypting data in some pipelines (https://docs.google.com/document/d/1UEIUrz22w8TiwAo-q1ZMbDv_m4AlEkHvJ6QPE6OUUQY/)).

2. **Python**:
  - Python is widely used in the data science community and has a rich ecosystem of libraries and tools for data manipulation and analysis (https://docs.databricks.com/languages/index.html).
  - Python can be used for ETL and data engineering tasks in Databricks, offering a robust ETL experience (https://docs.databricks.com/introduction/index.html)).
  - Python UDFs are supported and recommended for specific tasks where Scala UDFs are not ([Docs: UDF Issues with Unity Catalog](https://docs.google.com/document/d/1UEIUrz22w8TiwAo-q1ZMbDv_m4AlEkHvJ6QPE6OUUQY/)).

In summary, while both languages are supported and can be used effectively in Databricks for data engineering, the choice between Scala and Python would depend on your project's specific requirements and constraints.