Скачать или смотреть Apache Spark vs Databricks: Key Differences Explained for Big Data Projects | Data Engineering

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In this video, we dive into the key differences between Apache Spark and Databricks, two powerful tools for data processing and analytics. Whether you’re working on data engineering projects or machine learning workflows, understanding when to choose each tool is critical for success.

What You’ll Learn in This Video:

• The pros and cons of using Apache Spark and Databricks for Big Data Platforms.
• How Spark’s in-memory computation boosts performance and scalability of Big Data or large scale workloads.
• Unique features of Databricks, such as Delta Lake, integrated tools, and cloud-based optimization for Big Data Workloads.
• Ideal scenarios for using Apache Spark vs. Databricks in your Data Engineering or Big Data projects.
• Cost implications and the impact of vendor lock-in when choosing Databricks over Open Source Apache Spark.

Timestamps:
0:00 - Introduction to Apache Spark and Databricks
0:33 - What is Apache Spark?
1:16 - Spark's Modularity and Use Cases
1:53 - Managing Apache Spark: Pros and Cons
2:17 - Introduction to Databricks
2:30 - Databricks Features and Advantages
3:25 - Apache Spark vs. Databricks: Side-by-Side Comparison
5:05 - When to Choose Apache Spark
5:29 - When to Choose Databricks
6:23 - Cost and Vendor Lock-in Considerations
6:59 - Takeaway and Final Thoughts
7:05 - About Our Udemy Course on Apache Spark and Databricks

Why This Video Matters:

Choosing between Apache Spark and Databricks can significantly impact your project’s performance, scalability, and cost. This video equips you with the knowledge to make informed decisions tailored to your Big Data and Data Engineering workflows.

Want to learn Apache Spark using Free Databricks cluster set up? That too with practical assignments and use cases? Check out our Udemy course below
https://www.udemy.com/course/apache-s...

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