What is the difference between Databricks and Spark?

Question

I am trying to a clear picture of how they are interconnected and if the use of one always require the use of the other. If you could give a non-technical definition or explanation of each of them, I would appreciate it. Please do not paste a technical definition of the two. I am not a software engineer or data analyst or data engineer.

Dogbane

Answer 1

These two paragraphs summarize the difference (from this source) comprehensively:

Spark is a general-purpose cluster computing system that can be used for numerous purposes. Spark provides an interface similar to MapReduce, but allows for more complex operations like queries and iterative algorithms. Databricks is a tool that is built on top of Spark. It allows users to develop, run and share Spark-based applications.

Spark is a powerful tool that can be used to analyze and manipulate data. It is an open-source cluster computing framework that is used to process data in a much faster and efficient way. Databricks is a company that uses Apache Spark as a platform to help corporations and businesses accelerate their work. Databricks can be used to create a cluster, to run jobs and to create notebooks. It can be used to share datasets and it can be integrated with other tools and technologies. Databricks is a useful tool that can be used to get things done quickly and efficiently.

In simple words, Databricks has a tool that is built on top of Apache Spark, but it wraps and manipulates it in an intuitive way which is easier for people to use.

This, in principle, is the same as difference between Hadoop and AWS EMR.

Milore