Apache Spark and Apache Beam are frameworks that gained popularity as big data solutions in distributed processing. Both of them were created to avoid the feasible task of splitting the data into smaller pieces manually to be processed by single computers. Even though they are dedicated to the same industry and serve similar purposes, both frameworks differ from each other. What are the features that distinguish these two solutions?
It is a solution that enables writing software that can be executed simultaneously on multiple computers. Spark powers a stack of libraries, including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application and run it using the standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes.
What sets Apaches Spark apart from other technologies is the code execution process. Traditionally, you need to ask your computer’s operating system to run it for you. In the case of Spark, you shall ask the Spark server to perform this action. In order to have it done, you should have at least one Apache Spark Server installed in your computer’s network. You will have to submit your code into one instance of Spark, and once it is analyzed, it will be distributed among others in the network to perform work on it. Next, the results of the analysis can be transferred between single instances that work on data.
It is a unified programming model that is used for batch and data streaming processing. Apache Beam technology is highly successful due to the fact that it fuses streaming data processes and batch, while other solutions usually do it via separate APIs. As a consequence, it is straightforward to swap between streaming processes and batch processes, according to continually changing requirements.
Apache Spark and Apache Beam work on different levels. Since Beam uses different technologies in the process of distributional processing, Spark can be one of its many runners. Apache Beam itself does not perform any analyzing action on data. Instead, it defines the functions that are to be performed in pipelines. What is more, in Beam technology, pipelines are composed of transformations.
Since the Beam does not work on data itself, all the transformations and analysis of data can be done only when the pipeline is running. What differentiates Beam from Spark in this matter is that each of the pipelines can be run only once, and data cannot be moved back to the instance it was sent from.
To sum up, in Spark, the total sum of work and transformation returns to the node which started the data processing. In Beam, there is a defined transformation function that sums all the processes and stores it in the memory of one of its runners.
Even though technologies address the same problem and the difference between them is minor, lack of knowledge on how they differ can cause confusion. If you would like to learn which solution is better for your business, we’ve prepared a more extensive article (https://www.polidea.com/blog/apache-spark-vs-apache-beam-data-processing-in-2020/), which analyzes the pros and cons of these systems.