There have been a plethora of different tools and frameworks that have developed since the idea of big data was introduced to the programming community. Data processing may be divided into two categories, each of which has its own set of rules. Batch processing is one kind of processing, while Stream processing is another. For developers and businesses alike, maintaining a variety of software stacks and technologies may be very difficult and time-consuming. As a result, Apache Beam comes to the rescue!
Apache Beam (both batch and stream) is a strong tool for dealing with workloads that are embarrassingly parallel. In many ways, it is an extension of Google’s Flume, which offers batch and streaming data processing based on MapReduce principles and architecture. One of the unique characteristics of Beam is that it is not dependent on the platform on which the code is executed. Example: A pipeline may be built once and operate locally, across many Flink or Spark clusters, or on Google Cloud Dataflow, depending on the situation.
Apache Beam is a unified programming paradigm that can be used for both batch and streaming execution. It may be used with a variety of execution engines, including Apache Spark, to accomplish this. You may write a batch or streaming application in Python, Java, Go (or another programming language) and then run that program on Apache Apex, Apache Flink, Apache Spark, Apache Samza, Apache Gearpump, or Google Cloud, among other platforms. As a result, Apache Beam performs a distinct function. In addition to all of the execution engines, Apache spark integration services is capable of doing extremely specialized programming and execution tasks. It is the goal of Apache Beam to generalize the execution capabilities so that your application is portable across different platforms. So you’re asking for an apples-to-apples comparison between two different types of fruit. Finally, although Spark is very popular, it is not without its flaws.
Apache Beam has the following characteristics:
- The following are the distinguishing characteristics of Apache beam:
- Unified programming model – Use a single programming paradigm for both batch and streaming use cases to simplify development.
- Portability – Pipelines may be executed in a variety of different execution contexts. In this case, various execution contexts denote different runners. For example, Spark Runner, Dataflow Runner, and so on.
- Flexible – Create bespoke SDKs, IO connections, and transformation libraries to meet your specific needs.
Apache Beam vs. Apache Spark: Which is better?
The wonderful thing about open source projects and standards is that there are a plethora of options from which to select. Known as Beam, it is the most recent embodiment of Google’s newly discovered open technology approach. Apache Spark integration, on the other hand, is a technology that allows you to create software that will be run on many machines at the same time.
- Apache Beam may be categorized as a tool in the “Workflow Manager” area, while Apache Spark can be classed as a tool in the “Big Data Tools” category.
- Apache Spark is an open-source program with 22.9K GitHub stars and 19.7K GitHub forks, and it is developed by the Apache Software Foundation.
In general, Spark and Beam are attempting to solve the same issue. And although the differences between them are small, it would be perplexing if you didn’t know how they differed.
Although the costs are almost the same, it is important to note that the Spark task has much more room to optimize, whereas the Apache Beam project already includes Dataflow optimizations out-of-the-box.
2. Local run
There is no clear winner in this local race; it all depends on your choices.
When dealing with embarrassingly parallel data processing jobs, Testing Beam is especially helpful since it allows the issue to be broken into several smaller bundles of data that can be handled separately and in parallel.
Finally, we would like to point out that if you are familiar with Spark Job, it may be more helpful. A number of benefits of Apache Spark integration make it a highly appealing big data framework, and this is one of them. As a result, you may build complex parallel applications in Java, Scala, or Python in a short amount of time without having to limit yourself to thinking in terms of just the “map” and “reduce” operators. As a result, it is well-suited for use with machine learning techniques.