Before going through the comparison of Apache Beam and Apache Spark, we should have a glimpse of what these two exactly are.
Apache Beam means a unified programming model.
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines in multiple execution environments.
Apache Spark defines as a fast and general engine for large-scale data processing.
Spark is a fast and general processing engine compatible with Hadoop data.
Apache Beam is classified as a tool in the Workflow Manager category.
While Apache Spark is grouped under Big Data Tools.
Apache Beam (Batch + strEAM) fuses batch and streaming data processing, while others often do so via separate APIs.
It’s very easy to change a streaming process to a batch process and vice versa, say, as requirements change.
One of the main reasons to use Beam is the ability to switch between multiple runners such as –
Apache Spark, Apache Flink, Samza, and Google Cloud Dataflow.
Without Beam, a unified programming model, varied runners have different capabilities, making it difficult to provide a portable API.
Apache Beam raises portability and flexibility.
Apache Beam is based on so-called abstract pipelines that can be run on different executors.
This pipeline includes every stage of processing starting from data fetching, and transformation, and ending with the resulting output.
With these pipelines, Apache Beam hides low-level things like shuffling, repartitioning, etc. from the developer.
Unified – Use a single programming model for both batch and streaming use cases.
Portable – Execute pipelines in multiple execution environments.
Here, execution environments mean different runners. Ex. Spark Runner, Dataflow Runner, etc.
Extensible – Write custom SDKs, IO connectors, and transformation libraries.
Important key concepts
Simply put, a PipelineRunner executes a Pipeline, and a Pipeline consists of PCollection and Ptransform.
PCollection – represents a data set that can be a fixed batch or a stream of data.
PTransform – a data processing operation that takes one or more PCollections and outputs zero or more PCollections.
Pipeline – represents a directed acyclic graph of PCollection and PTransform and encapsulates the entire data processing job.
PipelineRunner – executes a Pipeline on a specified distributed processing backend.
With Beam, batch and streaming are merely two points on a continuum of latency, completeness, and cost.
There is no learning or rewriting cliff when moving from batch to streaming.
So, if you construct a batch pipeline today but your latency requirements change tomorrow, you can easily alter it to streaming within the same API.
The same pipeline may be operated in numerous ways. As data forms and runtime requirements are separated.
That implies there’s no need to rewrite code when migrating from a legacy system to something cutting-edge.
It’s possible to quickly compare choices to discover the optimal combination of environment and performance for current needs.
Apache Spark is an open-source, distributed processing system used for big data workloads.
It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size.
It provides development APIs in Java, Scala, Python, and R, and supports code reuse across multiple workloads—batch processing, interactive queries, real-time analytics, machine learning, and graph processing.
Based on the in-memory MapReduce model evolved from Hadoop MapReduce.
Hadoop MapReduce is a programming model for processing big data sets with a parallel, distributed algorithm.
Developers can write massively parallelized operators, without having to worry about work distribution, and fault tolerance.
With Spark, only one step is needed where data is read into memory, operations performed, and the results are written back—resulting in much faster execution.
Spark comes packed with a wide range of libraries for Machine Learning (ML) algorithms and graph algorithms.
Spark has a fast processing speed. Spark apps can run up to 100x faster in memory and 10x faster on disk in Hadoop clusters.
Easy to use – Spark allows you to write scalable applications in Java, Scala, Python, and R.
Developers get the scope to create and run Spark applications in their preferred programming languages.
Not only does Spark support simple “map” and “reduce” operations. Supports SQL queries, streaming data, and advanced analytics, including ML and graph algorithms.
Spark is designed to handle real-time data streaming.
Spark can run independently in cluster mode, and it can also run on-
Hadoop YARN, Apache Mesos, Kubernetes, and even in the cloud.
The best thing about Apache Spark is, it has a massive Open-source community behind it.
Apache Spark is opening up various opportunities for big data.
Apache Spark comes with the ability to run multiple workloads, including interactive queries, real-time analytics, machine learning, and graph processing.
One application can combine multiple workloads seamlessly.
Apache Spark can handle many analytics challenges because of its low-latency in-memory data processing capability.
It has well-built libraries for graph analytics algorithms and machine learning.
Spark Core – is the underlying general execution engine for the Spark platform that all other functionality is built.
It provides In-Memory computing and referencing datasets in external storage systems.
Spark Streaming – leverages Spark Core’s fast scheduling capability to perform streaming analytics.
It ingests data in mini-batches and performs RDD (Resilient Distributed Datasets) transformations on those mini-batches of data.
The machine learning library – is a distributed machine learning framework above Spark because of the distributed memory-based Spark architecture.
GraphX- is a distributed graph-processing framework on top of Spark. It provides an API for expressing graph computation that can model the user-defined graphs by using the Pregel abstraction API.
Hence, Spark and Beam are attempting to solve the same issue and the differences between them are small i.e.
Apache Beam looks more like a framework as it abstracts the complexity of processing and hides technical details, and Spark is the technology where you literally need to dive deeper.
Spark helps to simplify the challenging and computationally intensive task of processing high volumes of real-time or archived data, both structured and unstructured, seamlessly integrating relevant complex capabilities such as machine learning and graph algorithms.