Apache Beam vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Beam

Stacks183

Followers361

Votes14

Apache Beam vs Apache Spark: What are the differences?

Apache Beam and Apache Spark are both popular big data processing frameworks used for distributed data processing. Let's discuss the key differences between them.

Data Model: Apache Beam provides a unified programming model that is independent of any specific data processing engine. It allows developers to write data processing logic once and run it on various execution engines such as Apache Spark, Apache Flink, and Google Cloud Dataflow. On the other hand, Apache Spark has its own data model called Resilient Distributed Dataset (RDD), which is a fault-tolerant collection of elements that can be processed in parallel.
Ease of Use: Apache Beam provides a higher-level API and abstracts away the complexities of distributed processing. It offers a simple and consistent programming model which makes it easier for developers to write and maintain code. In contrast, Apache Spark has a steeper learning curve due to its more low-level API and complex execution model.
Flexibility: Apache Beam offers a wider range of options for data sources and sinks compared to Apache Spark. It provides connectors for various data storage systems and streaming platforms, allowing developers to process data from different sources and write the results to different destinations. Apache Spark, on the other hand, has a more limited set of built-in connectors.
Streaming and Batch Processing: Apache Beam is primarily designed with a focus on streaming data processing, although it also supports batch processing. It provides built-in windowing and triggering capabilities for handling event time-based computations. Apache Spark, on the other hand, was originally designed for batch processing but has added streaming capabilities. However, its streaming capabilities are not as advanced as those provided by Apache Beam.
Execution Engine Compatibility: Apache Beam is designed to be portable and run on different execution engines, making it more flexible in terms of deployment options. It can run on Apache Spark, Apache Flink, and Google Cloud Dataflow, among others. Apache Spark, on the other hand, is a standalone big data processing engine and does not have the same level of compatibility with other execution engines.
Ecosystem and Community: Apache Spark has a larger and more mature ecosystem compared to Apache Beam. It has a wide range of libraries, connectors, and tools built around it, making it easier to integrate with other big data technologies. Apache Beam, while growing in popularity, has a smaller ecosystem and community.

In summary, Apache Beam and Apache Spark both provide powerful distributed data processing capabilities, but Apache Beam offers a more flexible and portable programming model, while Apache Spark has a larger ecosystem and more mature streaming capabilities.

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Advice on Apache Spark, Apache Beam

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Apache Spark	Apache Beam
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.	It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	-
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 183
Followers 3.5K	Followers 361
Votes 140	Votes 14
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 5 Cross-platform 5 Open-source 2 Portable 2 Unified batch and stream processing

What are some alternatives to Apache Spark, Apache Beam?

Airflow

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Related Comparisons

Apache Beam vs Apache Spark: What are the differences?

Apache Beam and Apache Spark are both popular big data processing frameworks used for distributed data processing. Let's discuss the key differences between them.

Data Model: Apache Beam provides a unified programming model that is independent of any specific data processing engine. It allows developers to write data processing logic once and run it on various execution engines such as Apache Spark, Apache Flink, and Google Cloud Dataflow. On the other hand, Apache Spark has its own data model called Resilient Distributed Dataset (RDD), which is a fault-tolerant collection of elements that can be processed in parallel.
Ease of Use: Apache Beam provides a higher-level API and abstracts away the complexities of distributed processing. It offers a simple and consistent programming model which makes it easier for developers to write and maintain code. In contrast, Apache Spark has a steeper learning curve due to its more low-level API and complex execution model.
Flexibility: Apache Beam offers a wider range of options for data sources and sinks compared to Apache Spark. It provides connectors for various data storage systems and streaming platforms, allowing developers to process data from different sources and write the results to different destinations. Apache Spark, on the other hand, has a more limited set of built-in connectors.
Streaming and Batch Processing: Apache Beam is primarily designed with a focus on streaming data processing, although it also supports batch processing. It provides built-in windowing and triggering capabilities for handling event time-based computations. Apache Spark, on the other hand, was originally designed for batch processing but has added streaming capabilities. However, its streaming capabilities are not as advanced as those provided by Apache Beam.
Execution Engine Compatibility: Apache Beam is designed to be portable and run on different execution engines, making it more flexible in terms of deployment options. It can run on Apache Spark, Apache Flink, and Google Cloud Dataflow, among others. Apache Spark, on the other hand, is a standalone big data processing engine and does not have the same level of compatibility with other execution engines.
Ecosystem and Community: Apache Spark has a larger and more mature ecosystem compared to Apache Beam. It has a wide range of libraries, connectors, and tools built around it, making it easier to integrate with other big data technologies. Apache Beam, while growing in popularity, has a smaller ecosystem and community.

Apache Beam vs Apache Spark

Overview