Overview

Apache Spark

Stacks3.0K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs Apache Spark: What are the differences?

Apache Airflow and Apache Spark are both powerful tools used in the data engineering and data analysis domains. While they have some similarities, there are key differences between the two.

Distributed Computing Framework vs Workflow Management: Apache Spark is primarily a fast and general-purpose distributed computing system that provides in-memory processing capabilities. It is designed for big data processing and analytics, enabling processing of large datasets across multiple machines. On the other hand, Apache Airflow is a workflow management platform that allows users to coordinate and schedule tasks in a declarative manner, making it easier to manage complex data pipelines.
Data Processing vs Workflow Orchestration: Apache Spark focuses on data processing and provides a powerful processing engine with support for various data manipulation operations such as transformations, aggregations, and machine learning algorithms. It excels in parallel and distributed data processing using its resilient distributed dataset (RDD) and DataFrame APIs. In contrast, Apache Airflow focuses on workflow orchestration and provides a way to define and manage complex workflows using Directed Acyclic Graphs (DAGs). It allows for task dependency management, scheduling, and monitoring.
Real-time vs Batch Processing: Apache Spark offers both real-time and batch processing capabilities, making it suitable for a variety of use cases. It provides options for stream processing with its Structured Streaming API, allowing for real-time data analysis and processing. Apache Airflow, on the other hand, is more suited for batch processing as it focuses on orchestrating and scheduling tasks at a defined time or interval.
Language Support: Apache Spark has support for multiple programming languages, including Scala, Java, Python, and R. This allows users to develop applications and perform data analysis using their preferred language. In contrast, Apache Airflow is predominantly Python-based, making it a popular choice for Python developers and data engineers.
Built-in Libraries and Ecosystem: Apache Spark comes with a rich ecosystem of libraries and integrations that enhance its capabilities. It provides built-in support for various data formats, machine learning algorithms, graph processing, and more. Apache Airflow, while it has a smaller ecosystem compared to Spark, offers a wide range of operators and integrations for executing different types of tasks and interacting with various systems.
Data Storage and Execution Model: Apache Spark relies on distributed file systems, such as Hadoop Distributed File System (HDFS) or cloud storage systems like Amazon S3 or Azure Blob Storage, to store and process data. It utilizes a distributed computing model where data is partitioned and processed in parallel across a cluster of machines. Apache Airflow does not provide native data storage capabilities and relies on external systems such as databases or cloud storage to store and retrieve data.

In summary, Apache Spark is primarily focused on distributed data processing and provides fast, in-memory data analytics capabilities. It supports real-time and batch processing and has extensive language support and a rich ecosystem of libraries. Apache Airflow, on the other hand, is a workflow management platform that allows for task scheduling, monitoring, and dependency management. It is more suited for orchestrating complex data pipelines and batch processing workflows.

Advice on Apache Spark, Airflow

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.

576k views576k

Comments

Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

294k views294k

Comments

Detailed Comparison

Apache Spark	Airflow
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.	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.
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	Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.0K	Stacks 1.7K
Followers 3.5K	Followers 2.8K
Votes 140	Votes 128
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 Great for distributed SQL like applications 8 One platform for every big data problem 6 Easy to install and to use Cons 4 Speed	Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 10 Extensibility Cons 2 Running it on kubernetes cluster relatively complex 2 Observability is not great when the DAGs exceed 250 2 Open source - provides minimum or no support 1 Logical separation of DAGs is not straight forward

What are some alternatives to Apache Spark, Airflow?

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.