Airflow vs Amazon Athena

Overview

Airflow

Stacks1.7K

Followers2.8K

Votes128

Amazon Athena

Stacks521

Followers840

Votes49

Airflow vs Amazon Athena: What are the differences?

Introduction

Apache Airflow and Amazon Athena are both popular tools used in data processing and analysis. However, they differ in several key aspects. Let's explore the key differences between Airflow and Amazon Athena.

Architecture and Purpose: Apache Airflow is a workflow management platform that allows users to define, schedule, and monitor complex data pipelines. It provides a way to programmatically author, schedule, and monitor workflows. On the other hand, Amazon Athena is an interactive query service that allows users to analyze data in Amazon S3 using standard SQL queries. It is primarily used for ad-hoc querying and analysis of data stored in S3.
Data Sources and Formats: Airflow supports a wide range of data sources, including databases (like MySQL, PostgreSQL), cloud services (like Amazon S3, Google Cloud Storage), and more. It also supports various file formats such as CSV, JSON, Parquet, Avro, etc. On the other hand, Amazon Athena specifically focuses on querying data stored in Amazon S3 using standard SQL. It does not support data sources other than S3.
Data Processing Paradigm: Airflow allows users to define and schedule data processing tasks using a Directed Acyclic Graph (DAG) structure. Tasks can be chained together and dependencies can be defined between them. It provides a visual representation of the workflow and allows for easy monitoring and troubleshooting. Amazon Athena, on the other hand, follows a serverless query processing model where queries run on-demand without the need for provisioning or managing any infrastructure.
Query Performance and Cost: Airflow delegates data processing tasks to specific engines or services, such as Apache Spark or Google Cloud Dataproc, which can provide scalable and high-performance query processing capabilities. The performance of Airflow pipelines can be customized based on the chosen engines and resources allocated. On the other hand, Amazon Athena is optimized for querying data stored in Amazon S3 and leverages Presto, a distributed SQL query engine. While Athena provides on-demand query capabilities, the performance and cost efficiency depend on the size and structure of the data being queried.
Data Transformation and Manipulation: Airflow provides a rich set of operators and hooks that allow users to manipulate data and perform transformations as part of their workflows. Users can write custom Python code or use pre-defined operators to perform tasks like filtering, aggregating, joining, etc. Amazon Athena, however, focuses mainly on querying and analysis of data rather than providing extensive data manipulation capabilities. It is more suited for retrieving and analyzing data rather than transforming it.
Integration with Ecosystem and Services: Airflow integrates well with various external tools and services, such as cloud platforms (like Amazon Web Services, Google Cloud Platform), databases, message brokers, and more. It provides out-of-the-box integration with popular services like Spark, Hive, BigQuery, etc. On the other hand, Amazon Athena is tightly integrated with the AWS ecosystem, making it easy to access and analyze data stored in S3. It works seamlessly with other AWS services like AWS Glue, AWS Lambda, AWS CloudTrail, etc.

In summary, Apache Airflow provides a powerful workflow management platform for defining, scheduling, and monitoring complex data pipelines, while Amazon Athena is a serverless SQL query service specifically designed for analyzing data stored in Amazon S3 using SQL queries. The key differences lie in their architecture, data sources and formats supported, data processing paradigms, query performance and cost, data transformation capabilities, and integration with other tools and services.

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Advice on Airflow, Amazon Athena

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

Hi all,

Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?

522k views522k

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

Airflow	Amazon Athena
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.	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.
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
Stacks 1.7K	Stacks 521
Followers 2.8K	Followers 840
Votes 128	Votes 49
Pros & Cons
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	Pros 16 Use SQL to analyze CSV files 8 Glue crawlers gives easy Data catalogue 7 Cheap 6 Query all my data without running servers 24x7 4 No data base servers yay
Integrations
No integrations available	Amazon S3 Presto

What are some alternatives to Airflow, Amazon Athena?

Apache Spark

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.

Presto

Distributed SQL Query Engine for Big Data

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.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Related Comparisons

Airflow vs Amazon Athena: What are the differences?

Introduction

Architecture and Purpose: Apache Airflow is a workflow management platform that allows users to define, schedule, and monitor complex data pipelines. It provides a way to programmatically author, schedule, and monitor workflows. On the other hand, Amazon Athena is an interactive query service that allows users to analyze data in Amazon S3 using standard SQL queries. It is primarily used for ad-hoc querying and analysis of data stored in S3.
Data Sources and Formats: Airflow supports a wide range of data sources, including databases (like MySQL, PostgreSQL), cloud services (like Amazon S3, Google Cloud Storage), and more. It also supports various file formats such as CSV, JSON, Parquet, Avro, etc. On the other hand, Amazon Athena specifically focuses on querying data stored in Amazon S3 using standard SQL. It does not support data sources other than S3.
Data Processing Paradigm: Airflow allows users to define and schedule data processing tasks using a Directed Acyclic Graph (DAG) structure. Tasks can be chained together and dependencies can be defined between them. It provides a visual representation of the workflow and allows for easy monitoring and troubleshooting. Amazon Athena, on the other hand, follows a serverless query processing model where queries run on-demand without the need for provisioning or managing any infrastructure.
Query Performance and Cost: Airflow delegates data processing tasks to specific engines or services, such as Apache Spark or Google Cloud Dataproc, which can provide scalable and high-performance query processing capabilities. The performance of Airflow pipelines can be customized based on the chosen engines and resources allocated. On the other hand, Amazon Athena is optimized for querying data stored in Amazon S3 and leverages Presto, a distributed SQL query engine. While Athena provides on-demand query capabilities, the performance and cost efficiency depend on the size and structure of the data being queried.
Data Transformation and Manipulation: Airflow provides a rich set of operators and hooks that allow users to manipulate data and perform transformations as part of their workflows. Users can write custom Python code or use pre-defined operators to perform tasks like filtering, aggregating, joining, etc. Amazon Athena, however, focuses mainly on querying and analysis of data rather than providing extensive data manipulation capabilities. It is more suited for retrieving and analyzing data rather than transforming it.
Integration with Ecosystem and Services: Airflow integrates well with various external tools and services, such as cloud platforms (like Amazon Web Services, Google Cloud Platform), databases, message brokers, and more. It provides out-of-the-box integration with popular services like Spark, Hive, BigQuery, etc. On the other hand, Amazon Athena is tightly integrated with the AWS ecosystem, making it easy to access and analyze data stored in S3. It works seamlessly with other AWS services like AWS Glue, AWS Lambda, AWS CloudTrail, etc.

Airflow vs Amazon Athena

Overview