Airflow vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs Google BigQuery: What are the differences?

Introduction

Airflow and Google BigQuery are both powerful tools in the field of data processing and analysis. However, they have some key differences that set them apart from each other. In this article, we will explore and highlight the main differences between them.

Data Processing Methodology: Airflow is a platform used for orchestrating and managing complex workflows. It allows users to define and manage workflows as directed acyclic graphs (DAGs). Each task in the DAG represents a specific operation or process.

On the other hand, Google BigQuery is a fully-managed serverless data warehouse and analytics platform. It provides an SQL-like interface for querying and analyzing large datasets.

Use Case: Airflow is primarily used for data pipeline orchestration and scheduling of workflows. It enables users to create complex workflows that involve multiple tasks and dependencies. It provides a flexible and extensible framework for managing data processing pipelines.

Google BigQuery, on the other hand, is designed for ad-hoc querying and analysis of large volumes of data. It is optimized for handling big data analytics and can handle massive datasets with high performance.

Data Storage: Airflow does not provide its own data storage system. It relies on other tools or services to store and process data. It can work with various data storage systems such as Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage, and more.

Google BigQuery, on the other hand, provides its own data storage system. It uses a columnar storage format called Capacitor for efficient data storage and retrieval. Users can directly load data into BigQuery tables and perform analytics on them.

Pricing Model: Airflow is an open-source platform and is free to use. However, deploying and managing Airflow infrastructure may incur costs depending on the chosen hosting solution.

Google BigQuery follows a different pricing model. It charges users based on the amount of data processed and the amount of data stored. The pricing structure includes factors such as data storage, query costs, and streaming inserts.

Integration with other Tools: Airflow has extensive integration capabilities with various tools and services. It provides built-in integrations with popular tools such as Apache Spark, Hadoop, Snowflake, and more. It also supports custom integration through Operator and Hooks.

Google BigQuery integrates seamlessly with other Google Cloud Platform (GCP) services such as Google Cloud Storage, Cloud Dataflow, and Cloud Dataproc. It allows users to leverage the power of other GCP services for data processing and analytics.

Scalability and Performance: Airflow provides scalability by allowing users to distribute and parallelize tasks across multiple workers or nodes. However, the scalability is dependent on the infrastructure and resources available.

Google BigQuery, being a fully-managed service, automatically handles the scalability and performance aspects. It can handle massive datasets and provides high-performance querying and analytics capabilities.

In summary, Airflow is a workflow management platform used for orchestrating and managing complex data processing pipelines, while Google BigQuery is a fully-managed data warehouse and analytics platform designed for ad-hoc querying and analysis of large datasets. Airflow requires separate storage systems, has a different pricing model, offers extensive integration capabilities, and requires manual scalability management, whereas BigQuery provides its own data storage, follows a different pricing model, integrates seamlessly with other GCP services, and automatically handles scalability and performance.

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Advice on Google BigQuery, Airflow

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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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

Google BigQuery	Airflow
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.	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.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	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 1.8K	Stacks 1.7K
Followers 1.5K	Followers 2.8K
Votes 152	Votes 128
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 10 Extensibility Cons 2 Observability is not great when the DAGs exceed 250 2 Open source - provides minimum or no support 2 Running it on kubernetes cluster relatively complex 1 Logical separation of DAGs is not straight forward
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	No integrations available

What are some alternatives to Google BigQuery, Airflow?

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

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.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Airflow vs Google BigQuery: What are the differences?

Introduction

Data Processing Methodology: Airflow is a platform used for orchestrating and managing complex workflows. It allows users to define and manage workflows as directed acyclic graphs (DAGs). Each task in the DAG represents a specific operation or process.

On the other hand, Google BigQuery is a fully-managed serverless data warehouse and analytics platform. It provides an SQL-like interface for querying and analyzing large datasets.

Use Case: Airflow is primarily used for data pipeline orchestration and scheduling of workflows. It enables users to create complex workflows that involve multiple tasks and dependencies. It provides a flexible and extensible framework for managing data processing pipelines.

Data Storage: Airflow does not provide its own data storage system. It relies on other tools or services to store and process data. It can work with various data storage systems such as Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage, and more.

Pricing Model: Airflow is an open-source platform and is free to use. However, deploying and managing Airflow infrastructure may incur costs depending on the chosen hosting solution.

Integration with other Tools: Airflow has extensive integration capabilities with various tools and services. It provides built-in integrations with popular tools such as Apache Spark, Hadoop, Snowflake, and more. It also supports custom integration through Operator and Hooks.

Scalability and Performance: Airflow provides scalability by allowing users to distribute and parallelize tasks across multiple workers or nodes. However, the scalability is dependent on the infrastructure and resources available.

Airflow vs Google BigQuery

Overview

Airflow vs Google BigQuery: What are the differences?