Google BigQuery vs Athena: 7 Critical Differences

on AWS, AWS Athena, Data Warehouse, Google BigQuery • January 21st, 2022 • Write for Hevo

BigQuery vs Athena: Featured Image

Today every organization is moving to serverless cloud offerings to solve many of the data-related challenges. The primary issue these companies face occurs while trying to manage vast data repositories. In such a situation, they are drawn towards feature-rich cloud-based tools. However, businesses are often confused when comparing cloud-based products and services.

The essence of this article is to compare two such cloud-based tools, Google BigQuery and AWS Athena. The article will introduce you to these tools and provide a detailed discussion on BigQuery vs Athena using 7 critical aspects. Read along to decide the best tool for your business!

Table of Content

What is Google BigQuery?

BigQuery vs Athena: BigQuery Logo
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Google BigQuery is a popular cloud-based Data Warehouse that is known for its high-level analytic services that can process massive datasets easily. This serverless platform supports high-speed query processing using SQL can work with billions of rows in one go. Google BigQuery also automates the resource allocation process. Its storage works on a columnar structure that provides for seamless querying and aggregation tasks. This platform also offers data protection that allows you to check the identity and access status of clients.

You can learn more about Google BigQuery here.

What is AWS Athena?

BigQuery vs Athena: Athena Logo
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Amazon Athena is a serverless, interactive query service that makes it convenient for you to easily analyze your data stored in Amazon Simple Storage Service (S3) using standard SQL. Athena is easy to use by simply defining the schema of your data to start querying and does not have any infrastructure therefore, you do not have to manage or set up anything. Athena’s primary purpose is to analyze unstructured, semi-structured, and structured data stored in Amazon S3 meaning it is not a general-purpose database but excels with datasets that are up to petabytes in size. 

Athena can be used to run ad-hoc queries using ANSI SQL without needing to aggregate or load data into Athena. Moreover, you can easily integrate this platform with Amazon QuickSight and create engaging reports using business intelligence tools.

You can learn more about AWS Athena here.

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Comparing Google BigQuery vs Athena

Now you have gotten a general understanding of both BigQuery and Athena, let us analyze what makes both of them unique and different. This will be done by looking at the basics of both ranging from architecture, scalability, performance, data formats/types and sources, ease of use, security, and cost.

You can better understand the Google BigQuery vs Athena discussion using the following 7 parameters:

Google BigQuery vs Athena: Architecture

BigQuery vs Athena: Athena Architecture
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The major thing about cloud data warehousing architecture is if they separate storage and compute and what cloud platform they run on. 

BigQuery: BigQuery has a decoupled storage and computes architecture, it started as an on-demand serverless query engine making it different from the typical data warehouse. It has a petabit network and additional traffic used for transferring and caching data in shared memory over the network using slots. BigQuery supports the Google Cloud infrastructure with multi-tenant on-demand and reserved resources only.

Athena: Athena is built on a decoupled storage and compute architecture but it does not allow ingestion of data or storage. It is on multi-tenant shared resources that allow external writable storage but supports AWS only. 

Google BigQuery vs Athena: Scalability

The effectiveness of the scalability of a data warehouse is based on its decoupled storage and compute units, its dedicated resources to carry out functions, and if it gives room for continuous ingestion.

BigQuery: BigQuery ingests data first and later commits to storage using the automatic allocation of an on-demand model or a reserved and flex slots pattern. It is limited to 100 concurrent users by default, writes scalable batches of up to 1,500 load jobs/day, 100,000 per project, 15TB per job, and 6 hours max time. It also has continuous write scalability of up to 100k rows per second per table, 100k-500k per project by default making data scalability on BigQuery have no real limits.

Athena: Athena is a shared multi-tenant resource and by default supports a maximum of 20 concurrent users. It mostly supports batch-centric storage and data scalability can be up to 100 partitions per table and 100 buckets by default.

Google BigQuery vs Athena: Performance

BigQuery vs Athena: Performance
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The performance of a data warehouse is one of the biggest attributes that a user needs to know before committing to using it. 

BigQuery: BigQuery uses Google’s interactive query system Dremel to run queries and create tables, it is built for running queries on massive datasets that are natively stored in BigQuery. It uses the Jupiter petabit network to make the remote storage access operate fast but, using shared memory over the network for each stage of the query execution in the DAG can adversely affect performance though.

BigQuery does not use indexing rather, it uses slots to process data stored in large segments without going down to smaller ranges, it has a low latency for message-based ingestion by ingesting one row at a time and limits of 100k messages/sec by default making the data immediately available whenever it is required to be queried. 

Athena: Athena uses Presto to run queries and it is built for running queries on smaller single data sources. Athena’s performance is greatly affected by its design as it uses up storage-compute optimization to get support for federated queries across multiple data sources but, it is still very popular among users as its performance is still very efficient notwithstanding the challenge when you know how to manage the external storage. It does not support indexing and has a limited cost-based optimization, as well as separating its storage and ingestion. Athena is not well suited for low latency visibility and uses Apache hive in the creation of tables.

Google BigQuery vs Athena: Data Formats, Types, and sources

This has to do with the data formats and types of data both warehouses support and the sources the data is gotten from.

BigQuery: BigQuery supports several data formats such as CSV, JSON, Avro, Parquet, and ORC. It supports loading from Google Cloud Datastore backups, and it also supports UDFs as a JavaScript function that is called as part of a query. BigQuery data sources can be queried without loading it if it is already stored in BigQuery and external sources can be queried without having to load it but, a table must be created to reference the external data source. You can also stream data from outside the cloud and use BigQuery to run real-time analysis of the data.

Athena: Athena supports several Serializer/Deserializer (SerDe) libraries for parsing data from different data formats such as CSV, JSON, Avro, Logstash log files, Apache log files, CloudTrail log files, Parquet, and ORC. It also supports complex data types like arrays, maps, and structs. Athena’s only data source is data stored in an Amazon S3 bucket as this is the only data that can be queried on Athena.

Google BigQuery vs AWS Athena: Ease of Use

This has to do with the interface and features found on each of the warehouses used for analytics such as reporting, dashboards, and interactive or ad hoc analytics.

BigQuery: BigQuery is easy to use and supports reporting with a fixed view dashboard to enhance creating reports against historical or live data. It supports interactive or ad hoc analysis from sec-min first-time query performance but lacks the performance to handle it at scale. You can export up to 1GB max file size to Google Cloud and access BigQuery ML using BigQuery.

Athena: Athena is a great tool to use especially when you require a query engine for a one-off query as it can quickly pull together multiple data sources into S3 for querying. It supports reporting with a fixed view dashboard and exports query results.

Google BigQuery vs Athena: Security

Another difference to consider is the security used by both BigQuery and Athena to ensure the safety of your data.

BigQuery: BigQuery uses Google Cloud’s Identity and Access Management (IAM) in which users can only assign roles to groups, or service accounts when they have the access to resources. It also encrypts customer data stored by default, makes use of customer-managed encryption-store keys in the cloud for cloud services, uses customer-supplied encryption-store keys for on-premises, and uses them to encrypt cloud services thereby separating customers keys.

Athena: Athena uses Amazon’s Identity Access Management (IAM) for its security and users must have access to the S3 data locations. It only allows data stored in S3 therefore, you can easily query encrypted data and write encrypted results back to your S3 bucket.

Google BigQuery vs Athena: Cost

BigQuery: With BigQuery, you do not need to provision individual instances or virtual machines; it automatically allocates computing resources when needed. It has three different pricing models: on-demand pricing model with which you are charged $5/TB for the number of bytes processed by each query and the first 1 TB of query data processed per month is free, the second model is reserved, and lastly flex pricing with which you purchase slots that are dedicated processing capacity that can be used to run queries for $4 for 100 slots per hour, $1700/month per 100 slots. BigQuery also has pricing for storage, active storage for $20/TB, and $10/TB for inactive storage. For comprehensive information on BigQuery pricing, visit here.

Athena: With Amazon Athena, you only pay for the queries that you run as you are charged based on the amount of data scanned when performing each query, this leads to significant cost savings and performance hence, it is best suited for one-off analytics. Athena charges $5.00 per TB of data scanned and you can greatly reduce your cost by compressing, partitioning, and converting your data into columnar formats. For more information about Athena’s pricing models, visit here.

Conclusion

This write-up has explained the key differences between two cloud data warehousing platforms Google BigQuery and AWS Athena, giving you a comprehensive analysis of the strength of both platforms. It further showed that just as decoupled storage and compute architectures improve scalability, it may become a hindrance to performance as most data warehousing platforms fetch the entire partitions over the network when queried instead of the specific data needed for each query, therefore, affecting overall performance. 

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Ultimately, when trying to choose a cloud solution you will have to base your decision on supporting the needs of your business as both Google BigQuery and AWS Athena provide different functions and different approaches. For a more inclusive approach to handling and executing your data on the cloud without compromising your data or sacrificing performance, you can explore Hevo Data, which can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc. This platform allows you to transfer data from 100+ multiple sources to Cloud-based Data Warehouses like Amazon Redshift, Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.

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Share your understanding of the Google BigQuery vs Athena comparison in the comments below!

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