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Google BigQuery VS AWS CloudFormation

Compare Google BigQuery VS AWS CloudFormation and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.

AWS CloudFormation logo AWS CloudFormation

AWS CloudFormation gives developers and systems administrators an easy way to create and manage a...
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • AWS CloudFormation Landing page
    Landing page //
    2023-03-22

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

AWS CloudFormation features and specs

  • Infrastructure as Code
    CloudFormation allows you to define your infrastructure using code or templates, promoting version control, reviewability, and collaborative planning.
  • Automated Provisioning
    It automates the provisioning and updating of infrastructure, reducing the manual intervention required and minimizing human errors.
  • Consistency and Repeatability
    Ensures consistent configurations by deploying the same template multiple times across different environments, eliminating configuration drift.
  • Integration with Other AWS Services
    Tightly integrated with other AWS services, allowing for comprehensive infrastructure management, security policies, monitoring and logging.
  • Scalability and Flexibility
    Facilitates easy scaling and modifying of resources according to the application requirements without significant downtime.

Possible disadvantages of AWS CloudFormation

  • Complexity
    Large templates can become complex and difficult to manage, making troubleshooting and updating challenging.
  • Learning Curve
    Requires time and effort to learn and master, especially for newcomers to AWS or Infrastructure as Code (IaC) concepts.
  • Limited Cross-Platform Support
    Primarily tailored for AWS services, with limited support for managing infrastructure on other cloud platforms.
  • State Management
    Managing the state of your infrastructure can be complex, as creating or updating resources is highly dependent on the current state of your stack.
  • Debugging Issues
    Error messages and stack traces can sometimes be cryptic, making it difficult to pinpoint the exact cause of deployment failures.

Analysis of Google BigQuery

Overall verdict

  • Google BigQuery is a powerful and flexible data warehouse solution that suits a wide range of data analytics needs. Its ability to handle large volumes of data quickly makes it a preferred choice for organizations looking to leverage their data effectively.

Why this product is good

  • Google BigQuery is a fully-managed data warehouse that simplifies the analysis of large datasets. It is known for its scalability, speed, and integration with other Google Cloud services. It supports standard SQL, has built-in machine learning capabilities, and allows for seamless data integration from various sources. The serverless architecture means that users don't need to worry about infrastructure management, and its pay-as-you-go model provides cost efficiency.

Recommended for

  • Businesses requiring fast processing of large datasets
  • Organizations that already utilize Google Cloud services
  • Companies looking for a cost-effective, scalable analytics solution
  • Teams interested in using SQL for data analysis
  • Data scientists integrating machine learning with their data workflows

Analysis of AWS CloudFormation

Overall verdict

  • Good

Why this product is good

  • AWS CloudFormation can be a powerful tool for managing infrastructure as code, allowing you to model and set up your Amazon Web Services resources so that you can spend less time managing those resources and more time focusing on your applications. It provides a consistent, repeatable process for provisioning infrastructure, improves change management, enhances resource tracking, and reduces the possibility of human errors. Additionally, it integrates seamlessly with other AWS services and enables the deployment of infrastructure through code, which can be version controlled, tested, and automated.

Recommended for

  • Organizations that heavily utilize AWS services and wish to manage resources through a codified approach
  • Software teams that implement CI/CD pipelines and require infrastructure code to be included in those pipelines
  • DevOps teams striving for automation, consistency, and scalability in their cloud infrastructure management
  • Developers and IT professionals who need to manage complex infrastructures or regularly spin up and tear down environments

Google BigQuery videos

+ Add

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

AWS CloudFormation videos

+ Add

What is AWS Cloudformation? Pros and Cons?

More videos:

  • Demo - AWS CloudFormation Tutorial | AWS CloudFormation Demo | AWS Tutorial | AWS Training | Edureka
  • Tutorial - AWS CloudFormation Template Tutorial

Category Popularity

0-100% (relative to Google BigQuery and AWS CloudFormation)

Google BigQuery

AWS CloudFormation

Data Dashboard
100 100%
Data Dashboard
0% 0
DevOps Tools
0 0%
DevOps Tools
100% 100
Big Data
100 100%
Big Data
0% 0
Continuous Integration
0 0%
Continuous Integration
100% 100

User comments

Share your experience with using Google BigQuery and AWS CloudFormation. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Google BigQuery and AWS CloudFormation

Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQueryโ€™s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or โ€œheavyโ€ queries that operate using a large set of data. This means itโ€™s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io

AWS CloudFormation Reviews

5 Best DevSecOps Tools in 2023
There are multiple providers for Infrastructure as Code such as AWS CloudFormation, RedHat Ansible, HashiCorp Terraform, Puppet, Chef, and others. It is advised to research each to determine what is best for any given situation since each has pros and cons. Some of these also are not completely free while others are. There are also some that are specific to a particular...
Source: devopsauthority.tech
Do not use AWS CloudFormation
CloudFormation being a layer of indirection makes it difficult to work with in multi-region/multi-account scenarios. With CloudFormation you have to create Stack Sets and IAM policies that allow the CloudFormation service to impersonate other roles. The prerequisite steps you have to take to use CloudFormation across multiple accounts also must be taken just to have...
Source: gswallow.medium.com
Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation
Of course, there are downsides to declarative languages too. Without access to a full programming language, your expressive power is limited. For example, some types of infrastructure changes, such as a rolling, zero-downtime deployment, are hard to express in purely declarative terms. Similarly, without the ability to do โ€œlogicโ€ (e.g. if-statements, loops), creating...
Source: blog.gruntwork.io

Social recommendations and mentions

Based on our record, AWS CloudFormation should be more popular than Google BigQuery. It has been mentiond 129 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ€” we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 3 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 4 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 5 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโ€”while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 7 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 8 months ago
View more

AWS CloudFormation mentions (129)

  • Dynamic Looping Comes to AWS SAM
    AWS SAM CLI, the command-line tool for building and deploying serverless applications, now supports AWS CloudFormation Language Extensions. The one I am most excited about is Fn::ForEach, which brings dynamic looping to your YAML templates, but it's close. If you, like me, have been copy-pasting resource definitions to infinity, that stops today. - Source: dev.to / about 2 months ago
  • AWS CloudFormation Drift Detection & Remediation Guide
    AWS CloudFormation is an IaC service that helps users automate, scale, and manage their environments efficiently. On the other hand, GitOps has become one of the standard ways of ensuring the IaC configuration stored in code repositories is deployed live on the correct systems. - Source: dev.to / 6 months ago
  • Announcing AWS CDK Mixins: Composable Abstractions for AWS Resources
    The AWS Cloud Development Kit (CDK) is an open-source software development framework for defining cloud infrastructure in code and provisioning it through AWS CloudFormation. It contains pre-written modular and reusable cloud components known as constructs. Constructs are the basic building blocks representing one or more AWS CloudFormation resources and their configuration. - Source: dev.to / 8 months ago
  • Top 12 Puppet Alternatives for Automation
    Website: https://aws.amazon.com/cloudformation/. - Source: dev.to / 8 months ago
  • From Code to Cloud in Minutes: How AWS Amplify Supercharges Modern App Development
    When you deploy a cloud sandbox, Amplify creates an AWSโ€ฏCloudFormation stack following the naming convention of amplify--<$(whoami)>-sandbox in your AWS account with the resources configured in your amplify/ folder. - Source: dev.to / about 1 year ago
View more

What are some alternatives?

When comparing Google BigQuery and AWS CloudFormation, you can also consider the following products

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?

AWS Lambda - Automatic, event-driven compute service

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Codeship - Codeship is a fast and secure hosted Continuous Delivery platform that scales with your needs.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

Bamboo - Bamboo is a continuous integration and deployment tool that ties automated builds, tests and releases together in a single workflow.

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