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AWS Cloud9 VS Google BigQuery

Compare AWS Cloud9 VS Google BigQuery and see what are their differences

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

AWS Cloud9 logo AWS Cloud9

AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser.

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • AWS Cloud9 Landing page
    Landing page //
    2023-04-23
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

AWS Cloud9 features and specs

  • Integrated Development Environment
    AWS Cloud9 provides a set of tools for coding, running, and debugging applications, making the development process more efficient.
  • Collaboration
    Real-time collaboration features enable multiple developers to work on the same project simultaneously, making teamwork easier.
  • Preconfigured Workspaces
    Preconfigured environments speed up the setup process, allowing developers to start coding immediately without worrying about configuration.
  • Serverless Development
    Supports serverless apps and provides seamless integration with AWS Lambda, helping developers build modern applications.
  • Remote Development
    Enables development from any location without the need for a powerful local machine, as the IDE runs in the cloud.
  • Cost Management
    Cloud9 uses pay-as-you-go pricing, potentially reducing costs compared to maintaining and upgrading local development environments.

Possible disadvantages of AWS Cloud9

  • Internet Dependency
    Requires an internet connection to access, which can be a limitation in areas with unstable or no internet access.
  • Resource Limitations
    Dependent on the allocated AWS resources, which may require scaling and can incur additional costs for high usage.
  • Latency Issues
    Potential latency issues could affect productivity, particularly when used over slower internet connections.
  • Learning Curve
    Users unfamiliar with cloud-based IDEs or the AWS ecosystem may require time to learn how to effectively use Cloud9.
  • Vendor Lock-In
    Being tightly integrated with AWS services, it may contribute to vendor lock-in, making it harder to switch to other cloud providers.
  • Cost Management Complexity
    The pay-as-you-go model can lead to unexpected costs if resource usage is not closely monitored and managed.

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 Cloud9 videos

+ Add

Introducing AWS Cloud9 - AWS Online Tech Talks

More videos:

  • Review - Introduction to AWS Cloud9

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

Category Popularity

0-100% (relative to AWS Cloud9 and Google BigQuery)

AWS Cloud9

Google BigQuery

IDE
100 100%
IDE
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Text Editors
100 100%
Text Editors
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare AWS Cloud9 and Google BigQuery

AWS Cloud9 Reviews

8 Best Replit Alternatives & Competitors in 2022 (Free & Paid) - Software Discover
AWS cloud9 is a cloud-based integrated development environment (Ide) That lets you write, run, and debug your code with just a browser. AWS cloud9 amazon web services.
Source: www.softwarediscover.com
Top 10 Visual Studio Alternatives
AWS Cloud9 is a cloud-based coordinated advancement system. It is a server that allows the users to type, initiate or operate and repair the code by only using the browser. It contains an editor program for code, error-removing system, and endpoint. Cloud9 has all the important tools for general programming languages, that includes,
Source: blog.back4app.com
12 Best Online IDE and Code Editors to Develop Web Applications
There are no additional charges for using Cloud9. You can connect Cloud9 to an existing/new AWS compute instance, and you pay only for that instance. It’s also possible to connect to a third-party server over SSH — for exactly no fee! 🙂
Source: geekflare.com
Ruby IDE: The 9 Best IDEs for Ruby on Rails Development
Here we are talking about a different animal all together – Cloud9. Cloud9 offers development environment for almost all programming languages including Ruby. Cloud9 is fast becoming popular among medium to large enterprises and companies like Heroku, Soundcloud, Mailchimp and Mozilla etc. are already using Cloud9.
Source: noeticforce.com

Google BigQuery Reviews

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 BigQuery’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 “heavy” queries that operate using a large set of data. This means it’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
16 Top Big Data Analytics Tools You Should Know About
Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service that supports querying using ANSI SQL. It also has built-in machine learning capabilities.
Source: www.analytixlabs.co.in

Social recommendations and mentions

Google BigQuery might be a bit more popular than AWS Cloud9. We know about 42 links to it since March 2021 and only 39 links to AWS Cloud9. 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.

AWS Cloud9 mentions (39)

  • Serverless Data Processing on AWS : AWS Project
    AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser. It includes a code editor, debugger, and terminal. Cloud9 comes pre-packaged with essential tools for popular programming languages and the AWS Command Line Interface (CLI) pre-installed so you don’t need to install files or configure your laptop for this workshop. Your Cloud9... - Source: dev.to / 6 months ago
  • Codespaces but open-source, client-only, and unopinionated
    AWS has Cloud9[1] though it's worth pointing out that it's not an exact a 1:1 and may require some elbow grease to use in the same manner[2]. 1. https://aws.amazon.com/cloud9/ 2. https://aws.amazon.com/blogs/architecture/field-notes-use-aws-cloud9-to-power-your-visual-studio-code-ide/ (2021). - Source: Hacker News / almost 2 years ago
  • How does working with files through AWS work, do you save them onto the AWS console?
    If you just want to run an IDE for Python in the cloud, take a look at AWS Cloud9 (that would cost something however). You could get your code into AWS and sync your local changes using a source code repository, e.g. On GitHub or GitLab. Source: about 2 years ago
  • Best web-based IDEs?
    Not sure why you won't use replit but AWS has Cloud9 https://aws.amazon.com/cloud9/. Source: about 2 years ago
  • Taking my AWS CCP Exam today, any additional notes help, feel pretty good about the information I’ve reviewed, but please feel free to drop advice or notes.
    As I mentioned in a previous post, cloud9 was not in the course I was studying from, and not in the practice exams I solved. It came in my exam. Https://aws.amazon.com/cloud9/. Source: over 2 years ago
View more

Google BigQuery mentions (42)

  • Every Database Will Support Iceberg — Here's Why
    This isn’t hypothetical. It’s already happening. Snowflake supports reading and writing Iceberg. Databricks added Iceberg interoperability via Unity Catalog. Redshift and BigQuery are working toward it. - Source: dev.to / 30 days ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    Many of these companies first tried achieving real-time results with batch systems like Snowflake or BigQuery. But they quickly found that even five-minute batch intervals weren't fast enough for today's event-driven needs. They turn to RisingWave for its simplicity, low operational burden, and easy integration with their existing PostgreSQL-based infrastructure. - Source: dev.to / about 1 month ago
  • How to Pitch Your Boss to Adopt Apache Iceberg?
    If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / about 1 month ago
  • Study Notes 2.2.7: Managing Schedules and Backfills with BigQuery in Kestra
    BigQuery Documentation: Google Cloud BigQuery. - Source: dev.to / 4 months ago
  • Docker vs. Kubernetes: Which Is Right for Your DevOps Pipeline?
    Pro Tip: Use Kubernetes operators to extend its functionality for specific cloud services like AWS RDS or GCP BigQuery. - Source: dev.to / 6 months ago
View more

What are some alternatives?

When comparing AWS Cloud9 and Google BigQuery, you can also consider the following products

Codeanywhere - Codeanywhere is a complete toolset for web development. Enabling you to edit, collaborate and run your projects from any device.

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

Koding - A new way for developers to work.

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

Netbeans - NetBeans IDE 7.0. Develop desktop, mobile and web applications with Java, PHP, C/C++ and more. Runs on Windows, Linux, Mac OS X and Solaris. NetBeans IDE is open-source and free.

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.

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