Software Alternatives, Accelerators & Startups

Google BigQuery VS RegExr

Compare Google BigQuery VS RegExr and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Google BigQuery logo Google BigQuery

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

RegExr logo RegExr

RegExr.com is an online tool to learn, build, and test Regular Expressions.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • RegExr Landing page
    Landing page //
    2023-07-28

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.

RegExr features and specs

  • User-Friendly Interface
    RegExr offers an intuitive and visually appealing interface that makes it easy for users to write, test, and understand regular expressions.
  • Real-time Feedback
    Changes to the regular expression and input text are reflected immediately, allowing users to see the effects of their adjustments in real-time.
  • Built-in Cheatsheet
    RegExr includes a handy cheatsheet that provides quick access to common regex patterns and syntax, making it easier for users to learn and reference rules.
  • Community Examples
    Users can explore and share community-generated regex patterns, which can serve as valuable examples or starting points for creating their own regex.
  • Detailed Explanation
    Each part of the regex pattern can be hovered over to display detailed tooltips explaining its function, aiding in the understanding of complex expressions.
  • Cross-Platform Accessibility
    As a web-based tool, RegExr can be accessed from any modern browser without the need for installation, making it convenient to use on multiple devices.

Possible disadvantages of RegExr

  • Limited Offline Use
    Since RegExr is a web-based application, it requires an internet connection, limiting its utility for users who need to work offline.
  • Learning Curve
    While the tool is user-friendly, users still need to have a foundational understanding of regular expressions to use RegExr effectively.
  • Performance Issues
    For extremely large inputs or very complex regular expressions, the tool may experience performance lags or slowdowns.
  • Limited Advanced Features
    RegExr may lack some advanced features found in more specialized or professional regex tools, such as integration with development environments or extensive scripting capabilities.
  • Privacy Concerns
    Users inputting sensitive data need to be cautious, as the web-based nature of the tool could raise privacy or data security concerns.

Google BigQuery videos

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

RegExr videos

No RegExr videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Google BigQuery and RegExr)
Data Dashboard
100 100%
0% 0
Programming Tools
0 0%
100% 100
Big Data
100 100%
0% 0
Regular Expressions
0 0%
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 Google BigQuery and RegExr

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...
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.

RegExr Reviews

We have no reviews of RegExr yet.
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Social recommendations and mentions

Based on our record, RegExr should be more popular than Google BigQuery. It has been mentiond 367 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 (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 / 21 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 / 26 days 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 / 3 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

RegExr mentions (367)

  • The importance of the environment in Regex pattern matching
    However - here it becomes weird - when testing the original regex rule (the first one, without the \u00A0 part) on the same string in an interactive visualiser (https://regexr.com/ for instance), there is a match:. - Source: dev.to / 7 months ago
  • Ask HN: How did you learn Regex?
    Learned regex in the 90's from the Perl documentation, or possibly one of the oreilly perl references. That was a time where printed language references were more convenient than searching the internet. Perl still includes a shell component for accessing it's documentation, that was invaluable in those ancient times. Perl's regex documentation is rather fantastic. `perldoc perlre` from your terminal. Or... - Source: Hacker News / 9 months ago
  • Ask HN: How did you learn Regex?
    I read a lot on https://www.regular-expressions.info and experimented on https://rubular.com since I was also learning Ruby at the time. https://regexr.com is another good tool that breaks down your regex and matches. One of the things I remember being difficult at the beginning was the subtle differences between implementations, like `^` meaning "beginning of line" in Ruby (and others) but meaning "beginning of... - Source: Hacker News / 9 months ago
  • Ask HN: How did you learn Regex?
    Mostly building things that needed complex RegEx, and debugging my regular expressions with https://regexr.com/. - Source: Hacker News / 9 months ago
  • Form Validation In TypeScipt Projects Using Zod and React Hook Form
    For username: You are using the min() function to make sure the characters are not below three and, then the max() function checks that the characters are not beyond twenty-five. You also make use of Regex to make sure the username must contain only letters, numbers, and underscore. - Source: dev.to / 10 months ago
View more

What are some alternatives?

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

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

regular expressions 101 - Extensive regex tester and debugger with highlighting for PHP, PCRE, Python and JavaScript.

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.

rubular - A ruby based regular expression editor

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.

Expresso - The award-winning Expresso editor is equally suitable as a teaching tool for the beginning user of regular expressions or as a full-featured development environment for the experienced programmer with an extensive knowledge of regular expressions.