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Google BigQuery VS MATLAB

Compare Google BigQuery VS MATLAB 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.

MATLAB logo MATLAB

A high-level language and interactive environment for numerical computation, visualization, and programming
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • MATLAB Landing page
    Landing page //
    2022-10-30

We recommend LibHunt MATLAB for discovery and comparisons of trending MATLAB projects.

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.

MATLAB features and specs

  • Versatility
    MATLAB is versatile and can be used across a wide range of applications, including engineering, data analysis, robotics, and image processing.
  • Built-in Functions
    MATLAB comes with a vast library of built-in functions and toolboxes that simplify complex mathematical computations and data visualization tasks.
  • User-Friendly Interface
    The software offers an intuitive and user-friendly graphical interface that makes it accessible even for those who are not experts in programming.
  • Excellent Visualization
    MATLAB provides high-quality, customizable plots and graphs that facilitate the clear and effective presentation of data.
  • Strong Community and Support
    Users can benefit from extensive documentation, community forums, and customer support from MathWorks, which aids in troubleshooting and learning.
  • Integration Capabilities
    MATLAB integrates well with other programming languages like C, C++, and Java, and supports interfaces to SQL databases.

Possible disadvantages of MATLAB

  • Cost
    MATLAB is expensive to license, making it less accessible for small businesses, individual professionals, and students without institutional access.
  • Memory Usage
    MATLAB can be very memory-intensive, which could be a limitation when dealing with large datasets or running on devices with limited computational resources.
  • Speed
    Although MATLAB is efficient for rapid prototyping, it is generally slower in execution speed compared to compiled languages like C or Fortran, particularly for heavy computations.
  • Proprietary Nature
    Being a proprietary software, MATLAB does not offer the same level of transparency and flexibility that open-source alternatives provide.
  • Learning Curve
    For some new users, especially those who have no prior experience with numerical computing environments, it might have a steep learning curve.
  • Limited Cross-Platform Compatibility
    While MATLAB supports multiple operating systems, not all features and toolboxes are available on each platform, potentially limiting its utility in diverse environments.

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

MATLAB videos

Matlab Review Part 1

More videos:

  • Review - The Complete MATLAB Course: Beginner to Advanced!
  • Tutorial - Complete MATLAB Tutorial for Beginners

Category Popularity

0-100% (relative to Google BigQuery and MATLAB)
Data Dashboard
100 100%
0% 0
Technical Computing
0 0%
100% 100
Big Data
100 100%
0% 0
Numerical Computation
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 MATLAB

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.

MATLAB Reviews

25 Best Statistical Analysis Software
MATLAB is an exceptional choice for individuals seeking to perform advanced statistical analysis and data visualization. Its high-level programming environment and comprehensive range of tools enable users to efficiently process, analyze, and visualize their data.
7 Best MATLAB alternatives for Linux
MATLAB is a programming language and numeric computing environment. It is used for solving mathematical problems and displaying the result graphically. MATLAB is a paid tool, they provide a free trial for one month.
15 data science tools to consider using in 2021
Developed and sold by software vendor MathWorks since 1984, Matlab is a high-level programming language and analytics environment for numerical computing, mathematical modeling and data visualization. It's primarily used by conventional engineers and scientists to analyze data, design algorithms and develop embedded systems for wireless communications, industrial control,...
The 16 Best Data Science and Machine Learning Platforms for 2021
Description: MathWorks MATLAB combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, tested, and...
Matlab Alternatives
Matrix Laboratory also known as MATLAB is a high-level programming language. It provides an interactive environment to perform computations in various fields such as mathematics, sciences and engineering streams. The results can be visualized and generated as reports for further analysis. Matlab is the pioneer in combining these things. A team of professionals develop the...
Source: www.educba.com

Social recommendations and mentions

Based on our record, Google BigQuery seems to be more popular. It has been mentiond 42 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 / 11 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 / 16 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 / 23 days 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

MATLAB mentions (0)

We have not tracked any mentions of MATLAB yet. Tracking of MATLAB recommendations started around Mar 2021.

What are some alternatives?

When comparing Google BigQuery and MATLAB, 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?

Wolfram Mathematica - Mathematica has characterized the cutting edge in specialized processing—and gave the chief calculation environment to a large number of pioneers, instructors, understudies, and others around the globe.

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.

GNU Octave - GNU Octave is a programming language for scientific computing.

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.

Scilab - Scilab Official Website. Enter your search in the box aboveAbout ScilabScilab is free and open source software for numerical . Thanks for downloading Scilab!