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QPython 3L VS Google BigQuery

Compare QPython 3L VS Google BigQuery and see what are their differences

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QPython 3L logo QPython 3L

QPython is the Python engine for android. It contains some amazing features such as Python interpreter, runtime environment, editor, QPYI and SL4A library. It makes it easy for you to use Python on Android. QPython 3L is also an open source app.

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • QPython 3L Landing page
    Landing page //
    2020-08-18
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

QPython 3L features and specs

  • Ease of Use
    QPython 3L provides an intuitive interface and does not require complicated setup, making it convenient for beginners to start programming on Android devices.
  • Portable Python Development
    It offers the ability to write and run Python scripts directly on Android devices, allowing for development on the go without the need for a full computer setup.
  • Large Standard Library
    QPython 3L supports a wide range of Python standard libraries, enabling users to perform various tasks without needing to install additional modules.
  • Community Support
    There is a robust community of QPython users and developers who share knowledge, tutorials, and scripts that can help newcomers and seasoned developers alike.
  • Integration with SL4A
    Integration with the Scripting Layer for Android (SL4A) allows QPython scripts to directly interact with Android features, expanding its capabilities beyond typical Python execution.

Possible disadvantages of QPython 3L

  • Performance Limitations
    Running Python scripts on Android devices can be slower compared to running them on a PC due to hardware limitations and the interpreter environment.
  • Limited Third-Party Library Support
    Not all third-party Python libraries are compatible or available for installation on QPython, which can restrict the functionality for certain applications.
  • Platform Constraints
    As QPython 3L is designed for Android, it may not utilize the full potential of Python on desktop platforms and lacks cross-platform integration features.
  • User Interface Limitations
    Developing complex graphical user interfaces can be challenging due to limited support for GUI frameworks compared to desktop Python environments.
  • Dependency Management
    Handling dependencies and package management can be more cumbersome on QPython than in standard Python environments like Anaconda or virtualenv on PC.

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.

QPython 3L videos

+ Add

How to do python programming in Mobile For free (Easy) - using Qpython 3L 2022

More videos:

  • Review - QPython 3L : Python for android.

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 QPython 3L and Google BigQuery)

QPython 3L

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 QPython 3L and Google BigQuery

QPython 3L Reviews

We have no reviews of QPython 3L yet.
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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

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.

QPython 3L mentions (0)

We have not tracked any mentions of QPython 3L yet. Tracking of QPython 3L recommendations started around Mar 2021.

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 / 28 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 QPython 3L and Google BigQuery, you can also consider the following products

PyCharm - Python & Django IDE with intelligent code completion, on-the-fly error checking, quick-fixes, and much more...

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

NOMone Desktop - Linux and VR - Try our desktop experience running entirely on your smartphone/tablet/smart TV. Phone screen is too small, or just want to work from bed? Try our VR mode!

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.

NINJA-IDE - NINJA-IDE (from the recursive acronym: "Ninja-IDE Is Not Just Another IDE"), is a...

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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