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Google BigQuery VS nteract

Compare Google BigQuery VS nteract and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Google BigQuery logo Google BigQuery

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

nteract logo nteract

nteract is a desktop application that allows you to develop rich documents that contain prose...
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • nteract Landing page
    Landing page //
    2022-06-29

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.

nteract features and specs

  • Ease of Use
    nteract offers a user-friendly interface that is simple to set up and use, making it accessible to both beginners and experienced users in data science environments.
  • Interactivity
    The tool provides an interactive experience for running live code, displaying text, and visualizing data efficiently within a single notebook interface.
  • Multi-language Support
    nteract supports multiple programming languages, thanks to Jupyter kernels, which allows flexibility and integration within various data science workflows.
  • Open Source
    Being open source, nteract encourages community contributions and improvements, offering a level of transparency and customization to its users.
  • Extensibility
    The presence of numerous plugins and extensions enables users to enhance the functionality of nteract based on their specific requirements.

Possible disadvantages of nteract

  • Dependency Management
    Managing dependencies can be complex, as users need to handle different libraries and packages to ensure compatibility within their projects.
  • Limited Advanced Features
    Compared to other IDEs, nteract may lack some advanced features required by professional developers for large, intricate projects.
  • Performance Issues
    nteract may experience performance issues when managing large datasets or complex computations due to the resource-intensive nature of notebooks.
  • Learning Curve for Extensions
    While extensibility is a pro, understanding and integrating numerous plugins and extensions can present a learning curve for new users.
  • Community and Documentation
    Although growing, the nteract community and available documentation might not be as extensive as more established platforms like Jupyter Notebook.

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

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

nteract videos

+ Add

nteract weekly August 16, 2018

More videos:

  • Review - nteract weekly November 5, 2018
  • Review - nteract weekly October 1, 2018

Category Popularity

0-100% (relative to Google BigQuery and nteract)

Google BigQuery

nteract

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
100% 100
Big Data
100 100%
Big Data
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
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 nteract

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

nteract Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io

Social recommendations and mentions

Based on our record, Google BigQuery seems to be a lot more popular than nteract. While we know about 42 links to Google BigQuery, we've tracked only 4 mentions of nteract. 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 / about 1 month 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 2 months 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 2 months 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 / 7 months ago
View more

nteract mentions (4)

  • Best Python IDEs for Data Science!
    At the same time that already established and widely used IDEs like RStudio are renewed and provide support for new languages, other solutions appear almost out of nowhere and are adopted by the market as is the case of nteract, an open-source project to be the next interactive development experience adopted by Netflix, in practice it has support for Python, node.JS, R, Julia, C ++, Scala and .NET, in addition to... - Source: dev.to / over 3 years ago
  • Python IDE similar to Jupyter Notebook but not web based?
    Sounds like you're looking for nteract. Source: about 4 years ago
  • Installing Jupyter Notebook
    If you reach infuriation levels you can always cop out and use https://nteract.io/ Ultimately I would suggest jupyterlab over jupyter. Source: about 4 years ago
  • How to open .ipynb files with Jupyter Notebook by double-clicking from windows explorer?
    You can also try the software nteract (https://nteract.io). Source: about 4 years ago

What are some alternatives?

When comparing Google BigQuery and nteract, 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?

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.

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.

iPython - iPython provides a rich toolkit to help you make the most out of using Python interactively.

BeakerX - Open Source Polyglot Data Science Tool

Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)

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