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Google BigQuery VS ML.NET

Compare Google BigQuery VS ML.NET and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Google BigQuery logo Google BigQuery

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

ML.NET logo ML.NET

Machine Learning framework by Microsoft in .net framework and C#.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • ML.NET Landing page
    Landing page //
    2023-03-01

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.

ML.NET features and specs

  • Integration with .NET Ecosystem
    ML.NET allows seamless integration with the existing .NET ecosystem, leveraging the familiarity and resources available in .NET libraries and frameworks, making it easier for developers familiar with .NET to adopt machine learning practices.
  • Support for C# and F#
    Being built primarily for .NET developers, ML.NET supports C# and F#, which means developers can build, train, and implement machine learning models using languages they are already comfortable with.
  • Open Source and Free
    ML.NET is open source, which means developers can contribute to its development, view the source code, and it's free to use without licensing costs, encouraging a community-centric approach.
  • Comprehensive Machine Learning Workflows
    ML.NET provides end-to-end support for machine learning workflows, from data preparation to model training, evaluation, and deployment, offering a range of tools and features for various types of machine learning tasks.
  • Support for AutoML
    ML.NET includes support for automated machine learning (AutoML), which simplifies model creation by automating the process of selecting algorithms and optimizing hyperparameters, making it accessible to those with less expertise in machine learning.

Possible disadvantages of ML.NET

  • Limited Community and Resources
    Compared to more established frameworks like TensorFlow or PyTorch, ML.NET has a smaller user community and fewer learning resources, which can be a constraint for beginners seeking support and documentation.
  • Less Mature Compared to Other Frameworks
    ML.NET is relatively new compared to alternatives like TensorFlow and PyTorch, which means it may be less stable and optimized for certain complex tasks or scenarios.
  • Primarily for .NET Developers
    While beneficial for .NET developers, ML.NET's strong coupling to the .NET ecosystem may not appeal to those familiar with other programming languages who may find it less intuitive or flexible.
  • Limited Support for Deep Learning
    While ML.NET provides some capabilities for deep learning, its support and performance for deep learning tasks are limited compared to dedicated deep learning frameworks like TensorFlow.
  • Dependence on .NET Runtime
    ML.NET applications require the .NET runtime, which could be seen as a dependency when deploying models outside the typical .NET environment, potentially complicating deployment scenarios across different platforms.

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

ML.NET videos

+ Add

Announcing ML.NET 2.0 | .NET Conf 2022

More videos:

  • Review - ML.NET Model Builder: Machine learning with .NET
  • Review - What's New in ML.NET 2.0

Category Popularity

0-100% (relative to Google BigQuery and ML.NET)

Google BigQuery

ML.NET

Data Dashboard
100 100%
Data Dashboard
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Big Data
100 100%
Big Data
0% 0
AI
0 0%
AI
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 ML.NET

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

ML.NET Reviews

We have no reviews of ML.NET yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Google BigQuery seems to be a lot more popular than ML.NET. While we know about 42 links to Google BigQuery, we've tracked only 2 mentions of ML.NET. 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 / 18 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 / 22 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 / 29 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

ML.NET mentions (2)

  • what is the future of ML.NET?
    Documentation - You can find tutorials and how-to guides in our documentation site. Probably the easiest way to get started is with the Model Builder extension in Visual Studio. Here's install instructions and a tutorial to help you start out. Source: almost 3 years ago
  • What is the best way to get started with AI and ML in C#?
    I would start right here- ML.Net Documentation. Source: almost 4 years ago

What are some alternatives?

When comparing Google BigQuery and ML.NET, 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?

R MLstudio - The ML Studio is interactive for EDA, statistical modeling and machine learning applications.

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.

datarobot - Become an AI-Driven Enterprise with Automated Machine Learning

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.

Aureo.io - Aureo.io Makes AI Simple, Fast & Easy to Integrate

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