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Google BigQuery VS Entity Framework

Compare Google BigQuery VS Entity Framework 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.

Entity Framework logo Entity Framework

See Comparison of Entity Framework vs NHibernate.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • Entity Framework Landing page
    Landing page //
    2023-08-18

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.

Entity Framework features and specs

  • Productivity
    Entity Framework automates database-related code generation, reducing the amount of boilerplate code developers must write and maintain. This allows developers to work more efficiently and focus more on business logic.
  • Abstraction
    It abstracts the database interaction details, enabling developers to work with higher-level .NET objects instead of raw SQL queries, resulting in clearer and more manageable code.
  • Code First Approach
    This allows developers to define their database schema using C# classes, making it easy to evolve the database alongside the codebase using migrations.
  • Support for Multiple Databases
    Entity Framework supports a wide range of relational databases, including SQL Server, PostgreSQL, SQLite, and MySQL, providing flexibility and choice to the developers.
  • Change Tracking
    It provides automatic change tracking of entity objects, simplifying the process of updating data in the database without manually tracking object changes.

Possible disadvantages of Entity Framework

  • Performance Overhead
    The abstraction layer can lead to performance overhead compared to plain SQL queries, as the generated queries might not be as optimized as handcrafted SQL.
  • Complexity
    For simple or small applications, the complexity introduced by using an ORM like Entity Framework might be unnecessary and could complicate the architecture.
  • Learning Curve
    Developers need to learn the specific concepts and configurations of Entity Framework, which can be time-consuming compared to traditional database access methodologies.
  • Debugging Difficulty
    Debugging issues can be more challenging because of the abstraction, making it sometimes difficult to trace the exact query being executed and pinpoint performance bottlenecks.
  • Limited SQL Features
    While Entity Framework supports a wide range of SQL functionalities, there are advanced features specific to certain databases that may not be fully supported or could require custom implementation.

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

Entity Framework videos

Entity Framework Best Practices - Should EFCore Be Your Data Access of Choice?

More videos:

  • Tutorial - Entity Framework 6 Tutorial: Learn Entity Framework 6 from Scratch
  • Review - Getting the best out of Entity Framework Core - Jon P Smith

Category Popularity

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Data Dashboard
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Web Frameworks
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Big Data
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Development
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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 Entity Framework

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.

Entity Framework Reviews

We have no reviews of Entity Framework yet.
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Social recommendations and mentions

Based on our record, Google BigQuery should be more popular than Entity Framework. 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 / 23 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 / 28 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

Entity Framework mentions (15)

  • Create a Simple .NET Workflow App From Scratch – Your Ultimate Guide
    For the simplicity we will use MSSQLProvider to fetch the data from the database. This class has basic functionality, if you want to create complex database queries, for example JOIN, you'd better use something like Entity Framework. - Source: dev.to / 12 months ago
  • Entity Framework Core in .NET 7 7️⃣
    I only wanted to give a simple preview of what can be done with Entity Framework, but if this is something that interests you and you want to go further in-depth with all the possibilities, I recommend checking out the official docs where you can also find a great tutorial which will guide you through building your very own .NET Core web application. - Source: dev.to / almost 2 years ago
  • Got an internship, need help with .NET
    Entity Framework documentation hub - Entity Framework is a modern object-relation mapper that lets you build a clean, portable, and high-level data access layer with .NET (C#) across a variety of databases, including SQL Database (on-premises and Azure), SQLite, MySQL, PostgreSQL, and Azure Cosmos DB. It supports LINQ queries, change tracking, updates, and schema migrations. Source: almost 2 years ago
  • How to create a "Database Project" that can be used across multiple .NET apps?
    You can create the DAL using your existing code or start using a Object Relational Mapper like Entity Framework which will do a lot of the work for you, check this out here: https://learn.microsoft.com/en-us/ef/ also check out LINQ. Source: about 2 years ago
  • Website with Database. use C#
    And, possibly (not strictly speaking necessary but very useful) Entity framework as a backend part of it. Source: about 2 years ago
View more

What are some alternatives?

When comparing Google BigQuery and Entity Framework, 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?

Sequelize - Provides access to a MySQL database by mapping database entries to objects and vice-versa.

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.

Hibernate - Hibernate an open source Java persistence framework project.

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.

SQLAlchemy - SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.