Software Alternatives & Startups

Google BigQuery VS AppStruct

Compare Google BigQuery VS AppStruct and see what are their differences

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Google BigQuery logo Google BigQuery

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

AppStruct logo AppStruct

AppStruct — a new no-code platform built for web, mobile, desktop apps and telegram mini-apps development.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • AppStruct Full Frontend Contol
    Full Frontend Contol //
    2025-06-05
  • AppStruct Build Backend Flows
    Build Backend Flows //
    2025-06-05
  • AppStruct Direct Publishing
    Direct Publishing //
    2025-06-05

Hi, I’m Boris, co-founder of AppStruct — a new no-code platform built for web, mobile, and desktop apps development. We’re a team of no-code enthusiasts who set out to fix the two biggest pain points we kept running into: speed and complexity.

We’re not the first to build in the no-code space — but we felt the idea has never been pushed to its full potential. So we started fresh and built AppStruct from the ground up with one goal in mind:

Combine powerful functionality with simple UX — and make app creation faster than ever.

AppStruct

$ Details
freemium $45 / Monthly
Release Date
2024 January
Startup details
Country
Italy
State
Florence
City
Florence
Founder(s)
Boris Markarian, Vladimir Tambovtsev, Ilia Yasir
Employees
1 - 9

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.

AppStruct features and specs

  • 🖱️ Drag & Drop Editor
    Build your UI by dropping and stretching components on the canvas.
  • 🔗 API Integrations
    Connect to any API service in minutes: fetch data, send updates, and power your app with external APIs. Out-of-the-box integrations with Zapier, Stripe and Excel.
  • 🚀 One-Click Publishing
    Deploy to the App Store and Google Play in one click.
  • 📥 APK & PWA download
    Get installable apps with shareable links.
  • 📱 Adaptive Layouts
    Your UI automatically resizes for phones, tablets, desktops or any custom screen size.
  • 🗄️ Built-In & External Backends
    Use our database or plug in Firebase/Supabase.
  • 🧩 50+ UI Components
    Choose from a rich library of components — all fully customizable to match your brand.
  • 📡 WebSockets
    Real-time features like live chat and dashboards.
  • 💾 Local Storage
    Store temporary or persistent data in-app.
  • 🤖 AI Component Generator
    Describe what you need, we generate the component.
  • 🛠️ Custom Code Support
    Drop in your own React logic when needed.
  • 🔄 Visual Logic Builder
    Build complex conditionals and workflows with a node-based editor.
  • ➗ Math Engine
    Do live calculations and metrics in the UI. Build logic based on device data, geo position, and time.
  • 🎨 Design System
    Manage global fonts, colors, themes, and dark/light mode.
  • 📲 Deep Links
    Create shareable URLs that open specific screens or content directly within your app.
  • 🔍 SEO Control
    Meta tags, sitemaps, and prerendering built in.
  • 📍 Geolocation
    Access user location data to power maps, geo-fencing, location-based content and more.
  • 🔔 Push Notifications
    Send targeted notifications and real-time alerts. Works seamlessly with Deep Links to drive users directly to the right screen.
  • 📑 Prebuilt Templates
    E-commerce, delivery, AI chatbots, and more.
  • 📝 Localization
    Translate your app into multiple languages instantly.
  • 📚 Interactive Docs
    In-app docs and videos to help you every step of the way.

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

Analysis of AppStruct

Overall verdict

  • AppStruct.ai appears to be a capable no-code/AI-powered app building platform, but its suitability depends heavily on your specific needs, technical background, and the type of application you want to create. As with any tool in this space, it's best to evaluate it through a free trial before committing.

Why this product is good

  • It aims to lower the barrier to app development by leveraging AI, allowing non-technical users to build applications without writing code
  • AI-assisted platforms can significantly speed up prototyping and reduce development costs for simple to moderately complex apps
  • No-code/low-code approaches enable faster iteration and easier maintenance for small teams and solo builders
  • It may offer templates and pre-built components that accelerate getting a functional product to market

Recommended for

  • Entrepreneurs and startups wanting to quickly build an MVP without hiring developers
  • Small business owners needing custom internal tools or simple customer-facing apps
  • Non-technical founders who want to validate an idea before investing in full development
  • Designers and product managers who want to prototype rapidly
  • Teams looking to reduce development costs for straightforward applications

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

AppStruct videos

Welcome to AppStruct | A New Standard for No-Code

More videos:

  • Review - AppStruct & Earlybird – Live Webinar | A fresh look at no-code
  • Review - AppStruct Lifetime Deal - The Best AI-Assisted App Builder in 2025

Category Popularity

0-100% (relative to Google BigQuery and AppStruct)
Data Dashboard
100 100%
0% 0
No Code
0 0%
100% 100
Big Data
100 100%
0% 0
Application Builder
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 AppStruct

Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
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

AppStruct Reviews

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

Social recommendations and mentions

Based on our record, Google BigQuery seems to be more popular. It has been mentiond 47 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 (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery — we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 5 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 6 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 7 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferability—while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 9 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 10 months ago
View more

AppStruct mentions (0)

We have not tracked any mentions of AppStruct yet. Tracking of AppStruct recommendations started around Jun 2025.

What are some alternatives?

When comparing Google BigQuery and AppStruct, 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?

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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.

Floot - Build serious apps with AI without getting stuck

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.

Bubble.io - Building tech is slow and expensive. Bubble is the most powerful no-code platform for creating digital products.