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Google BigQuery VS import.io

Compare Google BigQuery VS import.io and see what are their differences

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Contents:
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  3. » Reviews
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Google BigQuery logo Google BigQuery

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

import.io logo import.io

Import. io helps its users find the internet data they need, organize and store it, and transform it into a format that provides them with the context they need.
  • Google BigQuery Landing page
    Landing page //
    2023-10-03
  • import.io Landing page
    Landing page //
    2023-06-12

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.

import.io features and specs

  • Ease of Use
    Import.io offers a user-friendly interface that allows users to easily extract data without needing to write code, making it accessible for non-technical users.
  • Data Integration
    The platform provides robust integration options with various analytics and data storage tools, enabling seamless data workflows.
  • Scalability
    Import.io can handle large volumes of data efficiently, making it suitable for both small and large-scale data extraction projects.
  • Speed
    The tool is designed to extract data quickly, minimizing the time required to obtain and process large datasets.
  • Data Transformation
    Offers features for data transformation and cleaning, allowing users to manipulate the data to fit their needs before export.

Possible disadvantages of import.io

  • Cost
    Import.io can be expensive, especially for businesses or users requiring extensive data extraction and processing capabilities.
  • Learning Curve for Advanced Features
    While basic features are easy to use, mastering the more advanced functionalities can require a significant amount of time and effort.
  • Limited Customization
    There are constraints on customization, which could be limiting for users with complex or highly specific data extraction needs.
  • Occasional Stability Issues
    Users have reported occasional performance and stability issues, which can cause interruptions during data extraction processes.
  • Dependency on Web Structure
    The tool is highly dependent on the structure of the target websites. Any changes in the website's layout can disrupt data extraction processes and require reconfiguration.

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

import.io videos

+ Add

mobile review extraction using import.io

More videos:

  • Review - Import.io Infinite Scroll Website Data Extraction

Category Popularity

0-100% (relative to Google BigQuery and import.io)

Google BigQuery

import.io

Data Dashboard
100 100%
Data Dashboard
0% 0
Web Scraping
0 0%
Web Scraping
100% 100
Big Data
100 100%
Big Data
0% 0
Data Extraction
0 0%
Data Extraction
100% 100

User comments

Share your experience with using Google BigQuery and import.io. For example, how are they different and which one is better?
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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 import.io

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

import.io Reviews

We have no reviews of import.io 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 import.io. While we know about 42 links to Google BigQuery, we've tracked only 2 mentions of import.io. 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 / 12 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 / 17 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 / 23 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

import.io mentions (2)

  • Woke up in hella good mood - I guess weekend - how are y’all
    Sort of, import.io is a portion. This could also automate tasks on your local computer as well. Source: about 4 years ago
  • Offering help for Free: If anyone's trying to get a custom internal tool built, I can Help
    This should be possible. But I think you can do this faster with import.io and google sheets. DM me, we'll figure it out. Source: about 4 years ago

What are some alternatives?

When comparing Google BigQuery and import.io, 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?

Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.

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.

Apify - Apify is a web scraping and automation platform that can turn any website into an API.

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.

ParseHub - ParseHub is a free web scraping tool. With our advanced web scraper, extracting data is as easy as clicking the data you need.

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