Google BigQuery vs Google Cloud Storage

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Google BigQuery vs Google Cloud Storage: What are the differences?

Google BigQuery and Google Cloud Storage are two popular services offered by Google Cloud Platform. Here are some key differences between the two.

  1. Data Storage and Structure: Google BigQuery is designed for storing and querying structured and semi-structured data. It is a fully-managed, serverless data warehouse that can handle large-scale data analysis. On the other hand, Google Cloud Storage is a scalable object storage service that can store unstructured data such as files, images, and videos.

  2. Data Querying and Analysis: BigQuery provides a SQL-like interface for querying data, making it easy to analyze large datasets. It supports advanced analytics functions and tools like machine learning integration. In contrast, Google Cloud Storage does not provide built-in querying capabilities and requires additional processing tools like Google Dataproc or Google Dataflow for data analysis.

  3. Data Transfer and Cost: Transferring data between Google BigQuery and Google Cloud Storage is faster and more efficient than transferring between other services. BigQuery allows data to be directly queried from Google Cloud Storage without any data transfer cost. However, storing data in BigQuery can be more expensive compared to Cloud Storage as BigQuery charges for both storage and analysis usage.

  4. Data Import and Export: Both BigQuery and Cloud Storage support data import and export, but they have different mechanisms. BigQuery supports direct data import from various sources including Google Cloud Storage, Google Drive, and other cloud platforms. It also provides export functionality to different formats such as CSV, JSON, and Avro. On the other hand, Google Cloud Storage is commonly used as a staging area for data ingestion and allows data to be easily exported to other storage or processing systems.

  5. Data Access Control: BigQuery offers fine-grained access control at the dataset and project level, allowing administrators to manage user permissions effectively. It also integrates with other Google Cloud Platform services like Cloud IAM for access control and Cloud Audit Logging for monitoring. In comparison, Google Cloud Storage provides access control at the bucket and object level, making it suitable for managing access to individual files or objects.

  6. Data Durability and Availability: Google Cloud Storage is designed for durability and availability, offering 99.999999999% (11 nines) durability for objects stored in multiple regions. It automatically replicates data across multiple locations to ensure high availability. On the other hand, BigQuery does not directly provide durability and availability metrics, as it focuses more on data analysis capabilities rather than long-term storage.

In summary, Google BigQuery is a fully-managed data warehouse designed for structured data analysis, while Google Cloud Storage is an object storage service for storing unstructured data. BigQuery supports SQL-like querying and advanced analytics, while Cloud Storage is more suitable for data ingestion and serving as a staging area.

Decisions about Google BigQuery and Google Cloud Storage
Julien Lafont

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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We choose Backblaze B2 because it makes more sense for storing static assets.

We admire Backblaze's customer service & transparency, plus, we trust them to maintain fair business practices - including not raising prices in the future.

Lower storage costs means we can keep more data for longer, and lower bandwidth means cache misses don't cost a ton.

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Gabriel Pa

We offer our customer HIPAA compliant storage. After analyzing the market, we decided to go with Google Storage. The Nodejs API is ok, still not ES6 and can be very confusing to use. For each new customer, we created a different bucket so they can have individual data and not have to worry about data loss. After 1000+ customers we started seeing many problems with the creation of new buckets, with saving or retrieving a new file. Many false positive: the Promise returned ok, but in reality, it failed.

That's why we switched to S3 that just works.

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Pros of Google BigQuery
Pros of Google Cloud Storage
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
  • 12
    Big Data
  • 11
    Full table scans in seconds, no indexes needed
  • 8
    Always on, no per-hour costs
  • 6
    Good combination with fluentd
  • 4
    Machine learning
  • 1
    Easy to manage
  • 0
    Easy to learn
  • 28
  • 19
  • 14
  • 9
  • 3
  • 2
    More praticlal and easy

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Cons of Google BigQuery
Cons of Google Cloud Storage
  • 1
    You can't unit test changes in BQ data
    Be the first to leave a con

    Sign up to add or upvote consMake informed product decisions

    What is Google BigQuery?

    Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

    What is Google Cloud Storage?

    Google Cloud Storage allows world-wide storing and retrieval of any amount of data and at any time. It provides a simple programming interface which enables developers to take advantage of Google's own reliable and fast networking infrastructure to perform data operations in a secure and cost effective manner. If expansion needs arise, developers can benefit from the scalability provided by Google's infrastructure.

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    Blog Posts

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    What are some alternatives to Google BigQuery and Google Cloud Storage?
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
    Google Analytics
    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications.
    See all alternatives