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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.
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.
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.
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.
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.
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.
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.
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
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.
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.
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
Pros of Google Cloud Storage
- Scalable28
- Cheap19
- Reliable14
- Easy9
- Chealp3
- More praticlal and easy2
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Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0