Overview

Google Cloud Storage

Stacks1.7K

Followers1.2K

Votes75

Google BigQuery

Stacks1.7K

Followers1.5K

Votes152

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.

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Advice on Google Cloud Storage, Google BigQuery

Julien

CTO at Hawk

Sep 19, 2020

Decided

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

193k views193k

Comments

Gabriel

CEO at NaoLogic Inc

Dec 24, 2019

Decided

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.

330k views330k

Comments

Ben

May 18, 2020

Decided

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.

120k views120k

Comments

Detailed Comparison

Google Cloud Storage	Google BigQuery
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.	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.
High Capacity and Scalability;Strong Data Consistency;Google Developers Console Projects;Bucket Locations;REST APIS;OAuth 2.0 Authentication;Authenticated Browser Downloads;Google Account Support for Sharing	All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
Statistics
Stacks 1.7K	Stacks 1.7K
Followers 1.2K	Followers 1.5K
Votes 75	Votes 152
Pros & Cons
Pros 28 Scalable 19 Cheap 14 Reliable 9 Easy 3 Chealp	Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas
Integrations
No integrations available	Xplenty Fluentd Looker Chartio Treasure Data

What are some alternatives to Google Cloud Storage, Google BigQuery?

Amazon S3

Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web

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.

Amazon EBS

Amazon EBS volumes are network-attached, and persist independently from the life of an instance. Amazon EBS provides highly available, highly reliable, predictable storage volumes that can be attached to a running Amazon EC2 instance and exposed as a device within the instance. Amazon EBS is particularly suited for applications that require a database, file system, or access to raw block level storage.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Azure Storage

Azure Storage provides the flexibility to store and retrieve large amounts of unstructured data, such as documents and media files with Azure Blobs; structured nosql based data with Azure Tables; reliable messages with Azure Queues, and use SMB based Azure Files for migrating on-premises applications to the cloud.

Minio

Minio is an object storage server compatible with Amazon S3 and licensed under Apache 2.0 License

OpenEBS

OpenEBS allows you to treat your persistent workload containers, such as DBs on containers, just like other containers. OpenEBS itself is deployed as just another container on your host.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

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.