Azure Storage vs Google BigQuery

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Azure Storage vs Google BigQuery: What are the differences?

Introduction

In this article, we will discuss the key differences between Azure Storage and Google BigQuery. Both Azure Storage and Google BigQuery are popular cloud-based storage and analytics platforms, but they differ in various aspects. Below are the key differences between the two:

  1. Data Storage Model: Azure Storage is a general-purpose object storage service that allows you to store unstructured data such as files, blobs, and tables. It provides different storage options like Blob storage, Table storage, Queue storage, and File storage. On the other hand, Google BigQuery is a fully-managed data warehouse that is designed for structured data. It supports automated data ingestion, schema definition, and SQL-like querying.

  2. Data Querying and Analysis: Azure Storage mainly offers storage capabilities and doesn't provide advanced analytics features out-of-the-box. In contrast, Google BigQuery is specifically built for data querying and analysis. It provides powerful SQL-like querying capabilities to process massive datasets quickly and easily. BigQuery also supports data visualization, machine learning integration, and advanced analytics functions.

  3. Data Scalability: Azure Storage is highly scalable and can handle large volumes of data. It can scale horizontally by sharding data across multiple storage accounts using partition keys. On the other hand, Google BigQuery is designed to handle extremely large datasets and can automatically scale computing resources based on the query workload. It can process petabytes of data without any manual scaling effort.

  4. Pricing and Cost Model: Azure Storage follows a pay-as-you-go pricing model based on the amount of storage used, data transfer, and operations performed. It offers different tiers with varying performance levels and associated costs. In contrast, Google BigQuery pricing is based on the amount of data processed by the queries and the storage used. It offers on-demand pricing and also provides flat-rate pricing for predictable workloads.

  5. Data Integration and Ecosystem: Azure Storage is part of Microsoft Azure cloud ecosystem, which provides a wide range of services for building applications and solutions. It integrates well with other Azure services like Azure Data Factory, Azure Functions, and Azure Databricks. Google BigQuery is part of the Google Cloud Platform (GCP) ecosystem and integrates seamlessly with other GCP services like Google Cloud Storage, Google Cloud Pub/Sub, and Google Cloud Dataproc.

  6. Data Security and Compliance: Azure Storage provides various security features like encryption at rest, encryption in transit, access control policies, and secure transfer protocols. It also offers compliance certifications like ISO, SOC, HIPAA, and GDPR. Similarly, Google BigQuery implements strong security measures like data encryption, access controls, and audit logs. It also complies with various industry standards and regulations.

In summary, Azure Storage is a versatile storage service that provides different storage options and integrates well with other Azure services. Google BigQuery, on the other hand, is a dedicated data warehouse platform with advanced querying and analytics capabilities. The choice between the two depends on the specific requirements of your projects, such as data types, analytics needs, scalability, and ecosystem preferences.

Decisions about Azure Storage and Google BigQuery
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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Pros of Azure Storage
Pros of Google BigQuery
  • 24
    All-in-one storage solution
  • 15
    Pay only for data used regardless of disk size
  • 9
    Shared drive mapping
  • 2
    Cost-effective
  • 2
    Cheapest hot and 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

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Cons of Azure Storage
Cons of Google BigQuery
  • 2
    Direct support is not provided by Azure storage
  • 1
    You can't unit test changes in BQ data

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What is 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.

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.

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What companies use Azure Storage?
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What are some alternatives to Azure Storage and Google BigQuery?
Azure Redis Cache
It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.
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
Azure Cosmos DB
Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.
OneDrive
Outlook.com is a free, personal email service from Microsoft. Keep your inbox clutter-free with powerful organizational tools, and collaborate easily with OneDrive and Office Online integration.
Dropbox
Harness the power of Dropbox. Connect to an account, upload, download, search, and more.
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