Azure Storage vs Google BigQuery

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Azure Storage

Stacks1.3K

Followers787

Votes52

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:

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.
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.
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.
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.
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.
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.

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Advice on Google BigQuery, Azure Storage

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

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Detailed Comparison

Google BigQuery	Azure Storage
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.	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.
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.	Blobs, Tables, Queues, and Files;Highly scalable;Durable & highly available;Premium Storage;Designed for developers
Statistics
Stacks 1.8K	Stacks 1.3K
Followers 1.5K	Followers 787
Votes 152	Votes 52
Pros & Cons
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	Pros 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 Cons 2 Direct support is not provided by Azure storage
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	Microsoft Azure

What are some alternatives to Google BigQuery, Azure Storage?

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.

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.

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.

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.

Related Comparisons

Azure Storage vs Google BigQuery: What are the differences?

Introduction

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.
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.
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.
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.
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.
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.

Azure Storage vs Google BigQuery

Overview

Azure Storage vs Google BigQuery: What are the differences?