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OpenStack VS Google BigQuery

Compare OpenStack VS Google BigQuery and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

OpenStack logo OpenStack

OpenStack software controls large pools of compute, storage, and networking resources throughout a datacenter, managed through a dashboard or via the OpenStack API.

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • OpenStack Landing page
    Landing page //
    2023-07-22
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

OpenStack features and specs

  • Open Source
    OpenStack is open source, which means there is no licensing fee and a broad community of users and developers contributes to its development and support.
  • Flexibility
    It supports a wide variety of hardware and software, allowing organizations to customize their cloud infrastructure to meet specific needs.
  • Scalability
    OpenStack can scale horizontally, allowing organizations to add or remove resources as their needs change, effectively managing large pools of compute, storage, and networking resources.
  • Vendor Neutrality
    Being vendor-neutral, OpenStack offers flexibility to avoid vendor lock-in and choose from a wide range of compatible technologies and service providers.
  • Community Support
    A large and active community provides extensive documentation, forums, and support, which can be very helpful for troubleshooting and development.

Possible disadvantages of OpenStack

  • Complexity
    Setting up and managing OpenStack can be complex and requires a significant level of expertise, which may necessitate specialized training for staff.
  • Performance Overhead
    Being a feature-rich platform, it often involves more performance overhead compared to other simpler, more streamlined services.
  • Resource Intensive
    OpenStack can be resource-intensive in terms of CPU, memory, and storage, which might not be suitable for all organizations, especially smaller ones with limited resources.
  • Interoperability Issues
    Integrating OpenStack with existing systems and third-party tools can sometimes present challenges, especially when dealing with legacy infrastructure.
  • Evolving Platform
    The platform is constantly evolving, which can be both a pro and a con. Keeping up to date with the latest releases and changes can be time-consuming and may require ongoing maintenance.

Google BigQuery features and specs

  • Scalability
    BigQuery can effortlessly scale to handle large volumes of data due to its serverless architecture, thereby reducing the operational overhead of managing infrastructure.
  • Speed
    It leverages Google's infrastructure to provide high-speed data processing, making it possible to run complex queries on massive datasets in a matter of seconds.
  • Integrations
    BigQuery easily integrates with various Google Cloud Platform services, as well as other popular data tools like Looker, Tableau, and Power BI.
  • Automatic Optimization
    Features like automatic data partitioning and clustering help to optimize query performance without requiring manual tuning.
  • Security
    BigQuery provides robust security features including IAM roles, customer-managed encryption keys, and detailed audit logging.
  • Cost Efficiency
    The pricing model is based on the amount of data processed, which can be cost-effective for many use cases when compared to traditional data warehouses.
  • Managed Service
    Being fully managed, BigQuery takes care of database administration tasks such as scaling, backups, and patch management, allowing users to focus on their data and queries.

Possible disadvantages of Google BigQuery

  • Cost Predictability
    While the pay-per-use model can be cost-efficient, it can also make cost forecasting difficult. Unexpected large queries could lead to higher-than-anticipated costs.
  • Complexity
    The learning curve can be steep for those who are not already familiar with SQL or Google Cloud Platform, potentially requiring training and education.
  • Limited Updates
    BigQuery is optimized for read-heavy operations, and it can be less efficient for scenarios that require frequent updates or deletions of data.
  • Query Pricing
    Costs are based on the amount of data processed by each query, which may not be suitable for use cases that require frequent analysis of large datasets.
  • Data Transfer Costs
    While internal data movement within Google Cloud can be cost-effective, transferring data to or from other services or on-premises systems can incur additional costs.
  • Dependency on Google Cloud
    Organizations heavily invested in multi-cloud or hybrid-cloud strategies may find the dependency on Google Cloud limiting.
  • Cold Data Performance
    Query performance might be slower for so-called 'cold data,' or data that has not been queried recently, affecting the responsiveness for some workloads.

Analysis of OpenStack

Overall verdict

  • OpenStack can be an excellent choice for businesses and enterprises looking to deploy a cloud infrastructure, particularly if they value flexibility, scalability, and control over their environment. Being open-source, it also offers cost advantages compared to proprietary solutions, provided the organization has the necessary expertise to manage and maintain it. However, it may be challenging for smaller teams without dedicated IT resources due to its complexity and the steep learning curve associated with its deployment and management.

Why this product is good

  • OpenStack is a popular open-source cloud computing platform that enables users to build and manage both public and private clouds. It offers a flexible and scalable solution for organizations that need to handle large amounts of data and infrastructure. OpenStack is developed by a vast community of developers and organizations, ensuring continuous improvement and adaptation to new technologies. It supports a wide range of APIs, which allows for customization and integration with other services and tools.

Recommended for

    OpenStack is particularly recommended for large enterprises, organizations with skilled IT teams, academic institutions, and service providers that need a highly customizable and scalable cloud solution. It's also a great fit for entities with specific compliance requirements or those that need to run a private cloud with tailored configurations.

Analysis of Google BigQuery

Overall verdict

  • Google BigQuery is a powerful and flexible data warehouse solution that suits a wide range of data analytics needs. Its ability to handle large volumes of data quickly makes it a preferred choice for organizations looking to leverage their data effectively.

Why this product is good

  • Google BigQuery is a fully-managed data warehouse that simplifies the analysis of large datasets. It is known for its scalability, speed, and integration with other Google Cloud services. It supports standard SQL, has built-in machine learning capabilities, and allows for seamless data integration from various sources. The serverless architecture means that users don't need to worry about infrastructure management, and its pay-as-you-go model provides cost efficiency.

Recommended for

  • Businesses requiring fast processing of large datasets
  • Organizations that already utilize Google Cloud services
  • Companies looking for a cost-effective, scalable analytics solution
  • Teams interested in using SQL for data analysis
  • Data scientists integrating machine learning with their data workflows

OpenStack videos

+ Add

OpenStack Summit Primer, The Who, What, Why and How of OpenStack

More videos:

  • Review - Red Hat OpenStack Platform GPU use case
  • Review - Performance Analysis Review for Production OpenStack Private Cloud in SaaS

Google BigQuery videos

+ Add

Cloud Dataprep Tutorial - Getting Started 101

More videos:

  • Review - Advanced Data Cleanup Techniques using Cloud Dataprep (Cloud Next '19)
  • Demo - Google Cloud Dataprep Premium product demo

Category Popularity

0-100% (relative to OpenStack and Google BigQuery)

OpenStack

Google BigQuery

Cloud Computing
100 100%
Cloud Computing
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
VPS
100 100%
VPS
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using OpenStack and Google BigQuery. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare OpenStack and Google BigQuery

OpenStack Reviews

35+ Of The Best CI/CD Tools: Organized By Category
OpenStack is a cloud framework. It provides users and enterprises with horizontal scale infrastructure. Its tools allow you to compute, store and share data and resources. It also provides self-service administration that users can interact with directly.
Source: www.cloudzero.com

Google BigQuery Reviews

Database for Data Analytics
Processing typeDescriptionUse casesCommon databasesProcessing typesProcesses data in scheduled intervals (hours, days). High-latency but cost-efficient for large datasets.Financial reporting, trend analysis, historical analyticsSnowflake, Amazon Redshift, Google BigQueryContinuously ingests and processes data with minimal latency for real-time decision-making.Fraud...
Source: blog.devart.com
Data Warehouse Tools
Google BigQuery: Similar to Snowflake, BigQuery offers a pay-per-use model with separate charges for storage and queries. Storage costs start around $0.01 per GB per month, while on-demand queries are billed at $5 per TB processed.
Source: peliqan.io
Top 6 Cloud Data Warehouses in 2023
You can also use BigQueryโ€™s columnar and ANSI SQL databases to analyze petabytes of data at a fast speed. Its capabilities extend enough to accommodate spatial analysis using SQL and BigQuery GIS. Also, you can quickly create and run machine learning (ML) models on semi or large-scale structured data using simple SQL and BigQuery ML. Also, enjoy a real-time interactive...
Source: geekflare.com
Top 5 Cloud Data Warehouses in 2023
Google BigQuery is an incredible platform for enterprises that want to run complex analytical queries or โ€œheavyโ€ queries that operate using a large set of data. This means itโ€™s not ideal for running queries that are doing simple filtering or aggregation. So if your cloud data warehousing needs lightning-fast performance on a big set of data, Google BigQuery might be a great...
Source: www.shipyardapp.com
Top 5 BigQuery Alternatives: A Challenge of Complexity
BigQuery's emergence as an attractive analytics and data warehouse platform was a significant win, helping to drive a 45% increase in Google Cloud revenue in the last quarter. The company plans to maintain this momentum by focusing on a multi-cloud future where BigQuery advances the cause of democratized analytics.
Source: blog.panoply.io

Social recommendations and mentions

Based on our record, Google BigQuery seems to be a lot more popular than OpenStack. While we know about 47 links to Google BigQuery, we've tracked only 2 mentions of OpenStack. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

OpenStack mentions (2)

  • Learn OpenStack by Example: Part 1 - Install DevStack
    In my first post, I looked into what is OpenStack and how, if done right, can be quite a powerful ally in our cloud deployment strategies. In this post, I want to start looking at how we can create an application to learn the basics and components of the system. - Source: dev.to / about 5 years ago
  • Learn OpenStack by examples: Part 0 - Summary and Goals
    While searching for solutions and documentation on the various problems I've come across, I would often see references to OpenStack and it got my curiosity going. What is OpenStack? What services does it offer and who owns it? How do I learn to use it? What are it's costs and limitations? - Source: dev.to / about 5 years ago

Google BigQuery mentions (47)

  • Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter
    We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ€” we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 4 months ago
  • How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
    Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 5 months ago
  • What if ML pipelines had a lock file?
    Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 6 months ago
  • Best SQL Courses with Certificates for 2026
    SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโ€”while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 8 months ago
  • Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach
    Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 9 months ago
View more

What are some alternatives?

When comparing OpenStack and Google BigQuery, you can also consider the following products

Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โ€ŽWhat is Apache Spark?

DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.

Looker - Looker makes it easy for analysts to create and curate custom data experiencesโ€”so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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