Software Alternatives, Accelerators & Startups
Register | Login

OpenShift VS Google BigQuery

Compare OpenShift VS Google BigQuery and see what are their differences

warmup.rocks
warmup.rocks keeps your CDN cache warm in every edge location worldwide: Cloudflare, Fastly, Akamai, CloudFront and more. Warming from 42 countries, hit-ratio analytics. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

OpenShift logo OpenShift

OpenShift gives you all the tools you need to develop, host and scale your apps in the public or private cloud. Get started today.

Google BigQuery logo Google BigQuery

A fully managed data warehouse for large-scale data analytics.
  • OpenShift Landing page
    Landing page //
    2023-10-15
  • Google BigQuery Landing page
    Landing page //
    2023-10-03

OpenShift features and specs

  • Comprehensive Platform
    OpenShift provides a complete Kubernetes-based container platform, including a strong set of integrated tools such as CI/CD pipelines, monitoring, and logging, which simplifies the development and deployment of applications.
  • Hybrid and Multi-Cloud Support
    OpenShift supports hybrid and multi-cloud deployments, enabling organizations to build, deploy, and manage applications across on-premises infrastructure and multiple cloud providers.
  • Enterprise-grade Security
    It offers robust security features, including role-based access control (RBAC), built-in authentication and authorization, and integrated vulnerability scanning, ensuring secure application development and deployment.
  • Developer Productivity
    OpenShift boosts developer productivity with features like source-to-image (S2I) builds, self-service environments, and a rich catalog of pre-configured application templates and runtimes.
  • Scalability and High Availability
    It is designed to scale applications seamlessly and ensure high availability with automated horizontal pod scaling, load balancing, and failover capabilities.

Possible disadvantages of OpenShift

  • Complexity
    The comprehensive nature of OpenShift can lead to increased complexity, particularly for small teams or organizations without prior Kubernetes or container orchestration experience.
  • Cost
    Enterprise-grade features come with significant licensing costs, which might be a barrier for startups and small to medium-sized enterprises.
  • Learning Curve
    Due to its extensive range of features and integrations, there can be a steep learning curve for administrators and developers new to the platform.
  • Vendor Lock-in
    While OpenShift supports hybrid and multi-cloud environments, there can be concerns about vendor lock-in due to the level of customization and proprietary features specific to Red Hat's implementation.
  • Resource Intensive
    Running OpenShift efficiently requires substantial computational resources and infrastructure, which might be challenging for organizations with limited IT resources.

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 OpenShift

Overall verdict

  • OpenShift is considered a good choice, especially for enterprises looking for a robust, scalable, and secure platform for deploying applications at scale. Its integration of Kubernetes with additional developer tools makes it an excellent option for facilitating DevOps practices.

Why this product is good

  • OpenShift is a solid platform as it combines containers and Kubernetes with developer-centric tools to accelerate application development and deployment. It offers built-in CI/CD, security features, and extensive scalability options. The platform ensures consistency across hybrid environments, which simplifies the management of containerized applications.

Recommended for

  • Organizations seeking a comprehensive platform for container orchestration.
  • Development teams focused on improving their CI/CD pipelines.
  • Enterprises adopting hybrid or multi-cloud strategies.
  • Teams that require robust security and compliance features.
  • Businesses aiming for rapid application development and deployment.

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

OpenShift videos

+ Add

OpenShift Container Platform by RedHat | Kubernetes Made Easy | Tech Primers

More videos:

  • Review - Open Source PaaS - OpenShift Review Part 1
  • Review - Red Hat OpenShift overview

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 OpenShift and Google BigQuery)

OpenShift

Google BigQuery

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

User comments

Share your experience with using OpenShift and Google BigQuery. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

OpenShift Reviews

Kubernetes Alternatives 2023: Top 8 Container Orchestration Tools
OpenShift is another container orchestration alternative for Kubernetes. It is a PaaS developed by Red Hat as a hybrid, enterprise-scale platform with extended Kubernetes capabilities for container orchestration. With a Linux OS, OpenShift helps you securely automate and scale the entire lifecycle of containerized applications. That means you can virtualize every host and...
Source: www.servertribe.com
OpenShift alternatives
The OpenShift platform was released by Red Hat โ€“ the maker of the professional Linux distribution โ€œRed Hat Enterprise Linuxโ€ (RHEL). The OpenShift alternative โ€œRancherโ€ has now been taken over by the traditional Linux provider SUSE. โ€œCanonical Kubernetesโ€, is another OpenShift alternative from an established Linux provider. Read on to find out more about these and other...
Source: www.ionos.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 more popular. It has been mentiond 47 times since March 2021. 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.

OpenShift mentions (0)

We have not tracked any mentions of OpenShift yet. Tracking of OpenShift recommendations started around Mar 2021.

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 / 4 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 / 5 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 / 8 months ago
View more

What are some alternatives?

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

Google App Engine - A powerful platform to build web and mobile apps that scale automatically.

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

Salesforce Platform - Salesforce Platform is a comprehensive PaaS solution that paves the way for the developers to test, build, and mitigate the issues in the cloud application before the final deployment.

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.

Dokku - Docker powered mini-Heroku in around 100 lines of Bash

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.

Loading...