Google BigQuery VS OpenScan

• 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

• 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.

• Open-Source
  Being open-source promotes transparency and community-driven improvements, ensuring the software remains up-to-date and secure.
• Cost-Effective
  Since it's available for free, both individuals and organizations can use the software without incurring licensing fees.
• Community Support
  The open-source nature allows for a large community of users and developers who can provide support, share tips, and contribute to feature enhancements.
• Customizability
  Users have the ability to modify the code base to better fit their specific needs, offering high levels of customization.
• Wide Platform Support
  OpenScan may support multiple platforms, making it versatile for use on different operating systems.

Possible disadvantages

• Technical Expertise Required
  Users may need significant programming knowledge to install, customize, and troubleshoot the software effectively.
• Limited Official Support
  There is often no official customer support, making it potentially difficult for users to resolve issues without community assistance.
• Documentation
  Documentation might be lacking or not up to professional standards, which can create challenges in understanding and utilizing all features.
• Potential for Bugs
  As with many open-source projects, the software might contain bugs or be less rigorously tested compared to commercial alternatives.
• Dependency Management
  Ensuring all dependencies are correctly installed and compatible can be a challenging and time-consuming process.

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Tracking OpenScan since Mar 2021.