BigQuery vs Redshift: What to Learn in 2024 with Trends Explained
Published in
4 min readJan 18, 2024
Bigquery Vs Redshift Trends
In this blog, we’ll look at the ever-evolving terrain of data engineering, with a focus on two giants: Amazon Redshift and Google BigQuery.
These data warehouses are well-known for their creativity and provide fully managed solutions for a wide range of data demands. The choice between RedShift and BigQuery is critical for enterprises negotiating the intricacies of cloud-based analytics.
Key Highlights:
- Fully managed data warehousing solution by Amazon.
- Storage capacity ranging from gigabytes to petabytes.
- Utilizes parallel processing and data compression for efficient data handling.
- Data stored in computed resources called nodes, forming clusters and slices.
- Offers security features at no additional cost.
Key Features:
Versatility:
- Serves a wide range of business requirements.
- RedShift Serverless for quick analytics scaling.
- Query Editor v2 for SQL ease in data engineering.
- Supports major programming languages through Redshift Data API.
Data Analysis Simplified:
- Enables querying and exporting data to/from the warehouse.
- Queries using ANSI SQL, supporting JSON and CSV formats in S3.
- AWS service integration and data sharing across different AWS accounts.
Scalability and Performance:
- Optimizes query speed with Advanced Query Accelerator and RA3 instances.
- Utilizes machine learning and result caching for faster query results.
Top-notch Security:
- End-to-end encryption and network isolation for robust security.
- Integration with AWS CloudTrail for auditing RedShift API calls.
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Google BigQuery
Features:
Multi-cloud Functionality:
- Analyzes data present in more than one cloud.
- Decouples storage and compute components for flexibility.
ML Integration:
- Built-in ML integration for creating and executing ML models.
- Executes ML models using simple SQL queries, eliminating the need for separate ML solutions.
Speed and Simplicity:
- Uses BI engine for high concurrency and quick response times.
- Processes billions of rows quickly.
Automated Data Transfer:
- Simple and automated data transfer.
- Supports loading data from other data warehouses into BigQuery.
Comparison: Amazon RedShift vs. Google BigQuery
Performance:
- Close call, both backed by tech giants.
- BigQuery’s pricing based on data volume; RedShift limited by running node.
- Free trial periods available for performance evaluation.
Scalability:
- BigQuery offers structured and well-planned scalability.
- On-demand pricing model provides control over resource assignment.
- RedShift has limitations in distributing workload across clusters.
Manageability:
- RedShift requires basic understanding of nodes and clusters.
- Periodic management activities needed.
- BigQuery is serverless, no knowledge required for setup; automatic scaling.
Usability:
- BigQuery’s SQL-like interface simplifies tasks.
- RedShift requires understanding of warehousing concepts; longer learning curve.
Security:
- Both have robust security; RedShift uses Amazon IAM, BigQuery uses Google Cloud IAM.
- Google has B2B identity management with OAuth for specific operations.
Pricing:
- BigQuery charges for storage, inserts, and queries individually.
- RedShift charges per node covering both storage and processing.
- RedShift ideal for everyday operations; BigQuery for variable workloads and data mining.
Current Google Trends for Bigquery vs Redhshift
Global Map
Based on current Google Trends, BigQuery is gaining popularity due to its user-friendly features.
In 2024, consider beginning your learning journey with BigQuery.
When to Choose Amazon RedShift or Google BigQuery
Choose Amazon RedShift When:
Stability and Consistency:
- Your organization values stability and consistency in data operations.
- You have predictable workloads and can benefit from a reserved-slots pricing model.
Deep AWS Integration:
- Your infrastructure heavily relies on AWS services, and you want seamless integration.
- AWS CloudTrail integration for auditing API calls is a priority.
Well-established Warehousing Concepts:
- Your team has a solid understanding of warehousing concepts, nodes, and clusters.
- You are willing to invest time in periodic management activities like cleaning and vacuuming tables.
Everyday Data Warehouse Operations:
- Your business primarily involves everyday data warehouse operations with relatively stable workloads.
- You prefer a pricing model that covers both storage and processing with a node-based approach.
Choose Google BigQuery When:
Flexibility and Scalability:
- Your organization requires flexibility and scalability for variable workloads.
- On-demand pricing model is preferred, providing complete control over resource assignment.
Multi-cloud Functionality:
- Analyzing data present in more than one cloud is crucial for your business.
- You appreciate the decoupling of storage and compute components for flexibility.
ML Integration and Automation:
- Machine learning integration and the ability to create and execute ML models with SQL queries is a priority.
- You prefer a serverless solution that requires minimal knowledge for setup and management.
Quick Response Times and Data Mining:
- Quick response times and the ability to process large volumes of data for data mining operations are essential.
- You want a simplified and automated data transfer process without coding.
If you’re interested in a blog about mastering BigQuery and essential techniques, feel free to leave a comment below.
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