Below is a comparison of Amazon Redshift, Snowflake, Google BigQuery, and Databricks in tabular format, covering various aspects:
| Aspect | Amazon Redshift | Snowflake | Google BigQuery | Databricks |
|---|---|---|---|---|
| Provider | Amazon Web Services (AWS) | Snowflake Inc. | Google Cloud Platform (GCP) | Databricks Inc. |
| Architecture | Columnar storage, MPP | Multi-cluster shared data, separated compute and storage | Serverless, MPP | Unified analytics platform |
| Storage | S3-backed, managed storage | Proprietary storage, AWS S3, Azure Blob, Google Cloud Storage | Colossus (Google’s distributed file system) | Delta Lake (open-source storage layer) |
| Compute | Redshift Clusters (nodes) | Virtual warehouses (independent compute clusters) | Serverless (on-demand compute) | Apache Spark-based clusters |
| Data Format Support | CSV, TSV, Parquet, ORC, JSON | CSV, TSV, Parquet, ORC, JSON | CSV, JSON, Avro, Parquet, ORC | Delta Lake, Parquet, ORC, JSON, CSV |
| Pricing Model | Pay per instance/hour (compute), per GB/month (storage) | Pay per second of compute, per TB of storage | Pay per query (analysis), per TB of storage | Pay per instance/hour (compute), per GB/month (storage) |
| Scaling | Manual or automatic scaling | Auto-scaling for compute, elastic scaling for storage | Automatic scaling, serverless architecture | Auto-scaling, serverless with Databricks SQL |
| Performance | Good performance, dependent on node type and size | High performance with automatic clustering | High performance, automatic optimization | High performance with optimized Spark execution engine |
| Concurrency | Limited concurrency, can be enhanced with concurrency scaling | High concurrency, multi-cluster architecture | High concurrency, suitable for ad-hoc queries | High concurrency, suitable for large-scale data processing |
| Security | VPC, encryption (at rest and in transit), IAM, KMS | End-to-end encryption, role-based access control, multi-factor auth | VPC, encryption (at rest and in transit), IAM, Cloud KMS | Encryption (at rest and in transit), IAM, role-based access |
| Data Sharing | Data sharing within Redshift clusters | Secure data sharing across different accounts and regions | Data sharing within and across organizations via datasets | Delta Sharing (open protocol for secure data sharing) |
| Integration with Ecosystem | Deep integration with AWS services (S3, Glue, IAM, etc.) | Integrates with AWS, Azure, and Google Cloud | Deep integration with GCP services (Dataflow, Dataproc, etc.) | Integrates with multiple cloud providers and services |
| ETL/ELT Support | Supports ETL/ELT with AWS Glue, external ETL tools (Fivetran, etc.) | Supports ETL/ELT with Snowpipe, external ETL tools (Matillion, etc.) | Supports ETL/ELT with Dataflow, Dataproc, external ETL tools | Supports ETL/ELT with Delta Live Tables, external ETL tools |
| Machine Learning | Integrates with Amazon SageMaker, built-in Redshift ML | Integrates with various ML platforms, Snowflake Data Science | Integrates with Vertex AI, BigQuery ML | Built-in ML capabilities with Databricks ML, integrates with MLflow |
| Availability | Regional availability with high availability features | Multi-zone availability, cross-cloud redundancy | Global availability with multi-region replication | Regional availability with high availability features |
| Data Governance | AWS Lake Formation, IAM, encryption | Data governance and compliance tools, role-based access | Google Data Catalog, IAM, encryption | Unity Catalog for data governance and compliance |
