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Amazon Redshift vs Google BigQuery vs Snowflake: What are the differences?
Amazon Redshift, Google BigQuery, and Snowflake are popular cloud-based data warehouse solutions that offer scalable and high-performance analytical capabilities. When evaluating these platforms, it's essential to understand their key differences to determine which one best suits your specific needs.
1. **Architecture**: Amazon Redshift uses a Massively Parallel Processing (MPP) architecture, Google BigQuery utilizes a serverless architecture, and Snowflake operates on a multi-cluster shared architecture. Redshift's MPP architecture allows for easy scalability by adding more nodes to the cluster, while BigQuery's serverless architecture eliminates the need for infrastructure management. Snowflake's multi-cluster shared architecture offers separation of storage and compute resources, providing more flexibility in managing workloads.
2. **Pricing Model**: Amazon Redshift follows a pay-as-you-go model with on-demand pricing, Google BigQuery charges based on the amount of data processed, and Snowflake offers separate pricing for storage and compute resources. Redshift may be more cost-effective for steady workloads, while BigQuery's pricing can result in savings for organizations with unpredictable query patterns. Snowflake's pricing model allows for optimizing costs by scaling compute resources based on performance requirements.
3. **Concurrency**: Amazon Redshift offers limited concurrent queries per cluster, Google BigQuery allows unlimited concurrent queries, and Snowflake provides automatic scaling for concurrent workloads. Redshift's concurrency limitations may impact performance during peak usage hours, while BigQuery's unlimited concurrency can efficiently handle multiple queries simultaneously. Snowflake's automatic scaling ensures optimal performance regardless of the level of concurrency.
4. **Data Loading**: Amazon Redshift employs a COPY command for bulk data loading, Google BigQuery supports streaming data ingestion in real-time, and Snowflake facilitates both batch and streaming data loading. Redshift's COPY command is suitable for bulk loading from S3 or DynamoDB, while BigQuery's streaming capabilities enable real-time analytics. Snowflake's support for both batch and streaming loading options offers flexibility in data ingestion methods.
5. **Query Performance**: Amazon Redshift provides performance tuning capabilities through distribution keys and sort keys, Google BigQuery's query performance is optimized with the Dremel execution engine, and Snowflake leverages a query optimizer for efficient data retrieval. Redshift's ability to define distribution and sort keys enhances query performance for specific use cases, while BigQuery's Dremel engine accelerates query processing for large datasets. Snowflake's query optimizer dynamically adapts to workload demands, optimizing query performance in real-time.
6. **Integration Ecosystem**: Amazon Redshift integrates seamlessly with other AWS services, Google BigQuery is designed to work with the Google Cloud Platform ecosystem, and Snowflake supports interoperability with various cloud providers. Redshift's tight integration with AWS services simplifies data pipeline management within the AWS environment, while BigQuery's compatibility with GCP tools enhances data analytics workflows. Snowflake's multi-cloud support allows organizations to leverage different cloud providers for specific use cases, facilitating a hybrid or multi-cloud strategy.
In Summary, understanding the key differences between Amazon Redshift, Google BigQuery, and Snowflake in terms of architecture, pricing, concurrency, data loading, query performance, and integration ecosystem can help organizations make informed decisions when selecting a cloud data warehouse solution.
We need to perform ETL from several databases into a data warehouse or data lake. We want to
- keep raw and transformed data available to users to draft their own queries efficiently
- give users the ability to give custom permissions and SSO
- move between open-source on-premises development and cloud-based production environments
We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
Pros of Amazon Redshift
- Data Warehousing41
- Scalable27
- SQL17
- Backed by Amazon14
- Encryption5
- Cheap and reliable1
- Isolation1
- Best Cloud DW Performance1
- Fast columnar storage1
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
Pros of Snowflake
- Public and Private Data Sharing7
- Multicloud4
- Good Performance4
- User Friendly4
- Great Documentation3
- Serverless2
- Economical1
- Usage based billing1
- Innovative1
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Cons of Amazon Redshift
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
Cons of Snowflake
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