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Amazon Redshift vs Google BigQuery vs Treasure Data: What are the differences?
Data Warehouse Architecture: Amazon Redshift is based on a traditional shared-nothing MPP architecture, while Google BigQuery utilizes a serverless architecture, and Treasure Data operates on a microservices-based architecture. This difference impacts how the data is stored, processed, and accessed within each platform.
Pricing Structure: Amazon Redshift follows a pay-as-you-go pricing model where users pay for the compute nodes they use. In contrast, Google BigQuery operates on a pay-per-query pricing model, and Treasure Data offers a flexible pricing structure based on the volume of data processed. This difference can significantly impact the cost of using each data warehousing solution.
Integration and Ecosystem: Amazon Redshift integrates seamlessly with other AWS services and has a robust ecosystem of tools and services. Google BigQuery integrates well with Google Cloud Platform services, and Treasure Data offers connectors to various third-party services. The level of integration and extensibility can affect the overall data workflow efficiency and flexibility.
Performance and Scalability: Amazon Redshift is known for its high performance and scalability, especially for complex queries and large datasets. Google BigQuery excels in processing ad-hoc queries and handling massive datasets quickly. Treasure Data focuses on real-time data processing and stream analytics, enabling high scalability for event-driven workloads. Understanding the specific performance and scalability requirements is crucial in choosing the right data warehousing solution.
Security and Compliance: Amazon Redshift offers robust security features, including encryption at rest and in transit, access control, and compliance certifications. Google BigQuery also provides strong security measures and compliance certifications. Treasure Data prioritizes data governance and compliance, offering features like data masking and privacy protection. Ensuring data security and compliance with industry regulations is paramount for all three data warehouse solutions.
Ease of Use and Management: Amazon Redshift offers a user-friendly interface and comprehensive management tools for monitoring and optimizing performance. Google BigQuery is known for its simplicity and ease of use, requiring minimal setup and maintenance. Treasure Data provides a unified platform for managing data pipelines, workflows, and analytics tasks. The level of ease of use and management convenience can impact user productivity and resource allocation for data teams.
In Summary, Amazon Redshift, Google BigQuery, and Treasure Data differ in their data warehouse architecture, pricing structure, integration and ecosystem, performance and scalability, security and compliance features, and ease of use and management capabilities.
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 Treasure Data
- Scaleability, less overhead2
- Makes it easy to ingest all data from different inputs2
- Responsive to our business requirements, great support1
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Cons of Amazon Redshift
Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
Cons of Treasure Data
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