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Amazon Redshift vs pgweb: What are the differences?
## Introduction
In this comparison, we will highlight key differences between Amazon Redshift and pgweb.
1. **Performance**: Amazon Redshift is a fully managed, high-performance data warehouse service, optimized for online analytical processing (OLAP) workloads, whereas pgweb is a web-based database browser for PostgreSQL databases without specific optimizations for data warehousing.
2. **Scalability**: Amazon Redshift is designed to scale effortlessly by adding additional nodes to the cluster, allowing for increased storage and compute power on-demand. Pgweb, on the other hand, lacks built-in scalability features as it mainly serves as a tool for database management and query execution.
3. **Pricing Model**: Amazon Redshift follows a pay-as-you-go pricing model based on the type and number of nodes used, along with the amount of data stored. Pgweb is an open-source tool and does not have any associated costs for usage.
4. **Data Backup and Recovery**: Amazon Redshift provides automated backups and point-in-time recovery to ensure data durability and reliability. Pgweb does not offer out-of-the-box data backup and recovery options and relies on external methods for these functionalities.
5. **Built-in Features**: Amazon Redshift includes various advanced features like data compression, columnar storage, and parallel query execution to optimize performance and efficiency. Pgweb, being a lightweight web tool, focuses more on providing a user-friendly interface for database interactions without advanced data warehousing features.
6. **Support and Documentation**: Amazon Redshift offers comprehensive support and documentation from Amazon Web Services (AWS) for troubleshooting, optimization, and best practices. Pgweb, being an open-source project, relies on community support and forums for assistance and may not have as extensive documentation and professional support services.
In Summary, Amazon Redshift and pgweb differ significantly in terms of performance optimization, scalability options, pricing model, data backup capabilities, built-in features, and support resources.
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 pgweb
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