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Amazon Redshift vs Census: What are the differences?
Introduction:
Amazon Redshift and Census are both data warehousing solutions used for storing and analyzing large volumes of data. However, there are key differences between the two that cater to different needs and requirements.
1. Scalability: Amazon Redshift is highly scalable and can easily handle petabytes of data, making it suitable for enterprise-level data analysis. On the other hand, Census is more focused on providing a simple and intuitive interface for smaller data sets, making it ideal for small to mid-sized businesses that do not require massive scalability.
2. Pricing Model: Amazon Redshift follows a pay-as-you-go pricing model, where users are charged based on the amount of data stored and processed. In contrast, Census offers a flat-rate pricing model, making it easier for users to budget and plan their expenses without unexpected costs.
3. Integration with External Tools: Amazon Redshift has strong integration capabilities with various external tools and services, allowing for seamless data transfer and analysis across different platforms. Census, on the other hand, is more limited in terms of integration options, which may be a consideration for companies with complex data pipelines involving multiple tools.
4. Data Security: Amazon Redshift offers comprehensive security features such as data encryption, access controls, and compliance certifications to ensure data protection and regulatory compliance. While Census also offers similar security measures, it may lack some of the advanced security features provided by Amazon Redshift, which could be a deciding factor for organizations with strict security requirements.
5. Query Performance: Amazon Redshift is known for its high query performance and can efficiently handle complex analytical queries on large volumes of data. Census, while efficient for smaller data sets, may not perform as well as Amazon Redshift when it comes to processing complex queries at scale, which could impact the user experience and overall data analysis capabilities.
6. Customization Options: Amazon Redshift allows for extensive customization and optimization of the data warehouse environment, enabling users to fine-tune performance and scalability based on their specific requirements. Census, on the other hand, may offer fewer customization options, which could limit the flexibility and adaptability of the data warehouse to unique business needs and workflows.
In Summary, Amazon Redshift and Census differ in scalability, pricing model, integration capabilities, data security, query performance, and customization options catering to diverse business requirements and data analysis needs.
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.
Pros of Amazon Redshift
- Data Warehousing41
- Scalable27
- SQL17
- Backed by Amazon14
- Encryption5
- Cheap and reliable1
- Isolation1
- Best Cloud DW Performance1
- Fast columnar storage1