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Amazon Redshift vs Azure Cosmos DB: What are the differences?
Introduction
In this article, we will compare Amazon Redshift and Azure Cosmos DB, two popular data storage and analytics solutions. While both platforms offer powerful features for managing data, there are several key differences that set them apart. Let's dive into the details.
Scalability and Performance: Amazon Redshift is built specifically for data warehousing and offers excellent scalability and performance for analytical workloads. It uses columnar storage and parallel processing to deliver fast query performance, especially for complex queries involving large datasets. On the other hand, Azure Cosmos DB is a globally distributed, multi-model database that provides low-latency, scalable storage and querying capabilities. It is designed to handle vast amounts of data with guaranteed low latency and high throughput.
Data Model and Query Language: Amazon Redshift follows a traditional relational database model with support for SQL as the primary query language. It offers a SQL-based interface for querying and managing data, making it easy for SQL-savvy users to work with. In contrast, Azure Cosmos DB is a NoSQL database that supports multiple data models, including key-value, document, graph, and column-family. It provides a JSON-like query language, SQL API, MongoDB API, and more, allowing developers to choose the most suitable model for their application's needs.
Data Consistency and Availability: Amazon Redshift guarantees high availability and durability of data through automated backups, replication, and fault tolerance mechanisms. It provides different levels of data consistency, such as eventual consistency and read-after-write consistency, depending on the configuration. Azure Cosmos DB, being a globally distributed database, ensures high availability and consistency across multiple regions. It offers strong consistency, bounded staleness, session consistency, and more, allowing developers to configure the desired level of consistency for their applications.
Geographical Presence: Amazon Redshift is available in multiple regions across the globe, allowing users to deploy their clusters in the nearest region to optimize performance. However, the geographical presence may be limited compared to Azure Cosmos DB, which is designed with global distribution in mind. Azure Cosmos DB provides global scale-out and automatic multi-region replication, ensuring data availability and low-latency access in any region where Azure is available.
Data Integration and Ecosystem: Amazon Redshift has strong integration with other AWS services like AWS Glue for data ingestion and transformation, Amazon S3 for data storage, and Amazon EMR for big data processing. It also integrates well with popular business intelligence (BI) tools like Tableau and Looker. Azure Cosmos DB integrates seamlessly with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Stream Analytics, allowing developers to build end-to-end solutions using a unified Azure ecosystem.
In summary, Amazon Redshift is a powerful data warehousing solution with excellent scalability and performance, suitable for complex analytical workloads. On the other hand, Azure Cosmos DB is a globally distributed, multi-model database that provides low-latency access, high scalability, and flexibility to handle diverse data models. The choice between the two depends on specific requirements, workload types, and the need for global data distribution.
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
Pros of Azure Cosmos DB
- Best-of-breed NoSQL features28
- High scalability22
- Globally distributed15
- Automatic indexing over flexible json data model14
- Tunable consistency10
- Always on with 99.99% availability sla10
- Javascript language integrated transactions and queries7
- Predictable performance6
- High performance5
- Analytics Store5
- Rapid Development2
- No Sql2
- Auto Indexing2
- Ease of use2
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Cons of Amazon Redshift
Cons of Azure Cosmos DB
- Pricing18
- Poor No SQL query support4
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What is Amazon Redshift?
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