Amazon Redshift is a cloud data warehouse that provides reporting and analytics capabilities for structured and semi-structured data. It was designed to handle analytical workloads on big data sets using column-oriented database principles similar to ClickHouse. While attractive to existing AWS users due to its tight integration with the Amazon ecosystem, Redshift users who adopt it to power real-time analytics applications find themselves in need of a more optimized solution for this purpose. As a result, they increasingly turn to ClickHouse to benefit from superior query performance and data compression. Redshift differs from ClickHouse in terms of its engine optimization for data warehousing workloads requiring complex reporting and analytical queries. However, ClickHouse achieves lower query latencies, including for varied query patterns, under high concurrency and while subjected to streaming inserts. It also places much higher limits on concurrent queries, which is vital for real-time application experiences. Additionally, ClickHouse offers superior data compression, allowing users to reduce their total storage (and thus cost) or persist more data at the same cost and derive more real-time insights from their data. Users appreciate ClickHouse for its wide-ranging support of real-time analytical capabilities, such as large range of specialized analytical functions designed to shorten and simplify query syntax, SQL query syntax designed to make analytical queries easier, superior data types support, file and data formats support, federated querying capabilities, secondary indexes & projections, etc. Redshift deployment options include serverless and provisioned instances, each with strengths and weaknesses for different workloads. The Ethereum dataset, which is not offered by AWS, can be generated using the excellent Ethereum ETL tooling or downloaded from a public bucket. ClickHouse vs Redshift storage efficiency comparison shows that ClickHouse compresses data more efficiently than the optimal Redshift schema, resulting in a combined rate of 2x for this dataset. Benchmarks compare query performance between ClickHouse and Redshift, with ClickHouse Cloud node completing queries in less time than a comparative Redshift cluster. Migrating Redshift tables to ClickHouse involves mapping equivalent ClickHouse types for each Redshift type, handling data types, compression, sorting keys, and primary key concepts. The approach of exporting data from Redshift to S3 using the UNLOAD command and then importing it into ClickHouse has limitations, such as relying on the latest timestamp in ClickHouse and potentially causing delays between export and import. Using AWS Lambda or an external script can help mitigate these issues by running periodically after exports are completed. Furthermore, ClickHouse provides superior data compression, allowing users to reduce their total storage (and thus cost) or persist more data at the same cost and derive more real-time insights from their data. ClickHouse vs Redshift query comparison shows that ClickHouse completes queries in less time than Redshift for various queries, such as Ethereum gas used by week and total Ethereum market capitalization. In conclusion, moving data to ClickHouse from Redshift can accelerate queries for real-time analytics, and leveraging ClickHouse for real-time analytics on top of this data can provide superior performance and insights.