How to copy large data sets from a database to a data warehouse

Loading petabytes of data from servers to a warehouse every day can be a challenging task, as it requires a high-performance and scalable infrastructure that can handle large data volumes and high data transfer rates. Here are some best practices for loading large amounts of data into a data warehouse on a daily basis:

Use a high-performance data transfer protocol: When transferring large amounts of data, it’s important to use a high-performance data transfer protocol that can efficiently handle large data volumes. Some popular options include Aspera and GridFTP, which are optimized for large data transfers, or other optimized protocols like S3-S3 data transfer.
Use a data replication tool: As previously mentioned, data replication tools can be used to replicate data from your servers to the data warehouse in real-time. This allows you to keep your data warehouse updated with the latest data from your servers, without the need for manual data exports or imports.
Use Data integration service: Utilize a cloud-based data integration service like AWS Glue, Data Factory, or Data Pipeline to move data from various sources, including servers, into the data warehouse. These services provide a scalable and fault-tolerant infrastructure for data movement and transformation.
Use partitioning: Partitioning your data can help you to speed up the loading process by breaking the data into smaller subsets, allowing you to load the data in parallel. This can be done based on the time stamp of the data, primary key or any other attributes.
Use Compression: If the data is compressible, using a compressed format can help reducing the data transfer time as well as the storage cost.
Use a Cloud-based warehousing service: Cloud-based data warehousing services like Google BigQuery, Microsoft Azure Synapse Analytics, Snowflake and others have the capability to handle very large data sets, and can automatically scale up or down as needed to handle the data volume and transfer rate.
Monitor and measure: Make sure to monitor and measure the performance of your data loading process and tune the process as needed to ensure optimal performance.

It’s worth noting that some of these best practices can be combined together, to achieve better performance and scalability. In addition, you may also want to consider using a data warehousing framework like Hadoop or Spark, which can help you to process and analyze large data sets in parallel, making it possible to load large amounts of data into a data warehouse on a daily basis.