Lake house architecture
Game developers often use data warehouse alongside a data lake. Data
warehouse can provide lower latency and better performance of SQL
queries working with local data. That’s why one of the common
use-cases for the data warehouse in games analytics is building
daily aggregations to be consumed from business intelligence (BI)
solutions. Games can generate a lot of data, even logging activity
down to the key stroke. This can result in having to process
terabytes of data every day, and the data may reside in, or need to
be loaded into, different data stores. These data stores can be a
cache such as
Amazon ElastiCache (Redis OSS)
For example, DynamoDB performs well for specific use cases such as reading and writing data with single digit millisecond latency. But it should not be used as a source for your analytical queries, as there is no one tool that is perfect for every job. Having a lake house architecture allows customers to easily move data to and from their data stores in a fast and secure manner. This also allows customers to connect their data lake to their databases and data warehouses using the AWS Glue Data Catalog, which is integrated with many AWS services.
Instead of building a siloed data warehouse, you can use
technologies to integrate data lake with it. For example, use
Redshift Spectrum to query data directly from the S3 data lake, or the
Amazon Redshift
Refer to
Derive
Insights from AWS Modern Data for more details, and the
Build
a Lake House Architecture on AWS
![A diagram depicting AWS lake house architecture.](/images/whitepapers/latest/best-practices-building-data-lake-for-games/images/lake-house-architecture.png)
AWS lake house architecture