How Netflix Built Their Data Pipeline with Amazon Redshift
The data infrastructure at Netflix is certainly one of the most complex ones, having in mind that they serve over 550 billion events per day, equaling roughly to 1.3 petabytes of data. In general, Netflix’s architecture is broken down into smaller systems, such as systems for data ingestion, analytics, predictive modeling etc. The data stack employed in the core of Netflix is mainly based on Apache Kafka for real-time (sub-minute) processing of events and data. Data needed in the long-term is sent from Kafka to AWS’s S3 and EMR for persistent storage, but also to Redshift, Hive, Snowflake, RDS and other services for storage regarding different sub-systems. Metacat is built to make sure the data platform can interoperate across these data sets as a one “single” data warehouse. Its task is to actually connect different data sources (RDS, Redshift, Hive, Snowflake, Druid) with different compute engines (Spark, Hive, Presto, Pig). Other Kafka outputs lead to a secondary Kafka sub-system, predictive modeling with Apache Spark, and Elasticsearch. Operational metrics don’t flow through the data pipeline but through a separate telemetry system named Atlas.
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