Thanks for the great content. But, I am unable to create the materialized view and getting the following error: ERROR: Operations on local objects in external schema are not enabled. [ErrorId: 1-62f52b1e-5ea3ac1d18bf9a1234e86e4f] Am I missing any step ? I have followed all the steps as per the tutorial.
So sir.. Once data is read into materialized view then it is vanished from kinesis data stream? Or we can read same data with some other aws components/tools also?
Is it require to insert data into other staging layer the moment we query it from datastream very first time Or will data persist in datastream until we manually purge it?
In Kinesis, the data is immutable. It remains there for the retention period configured. When you refresh materialized view; it fetches the latest data from the Kinesis data stream.
The ingestion code I run from SageMaker notebook. Sample code is given in a link in the description. And yes, you can run python code from EC2. You need to setup authorization either using EC2 instance profile or using access/secret key profile or using cognito.
@@AWSTutorialsOnline I got a response from AWS that MSK and Avro deserialization are on the roadmap for Redshift Streaming Ingestion. We do already do Spark Streaming of MSK Avro serialized message into an S3 data lake, so I can confirm that does indeed work. I was hoping to PoC using this to go directly to RedShift for a materialized view but will have to wait until later this year. Thank you for the video and the reply!
Thank you for the Video, it is great. I think this is a nice feature. my question is similar to @Gunjan Jain one, but not about the data purge? I am wondering whether Redshift persists the data automatically during the process so that we won't rely on Kinesis Data Stream for its data retention.
Materialized view does not store data but provides a way to query data from Kinesis in the real-time. So you need to process data using Materialized View within the retention period of Kinesis data stream otherwise the data is not available anymore for processing.
@@AWSTutorialsOnline Materialized views are especially useful for queries that are predictable and repeated over and over. Instead of performing resource-intensive queries on large tables, applications can query the pre-computed data stored in the materialized view. Data is stored in the materialized view