firehose | Firehose - Spark streaming 2.2 Kafka

Sadly there is not. It still writes entire content of its buffer to s3. You would have to setup lambda transaction for the records and do the writing yourself using the lambda function.

Here is the sample output that worked(in python):

Spent few days but figured it out. You can use **kwargs to pass arguments like this

the second stack fails to find it

This is because nested CloudFormation stacks are created in parallel by default.

This means that if one of your child stacks - e.g. the stack which contains KinesisFirehoseRole - is importing the output from another child stack - e.g. the stack which contains KinesisStream - then the stack creation will fail.

This is because as they're created in parallel, how does CloudFormation ensure that the export value has been exported by the time another child stack created is importing it?

To fix this, use the DependsOn attribute on the stack which contains KinesisFirehoseRole.

This should point to the stack which contains KinesisStream as KinesisFirehoseRole has a dependency on it.

DependsOn makes this dependency explicit and will ensure correct stack creation order.

Something like this should work:

I suspect that your bench may be in "autocommit" mode and a COMMIT is being send at the end of each run block of code. This will end your transaction and release the lock. Can you confirm that your lock is still in place by viewing it from another session?

There are other ways that the lock was released, like a conflict being resolved, but you would have seen an error message in your bench if this was the case. Seeing which locks are in place during your firehose execution would be the direct way to detect what is happening.

Generally there are two choices:

1. Export the arn of your KinesisStreamARNParameter in the outputs. Then use ImportValue to reference it your second template.

2. Pass the arn as an input parameter to your second template. This will require you to manually provide the value when you deploy the second template, or create some automation wrapper that will populate that value for you before deployment.

Kinesis Firehose splits your data based on buffer size or interval.

Let's say you have a buffer size of 1MB and an interval of 1 minute. If you receive less than 1MB within the 1 minute interval, Kinesis Firehose will anyway create a batch file out of the received data, even if it is less than 1MB of data.

This is likely happening in scenarios with few data arriving. You can adjust your buffer size and interval to your needs. E.g. You could increase the interval to collect more items within a single batch.

You can choose a buffer size of 1–128 MiBs and a buffer interval of 60–900 seconds. The condition that is satisfied first triggers data delivery to Amazon S3.

From the AWS Kinesis Firehose Docs: https://docs.aws.amazon.com/firehose/latest/dev/create-configure.html

Since the shape and content of Pinpoint events are different than the format of interactions required by Personalize (either imported in bulk as an interactions CSV or incrementally via the PutEvents API), some transformation is going to be required to get these events into the right format. The solution you noted uses periodic bulk imports by using Athena to extract and format the event data saved in S3 (through Kinesis Firehose) into the CSV format expected by Personalize and then imported into Personalize. You can find the Athena named queries in the CloudFormation template for the solution here.

You have mentioned the resource without unit : https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/#resource-units-in-kubernetes