Exam AWS Certified Solutions Architect - Associate SAA-C02 topic 1 question 244 discussion

Answer is B. Kinesis data streams consists of shards. The more througput is needed, the more shards you add, the less throughput, the more shards you remove, so it’s scalable. Each shard can handle up to 1MB/s of writes. However Kinesis data streams stores ingested data for only 1 to 7 days so there is a chance of data loss. Additionally, Kinesis data analytics and kinesis data streams are both for real-time ingestion and analytics. Firehouse on the other hand is also scalable and processes data in near real time as per the requirement. It also transfers data into Redshift which is a data warehouse so data won’t be lost. Redshift also has a SQL interface for performing queries for data analytics. This information was sourced from ultimate AWS certified solutions architect 2020 course with Stephane Maarek.

I am completely confused with the comments here. More than 80 comments saying ALL options are correct. This is sad! I wish people will comment only if they are sure of the answer.

B.Amazon Kinesis Data Firehose could lose data C.EC2 instance store could lose data D:Elastic Block Store and Redis could lose data

The answer is B, it's more resilient than A and compatible with the near-real-time requirements.

Query ingested data in near-real time= kinnesis data analytics.

AAAAAAAAA

Kinesis Data Streams for real time data ingestion and KDA for "real-time" analytics. REal time analytics is the key. Load in Redshift is not real time..

For near real time streaming and processing answer A is correct.

Answer is A , Amazon Kinesis Data Streams to collect and store streaming data Amazon Kinesis Data Analytics to process and analyze the streaming data continuously https://aws.amazon.com/blogs/big-data/perform-near-real-time-analytics-on-streaming-data-with-amazon-kinesis-and-amazon-elasticsearch-service/ Redshift is nowhere required.

Kinesis Strem = Real time. so A for me.

Near Real time -> firehouse JSON -> so redshift is not suitable I will go with A as KDS ingest data in real-time but KDA is near real time plus JSON data lead me to not use redshift I hope this one is not in my exam

- Wrong. Answer is A. - "near-real-time data querying" --> Kinesis Data Analytics - "minimal data loss" --> Kinesis Data Streams, which has configurable persistence (1 - 7 days) - https://docs.aws.amazon.com/streams/latest/dev/kinesis-extended-retention.html

My bad ... after analysing the question. Answer is B

1. an Amazon Kinesis Data Analytics for Apache Flink application transforms and aggregates streaming data in near-real time 2. Amazon Kinesis Data Firehose extends data from Kinesis Data Streams to your data lake stored in Amazon Simple Storage Service (Amazon S3) Answer is A. https://aws.amazon.com/blogs/database/near-real-time-processing-with-amazon-kinesis-amazon-timestream-and-grafana/

Ans is A

Kinesis Data Firehose Load streaming data into S3 / Redshift /ES / 3rd party / custom HTTP Near real-time (buffer time min. 60 sec) Automatic scaling

This is a data analytics question, the answer should be A, you need to ingest the data in real time so you should use Kinesis Data Stream, the do the analytics with near real time analysis tool which is Kinesis Data Analytics. Kinesis Data Firehose can't ingest 1MB/sec.