ZNews, one of the largest media and information companies supporting digital distribution hosts their entire infrastructure on AWS, interested to analyze near real time clickstream events such as readership statistics, impressions, and page views for their 100’s of web applications generating 30 TB of data every day, analyze trending content in order to promote cross-platform sharing and increase consumer engagement and use clickstream data to perform data science, develop algorithms, and create visualizations and dashboards.
Hosting such a massive scale infrastructure, ZNews is looking at a streaming platform with the following capabilities
Ingest data from variety of streaming sources
Auto-Scaling, Support delivery to new destinations without diminishing throughput
Shall support aggregation and de-aggregation of data
Ease of configuration, implementation, management and maintenance
Support data standardization in JSON format, record format conversion, and compression
What kind of artifacts supports the above requirements? Select 3 options

Answer options:

A.Kinesis Data Streams
B.KPL Library
C.Streams API
D.Kinesis Data Firehose
E.Lambda Blueprints, Record Format Conversion
F.Kinesis Client Library + Enhanced Customers + Kinesis Connector Library

 Answer : B, D and E
Option A is incorrect -
Option A explanation does mention that Kinesis streams is not good option for above mentioned use case. Kinesis Data Streams is not the right platform to fulfil the requirements since Kinesis Data Streams does not provide data transformation and record format conversion. Besides the customer is looking for near real time response in minutes.
Besides Kinesis Data Streams to collect and process large streams of data records in real time. You can create data-processing applications, known as Kinesis Data Streams applications. A typical Kinesis Data Streams application reads data from a data stream as data records. These applications can use the Kinesis Client Library, and they can run on Amazon EC2 instances. se Kinesis Data Streams for rapid and continuous data intake and aggregation. The type of data used can include IT infrastructure log data, application logs, social media, market data feeds, and web clickstream data. Because the response time for the data intake and processing is in real time, the processing is typically lightweight.
The following are typical scenarios for using Kinesis Data Streams:
Accelerated log and data feed intake and processing
Real-time metrics and reporting
Real-time data analytics
Complex stream processing
Option B is correct - The KPL is an easy-to-use, highly configurable library that helps you write to a Kinesis data stream. It acts as an intermediary between your producer application code and the Kinesis Data Streams API actions.
https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-kpl.html
Option C is incorrect - A STREAM API does not provide aggregation of data. It provides collection of data in a single HTTP request. Streams API, using PutRecords operation sends multiple records to Kinesis Data Streams in a single request. By using PutRecords, producers can achieve higher throughput when sending data to their Kinesis data stream. Each PutRecords request can support up to 500 records. Each record in the request can be as large as 1 MB, up to a limit of 5 MB for the entire request, including partition keys. Also the platform programmatically supports changing between submissions of single records versus multiple records in a single HTTP request.
https://docs.aws.amazon.com/streams/latest/dev/developing-producers-with-sdk.html
Option D is correct - Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), and Splunk. Kinesis Data Firehose can invoke your Lambda function to transform incoming source data and deliver the transformed data to destinations. You can enable Kinesis Data Firehose data transformation when you create your delivery stream. Amazon Kinesis Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3. Parquet and ORC are columnar data formats that save space and enable faster queries compared to row-oriented formats like JSON. If you want to convert an input format other than JSON, such as comma-separated values (CSV) or structured text, you can use AWS Lambda to transform it to JSON first.
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
Option E is correct - enable Kinesis Data Firehose data transformation when you create your delivery stream. Amazon Kinesis Data Firehose can convert the format of your input data from JSON to Apache Parquet or Apache ORC before storing the data in Amazon S3. Parquet and ORC are columnar data formats that save space and enable faster queries compared to row-oriented formats like JSON. If you want to convert an input format other than JSON, such as comma-separated values (CSV) or structured text, you can use AWS Lambda to transform it to JSON first.
https://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html
https://docs.aws.amazon.com/firehose/latest/dev/record-format-conversion.html
Option F is incorrect - KCL library provides de-aggregation, enhanced consumers but does not provide Autoscaling. KCL library needs Connector library to write data into Redshift but ease of maintenance cannot be achieved. Consumers based on KCL library use push mechanism. use the record processor support provided by the Kinesis Client Library (KCL) to retrieve stream data in consumer applications. This is a push model, where you implement the code that processes the data. The KCL retrieves data records from the stream and delivers them to your application code. Enhanced Consumers provide scaling for additional destinations but the process is manual and need to have a clear idea of data throuput
https://docs.aws.amazon.com/streams/latest/dev/developing-consumers-with-sdk.html