Kinesis Analytics Java Connectors, Operations, and Processing Mechanism

Answer : A,D and E.

With Amazon Kinesis Data Analytics for Java Applications, you can use Java to process and analyze streaming data.

The service enables you to author and run Java code against streaming sources to perform time-series analytics, feed real-time dashboards, and create real-time metrics.

Build Java applications in Kinesis Data Analytics using open-source libraries based on Apache Flink.

Apache Flink is a popular framework and engine for processing data streams.

Kinesis Data Analytics provides the underlying infrastructure for your Apache Flink applications.

It handles core capabilities like provisioning compute resources, parallel computation, automatic scaling, and application backups (implemented as checkpoints and snapshots)

In Amazon Kinesis Data Analytics for Java Applications, connectors are software components that move data into and out of an Amazon Kinesis Data Analytics application.

Connectors are flexible integrations that enable you to read from files and directors.

Connectors consist of complete modules for interacting with AWS services and third-party systems.

Types of connectors include the following:

Sources: Provide data to your application from a Kinesis data stream, file, or other data source.

Sinks: Send data from your application to a Kinesis data stream, Kinesis Data Firehose delivery stream, or other data destination.

Asynchronous I/O: Provides asynchronous access to a data source (such as a database) to enrich stream events.

To transform incoming data in a Kinesis Data Analytics for Java application, you use an Apache Flink operator.

An Apache Flink operator transforms one or more data streams into a new data stream.

The new data stream contains modified data from the original data stream.

Kinesis Applications for Java supports.

Transform Operators.

Aggregation Operators.

https://docs.aws.amazon.com/kinesisanalytics/latest/java/what-is.html https://docs.aws.amazon.com/kinesisanalytics/latest/java/how-operators.html

Kinesis Analytics Java is a fully managed service by AWS which helps in processing streaming data in real-time. It integrates with Kinesis Data Streams, and provides the capability to run SQL queries on streaming data to perform various operations such as filtering, transformation, and aggregation.

The question asks about the connectors, operations, and processing mechanism used by Kinesis Analytics Java. Let's discuss the possible options:

A. Sources, Sinks, Asynchronous IO: Sources refer to the input streams that are used to provide data to Kinesis Analytics Java for processing. Sinks refer to the output streams where the processed data is sent for further consumption. Asynchronous IO refers to the non-blocking mode of data transfer where the data is processed without waiting for a response from the recipient. These concepts are relevant to Kinesis Analytics Java as it needs to read data from Kinesis Data Streams as a source, and write the processed data to Redshift or QuickSight as a sink. Hence, option A is correct.

B. Sources, Sinks, Channels, Agents: Channels and agents are not relevant to Kinesis Analytics Java. Channels are a concept used in Apache Flume, which is not mentioned in the question. Agents are used in the context of monitoring and managing resources, which is not relevant to the question. Hence, option B is incorrect.

C. Apache Flume: Apache Flume is a tool used for efficiently collecting, aggregating, and moving large amounts of log data. It is not relevant to the question, as L-Finance is not mentioned to be using it. Hence, option C is incorrect.

D. Apache Flink: Apache Flink is an open-source stream processing framework used for real-time analytics and data processing. Although it is relevant to the context of the question, it is not mentioned as a tool being used by L-Finance. Hence, option D is incorrect.

E. Transformation and Aggregation: Transformation and aggregation are the key operations performed by Kinesis Analytics Java on the streaming data. Transformation involves modifying the structure and content of the data, while aggregation involves summarizing or grouping the data based on certain criteria. These operations are performed through SQL queries that are run on the streaming data. Hence, option E is correct.

F. Format Conversion and Accumulation: Format conversion and accumulation are not key operations performed by Kinesis Analytics Java. Although some format conversion may occur as a part of transformation, it is not mentioned as a separate operation. Accumulation is not mentioned as a concept relevant to the question. Hence, option F is incorrect.

Therefore, the three options that are relevant to Kinesis Analytics Java are Sources, Sinks, Asynchronous IO, Transformation and Aggregation.