AWS Kinesis is a widely adopted message queue used by AWS users, much like a cloud service version of Apache Kafka. Support for AWS Kinesis will be a great addition to the handful of Flink's streaming connectors to external systems and a great reach out to the AWS community.
AWS supports two different ways to consume Kinesis data: with the low-level AWS SDK , or with the high-level KCL (Kinesis Client Library) . AWS SDK can be used to consume Kinesis data, including stream read beginning from a specific offset (or "record sequence number" in Kinesis terminology). On the other hand, AWS officially recommends using KCL, which offers a higher-level of abstraction that also comes with checkpointing and failure recovery by using a KCL-managed AWS DynamoDB "leash table" as the checkpoint state storage.
However, KCL is essentially a stream processing library that wraps all the partition-to-task (or "shard" in Kinesis terminology) determination and checkpointing to allow the user to focus only on streaming application logic. This leads to the understanding that we can not use the KCL to implement the Kinesis streaming connector if we are aiming for a deep integration of Flink with Kinesis that provides exactly once guarantees (KCL promises only at-least-once). Therefore, AWS SDK will be the way to go for the implementation of this feature.
With the ability to read from specific offsets, and also the fact that Kinesis and Kafka share a lot of similarities, the basic principles of the implementation of Flink's Kinesis streaming connector will very much resemble the Kafka connector. We can basically follow the outlines described in StephanEwen's description  and rmetzger's Kafka connector implementation . A few tweaks due to some of Kinesis v.s. Kafka differences is described as following:
1. While the Kafka connector can support reading from multiple topics, I currently don't think this is a good idea for Kinesis streams (a Kinesis Stream is logically equivalent to a Kafka topic). Kinesis streams can exist in different AWS regions, and each Kinesis stream under the same AWS user account may have completely independent access settings with different authorization keys. Overall, a Kinesis stream feels like a much more consolidated resource compared to Kafka topics. It would be great to hear more thoughts on this part.
2. While Kafka has brokers that can hold multiple partitions, the only partitioning abstraction for AWS Kinesis is "shards". Therefore, in contrast to the Kafka connector having per broker connections where the connections can handle multiple Kafka partitions, the Kinesis connector will only need to have simple per shard connections.
3. Kinesis itself does not support committing offsets back to Kinesis. If we were to implement this feature like the Kafka connector with Kafka / ZK to sync outside view of progress, we probably could use ZK or DynamoDB like the way KCL works. More thoughts on this part will be very helpful too.
As for the Kinesis Sink, it should be possible to use the AWS KPL (Kinesis Producer Library) . However, for higher code consistency with the proposed Kinesis Consumer, I think it will be better to stick with the AWS SDK for the implementation. The implementation should be straight forward, being almost if not completely the same as the Kafka sink.