Low latency Streaming ingest at scale
It is used to load streams into S3, Redshift, ElasticSearch, and Splunk.
Perform real-time analytics on streaming using SQL.
With the main purpose of streaming video on real time auto-scaling the infrastructure required.
Some consider that Kinesis is a managed alternative to Apache Kafka.
The main purpose is to gather a lot of data in real-time. Manage real-time big data.
Great to gather data such as applications logs, metrics IoT data or Click streams.
It can integrate with processing frameworks for string pressing frameworks such as Spark or NiFi, to mention a few.
Data with in Kinesis is automatically replicated synchronously to 3 AZ
Great to gather data such as applications logs, metrics IoT data or Click streams.
This service can take a lot of data from streams or IoT devices or metrics and logs from a server directly. In less words will ingest a lot of data from different sources.
Streams are divided into ordered Shards(Similar to partition). The flow starts on the producer, sending to our stream on Kinesis, and the stream is made of Shards, the shards will be delivered to consumers.
The time that the data is available in the stream we will refer to as Data retention time.
The retention time is 24 hours by default and can go up to 7 days, the data does not available forever. On the retention time has the ability to reprocess or replay data as many times you want on the period of time set in the retention time.
Multiple applications can consume the same stream,
The retention time is 24 hours by default and can go up to 7 days, the data does not available forever. On the retention time has the ability to reprocess or replay data as many times you want on the period of time set in the retention time.
A single stream is made of many shards, you need to take into consideration the billing is per shard provisioned, and there are no limits on the number of shards.
The records are ordered by shard, this means that the records within the shard will be ordered base on when they are received.
The number of shards can evolve over time, due it is possible to merge the shards. Changing the final count of shards.
It is the data being sent, serialized as bytes, can be up to 1MB and can represent anything.
Data is sent alongside a record with the purpose of group records in Shards, meaning that Kinesis knows to reach the correct shard to send that data.
This is something the producer doesn’t send. It is added by Kinesis after it is ingested, this represents the sequence number in the shard when the data is added to the chart.
The producer can generate one megabyte of one thousand messages per second per Shard, if this restriction is exceeded it returns “ProvisionedThrougputException”.
The Classic consumer can read up to 2 MB/s per shard across all consumers, and a maximum of % API calls per second for each Shard across all consumers.
In the case, the consumer is Enhanced Fan-Out it only has the restriction of reading at a maximum of 2 MB/s per shard and for each enhanced consumer.
At the moment you need to compute a metric to analyze the data in real-time, creating alerts to notify.
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These services ensure the data to be stored somewhere for later analysis or real-time dashboards. With the benefit of the near real-time service, could be S3 bucket or Amazon RedShift.
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