We live in a world driven by data and every second we are processing a large amount of data, using it, analyzing it, and transforming it. Data is very essential for businesses these days. Therefore need for handling the Dynamically generating data is important. As the number, variety, and velocity of data sources grow, new architectures and technologies are needed. Technologies like Amazon Kinesis are focused on ingesting the massive flow of data from multiple fire hoses and then routing it to the systems that need it – optionally filtering, aggregating, and analyzing en-route. This is where the need for data streaming services is needed. One of these streaming services is AWS Kinesis firehose.

In this blog, we will have an Introduction to Amazon Kinesis firehose and see how you can stream your data using Amazon Kinesis firehose.

What is Kinesis firehose?

Amazon firehose Kinesis is the data streaming service provided by Amazon which lets us Stream data in real-time for storing data and for analytical and logging purposes. It can easily capture data from the source, transform that data, and then put it into destinations supported by Kinesis Firehose.

The most amazing thing about firehose is that you do not need to worry about scaling, It automatically scales up according to the data throughput. You can easily

• Batch
• Compress
• Transform
• Encrypt

Your data stream before loading saving you storage and minimising cost.

Your data stream before loading saving you storage and minimizing cost.

Some common data sources for Amazon kinesis:

• IoT devices
• Log files
• Game activities
• Events or data generated by other AWS services

There are actually 3 services which make up Amazon Kinesis:

• Amazon Kinesis Firehose is the simplest way to load massive volumes of streaming data into AWS. The capacity of your Firehose is adjusted automatically to keep pace with the streaming throughput. It can optionally compress and encrypt the data before it’s stored.
• Amazon Kinesis Streams are similar to the Firehose service but give you more control, allowing for:
• Multi-stage processing
• Custom stream partitioning rules
• Reliable storage of the stream data until it has been processed.
• Amazon Kinesis Analytics is the simplest way to process the data once it has been ingested by either Kinesis Firehose or Streams. The user provides SQL queries which are then applied to analyse the data; the results can then be displayed, stored, or sent to another Kinesis stream for further processing.

#aws #aws services #scala ##kinesis #firehose