Stream your data using Amazon Kinesis Firehose

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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.

We are only going to focus on Kinesis firehose and focus on the other two in later blogs.

Why use Kinesis firehose?

We do know what Amazon Kinesis is but let us now see why we should use amazon kinesis firehose

  1. Easy to use: creating a stream and transform the data can be a time-consuming task but kinesis firehose makes it easy for us to create a stream where we just have to select the destination where we want to send the data from hundreds of thousands of data sources simultaneously. 
  2. Easy integration with other AWS services: It is very easy to integrate firehose with other AWS services such as S3, Elasticsearch, Redshift. It can also deliver the data to an HTTP  endpoint and can also deliver to service providers like Datadog, New Relic, MongoDB, and Splunk.
  3. Real-time data loading: Kinesis Data Firehose captures and loads data in near real-time. It loads new data into your destinations within 60 seconds after the data is sent to the service. 
  4. Easy management of service: Kinesis Data Firehose is a fully managed service that automatically provisions, manages, and scales compute memory, and network resources required to process and load your streaming data.
  5. Pay for what you use: Kinesis Data Firehose, you pay only for the volume of data you transmit through the service, and if applicable, for data format conversion. 

How does firehose work?

Figure: Kinesis Firehose flow

It is very easy to understand the working of Kinesis Firehose and to get started with it. The flow for working with Firehose is as follows.

  1. Create a delivery stream.
  2. Configure the source of the data.
  3. Transform source data in firehose using AWS lambda.
  4. Dump the data in the preferred destination.

Figure: Destinations provided by Kinesis Firehose

That’s it! It’s that simple to use Kinesis Firehose. You can follow simple steps given in the AWS console and get started with your first Kinesis Firehose stream.

Where can I use Kinesis firehose?

Till now we have discovered all we can about kinesis firehose. We understood the data flow for it. Now we will look at some use cases where kinesis firehose can help us make our lives easier.

  1. IoT Analytics: capture data from consumer appliances such as sensors and setup boxes 
  2. Clickstream Analytics: Kinesis Data Firehose can ingest real-time clickstream data, enabling marketers to connect with their customers in the most effective way. 
  3. Log Analytics: You can detect application errors as they happen and identify the root cause by collecting, monitoring, and analyzing log data.

4. Security monitoring: Kinesis Data Firehose supports Splunk as a destination so you can monitor network security in real-time and alert when a potential threat arises.

So now we have an basic understanding of what Kinesis Firehose is and how and where we can leverage it to make our lives easier!

For more blogs on AWS check out Knoldus blogs.

You can check out the documentation for AWS Kinesis Firehose here.

References:

  1. https://aws.amazon.com/kinesis/data-firehose/?kinesis-blogs.sort-by=item.additionalFields.createdDate&kinesis-blogs.sort-order=desc
  2. https://www2.realm.io/blog/post/processing-data-streams-with-amazon-kinesis-and-mongodb-atlas
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