data streaming

Streaming data services on AWS

The digital universe is evolving, and with it comes an ocean of data generated every split second. As data-driven decision making becomes the norm, it is critical to understanding the power of real-time data streaming and how it can revolutionise the way we harness this data. In this edition, we delve into the world of real-time data streaming in AWS and explore its components, services and practical use cases. Buckle up for an enriching journey.


What is real-time data streaming?

Streaming, often referred to as stream processing, is the continuous transfer, processing and analysis of large volumes of data in real or near-real time. Unlike batch processing, which accumulates data and processes it in chunks, streaming manages data as it is created. This ensures timely insight and enables organisations to respond to information almost as soon as it is generated.


Components of the real-time data stream

  • Data producers: These are the data sources. It can be anything from IoT devices, web applications, logs or even user activity in an application.
  • Data flow: It is like a pipeline where data flows from the producer to the consumer. The flow ensures the continuous movement of data without any delay.
  • Data stream: It is like a pipeline where data flows from the producer to the consumer. The flow ensures the continuous movement of data without any delay.
  • Flow processing: This is where the magic happens. As data flows through the stream, it is processed in real time by complex algorithms and analysis tools.
  • Data consumers: After processing, the data is sent to consumers, which can be databases, dashboards or even other applications.

data streaming

  • Source: Up to hundreds and thousands of devices or applications producing large volumes of continuous data at high speed. Examples include mobile devices, web applications (clickstream), application logs, IoT sensors, smart devices and gaming applications.
  • Flow ingestion: Easy integration with more than 15 AWS services (Amazon API Gateway, AWS IoT Core, Amazon Cloudwatch, etc.) allows you to capture the continuous data produced from thousands of devices in a durable and secure way.
  • Streaming storage: Choose a solution that meets your storage needs based on scaling, latency and throughput requirements such as Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose and Amazon Managed Streaming for Apache Kafka (Amazon MSK).
  • Streaming processing: Choose from a selection of services ranging from solutions that require just a couple of clicks to transform and deliver data continuously to a destination such as Amazon Kinesis Data Firehose, to powerful, custom-built real-time applications and machine learning integration using services such as Amazon Kinesis Data Analytics and AWS Lambda.
  • Destination: Deliver streaming data to a selection of fully integrated data lakes, data warehouses and analytics services for further analysis or long-term storage, such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service and Amazon EMR.


Streaming data services on AWS

AWS, with its commitment to delivering cutting-edge solutions, provides a set of tools for data streaming and real-time analytics:

  • Amazon Kinesis: This fully managed service facilitates real-time data streaming. It is divided into four main components:
  • Kinesis Data Streams: It captures, processes and stores data streams for real-time analysis.
  • Kinesis Data Firehose: Upload data streams to other AWS services such as S3, Redshift or even external tools such as Splunk.
  • Kinesis Data Analytics: Analyse data streams using SQL or integrate with popular stream processing frameworks.
  • Kinesis Video Streams: Process and analyse video streams for machine learning and other analytics.
  • AWS Lambda: While not exclusively a streaming service, Lambda can process data as it is ingested into AWS, making it a perfect tool to pair with Kinesis.
  • Amazon Managed Streaming for Apache Kafka (MSK): Apache Kafka is a popular open source tool for real-time data streaming. MSK manages the operations of Apache Kafka, making it easy to configure, scale and manage your streaming applications on AWS.


Examples of use cases

  • Financial transactions: Banks and financial institutions use real-time data transmission to monitor transactions. This helps to detect fraud, as unusual patterns can be detected and acted upon instantly.
  • Personalisation of e-commerce: E-commerce platforms can analyse a user's real-time activity, such as products viewed, searches performed, etc., and provide personalised product recommendations on the fly.
  • Monitoring of registers: For companies with large-scale operations, logging errors can signal larger underlying problems. Real-time data transmission can alert teams instantly when an anomaly occurs in system logs.
  • Health supervision: Wearable devices can send real-time patient data to medical databases. If any irregularities are detected, immediate and potentially life-saving action can be taken.
  • Supply chain optimisation: For logistics companies, real-time data on vehicle location, traffic conditions, etc. can be processed to optimise routes and ensure on-time deliveries.



The wave of real-time data streaming and analytics is here, and it's changing the way businesses operate and serve their customers. With the AWS toolset, harnessing this power has never been easier. Whether you're a startup looking to deliver personalised, real-time content to users or an enterprise looking to monitor a global supply chain, AWS provides everything you need.

Stay tuned for our next edition, where we'll dive deeper into best practices for configuring your AWS data streaming pipeline.

Happy streaming!

Raúl Martínez Cordón

Raúl Martínez Cordón

Big Data consultant at Bosonit

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