Implementing Real-Time Data Analytics using AWS Managed Services

Mohammed Shafeek
DevOps.dev
Published in
2 min readMay 22, 2023

In today’s business world, data analytics is vital, providing valuable insights into customer behavior, market trends, operational performance, forecast etc.. However, as the volume and velocity of data increase, it becomes increasingly necessary to analyze the data properly.

Designing and implementing a real-time analytics architecture can be a difficult and time-consuming task, requiring expertise in data engineering, computing, and analytics.

This blog post will go through how to use AWS services, one of the most well-liked cloud platforms for data analytics, to perform real-time data analytics.

Step 1: Real-Time/Near Real-Time Data collection

Collecting real-time data from diverse sources is an essential step in implementing real-time data analytics. For real-time data collection, AWS offers a number of services; however, in this design, we’ll be using Kinesis agent and Kinesis Firehose. You can collect, and analyze data in real-time using Kinesis Streams, whereas you can load near-real-time streaming data to AWS storage services like S3 or Redshift using Kinesis Firehose.

Step 2: Process Data in Real-Time

Lambda is a go-to service when it comes to serverless workloads. In this architecture, we use Lambda for processing data based on the business logic and store them in a data store.

Step 3: Store Data in a Data Store

Once we collect data, we need to store it in a data store that can handle the volume and velocity of incoming data. AWS offers several data storage services, including S3, DynamoDB, and Redshift.

S3 is a scalable and durable object storage service that can store any type of data, including unstructured data. We will use S3 as data storage in this architecture implementation.

Step 4: Visualize Data in Real-time

For data visualization, we chose Amazon Managed Grafana. The setup is super quick since it is a managed service that enables developers/Analysts to create dashboards with SQL queries.

Grafana helps to connect with various data sources including S3, here we used the Athena plugin within Grafana to communicate with S3.

AWS Managed Grafana provides SSO ( AWS IAM Identity Center) and SAML integration for authentication.

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Published in DevOps.dev

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