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AWS Solutions Library

Guidance for Implementing Near Real-Time Analytics with Spark Streaming on AWS

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Overview

This Guidance demonstrates how to configure a self-service data analytics environment that is simple to launch and access for data engineers and data scientists. The integrated development environment (IDE) is based on Jupyter Notebooks, providing an interactive interface for easy data exploration, and includes all the necessary tools to debug, build, and schedule near real-time data pipelines. The environment supports secure team collaboration with workload isolation, and allows administrators to self-provision, scale, and de-provision resources from a single interface without exposing the complexities of the underlying infrastructure or compromising security, governance, and costs. Administrators can independently manage cluster configurations and continuously optimize for cost, security, reliability, and performance.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

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Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon EMR Studio provides a fully managed, web-based integrated development environment (IDE) with Jupyter Notebooks, allowing data engineering or data science teams to develop, visualize, and debug Spark streaming applications interactively without managing additional servers. Teams can self-provision Amazon EMR clusters that have been predefined using infrastructure as code (IaC) templates in the Service Catalog. This reduces the dependency on cloud operations teams, improves development agility, and helps organizations follow security and governance best practices with minimal overhead.

Read the Operational Excellence whitepaper 

Amazon EMR Studio supports authentication and authorization with AWS Identity and Access Management (IAM), or AWS Identity Center, removing the need to connect with SSH (Secure Shell) directly into Spark clusters. Lake Formation allows for granular and centralized access control to the data in your data lakes, centralizing user access management and augmenting a strong security and governance posture on your data pipelines.

Read the Security whitepaper 

Kinesis Data Streams and Amazon EMR provide autoscaling capabilities to meet the throughput demand of your real-time data streaming workflow. Amazon EMR uses the Apache Spark framework, which automatically distributes and retries jobs in the event of application or network failures. Kinesis Data Streams also scales capacity automatically and synchronously replicates data across three Availability Zones, providing high availability and data durability.

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Kinesis Data Streams automatically scales capacity in response to varying data traffic, allowing your real-time processing workflow to meet throughput demands. Amazon EMR provides multiple performance optimization features for Spark, allowing users to run 3.5 times faster without any changes to their applications. In addition, Athena automatically processes queries in parallel and provisions the necessary resources. Also, data can be stored in Amazon S3 partition keys and columnar formats to increase query performance.

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This Guidance provides a sample Amazon EMR cluster template that uses instance fleets with Amazon EC2 Spot Instance capacity and specifies Amazon EC2 Graviton3 instance types. This can provide up to 20 percent in cost savings over comparable x86-based Amazon Elastic Compute Cloud (Amazon EC2) instances. Further, the use of idle timeouts and Amazon S3 storage tiers allows for better utilization of compute and storage resources with optimized costs.

Read the Cost Optimization whitepaper 

Amazon EC2 Graviton3 instance types use up to 60 percent less energy for the same performance as comparable Amazon EC2 instances, helping to reduce the carbon footprint. The use of Amazon EC2 Spot Instances and Amazon EMR idle timeout settings helps ensure better utilization of resources and minimizes the environmental impact of the workload.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.