Streaming analytics is an invaluable capability for organizations seeking to extract real-time insights from the log data they continuously generate through applications and cloud services.
To help our community get started with streaming analytics on AWS, we published a piece last year called An Overview of Streaming Analytics in AWS for Logging Applications, where we covered all the basics.
Now, we’re expanding on this blog with a new piece all about best practices for streaming analytics with S3 and AWS. Keep reading to discover five best practice recommendations that will help you optimize your streaming analytics architecture, reduce costs, and extract powerful insights from your data.
A modern streaming analytics solution on AWS consists of five logical layers, each incorporating one or more microservices that facilitate a specific step in the streaming analytics workflow.
Five layers of a modern streaming analytics architecture.
The five layers are:
Reference architecture for a modern streaming analytics solution using AWS services.
After the stream processing phase, streaming data may be sent to a data warehouse tool like Amazon Redshift for SQL/relational querying, to Amazon OpenSearch Service for text-based search, to third-party analytics applications, or to another destination.
But whether you’re tracking user behavior on customer-facing applications, monitoring cloud infrastructure and services, or securing your network, we recommend building a data lake in Amazon S3 to securely and cost-effectively store all of your streaming data in its raw form.
Storing raw data gives you access to long-term analytics use cases, and Amazon S3 is the best choice for a data lake storage backing, thanks to its high availability, unlimited scalability, and low data storage costs.
AWS Lake Formation is a managed service that helps AWS customers set up and manage an Amazon S3 data lake. Lake Formation gives enterprises access to vital capabilities when it comes to managing and administering the data lake. These include:
Reference architecture for leveraging AWS Lake Formation to transform Amazon S3 into a data lake.
Indexing and compressing data after it reaches your data lake is a strategic choice that can help you accelerate query performance, reduce costs, and overcome data retention limitations.
Indexing involves re-ordering, re-structuring, or re-formatting your data to improve the speed and performance of analytical queries. Where vast quantities of data are present, indexing acts as a crucial navigational aid for your analytics engine and allows it to quickly locate specific data points without scanning the entire data set.
Data compression uses specialized algorithms to reduce the size of your data, resulting in faster data transfer speeds and lower storage costs in your data lake. Data compression algorithms remove redundant or unnecessary data, then encode what’s left into a more efficient format that’s cheaper to store.
Modern data compression and indexing technologies like Chaos Index provide up to 20x data compression with no loss of resolution, resulting in faster query times and lower data storage costs.
Streaming analytics is well-suited for resource demanding or performance-critical applications that demand low-latency data access. The ability to continuously process data in real time as it is created contributes to accelerated insights and faster response times between customer-facing applications and back-end systems.
S3 Express One Zone delivers low request latency to facilitate high-performance computing applications like financial modeling and AI/ML model training.
For the most latency-sensitive applications, Amazon S3 Express One Zone is a unique S3 storage class, purpose-built to consistently deliver the fastest possible cloud object storage capabilities. Here’s how it works:
According to AWS, Express One Zone can provide data access speeds up to 10x faster and with request costs 50% lower than S3 Standard.
Multi-model analytics is the capability to analyze and derive insights from diverse data models (e.g. structured, semi-structured, and unstructured) using a single analytics engine.
Enterprises can enable multi-model data access on Amazon S3 data lakes with Chaos LakeDB, the first and only data lake database to enable true multi-model data access with support for:
Enterprise ITOps teams can use streaming analytics to collect, aggregate, and analyze log data from cloud infrastructure and services in real time. Live visibility of the performance, health, and status of cloud services empowers ITOps teams to effectively monitor cloud resource utilization, quickly detect and diagnose issues (e.g. security, performance, availability, etc.), and optimize costs.
Enterprise SecOps teams can use streaming analytics to support their security operations and threat hunting programs. Security log data is generated throughout the network, including from enterprise applications, access control services, and security tools.
Some of this data should be transferred to a SIEM tool for monitoring/alerting and short-term storage, but all security logs should be stored in an Amazon S3 security data lake to enable long-term security analytics and advanced persistent threat (APT) hunting for security teams.
Enterprise DevOps teams can leverage streaming analytics to capture and analyze user behavior data from customer-facing applications and microservices in real time. With this approach, DevOps teams can gain deeper user insights and shorten feedback cycles to better understand their users and drive improvements to the customer experience.
Storing user behavior data at scale in a cost-efficient S3 data lake can help DevOps teams track and analyze user behavior trends over time.
Generate AI is an emerging area of technology that uses deep-learning models to generate novel, high-quality images, text, or audio based on large volumes of training data. Streaming data fed into a Generative AI analytics engine can be used to power conversational chatbots, generate code, automate reporting tasks, create content for marketing or sales, and optimize key business processes.
Chaos LakeDB is a Software-as-a-Service (SaaS) data platform for enterprises that transforms your Amazon S3 cloud object storage into a live analytics database with multi-model analytics capabilities and unlimited hot data retention.
With Chaos LakeDB, enterprises can aggregate data streams into one centralized data lake database, automate and adaptively scale data pipelines (eliminating the need for manual intervention), and make streaming data available for immediate querying.
Download our free Chaos LakeDB white paper to learn more about the fundamental database innovation behind Chaos LakeDB, along with real-world use cases and testimonials.