This put up was cowritten with Steven Aerts and Reza Radmehr from Airties.
Airties is a wi-fi networking firm that gives AI-driven options for enhancing house connectivity. Based in 2004, Airties focuses on growing software program and {hardware} for wi-fi house networking, together with Wi-Fi mesh methods, extenders, and routers. The flagship software program as a service (SaaS) product, Airties Dwelling, is an AI-driven platform designed to automate buyer expertise administration for house connectivity, providing proactive buyer care, community optimization, and real-time insights. By utilizing AWS managed providers, Airties can deal with their core mission: bettering house Wi-Fi experiences by means of automated optimization and proactive challenge decision. This contains minimizing community downtime, enabling sooner diagnostic capabilities for troubleshooting, and enhancing total Wi-Fi high quality. The answer has demonstrated vital impression in decreasing each the frequency of assist desk calls and common name length, resulting in improved buyer satisfaction and diminished operational prices for Airties whereas delivering enhanced service high quality to their clients and the end-users.
In 2023, Airties initiated a strategic migration from Apache Kafka working on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Kinesis Information Streams. Previous to this migration, Airties operated a number of fixed-size Kafka clusters, every deployed in a single Availability Zone to reduce cross-AZ visitors prices. Though this structure served its goal, it required fixed monitoring and handbook scaling to deal with various knowledge hundreds. The transition to Kinesis Information Streams marked a major step of their cloud optimization journey, enabling true serverless operations with automated scaling capabilities. This migration resulted in substantial infrastructure price discount whereas bettering system reliability, eliminating the necessity for handbook cluster administration and capability planning.
This put up explores the methods the Airties workforce employed throughout this transformation, the challenges they overcame, and the way they achieved a extra environment friendly, scalable, and maintenance-free streaming infrastructure.
Kafka use circumstances for Airties workloads
Airties constantly ingests knowledge from tens of tens of millions of entry factors (resembling modems and routers) utilizing AWS IoT Core. Earlier than the transition, these messages have been queued and saved inside a number of siloed Kafka clusters, with every cluster deployed in a separate Availability Zone to reduce cross-AZ visitors prices. This fragmented structure created a number of operational challenges. The segmented knowledge storage required advanced extract, remodel, and cargo (ETL) processes to consolidate data throughout clusters, growing the time to derive significant insights. The info collected serves a number of important functions—from real-time monitoring and reactive troubleshooting to predictive upkeep and historic evaluation. Nevertheless, the siloed nature of the info storage made it significantly difficult to carry out cross-cluster analytics and delayed the power to establish network-wide patterns and tendencies.
The info processing structure at Airties served two distinct use circumstances. The primary was a conventional streaming sample with a batch reader processing knowledge in bulk for analytical functions. The second use case used Kafka as a queryable knowledge retailer—a sample that, although unconventional, has grow to be more and more widespread in large-scale knowledge architectures.
For this second use case, Airties wanted to supply rapid entry to historic machine knowledge when troubleshooting buyer points or analyzing particular community occasions. This was carried out by sustaining a mapping of knowledge factors to their Kafka offsets in a database. When buyer help or analytics groups wanted to retrieve particular historic knowledge, they might rapidly find and fetch the precise data from high-retention Kafka matters utilizing these saved offsets. This method eradicated the necessity for a separate database system whereas sustaining quick entry to historic knowledge.
To deal with the huge scale of operations, this answer was horizontally scaled throughout dozens of Kafka clusters, with every cluster liable for managing roughly 25 TB of data.
The next diagram illustrates the earlier Kafka-based structure.
Challenges with the Kafka-based structure
At Airties, managing and scaling Kafka clusters has offered a number of challenges, hindering the group from specializing in delivering enterprise worth successfully:
- Operational overhead: Sustaining and monitoring Kafka clusters requires vital handbook effort and operational overhead at Airties. Duties resembling managing cluster upgrades, dealing with {hardware} failures and rotation, and conducting load testing continually demand engineering consideration. These operational duties take away from the workforce’s skill to focus on core enterprise capabilities and value-adding actions inside the firm.
- Scaling complexities : The method of scaling Kafka clusters includes a number of handbook steps that create operational burden for the cloud workforce. These embrace configuring new brokers, rebalancing partitions throughout nodes, and offering correct knowledge distribution—all whereas sustaining system stability. As knowledge quantity and throughput necessities fluctuate, scaling usually includes including or eradicating complete Kafka clusters, which is a fancy and time-consuming course of for the Airties workforce.
- Proper-sizing cluster capability: The static nature of Kafka clusters created a “one-size-fits-none” state of affairs for Airties. For big-scale deployments with excessive knowledge volumes and throughput necessities, including new clusters required vital handbook work, together with capability planning, dealer configuration, and partition rebalancing, making it inefficient for dealing with dynamic scaling wants. Conversely, for smaller deployments, the usual cluster dimension was outsized, resulting in useful resource waste and pointless prices.
How the brand new structure addresses these challenges
The Airties workforce wanted to discover a scalable, high-performance, and cost-effective answer for real-time knowledge processing that might permit seamless scaling with growing knowledge volumes. Information sturdiness was a important requirement, as a result of shedding machine telemetry knowledge would create everlasting gaps in buyer analytics and historic troubleshooting capabilities. Though non permanent delays in knowledge entry could possibly be tolerated, the lack of any machine knowledge level was unacceptable for sustaining service high quality and buyer help effectiveness.
To handle these challenges, Airties carried out two completely different approaches for various situations.
The first use case was real-time knowledge streaming with Kinesis Information Streams. Airties changed Kafka with Kinesis Information Streams to deal with the continual ingestion and processing of telemetry knowledge from tens of tens of millions of endpoints. This shift supplied vital benefits:
- Auto-scaling capabilities : Kinesis Information Streams may be scaled by means of easy API calls, assuaging the necessity for advanced configurations and handbook interventions.
- Stream isolation : Every stream operates independently, which means scaling operations on one stream don’t have any impression on others. This alleviated the dangers related to cluster-wide adjustments of their earlier Kafka setup.
- Dynamic shard administration : In contrast to Kafka, the place altering the variety of partitions requires creating a brand new matter, Kinesis Information Streams permits including or eradicating shards dynamically with out shedding message ordering inside a partition.
- Software Auto Scaling: Airties carried out AWS Software Auto Scaling with Kinesis Information Streams, permitting the system to robotically regulate the variety of shards primarily based on precise utilization patterns and throughput necessities.
These options empowered Airties to effectively handle sources, optimizing prices in periods of decrease exercise whereas seamlessly scaling as much as deal with peak hundreds.
For offering on-demand entry to historic machine knowledge, Airties carried out a decoupled structure that separates streaming, storage, and knowledge entry considerations. This method changed the earlier answer the place historic knowledge was saved instantly in Kafka matters. The brand new structure consists of a number of key elements working collectively:
- Information assortment and processing : The structure begins with a shopper utility that processes knowledge from Kinesis Information Streams. This utility implements analyzing the info, as making it out there for detailed historic evaluation. The results of the info evaluation is written to Amazon Information Firehose, which buffers the info, writing it repeatedly to Amazon Easy Storage Service (Amazon S3), the place it may well later be picked up by Amazon EMR. This path is optimized for environment friendly storage and bulk studying from Amazon S3 by Amazon EMR. For uncooked knowledge storage, a number of uncooked knowledge samples are batched collectively in bulk recordsdata, that are saved in a separate Amazon S3 path. This path is optimized for storage effectivity and fetching uncooked knowledge utilizing Amazon S3 vary queries.
- Indexing and metadata administration: To allow quick knowledge retrieval, the structure implements a complicated indexing system. For every file within the uploaded bulk recordsdata, two essential items of data are recorded in an Amazon DynamoDB desk: the Amazon S3 location (bucket and key) the place the majority file was written, and the sequence variety of the corresponding knowledge file within the Kinesis Information Streams queue. This indexing technique gives low-latency entry to particular knowledge factors, environment friendly querying capabilities for each real-time and historic knowledge, automated scaling to deal with growing knowledge volumes, and excessive availability for metadata lookups.
- Advert-hoc knowledge retrieval: When particular historic knowledge must be accessed, the system follows an environment friendly retrieval course of. First, the applying queries the DynamoDB desk utilizing the related identifiers. The question returns the precise Amazon S3 location and offset the place the required knowledge is saved. The applying then fetches the precise knowledge instantly from Amazon S3 utilizing vary queries. This method permits fast entry to historic knowledge factors, minimal knowledge switch prices by retrieving solely wanted data, environment friendly troubleshooting and evaluation workflows, and diminished latency for buyer help operations.
This decoupled structure makes use of the strengths of every AWS service: Amazon Kinesis Information Streams gives scalable and dependable real-time knowledge streaming, whereas Amazon S3 delivers sturdy and cost-effective object storage for uncooked knowledge, and Amazon DynamoDB permits quick and versatile storage of metadata and indexing. By separating streaming from storage and using every service for its particular strengths, Airties created a cheaper and scalable answer for ad-hoc knowledge entry wants, aligning every part with its optimum AWS service. The brand new structure not solely improved knowledge entry efficiency but in addition considerably diminished operational complexity. As an alternative of managing Kafka matters for historic knowledge storage, Airties now advantages from absolutely managed AWS providers that robotically deal with scaling, sturdiness, and availability. This method has confirmed significantly useful for buyer help situations, the place fast entry to historic machine knowledge is essential for resolving points effectively.
Answer overview
Airties’s new structure includes a number of important elements, together with environment friendly knowledge ingestion, indexing with AWS Lambda capabilities, optimized knowledge aggregation and processing, and complete monitoring and administration practices utilizing Amazon CloudWatch. The next diagram illustrates this structure.
The brand new structure consists of the next key phases:
- Information assortment and storage: The info journey begins with Kinesis Information Streams, which ingests real-time knowledge from tens of millions of entry factors. This streaming knowledge is then processed by a shopper utility that batches the info into bulk recordsdata (also called briefcase recordsdata) for environment friendly storage in Amazon S3. This method of streaming, batching, after which storing minimizes write operations and reduces total prices, whereas offering knowledge sturdiness by means of built-in replication in Amazon S3. When the info is in Amazon S3, it’s available for each rapid processing and long-term evaluation. The processing pipeline continues with aggregators that learn knowledge from Amazon S3, course of it, and retailer aggregated outcomes again in Amazon S3. By integrating AWS Glue for ETL operations and Amazon Athena for SQL-based querying, Airties can course of massive volumes of knowledge effectively and generate insights rapidly and cost-effectively.
- Information aggregation and bulk file creation: The aggregators play a vital function within the preliminary knowledge processing. They mixture the incoming knowledge primarily based on predefined standards and create bulk recordsdata. This aggregation course of reduces the quantity of knowledge that must be processed in subsequent steps, optimizing the general knowledge processing workflow. The aggregators then write these bulk recordsdata on to Amazon S3.
- Indexing: Upon profitable add of a bulk file to Amazon S3 by the aggregators, the aggregator will write an index entry for the majority file an Amazon DynamoDB desk. This indexing mechanism permits for environment friendly retrieval of knowledge primarily based on machine IDs and timestamps, facilitating fast entry to related knowledge utilizing S3 vary queries on the majority recordsdata.
- Additional processing and evaluation: The majority recordsdata saved in Amazon S3 at the moment are in a format optimized for querying and evaluation. These recordsdata may be additional processed utilizing AWS Glue and analyzed utilizing Athena, permitting for advanced queries and in-depth knowledge exploration with out the necessity for extra knowledge transformation steps.
- Monitoring and administration: To keep up the reliability and efficiency of the Kafka-less structure, complete monitoring and administration practices have been carried out. CloudWatch gives real-time monitoring of system efficiency and useful resource utilization, permitting for proactive administration of potential points. Moreover, automated alerts and notifications be certain anomalies are promptly addressed.
Outcomes and advantages
The transition to this new structure yielded vital advantages for Airties:
- Scalability and efficiency: The brand new structure empowers Airties to scale seamlessly with growing knowledge volumes. The power to independently scale reader and author operations has diminished efficiency impacts throughout high-demand intervals. It is a vital enchancment over the earlier Kafka-based system, the place scaling typically required advanced reconfigurations and will have an effect on your entire cluster. With Kinesis Information Streams, Airties can now deal with peak hundreds effortlessly whereas optimizing useful resource utilization throughout quieter intervals.
- Reliability and fault tolerance: By utilizing AWS managed providers, Airties has considerably diminished system latency and improved total uptime. The automated knowledge replication and restoration processes of Kinesis Information Streams present enhanced knowledge sturdiness, a important requirement for Airties’s operations. The improved excessive availability implies that Airties can now supply extra dependable providers to their clients, minimizing disruptions and enhancing the general high quality of their house connectivity options.
- Operational effectivity: The brand new structure has dramatically diminished the necessity for handbook intervention in capability administration. This shift has freed up useful engineering sources, permitting the workforce to deal with delivering enterprise worth moderately than managing infrastructure. The simplified operational mannequin has elevated the workforce’s productiveness, empowering them to innovate sooner and reply extra rapidly to buyer wants. The discount in operational overhead has additionally led to sooner deployment cycles and extra frequent function releases, enhancing Airties’s competitiveness out there.
- Environmental impression and sustainability: The transition to a serverless structure demonstrated vital environmental advantages, attaining a exceptional 40% discount in vitality consumption. This substantial lower in vitality utilization was achieved by eliminating the necessity for continually working EC2 situations and utilizing extra environment friendly, managed AWS providers. This enchancment in vitality effectivity aligns with Airties’s dedication to environmental sustainability and establishes them as an environmentally accountable chief within the tech business.
- Price optimization: The monetary advantages of transitioning to a Kafka-less structure are clearly demonstrated by means of complete AWS Price Explorer knowledge. As proven within the following diagram, the overall price breakdown throughout all related providers from January to July contains EC2 situations, DynamoDB, different Amazon EC2 prices, Kinesis Information Streams, Amazon S3, and Amazon Information Firehose. Probably the most notable change was a 33% discount in complete month-to-month infrastructure prices (in comparison with January baseline), primarily achieved by means of vital lower in Amazon EC2 associated prices because the migration progressed, elimination of devoted Kafka infrastructure, and environment friendly use of the AWS pay-as-you-go mannequin. Though new prices have been launched for managed providers (DynamoDB, Kinesis Information Streams, Amazon Information Firehose, Amazon S3), the general month-to-month AWS prices maintained a transparent downward development. With these price financial savings, Airties can supply extra aggressive pricing to their clients. The diagram under reveals month-to-month price breakdown through the transition.
Conclusion
The transition to this new structure with Kinesis Information Streams has marked a major milestone in Airties’s journey in direction of operational excellence and sustainability. These initiatives haven’t solely enhanced system efficiency and scalability, however have additionally resulted in substantial price financial savings (33%) and vitality effectivity (40%). By utilizing superior applied sciences and modern options on AWS, the Airties workforce continues to set the benchmark for environment friendly, dependable, and sustainable operations, whereas paving the best way for a sustainable future. So as to discover how one can modernize your streaming structure with AWS, see the Kinesis Information Streams documentation and watch this re:invent session on serverless knowledge streaming with Kinesis Information Streams and AWS Lambda.
Concerning the Authors
Steven Aerts is a principal software program engineer at Airties, the place his workforce is liable for ingesting, processing, and analyzing the info of tens of tens of millions of properties to enhance their Wi-Fi expertise. He was a speaker at conferences like Devoxx and AWS Summit Dubai, and is an open supply contributor.
Reza Radmehr is a Sr. Chief of Cloud Infrastructure and Operations at Airties, the place he leads AWS infrastructure design, DevOps and SRE automation, and FinOps practices. He focuses on constructing scalable, cost-efficient, and dependable methods, driving operational excellence by means of good, data-driven cloud methods. He’s obsessed with mixing monetary perception with technical innovation to enhance efficiency and effectivity at scale.
Ramazan Ginkaya is a Sr. Technical Account Supervisor at AWS with over 17 years of expertise in IT, telecommunications, and cloud computing. He’s a passionate problem-solver, offering technical steering to AWS clients to assist them obtain operational excellence and maximize the worth of cloud computing.
