This submit 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 creating 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. Through the use of AWS managed companies, Airties can give attention to their core mission: enhancing house Wi-Fi experiences by automated optimization and proactive concern decision. This consists of minimizing community downtime, enabling sooner diagnostic capabilities for troubleshooting, and enhancing total Wi-Fi high quality. The answer has demonstrated important influence in lowering each the frequency of assist desk calls and common name length, resulting in improved buyer satisfaction and lowered operational prices for Airties whereas delivering enhanced service high quality to their prospects and the end-users.
In 2023, Airties initiated a strategic migration from Apache Kafka operating on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Kinesis Knowledge Streams. Previous to this migration, Airties operated a number of fixed-size Kafka clusters, every deployed in a single Availability Zone to attenuate cross-AZ visitors prices. Though this structure served its objective, it required fixed monitoring and guide scaling to deal with various information masses. The transition to Kinesis Knowledge Streams marked a big step of their cloud optimization journey, enabling true serverless operations with automated scaling capabilities. This migration resulted in substantial infrastructure price discount whereas enhancing system reliability, eliminating the necessity for guide cluster administration and capability planning.
This submit explores the methods the Airties staff 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 repeatedly ingests information from tens of tens of millions of entry factors (reminiscent of 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 attenuate cross-AZ visitors prices. This fragmented structure created a number of operational challenges. The segmented information storage required advanced extract, rework, and cargo (ETL) processes to consolidate data throughout clusters, rising the time to derive significant insights. The info collected serves a number of crucial functions—from real-time monitoring and reactive troubleshooting to predictive upkeep and historic evaluation. Nevertheless, the siloed nature of the information storage made it significantly difficult to carry out cross-cluster analytics and delayed the flexibility to establish network-wide patterns and developments.
The info processing structure at Airties served two distinct use circumstances. The primary was a standard streaming sample with a batch reader processing information in bulk for analytical functions. The second use case used Kafka as a queryable information retailer—a sample that, although unconventional, has turn out to be more and more widespread in large-scale information architectures.
For this second use case, Airties wanted to supply quick entry to historic system information when troubleshooting buyer points or analyzing particular community occasions. This was applied 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 information, they might rapidly find and fetch the precise information from high-retention Kafka subjects utilizing these saved offsets. This method eradicated the necessity for a separate database system whereas sustaining quick entry to historic information.
To deal with the large scale of operations, this answer was horizontally scaled throughout dozens of Kafka clusters, with every cluster accountable for managing roughly 25 TB of information.
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 important guide effort and operational overhead at Airties. Duties reminiscent of managing cluster upgrades, dealing with {hardware} failures and rotation, and conducting load testing continually demand engineering consideration. These operational duties take away from the staff’s potential to focus on core enterprise features and value-adding actions throughout the firm.
Scaling complexities : The method of scaling Kafka clusters entails a number of guide steps that create operational burden for the cloud staff. These embrace configuring new brokers, rebalancing partitions throughout nodes, and offering correct information distribution—all whereas sustaining system stability. As information quantity and throughput necessities fluctuate, scaling usually entails including or eradicating whole Kafka clusters, which is a posh and time-consuming course of for the Airties staff.
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 information volumes and throughput necessities, including new clusters required important guide 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 measurement was outsized, resulting in useful resource waste and pointless prices.
How the brand new structure addresses these challenges
The Airties staff wanted to discover a scalable, high-performance, and cost-effective answer for real-time information processing that might permit seamless scaling with rising information volumes. Knowledge sturdiness was a crucial requirement, as a result of dropping system telemetry information would create everlasting gaps in buyer analytics and historic troubleshooting capabilities. Though non permanent delays in information entry could possibly be tolerated, the lack of any system information level was unacceptable for sustaining service high quality and buyer help effectiveness.
To deal with these challenges, Airties applied two completely different approaches for various eventualities.
The first use case was real-time information streaming with Kinesis Knowledge Streams. Airties changed Kafka with Kinesis Knowledge Streams to deal with the continual ingestion and processing of telemetry information from tens of tens of millions of endpoints. This shift supplied important benefits:
Auto-scaling capabilities : Kinesis Knowledge Streams might be scaled by easy API calls, assuaging the necessity for advanced configurations and guide interventions.
Stream isolation : Every stream operates independently, that means scaling operations on one stream don’t have any influence on others. This alleviated the dangers related to cluster-wide adjustments of their earlier Kafka setup.
Dynamic shard administration : Not like Kafka, the place altering the variety of partitions requires creating a brand new subject, Kinesis Knowledge Streams permits including or eradicating shards dynamically with out dropping message ordering inside a partition.
Software Auto Scaling: Airties applied AWS Software Auto Scaling with Kinesis Knowledge Streams, permitting the system to mechanically alter the variety of shards primarily based on precise utilization patterns and throughput necessities.
These options empowered Airties to effectively handle assets, optimizing prices during times of decrease exercise whereas seamlessly scaling as much as deal with peak masses.
For offering on-demand entry to historic system information, Airties applied a decoupled structure that separates streaming, storage, and information entry considerations. This method changed the earlier answer the place historic information was saved immediately in Kafka subjects. The brand new structure consists of a number of key elements working collectively:
Knowledge assortment and processing : The structure begins with a shopper software that processes information from Kinesis Knowledge Streams. This software implements analyzing the information, as making it accessible for detailed historic evaluation. The results of the information evaluation is written to Amazon Knowledge Firehose, which buffers the information, writing it repeatedly to Amazon Easy Storage Service (Amazon S3), the place it may 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 information storage, a number of uncooked information samples are batched collectively in bulk information, that are saved in a separate Amazon S3 path. This path is optimized for storage effectivity and fetching uncooked information utilizing Amazon S3 vary queries.
Indexing and metadata administration: To allow quick information retrieval, the structure implements a classy indexing system. For every report within the uploaded bulk information, 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 information report within the Kinesis Knowledge Streams queue. This indexing technique gives low-latency entry to particular information factors, environment friendly querying capabilities for each real-time and historic information, automated scaling to deal with rising information volumes, and excessive availability for metadata lookups.
Advert-hoc information retrieval: When particular historic information 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 information is saved. The appliance then fetches the particular information immediately from Amazon S3 utilizing vary queries. This method permits fast entry to historic information factors, minimal information switch prices by retrieving solely wanted information, environment friendly troubleshooting and evaluation workflows, and lowered latency for buyer help operations.
This decoupled structure makes use of the strengths of every AWS service: Amazon Kinesis Knowledge Streams gives scalable and dependable real-time information streaming, whereas Amazon S3 delivers sturdy and cost-effective object storage for uncooked information, 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 more cost effective and scalable answer for ad-hoc information entry wants, aligning every element with its optimum AWS service. The brand new structure not solely improved information entry efficiency but additionally considerably lowered operational complexity. As an alternative of managing Kafka subjects for historic information storage, Airties now advantages from totally managed AWS companies that mechanically deal with scaling, sturdiness, and availability. This method has confirmed significantly beneficial for buyer help eventualities, the place fast entry to historic system information is essential for resolving points effectively.
Resolution overview
Airties’s new structure entails a number of crucial elements, together with environment friendly information ingestion, indexing with AWS Lambda features, optimized information 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:
Knowledge assortment and storage: The info journey begins with Kinesis Knowledge Streams, which ingests real-time information from tens of millions of entry factors. This streaming information is then processed by a shopper software that batches the information into bulk information (also referred to as briefcase information) for environment friendly storage in Amazon S3. This method of streaming, batching, after which storing minimizes write operations and reduces total prices, whereas offering information sturdiness by built-in replication in Amazon S3. When the information is in Amazon S3, it’s available for each quick processing and long-term evaluation. The processing pipeline continues with aggregators that learn information 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.
Knowledge aggregation and bulk file creation: The aggregators play an important position within the preliminary information processing. They mixture the incoming information primarily based on predefined standards and create bulk information. This aggregation course of reduces the quantity of knowledge that must be processed in subsequent steps, optimizing the general information processing workflow. The aggregators then write these bulk information 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 system IDs and timestamps, facilitating fast entry to related information utilizing S3 vary queries on the majority information.
Additional processing and evaluation: The majority information saved in Amazon S3 at the moment are in a format optimized for querying and evaluation. These information might be additional processed utilizing AWS Glue and analyzed utilizing Athena, permitting for advanced queries and in-depth information exploration with out the necessity for added information transformation steps.
Monitoring and administration: To take care of the reliability and efficiency of the Kafka-less structure, complete monitoring and administration practices have been applied. 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 that anomalies are promptly addressed.
Outcomes and advantages
The transition to this new structure yielded important advantages for Airties:
Scalability and efficiency: The brand new structure empowers Airties to scale seamlessly with rising information volumes. The power to independently scale reader and author operations has lowered efficiency impacts throughout high-demand durations. This can be a important enchancment over the earlier Kafka-based system, the place scaling typically required advanced reconfigurations and will have an effect on the whole cluster. With Kinesis Knowledge Streams, Airties can now deal with peak masses effortlessly whereas optimizing useful resource utilization throughout quieter durations.
Reliability and fault tolerance: Through the use of AWS managed companies, Airties has considerably lowered system latency and improved total uptime. The automated information replication and restoration processes of Kinesis Knowledge Streams present enhanced information sturdiness, a crucial requirement for Airties’s operations. The improved excessive availability implies that Airties can now provide extra dependable companies to their prospects, minimizing disruptions and enhancing the general high quality of their house connectivity options.
Operational effectivity: The brand new structure has dramatically lowered the necessity for guide intervention in capability administration. This shift has freed up beneficial engineering assets, permitting the staff to give attention to delivering enterprise worth fairly than managing infrastructure. The simplified operational mannequin has elevated the staff’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 influence and sustainability: The transition to a serverless structure demonstrated important environmental advantages, attaining a exceptional 40% discount in power consumption. This substantial lower in power utilization was achieved by eliminating the necessity for continually operating EC2 cases and utilizing extra environment friendly, managed AWS companies. This enchancment in power effectivity aligns with Airties’s dedication to environmental sustainability and establishes them as an environmentally accountable chief within the tech business.
Value optimization: The monetary advantages of transitioning to a Kafka-less structure are clearly demonstrated by complete AWS Value Explorer information. As proven within the following diagram, the overall price breakdown throughout all related companies from January to July consists of EC2 cases, DynamoDB, different Amazon EC2 prices, Kinesis Knowledge Streams, Amazon S3, and Amazon Knowledge Firehose. Essentially the most notable change was a 33% discount in whole month-to-month infrastructure prices (in comparison with January baseline), primarily achieved by important 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 companies (DynamoDB, Kinesis Knowledge Streams, Amazon Knowledge Firehose, Amazon S3), the general month-to-month AWS prices maintained a transparent downward pattern. With these price financial savings, Airties can provide extra aggressive pricing to their prospects. The diagram under exhibits month-to-month price breakdown in the course of the transition.
Conclusion
The transition to this new structure with Kinesis Knowledge Streams has marked a big 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 power effectivity (40%). Through the use of superior applied sciences and progressive options on AWS, the Airties staff continues to set the benchmark for environment friendly, dependable, and sustainable operations, whereas paving the way in which for a sustainable future. To be able to discover how one can modernize your streaming structure with AWS, see the Kinesis Knowledge Streams documentation and watch this re:invent session on serverless information streaming with Kinesis Knowledge Streams and AWS Lambda.
Concerning the Authors
Steven Aerts is a principal software program engineer at Airties, the place his staff is accountable for ingesting, processing, and analyzing the information of tens of tens of millions of houses 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 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 prospects to assist them obtain operational excellence and maximize the worth of cloud computing.
