How Apache Pulsar Functions are Enablers for ML and Event Stream Processing

Apache Pulsar functions allow programmers to script automated data processing of event streams and messages on cloud TPU/GPU hardware for AI/ML/DL functionality support using custom-written code in Java, Python, or Go. Many developers are also using Apache Pulsar for Serverless computing implementation after the launch of AWS Lambda and other platforms based on Apache OpenWhisk. Pulsar functions draw upon open source code and data streaming architecture with support for the Apache Bookkeeper and Zookeeper projects. 

Apache Pulsar enables software developers to implement AI/ML/DL and Function as a Service (FaaS) solutions in enterprise web/mobile applications operating at hyperscale with millions of simultaneous users. This can be done without refactoring existing hardware and data center installations using Software-Defined Networking (SDN) concepts for multi-cloud orchestration. These values are coded into cloud web/mobile applications to enable enterprise software to run on distributed hardware in production with secure, encrypted backend connections and routing.

Introduction: Enterprise Ecommerce and Social Networking

Apache Pulsar began as a project similar to Kafka which would create a new cloud architecture based on data stream event processing in hyperscale applications. The platform was donated by Yahoo! Labs to the Apache Software Foundation to further advance the development of the open cloud through cooperation under open source standards in the IT industry.

  • Enterprise companies running mega-brands in social networking and ecommerce have millions of simultaneous users building content, accessing files, shopping, making payments, and receiving personalized data feeds for a myriad of device displays.
  • Some of the world’s most talented programming teams collaborate to bring these software services to market with an increasing amount of regional oversight and legal requirements that must be implemented across data center operations globally.
  • Many data privacy agreements mandate multinational companies collecting user information to adhere to strict rules for encryption and cloud data storage in order to operate in a particular region which requires custom routing and namespace support.

Amazon, Google, Microsoft, Apple, and other public cloud companies have invested billions of dollars in cloud computer research for building new hyperscale architecture through open source platforms. The Apache Pulsar, Bookkeeper, and Zookeeper suite are developed in cooperation across many IT companies to provide cutting-edge innovation with enterprise security in event messaging for streaming architecture deployed on multi-cloud resources.

Apache Pulsar Functions: AI / ML / DL and Serverless Platforms

The latest industry trends for social networking and ecommerce are for personalized content delivered to the users that is generated by AI algorithms based in machine learning. AI/ML has advanced to all sectors of industry and manufacturing, including mining, oil/gas exploration, and other natural resource companies. Enterprise corporations use AI/ML for a wide range of functions such as network anti-virus security, automated assembly line production with robotics, self-driving vehicles, content/product recommendations, language translation, call center support, logistics, and supply chain modelling. All of these software applications are based on “Big Data” and cloud technology where Apache Pulsar can provide the open source code fundamentals for the data streaming architecture. 

Each event stream packet can be routed through AI/ML/DL processing on parallel cloud TPU hardware using pre-trained algorithmic analysis for text interpretation. This functionality is then implemented in an object-oriented manner to build keyword analysis support, product/content recommendation systems, and automated translation services into existing web/mobile applications. Apache Pulsar functions work in coordination with Bookkeeper to direct hardware packet routing to the specific web servers or TPU/GPU units required for AI/ML processing.

Apache Pulsar functions allow Agile programming teams within any organization to swiftly and securely implement custom code processing in Java, Python, and Go to information or files in an event/message queue. Apache Pulsar functions give enterprise software development teams the power and flexibility needed to implement “Big Data” analytics solutions with AI/ML processing at hyperscale on distributed architecture. This includes support for TPU/GPU servers in the public cloud through namespace allocation and the secure routing of network packet data between servers for processing in the assembly of cloud software applications.

  • The multi-tenant features of Apache Pulsar support integration with Apache Bookkeeper so that programmers can script the software to support load balancing across geo-locations as required for legal compliance in the EU, US, Australia, etc. 
  • The messaging and event queue architecture in Pulsar leads to the generation of more advanced metrics from data lakes that is valuable for IT pros for network security, supporting the requirements of sales, marketing, production, and manufacturing teams.
  • Support for Serverless computing platforms like AWS Lambda and OpenWhisk allow programmers to quickly add parallel processing services to support image customization according to individual device display requirements (mobile/tablet/desktop/IoT).

By allowing development teams to custom script simple or complex code functions to each message or event in a user data stream, advanced AI/ML/DL features from TPU/GPU servers can be introduced to enterprise software products more quickly and efficiently with a higher level of feature innovation. Agile project management typically requires CI/CD with version control and modular isolation between different layers of a software application in production. 

The underlying theme is that enterprise companies with “Big Data” requirements for support of their web/mobile applications in cloud data centers can use Apache Pulsar to build a custom Serverless architecture. Programmers can implement Pulsar functions to customize scripts for the unique purposes of their organizational requirements. Businesses now trust and invest in the longevity of the Apache Pulsar architecture for continuity of service over the next 10 years of cloud expansion. Developers can use these formats to quickly iterate AI/ML/DL functionality for enterprise software support without needing to abandon existing code and hardware services.

Apache Pulsar For AI / ML / DL: Event and Message Stream Architecture

Apache Pulsar powers web/mobile software that operates with the user traffic requirements of social networking sites like LinkedIn, Pinterest, and Facebook, or ecommerce platforms the size of Amazon. Other enterprise companies seek to emulate the cutting edge practices of these cloud corporations in data center administration in support of their own DevOps procedures. 

  • The advantages of using Apache Pulsar for AI/ML/DL software support are shared by enterprise corporations across a wide range of competing and divergent industries.
  • Manufacturing companies have different requirements for “Big Data” in robotics and industrial production than ecommerce or social media publishing companies.
  • Enterprise corporations can adopt Apache Pulsar to manage their cloud data center, network security, legal compliance, and software development requirements. 

Apache Pulsar is specifically designed to support the requirements of hyperscale application data processing on cloud cluster hardware to support billions of I/O requests per minute for the world’s most popular web and mobile applications in the cloud. Learn more about Apache Pulsar function support at Pandio.

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