Apache Pulsar’s Role in the Future of AI and ML

The future of AI and ML is of great interest to many different people across verticals. Everyone, ranging from business leaders and engineers to dev-ops and data architects, is interested in technologies that can enable AI and ML.

AI and ML offer way too many benefits for businesses to pass. Finding a reliable tech to streamline their implementation and use can benefit both companies and developers of AI and ML solutions.

Today, we will take a closer look at Apache Pulsar and its role in the future of AI and ML. Before we continue with analysis, let’s make a quick stop to see what Apache Pulsar really is.

Apache Pulsar – A Quick Recap

When it comes to the server to server messaging systems, Apache Pulsar is definitely one that deserves your attention. Pulsar broke out of Yahoo’s kitchen as an open-source project to become a top-level project under the Apache Software Foundation.

Apache Pulsar is a distributed messaging and streaming platform that works on the pub-sub pattern with a topic at its core. Topics work as channels to enable communication between producers and consumers. It comes with different subscription models, unified messaging models, routing modes, and state-of-the-art multi-layered architecture.

Apache Pulsar Enables ML and AI

Distributed messaging and streaming platforms are built to enable the use of AI and ML. However, using ML and AI comes with many challenges – the most common one resource allocation. ML and AI require huge storage capacity and processing power, but the tricky part is that these requirements are not linear.

You can’t predict the ML and AI requirements in real-time, which becomes a considerable challenge in use-cases where scaling becomes dynamic. The previous solutions tied computational power and storage together. Scaling both at the same time is not feasible.

Enter Apache Pulsar. It facilitates scaling because it enables the storage and computes to scale independently. ML and AI solutions have robust distributed messaging systems, so a cloud-native distributed messaging and streaming platform with flexible scaling can help even the most robust AI and ML initiatives succeed.

The question now is how precisely Apache Pulsar does it? What makes it stand above other solutions on the market? To answer these questions, we have to take a deep dive into the features and functionality of Apache Pulsar.

What Makes Apache Pulsar Technology Unique

To truly enable ML and AI, technology has to support movement, analysis, and connectivity of vast amounts of data. Apache Pulsar does it in several unique ways. Let’s start with the Pulsar’s Brokers.

Pulsar keeps the storage layer and broker layer completely separate. Some other solutions store data on the brokers. In case you need to add more brokers down the line, you need to re-configure the topic. It becomes a big problem if you need to move a lot of data. With Pulsar, you can add additional brokers without even touching, let alone re-partitioning the data.

Apache Pulsar also delivers tiered storage. Why is this important? Even if you have pristine data retention policies, at some point, you’ll need to store more data, and it can significantly increase the costs. Pulsar enables you to achieve the ultimate storage cost-efficiency for all types of data. By default, it will discard the data that has been acknowledged and retain unacknowledged data. The key here is flexibility – Pulsar can be configured any way that is needed (ie.e retain acknowledged and unacknowledged, retain only acknowledged).

Apache Pulsar leverages multiple storage nodes to store the data depending on how the quorum is configured. Data is organized into segments, and all the segments and storage nodes make a unique storage layer. One that you can customize to deliver the best performance. For instance, you can store the data required for ultra-fast operations on main SSD-based storage and offload historic data to HDD-based storage.

When working with large sets of data, you have to take into account latency. Apache Pulsar meets this challenge head one with its pristine quorum-based replication algorithm. This algorithm delivers more consistent latencies, which is great news for dev-ops who build apps that frequently send queries to the server.

Pulsar also streamlines management and delivers simplified infrastructure. There’s no need to have abstraction layers on top of the distributed messaging system or create new groups of users. Thanks to multi-tenacity, Apache Pulsar supports multiple user groups under the same cluster. ML and AI implementation often comes with certain cybersecurity concerns. Pulsar addresses it with industry-grade end-to-end encryption.

Many organizations already use distributed messaging and streaming platforms. Making a switch to Pulsar can appear challenging to them. However, Pulsar has support for many protocols to facilitate the transition to a system that truly enables ML and AI, even on a large scale. Currently, Pulsar supports AMQP, Presto, RabbitMQ, and Kafka.

Analyzing enormous amounts of data can eat up too much computation power, especially if data streams run as separate applications. Apache Pulsar comes with turn-key stream processing. What is it? It’s a lightweight processing solution for long data streams, directly deployed on the broker nodes to reduce the need for complex operational setups. Now, stream processing tasks are handled directly within the Pulsar.

Final Thoughts

Apache Pulsar has gone a long way from being a distributed messaging and streaming platform used internally at Yahoo to becoming a platform that enables Tencent, Splunk, China Cloud, Comcast, and many other real-time applications.

At the moment, it’s the most powerful stream-processing platform out there. The new approach to subscription and routing modes, implementation of the quorum-based replication algorithm for consistent latency, tiered storage, and separate storage and broker layers make Apache Pulsar a mission-critical platform for ML and AI projects.

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