Apache Pulsar & Apache Cassandra: Designed to Enable Machine Learning
Machine learning algorithms’ effectiveness depends on the amount of data you manage to feed to them. Ensuring uninterruptible data streams is a priority here. However, when working in cloud-based environments streaming and processing data from multiple sources is heavy on resources and processing power.
Given the common scalability issues and limited resources, no wonder many businesses struggle in the cloud when implementing ML. Simply put, it can not be done without using a cutting edge database and a distributed messaging system.
Today we will take a closer look at Pulsar and Cassandra. Let’s see why these two are a perfect match for enabling machine learning and what they offer compared to alternative solutions.
What is Pulsar?
Pulsar is a cloud-native distributed messaging system and streaming platform. It’s capable of managing billions of events daily. It was originally developed at Yahoo and used to connect various apps such as Yahoo Mail and Yahoo Finance. In 2016, Yahoo contributed Pulsar to open source. Apache Software Foundation picked it up in 2018 and made it one of its top projects.
We will return to discuss technical specifications when we compare it to other distributed messaging systems and event streaming platforms.
What is Cassandra?
Cassandra is a code-name for a NoSQL database. It is quite a unique database. Cassandra comes with all the features any data enthusiast dreams of. The features include no single point of failure, ultra-fast speed, great scalability, and guaranteed durability. It shares somewhat the same history as one of Pulsar.
It was originally developed at Facebook to be used under the hood of the Inbox Search feature. Facebook released it on Google as an open-source project in 2008. A year later, Apache took it and made it one of the top Apache Incubator projects.
It is a proven NonSQL database with some of the largest production deployments. Cassandra stands at the heart of operation in Apple, Netflix, Activision, Spotify, Uber, and many other companies, where it is used to handle trillion requests per day on several thousand nodes.
The Common Challenges When Working in the Cloud
Many organizations have a multi-faceted IT infrastructure. They deploy several data centers that are very often in different geographical locations. Without a proper strategy, the entire network is prone to failure due to a single data center failure or an event that makes data streaming impossible.
Furthermore, enterprises often serve the same data feeds to hundreds of clients or customers. The same applies to employees who are geographically dispersed, trying to access the same data set. Creating multiple instances of a data stream snapshot is not feasible when you are working with terabytes of data.
Add machine learning to this, and complexity increases by orders of magnitude.
How do you ensure a non-stop data stream to an ML on a cloud-based infrastructure with fluctuating data flow and hundreds of other users that need access to data? What happens when the amount of data generated by software tools, devices, and users spikes? What happens if the data stream fails? How do you manage the fluctuating demand for both processing and storage resources?
How Pulsar Outperforms the Competition
While Pulsar was not built specifically to outperform other distributed messaging systems and event streaming platforms, the results undeniably show its supremacy regarding performance and easy implementation.
For instance, organizations that depend on Kafka cannot serve a single instance of software to multiple tenants. In this case, “tenants” refers to a group of users with the same view of the software. That’s because Kafka is built as a log abstraction system.
On the other hand, Pulsar handles multi-tenacity natively because it was built to support it from day one. It was made to share real-time data across departments without creating data silos.
When it comes to geo-replication, Pulsar is preferable to Kafka because Kafka creates clusters in multiple data centers and uses MirrorMaker to streamline this process. While it is able to make copies of a message from one cluster to another, it doesn’t keep clusters in sync. It can affect topics in several ways:
- Same topics on two clusters can end up with different numbers of partitions;
- Topics can have different replication factors;
- Topics can have different topic-level settings;
Pulsar was built to keep all data centers in sync. Both synchronous geo-replication and asynchronous geo-replication are available with Pulsar. While the methodology behind these two geo-replication processes is different, the result is the same. All data centers in the network store the same set of write/read data sets.
The difference is that with asynchronous geo-replication, clients don’t have to wait for a response from at least 2 data centers. Add the ability to store data in SSD and HDD storage nodes to the immediate server response, and you have a lighting-fast distributed messaging system.
Pulsar & Cassandra:
ML is the next-gen cloud application. While it offers plenty of insights and enables an organization to adopt data-driven strategies, it operates with big data volume and data velocity. That brings us to scalability.
Both Pulsar and Cassandra were built to be scalable. Pulsar is horizontally scalable, meaning that you can scale capacity and throughput separately. Cassandra is linearly scalable, and you can choose the appropriate amount of capacity based on the current data flow.
When it comes to preventing disruptions and delivering a consistent experience to end-users, both Pulsar and Cassandra feature impressive mechanisms. The Pulsar disaster-prevention solution via geo-replication is excellent. Coupled with the masterless architecture of Cassandra, Pulsar becomes completely fault-tolerant.
Finally, both Cassandra and Pulsar can support high-velocity machine learning algorithms. With predictable scalability and no single point of failure, you will be able to build a timeless cloud-based system for your organization.
Both Pulsar and Cassandra were built to help organizations tackle the challenges with big data in the cloud. They can be easily integrated into a unified enterprise solution. As you could see, they offer unique solutions to common challenges in the cloud and can potentially help organizations to deploy and use ML.