Five Ways Apache Cassandra is Designed to Support Machine Learning Use Cases

Organizations across verticals have figured out that the answer to most of the challenges they face lies in analyzing data, using it in forecasts, and leveraging it to make the decision-making process efficient. There’s only one thing that can help organizations use data in such a fashion – Machine Learning (ML). 

ML enables computers to learn. It uses different algorithms to process the data and deliver outputs useful to businesses. However, to do it, ML needs access to data. To feed the information efficiently to an ML, organizations need to use a database – not just any database, but one built to support predictable linear scalability and masterless architecture.

No Single Point of Failure

For ML to work correctly, you have to continue feeding data into it. A no data stream equals a no ML. That’s why organizations have to consider using databases that are decentralized and fault-tolerant. If organizations use commonly used SQL databases, in the event of a fault, the entire database needs to be brought down and repaired. During this time, the ML is of no use. 

Cassandra is a completely decentralized database. Its network is architectured so that every node in the cluster is identical. Cassandra simply doesn’t have bottlenecks. Organizations can continue to use ML to facilitate decision-making and get valuable insights even if a Cassandra node becomes unavailable at some point, for whatever reason.

Cassandra also takes fault tolerance to a new level with data replication, making replacing nodes possible without taking the entire database offline. As a NoSQL database, Cassandra automatically replicates data to multiple nodes. Organizations can even set it up to replicate data across multiple data centers to extend fault tolerance initiatives and ensure 24/7/365 uptime even during regional outages.

Ultra-Fast Speed

When talking about ML implementation in the business landscape, it’s essential to address the operational speed challenges. There are two main questions here – “Can a database feed data to an ML efficiently enough?” and “Will ML be able to feed data back to the database so that users can get access to the output in a matter of milliseconds?”

We can’t talk about speed without taking a look at some architectural best practices incorporated in Cassandra.

First of all, it uses a log-structured storage engine. Cassandra avoids overwrites when turning updates into sequential input/output, even in situations when datasets that need to be stored exceed available RAM. The data is stored on hard disk drives and solid-state drives for the best possible performance.  

A distributed replication engine enables Cassandra to enable the same throughput to all users. It enables ML to work on multiple data streams at the same time without sacrificing the performance. 

Finally, Cassandra supports locally-managed storage and can be further optimized for specific ML use-cases and the organization’s needs. A single Cassandra cluster can include both HDDs and SSDs. Users can set Cassandra to store particular data on HDD or SSD in a single cluster, thus taking performance to a new level.

Great Scalability

It all appears fine and dandy when your organization uses a database to record, store, and process a few gigabytes of data. However, what happens when you need to add new machines and the number of users exponentially grows? What happens when data read and write throughput exceeds terabytes and starts counting in petabytes?

We’ve already established that to benefit from ML – you have to maintain a stable data stream. To do it, you need a scalable database. Cassandra tackles this challenge perfectly. Users can add new machines with no downtime, thanks to its fault-tolerant architecture. But, more importantly, Cassandra is the most elastic database on the market. What does it mean?

Cassandra is capable of supporting linearly increasing read and write throughput. The modular nature of the database allows great scaling at no operational costs. Scaling it up will cause no disruptions and interruptions to the data stream, and ML will continue working. 

Read and Write Best Practices For Minimal Data Inconsistency

The more accurate data you feed to an ML model, the more viable the results will be. Data inconsistencies result in faulty ML operations and outputs that can contain errors. Organizations want to base the decision process on accurate data. 

On the other hand, app users want to get personalized ML-driven recommendations. Cassandra tackles read and write data inconsistency challenges like no other NoSQL alternative. It can prove vital in cases when you have to use asynchronous replication for updates. Asynchronous replication requires some fine-tuning while synchronous one works out-of-the-box.

Hinted Handoff refers to a process Cassandra uses to apply hints to failed nodes. It is essential to write operations during maintenance. Cassandra enables all replicas of a key to store mutations even without reaching a consensus to guarantee the availability of data. Administrators can configure hints to ensure data consistency. 

Cassandra also uses the Read Repair process to repair data replicas when a read request is made. The processes ensure that the client receives the most up-to-date data, whether it is a user or data-processing framework. You can find out more about the Read Repair process here

Guaranteed Durability

ML-based operations can’t afford to lose a single bit of data. To ensure its use case in the ML landscape, Cassandra offers something rarely seen in the world of databases – guaranteed durability. It makes guarantees about: 

  • High scalability and availability
  • Eventual consistency of writes and reads
  • Durability
  • Batched writes across multiple tables (either all will succeed or none at all)
  • Consistency in secondary indexes and their local replicas data

Cassandra is a NoSQL database that has a lot to offer to organizations planning to implement ML. It’s also a viable option if you want to switch to a new database to empower your ML even more efficiently. It’s a proven solution already used by some fortune 500 companies, including Apple, Netflix, Easou, and eBay. 

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