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Kubernetes-Based Analytics Platform

Containerisation has proven to be a boon for developers and big businesses – especially in this world of microservices. And Kubernetes has streamlined the orchestration of containers. But the question is, why should developers have all the fun? A lot of Data Analytics processes appear similar to what a developer does. Why can’t we streamline these processes and make them more efficient with Kubernetes? We can! Aceso Analytics builds a Kubernetes-based Data Analytics platform, streamlining batch Data Science jobs, managing configurations effectively, and extending the DevOps paradigm by facilitating smooth collaboration between Data Analytics and the operations team.

Do We Really Need Kubernetes For Data Analytics?

We use newer technologies to solve specific problems. So what are the challenges pertaining to Data Analytics that Kubernetes aims to address? To be honest, you don’t need Kubernetes when it comes to standard enterprise data. 

 

However, as the data starts to grow, managing it becomes an issue. Existing applications become insufficient for data exploration, ML model creation, and data deployment. There are too many moving parts in the process of managing and analysing big data. This is where the Kubernetes-based analytics platform comes in. Kubernetes can simplify big data analytics, containerise the data analytics processes and make each of the processes portable.

Big Data Analytics Has a Lot in Common with Software Development - Hence It Includes Similar Challenges

To create a Machine Learning model for big data analytics, Data Scientists need data regarding the problem. For example, if a Data Scientist wants to create a model that can predict cart abandonment, she would need to identify the data that she thinks will be helpful for the ML model to predict. The workflow would look like this:

  • Identifying the data (from a larger chunk of data) that the ML model would need to predict cart abandonment. The data can be anything - from the time taken to check each product, the number of products viewed by a particular customer, the total amount spent by the customer in the last six months and so on. All the data that the Data Scientist thinks would be useful for data. This kind of data is called feature data.
  • In the next step, the Data Scientist would compare the feature data with the historical data of customer behaviour and see if there is a correlation between cart abandonment and the data chosen to train the ML model.
  • The analysis of historical data will help the Data Scientist to know which feature data should be included in the ML model training, how to encode the feature data and so on…
  • Based on the exploratory analysis of historical data and the outcome of the comparison between historical data and feature data, Data Scientists might have to tweak the previous steps in the process of ML model training, add new feature data or change the definition of the problem altogether.

Can you spot the similarities between Big Data Analysis and software development?

  • You can create a pipeline (comparable to CI/CD) out of the stages involved in Big Data Analytics. From extracting data to transforming and encoding it, from training ML models with the data to deploying the model into production - each stage needs to be orchestrated carefully in order to make Big Data analytics accurate.
  • Just like application development, microservices enable ML model development with the provision to debug just that part of the pipeline where the problem exists.
  • Similar to software development, a Data Analyst creates an ML model first in the development and then releases it to production. Hence, the infrastructure in the development phase can differ from that in the production.

Challenges Involved in Big Data Analytics

Since Big Data analytics with Machine Learning has a lot in common with application development, they both share some common challenges…

The Management Problem

Building a Machine Learning model for Big Data analytics involves a lot of moving parts. From data ingestion to feature selection, feature engineering, ML model training and deployment. There are multiple technologies, multiple infrastructures and multiple paradigms involved. This makes it hard to manage the moving parts of the Big Data analytics process. Such heterogeneous technologies make it challenging to keep the workload secure.

The Annoying “It worked well on my computer” problem.

Data Scientists work with various devices while creating an ML model. For example, a lot of Data Scientists love to work in Jupyter. But Jupyter notebooks depend on the host environment to work optimally. So, the performance of a Big Data analytics model might differ in the production environment from that in the dev environment.

The Challenge of Scalability

The very purpose of Big Data analytics models is to handle massive amounts of data. Data Scientists need to shape the ML infrastructure in such a way that scaling up or down remains smooth with minimal hiccups.

How Aceso Analytics Helps You Address These Challenges

Most of the Big Data analytics challenges are akin to the challenges in software development. Why not use the same technologies that software developers use to address similar challenges?

Presenting: Kubernetes-Based Analytics Platform

Building ML models requires a lot of phases. We at Aceso Analytics containerise each of these phases, thereby solving the three biggest challenges in Big Data analytics mentioned above. 

We Containerise Machine Learning Models

Aceso Analytics abstracts each step in Big Data analytics. We containerise Jupyter notebooks, training models and the end product so that you can run them anywhere confidently- we make your workload portable. Scalability is not an issue anymore!

Experience The Ease of KubeFlow

Kubernetes solves many problems – no doubt about it. But Big Data scientists would not want to dive deep into the complexities of containers, API, Kubernetes services, network configuration and whatnot.

 

We at Aceso Analytics simplify Big Data analytics on Kubernetes with Kubeflow. Want to simplify the orchestration of Jupyter notebooks? Kubeflow can do it. Want to build models with TensorFlow? Kubeflow is there for you. Want to deploy your ML models with Seldon? Kubeflow simplifies that as well.

 

Aceso Analytics streamlines Big Data workflow by abstracting away the entire ML pipeline with Kubeflow.

Apache Spark Meets Kubernetes

Apache Spark is one of the most popular Big Data processing engines enabling Data Scientists to work with data at scale. However, as with any other technology, Apache Spark comes with its own set of challenges. Managing Spark-based deployment becomes a problem when the data gets too massive. On top of that, you need to take care of the dependencies for Spark to perform optimally. Above all, most companies depend on cloud platforms to run Spark in an efficient manner which invites the very real fear of vendor locking.

 

Running Spark under Kubernetes solves most of the challenges associated with Spark. You are left with just the best parts of this data processing platform.

 

We at Aceso Analytics enable businesses to be able to orchestrate Spark jobs with Kubernetes. The result? Streamlined deployment with no fear of missing dependencies, standalone deployment with no fear of vendor lock-in, and a more cost-effective data processing solution.

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Working With Big Data Is Challenging. Let’s Offload Your Problems With Kubernetes-Based Big Data Analytics Platform

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