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.
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.
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:
Can you spot the similarities between Big Data Analysis and software development?
Since Big Data analytics with Machine Learning has a lot in common with application development, they both share some common challenges…
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.
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 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.
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?
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.
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!
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 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.
Working With Big Data Is Challenging. Let’s Offload Your Problems With Kubernetes-Based Big Data Analytics Platform