(AGENPARL) – THE HAGUE (THE NETHERLANDS), lun 14 dicembre 2020
Now that we’re aware of the existence of big data, everyone wants to extract valuable information from it. For companies, this could mean any number of advantages in the market. That’s why so many enterprises, both big and small, are looking for data scientists.
You see, data scientists are the ones who can analyze big data and make sense of it, but even they can’t do everything without data engineers. Data engineers are just as important as scientists, but engineers are less visible due to the fact that they are separated from the end product.
Today, both data scientists and engineers can work together seamlessly by relying on IaC (Infrastructure as Code), one of the latest cloud-based models that can revolutionize data analysis. With that in mind, here’s how to accelerate data engineering in the cloud with IaC.
Adopted by various platforms
The thing about IaC is that it can be easily integrated into existing systems. That said, some of the main cloud platforms in the market, such as Microsoft Azure, AWS (Amazon Web Services), and GCP (Google Cloud). Therefore, whichever platform your company is currently using or whichever platform you’re familiar with can easily support IaC.
So what exactly is IaC, to begin with? Simply put, infrastructure as a code is a cloud-based model for automated infrastructure management. You can create an ideal environment for data analysis and engineering that you can deploy any time you want. In other words, you won’t have to start from scratch at the beginning of every new project. Moreover, modification can be implemented seamlessly without having to redo the entire template.
Enter the DataOps
IaC is ideally suited for DataOps. Before we get to that, let’s have a closer look at what DataOps really is. First of all, DataOps is not DevOps applied to data analysis. Yes, they’re inherently similar in purpose and design, but they do have subtle differences.
That said, DataOps is designed to reduce end-to-end cycle times for data analysis from the point of brainstorming ideas to creating useful graphs, charts, and valuable models. Of course, it uses the DevOps approach to do so; only this time, the focus is on both data and code, not just code alone. The key component here is IaC.
So, what does IaC bring to the table, aside from a high-level infrastructure? Simply put, it brings templates with various resources and other relevant sections, such as parameters and outputs. This allows the DataOps team (engineers and scientists) to create a seamless data pipeline infrastructure.
As you may already know, the infrastructure for the data science project consists of hardware and software configuration that creates an ideal environment for the experiment. Scalability is one of the advantages of Infrastructure as Code that really comes in handy. So whether you’re using Python environments, R libraries, or any other parameter, you’d want to have the right resources available.
However, you won’t have to use the same amount of resources for every project. Scalability allows you to, well obviously, scale up and down to meet the current needs. This is more of a corporate advantage as it involves cost savings, time to market speed, etc.
Still, it’s of vital importance to data engineers as well as it allows them to utilize all the resources they need when creating data pipelines. On the other hand, data scientists are able to scale their needs as well based on the magnitude of the experiment.
In other words, scalability reduces waste and saves both time and resources based on current demands or needs. Every resource required can be converted into a YAML or JSON file that can be activated with a simple click.
Ability to solve various issues
It’s no secret that working in traditional or cloud environments (virtual or otherwise) can be problematic. A lot of issues can hinder the project or make the infrastructure unstable, which leads to unnecessary delays, increased costs, maintenance issues, and so on.
Infrastructure as code can solve the majority of such issues as well as provide additional benefits and advantages that will ensure that the entire process goes as smoothly as possible. That said, here are some of the issues IaC can easily solve.
Environment drifts – This is the issue that gave birth to IaC, in the first place. Individual deployment environments that don’t utilize IaC have a lot of maintenance issues. The problem occurs when resource demands increase and environments are no longer able to be reproduced automatically, so developers need to do it manually. IaC eliminates the one-of-a-kind configurations altogether.
Lack of Idempotence – IaC introduces idempotence that ensures that deployment command places the target environment in the same configuration all the time.
Lack of workflow automation – IaC’s main advantage over legacy systems is the all-encompassing workflow automation. This reduces time, efforts, specialized skill requirements, and manual interference for scaling and successful provisioning of the infrastructure.
Lack of reusability – Legacy systems lacked reusability in terms of infrastructure. You simply couldn’t use coded elements from scripts in multiple environments and had to start from scratch every time. IaC eliminates this issue by allowing you to reuse the same script multiple times.
Analyzing big data is troublesome work even for data scientists. The information included in the big data is simply too vast, and extracting relevant information can be more than time-consuming. That’s why data engineers and scientists work together to make the process much more seamless. In addition, accelerating data engineering in the cloud is not only possible now but also more efficient thanks to the infrastructure as code.
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