DevOps and data science are two of the hottest fields in tech, but they serve very different purposes. DevOps focuses on collaboration, automation, and integration to improve software delivery and IT operations. Data science utilizes statistical and analytical methods to extract insights from data. These two disciplines actually complement each other quite well. Organizations are increasingly looking to build effective data pipelines and infrastructure to support data science initiatives. Understanding the differences between DevOps and data science roles, skills, and tools can help IT leaders optimize their teams and processes to enable data-driven decision making.
In this article, you will learn the key differences between DevOps vs Data Science and how they differ in terms of focus, skills required, and outlook.
What is DevOps?
DevOps refers to the combination of software development (Dev) and IT operations (Ops). The goal of DevOps is to enable faster software delivery and improve reliability through better collaboration between developers and operations teams.
Some key aspects of DevOps are given below:
Focus on Automation
DevOps relies heavily on automation to improve efficiency. Tools like Ansible, Puppet, and Chef allow teams to automate infrastructure provisioning, configuration management, and deployment.
The goal is to release software updates in smaller batches more frequently. This is enabled through practices like continuous integration and continuous delivery.
Infrastructure as Code
Treating infrastructure configuration as code enables version control and facilitates replication. Infrastructure provisioning can be automated using this approach.
Robust monitoring using tools like Nagios, Datadog, and Prometheus lets teams to measure application performance and detect issues early.
DevOps breaks down silos between developers and ops teams through practices like daily standups and blameless postmortems. This improves communication and collaboration.
What is Data Science?
Data Science is an interdisciplinary field that uses scientific processes and algorithms to extract knowledge and insights from data.
Some key aspects of data science are given below:
Focus on Analytics
A significant portion of a data science project involves collecting, cleaning, and transforming data to prepare it for analysis. Data scientists need skills to handle both structured and unstructured data.
Visualizing and presenting data insights is an important part of the job. Data scientists need to create charts, graphs, and dashboards to communicate findings.
Analytical work can be done through GUI-based tools, programming skills are required for activities like web scraping, ETL, and modelling. Common data science languages are Python, R, and SQL.
Understanding the business domain is key for identifying the right problems to solve and relevant data sources. Data scientists often need domain expertise in industries like finance, healthcare, and manufacturing.
DevOps vs Data Science: Head to Head Comparison
Here is a comparison table between DevOps and Data Science:
|Applications, software delivery
|Analytics, data insights
|Running software, applications
|Analytical models, data visualizations
|CI/CD pipelines (A procedure that leads software development along the path of constructing, testing, and deploying code.)
|CRISP-DM, OSEMN (Describes the basic data project life cycle in which the data is Obtained, Scrummed, Explored, Modeled, and Interpreted.)
|ETL pipelines, model training
|Developers, IT operations
|Data engineers, analysts, business users
|Infrastructure as code, automation, monitoring
|Statistical modeling, data wrangling, visualization
|Infrastructure hardening, access controls, secrets management
|Data governance, model explainability, ethical AI
|Data engineering, MLOps
|Software engineering, decision intelligence
Difference Between DevOps vs Data Science
While there are some common skills like programming, automation, and data analysis, there are some fundamental differences between the two roles:
1- Application focus
DevOps engineers work on building, testing, deploying, and monitoring applications. The software product is their core focus. Data scientists work on extracting insights from data. The data and analytical models are their primary concern.
The output of DevOps is running software and applications. Data science outputs are analytical models and data-driven insights.
3- Process Orientation
DevOps focuses on the software development and infrastructure management process. Data science focuses on the analytical modeling process. DevOps improves agility of software delivery through CI/CD pipelines. Data science relies on CRISP-DM or OSEMN to structure the analytical process.
4- Infrastructure vs Data Pipes
DevOps engineers build pipelines for faster software delivery. Data engineers build data pipelines for ETL and data movement.
5- Automation Focus
DevOps focuses on automating operational tasks like provisioning infrastructure, deploying code, and monitoring apps. Data science automation involves ETL pipelines, model training pipelines, etc.
6- Collaboration approach
DevOps brings developers and IT ops together. Data science involves collaboration between data engineers, analysts, and business teams.
DevOps incorporates security practices like infrastructure hardening, access controls, and secrets management. Data science includes data security, model governance, and ethical AI practices.
Overlapping and Emerging Skills
Some skill areas intersect or evolve to bridge the two domains:
Increasingly, data science teams require access to scalable cloud infrastructure for activities like big data processing and model training. Some data engineers take on DevOps-like responsibilities for provisioning and managing this infrastructure.
MLOps applies DevOps-style practices like CI/CD and automation to machine learning projects. This helps data scientists integrate models into production applications efficiently.
Monitoring & Observability
Monitoring systems like Prometheus permits both DevOps and data science teams to monitor metrics and logs effectively. Platforms like Grafana provide visibility through dashboards.
DataOps improves collaboration between data engineers and consumers to speed up analytics. Drawing inspiration from DevOps, it focuses on making data easily available for different use cases.
Low Code Tools
Emerging low code platforms helps it faster delivery of applications with integrated analytics and ML capabilities, blurring the boundaries between the two domains.
Career paths and transitions
Here are some common career options and transitions between the two areas:
Becoming a DevOps Engineer
Data professionals can become DevOps engineers by gaining skills in infrastructure automation, CI/CD, and cloud platforms like AWS. Understanding application architectures and software development processes is also important.
DevOps engineers can transition to data science by building skills in statistics, Python/R programming, SQL, and machine learning techniques. Gaining domain knowledge in the industry of interest is also key.
Site Reliability Engineering
Both data scientists and DevOps engineers can become SREs by developing skills in production system design, advanced monitoring, and software troubleshooting.
Data engineering is an intersection of software engineering, data science, and DevOps. Data pipeline development uses DevOps tools, whereas data wrangling utilizes data science skills.
MLOps combines software engineering, ML, and DevOps to operationalize models. Skills from both fields are required spanning data, modelling, CI/CD, and monitoring.
Decision intelligence applies data science with business context for executive decision-making. Data storytelling, metrics, and technical communication skills are important here.
More to read
- Introduction to Data Science
- Brief History of Data Science
- Components of Data Science
- Data Science Lifecycle
- Data Science Techniques
- 24 Skills for Data Scientist
- Data Science Languages
- Data Scientist Job Description
- 15 Data Science Applications in Real Life
- 15 Advantages of Data Science
- Statistics for Data Science
- Probability for Data Science
- Linear Algebra for Data Science
- Data Science Interview Questions and Answers
- Data Science Vs. Artificial Intelligence
- Data Science Vs. Statistics
- Best Books to learn Python for Data Science
- Best Books on Statistics for Data Science