Platform Engineering

Toolchain and self-service workflow development is the emphasis of the software engineering field.  By establishing a common platform, it seeks to improve productivity and streamline procedures by empowering software engineers to effectively design, implement, and oversee computer-coded applications.  Software development teams looking to increase their operational and development efficiency are the main beneficiaries of platform engineering.

What is a platform?

A platform is software that is made available to a certain audience and uses exposed transactional services to empower and enable an audience inside a specific business segment.

For instance, the platform Heroku makes it easier to distribute software libraries, typically through a publicly accessible web front end or API.  For instance, Heroku offers logging services that you can buy, configure, and use through the platform in place of writing your logger for your deployed applications.

Platforms are companies that scale issues across solutions to address specific technological issues.  Performance, data access, security, and logging are a few examples of issues that are relevant to all programs yet may be addressed more or less consistently, independent of the function of the software.

The second half is simple.  An “engineer” is a person who finds solutions by weighing value against few resources.  A platform engineer, therefore, recognizes the limitations of a particular platform and determines how to offer value that aligns with the platform’s goal.

Who works as a “platform software engineer” rather than a “software engineer, platform engineering”?  What distinguishes it from the title of “software engineer”?  To become a platform software engineer, what knowledge is required?  

What qualities do employers seek in such applicants?

Software Engineer vs. Platform Software Engineer? 

The following summarizes the distinctions between a software engineer and a platform software engineer.

Software engineer

Focus:– Develops and manages software features or applications that people may engage with directly (e.g., online applications, mobile apps).

Software design, development, testing, deployment, and maintenance are among the duties.

need proficiency in; 

• Software development processes, 
• frameworks, and 
• programming languages.

1. Software Engineer, 
2. Platform Engineering, or 
3. Platform Software Engineer?

Emphasis- Develops and manages the internal platform used by other software developers to create apps.

 Designing, constructing, implementing, and managing systems, tools, and infrastructure that facilitate other engineers’ development processes are among the duties.  This might consist of: Continuous Integration & Continuous Delivery (CI/CD) pipelines, Development of APIs for cloud infrastructure management, Platform scalability and dependability, Tools for the developer experience

Proficiency-  Platform engineers frequently possess the following abilities in addition to those of software developers: DevOps procedures, Automation of infrastructure, Administration of the system Best practices for security.

The automobiles are assembled by a software developer.

 The assembly line itself is constructed and maintained by a platform software engineer, who makes sure it functions effectively and efficiently for the manufacture of automobiles.

Have strong Knowledge to be a Platform Software Engineer

Technical Proficiency.

• Cloud computing systems, such as AWS, Azure, and GCP
• Languages for programming (such as Python, Java, and Go)
• Tools for infrastructure automation (such as Terraform and Ansible)
• CI/CD tools (such as GitLab CI/CD and Jenkins)
• Technologies for containerization (such as Docker and Kubernetes)
• Principles of system management
• Best practices for security

What are the non-technical competencies?

• Analytical and problem-solving abilities.
• Collaboration and communication abilities.
• Capacity to work both individually and collaboratively, Knowledge of DevOps techniques.
• Love of infrastructure and construction tools

What Employers See in Candidates for Platform Software Engineer Positions?

Employers need applicants who have a solid grasp of infrastructure and software development.  

They may search for the following specific items-

1. Familiarity with creating and managing internal development platforms. 
2. Familiarity with DevOps procedures.
3. Excellent analytical and problem-solving abilities.
4. The capacity to pick up new technology fast.
5. Outstanding teamwork and communication abilities,
6. A Drive to provide infrastructure and technologies that increase developer productivity.
7. Platform software engineers are, all things considered, a great asset to any company looking to empower its development teams and expedite the software development process.

Are DevOps and Platform Engineers the same thing?

Nope, DevOps and platform engineering are not the same thing.  Platform engineering concentrates on creating and managing the tools and underlying infrastructure needed for software development & deployment, whereas DevOps is a more general approach meant to enhance cooperation between the development and operations teams to optimize the software delivery pipeline.  Although platform engineering addresses diverse facets of the software lifecycle, it frequently supports DevOps approaches.

How does platform engineering differ from DevOps?

By constructing the required infrastructure, Platform Engineering improves the security and efficiency of development operations.  By combining development and operations, DevOps, on the other hand, promotes a continuous integration & delivery culture.

What difficulties arise while putting contemporary Kubernetes infrastructure onto the cloud?

Problems show how crucial it is to have a clear platform engineering plan in place for setting up and maintaining contemporary cloud-based Kubernetes infrastructure.  Through proactive resolution of these issues, platform engineering teams may guarantee a seamless transition, effective use of resources, and a safe, expandable environment for containerized applications.

Let’s check how this works…

For container orchestration, contemporary Kubernetes installations provide a strong and expandable option.  Platform engineering teams must overcome several obstacles when implementing this technology in the cloud, though:

1. Handling Complexity:

With many moving components, Kubernetes is a complicated system in and of itself.  Platform engineering teams may find it difficult to manage settings across various environments (such as development, testing, and production) and integrate them with cloud provider-specific solutions.

 For IaC- Infrastructure as Code and configuration management, tools and best practices are essential since they may assist in enforcing consistency and lowering human mistakes.

2. Security Issues:

Because containerized environments increase the attack surface, strong security measures are necessary.  Secure container images, appropriate network segmentation inside the cluster, & RBAC-Role-Based Access Control for user authorization management are all requirements for platform engineering teams.

3. Observability and Monitoring:

Kubernetes is a distributed system that requires extensive monitoring and logging to be managed.  Platform engineering teams must set up dashboards and tools to monitor application performance, resource usage, and cluster health.  Certain observability strategies are necessary for detecting and resolving problems in containerized settings.

4. Optimization of Costs

It may be costly to use cloud resources, and ineffective Kubernetes installations might result in resource waste.  To maintain a balance between affordability and effectiveness, platform engineering teams must use autoscaling capabilities, optimize allocation of resources for pods along with containers, and keep an eye on expenses.

5. Knowledge and Skill Gap:

To administer Kubernetes effectively, certain knowledge is needed.  Platform engineering teams could need to employ or upskill staff with knowledge of cloud-specific technologies, Kubernetes management, and containerization.

6. Multi-cloud and hybrid environments:

There is an extra layer of complexity involved in managing Kubernetes deployments among several cloud providers or hybrid settings (on-premises and cloud).  To maintain uniformity and centralized administration, platform engineering teams must provide configurations that are both portable and flexible enough to be used in many cloud environments.

7. Upgrades and Versioning

It might be difficult to maintain consistency across several cloud-based Kubernetes versions.  To reduce interruptions during version rollouts, platform engineering teams must set up a clear upgrade strategy and rigorous testing protocols.

8. High Availability and Disaster Recovery.

It is essential to have a strong DR (Disaster Recovery) strategy for cloud-based Kubernetes installations.  To guarantee a speedy recovery in the event of failures, platform engineering teams must have backup plans in place for persistent volumes, configuration data, and container images.

How does the work of a “data platform engineer” vary from that of a “data engineer”?

Important Distinctions:-

Focus: While data platform engineers concentrate on the tools and infrastructure that support data engineering, data engineers are primarily concerned with data processing and transformation.

Skills:- While data platform engineers need to be knowledgeable with cloud platforms, containerization, & system design, data engineers frequently need to be proficient in data modeling, 

1. ETL tools
2. SQL, 

Although their duties may overlap, data engineers and data platform engineers concentrate on distinct facets of dealing with data.

 Engineer for Data

Designing, building, and maintaining the architecture and systems that enable data gathering, processing, and storage is the core responsibility of a data engineer.  Usually, their duties consist of:

The process of creating and managing pipelines that receive data from several sources is known as data pipeline development.

Designing and overseeing databases along with data warehouses, is known as database management.

Data Transformation: To get data ready for analysis, ETL (Extract, Transform, Load) procedures are implemented.

Performance optimization is the process of making sure data systems can manage high data volumes and operate effectively.

Cooperation: Gaining a thorough understanding of the data requirements of data scientists and analysts.

Platform Engineer for Data

The platforms and infrastructure that underpin data engineering initiatives are the main focus of a data platform engineer.  

Among their duties might be

Platform design is the process of creating and putting into use data platforms that offer dependable and scalable data services.

Managing cloud services for big data technologies,

such as; 

• Spark,
• Hadoop  
• And container orchestration (like Kubernetes) is known as infrastructure management.

CI/CD and automation; combining continuous integration/continuous deployment techniques for data applications and automating data operations.

Security and Compliance: Making certain that data platforms adhere to legal requirements and security standards.

Monitoring and Upkeep: Configuring monitoring instruments and keeping data platforms in good condition.

Finally, keep in mind…

Despite being crucial to the data ecosystem, these positions have unique duties and expertise that meet various organizational demands.

Now, shall we move on to the important point…

9 Top digital platform engineering solutions can make potential trends?

 Digital platform engineering’s future is changing quickly due to both a growing need for innovation and technological breakthroughs.  The landscape is anticipated to change in the coming years due to a number of important services.

1.0 cloud native Apps:- 

Building and managing cloud-native apps will be the major emphasis of digital platform engineering solutions as more companies migrate to the cloud.  This entails creating microservices architectures, utilizing Kubernetes and containers, and putting serverless computing into practice for increased flexibility and scalability.

2.0 Edge Computing:- 

The need for edge technology will increase as IoT devices proliferate.  For applications like driverless cars or smart cities that need real-time processing, engineering teams will create platforms that can analyze data adjacent to the source of information, lowering latency and enhancing performance.

3.0 Blockchain Solutions:- 

Beyond cryptocurrencies, blockchain technology is probably going to increase.  Blockchain integration for identity management, secure transactions, and decentralized apps will be part of the digital platform engineering services, especially for supply chain management and finance.

4.0 Low-Code/No-Code Development:-

 Low-code & no-code platforms will emerge as a crucial product to meet the increasing need for quicker development cycles.  These platforms enable companies to create apps with little coding, making them accessible to non-technical consumers. And then came to creation of digital solutions. 

5.0 API-First Development:- 

The need for API-first service providers will increase as platforms grow more integrated.  In order to facilitate interoperability across ecosystems, engineers will create and deploy strong API designs that guarantee platforms can readily interface with third-party apps and services.

6.0 Cybersecurity Engineering:- 

As cyberattacks get more complex, cybersecurity services will become increasingly important.  To protect user data and prevent breaches, digital platform developers will concentrate on developing secure platforms, putting encryption into practice, and setting up threat detection systems.

7.0 Quantum computing:-

Even though it is still in its early stages, quantum computing will begin to affect some industries.  Engineers working on digital platforms will investigate how to include quantum algorithms for particular applications, including cryptography or intricate data analysis.

8.0 Integration of AI:- 

Machine learning and AI will be essential components of digital platforms.  In order to improve decision-making, automate procedures, and forecast consumer behavior, services will use AI models.  Data management and backend system optimization will also be greatly aided by AI.

9.0 RPA-Robotic Process Automation:-

 RPA services will rise significantly as companies want to automate monotonous operations.  By automating laborious procedures, these services will assist businesses in increasing productivity and lowering human error on digital platforms.

This trend looks like digital platform engineering will be shaped by many fascinating trends, some of which are represented by these services.  Businesses will want solutions that are quicker, more secure, and equipped to handle challenging problems as technology develops.

Summary

The future is unpredictable. But some assumptions may really make a change positively. So, platform engineering may take the lead in all digital businesses as well.

Hope this will help.

Read more on related topics with the above internal links. Reverse ETL, data engineering