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Golang-Beego Framework and its Applications

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4 minutes read

There are usually some concerns when implementing a new technology such as what would happen if we get stuck somewhere and end up wasting our time and effort. It’s possible that we’ll have to go back to the original solution. We faced similar issues a year ago but after long and in-depth research, we found a solution that was more secure and safe-Golang and its frameworks. The way it is documented is really helpful. However, we were quite certain that we would not find all the answers online which was a challenge we accepted in the spirit of Lailah Gifty Akita’s renowned adage, “THERE IS ALWAYS A SOLUTION TO EVERY CHALLENGING SITUATION.”

This blog mainly talks about Golang-Beego framework and its applications. We’ll be discussing how Golang is used in Web Development and why most of the developers shift from Python, Node, or other languages to Go.

Let’s understand the Golang framework in order to know how it works.

What is Golang?

First appearing in 2009, Golang (popularly known as Go) quickly gained popularity among developers, becoming a preferred language for more than 90% of users. Its ancestor languages are C and C++ programming languages which is quite evident by looking into its syntax and compiling features. 

Primarily used for backend development, Go has 4 other use cases- 

  1. Cloud & Network Services
  2. Command-line Interfaces (CLIs) 
  3. Web Development
  4. Development Operations & Site Reliability Engineering. 

Here are some of the main features of Golang that make this framework the preferred choice for developers:

1. Simplicity 

Go syntax is straightforward as shown here and its compiler can smell trouble and raise errors during the build process — that is before the program is run.

Go Syntax in Golang-Beego Framework

The flexibility, usability, and incredibly cool concept behind Go (how it handles native concurrency, garbage collection, and safety+speed) are some of the features that are quite useful for developers.

2. Speed

Built-in concurrency ( Goroutines and Channels ) is one of the main reasons for its high performance. Analyzing this stack overflow will allow us to estimate its speed.

“I may have implemented this incorrectly because the results do not make sense. I have a Go program that counts to 1000000000; it finishes in less than a second. On the other hand, I have a Python script; it finishes in a few minutes. Why is the Go version so much faster? Are they both counting up to 1000000000 or am I missing something?” 

If you’re still unsure about the speed, here’s a comparison between Go, Node JS, Java, and Python that will help in gaining more clarity about its usage:

My Device Specification:

Device name- LAPTOP-Q8U9LM8P

Processor- Intel(R) Core(TM) i5-10210U CPU @ 1.60GHz   2.10 GHz

Installed RAM- 16.0 GB (15.6 GB usable)

System type- 64-bit operating system, x64-based processor

N-body print:

Source Time To Count 

Go: 6.34   seconds

Python3: 545.25 seconds

GO

Output:

Factorial   Time To calculate factorial

10000       0.008 seconds

50000       0.506 seconds

100000      3.154 seconds

500000      82.394 seconds

1000000     284.445 seconds

NodeJS (Javascript )

Output:

Factorial   Time To calculate factorial

10000       0.113 seconds

50000       1.974 seconds

100000      22.730 seconds

500000      477.534 seconds 

1000000     1175.795 seconds 

Python

Output:

Factorial   Time To calculate factorial

10000       0.046 seconds

50000       1.187 seconds

100000      6.051 seconds

500000      388.607 seconds 

1000000     813.725 seconds 

JAVA

Output:

Factorial   Time To calculate factorial

10000       0.064 seconds

50000       1.607 seconds

100000      5.363 seconds

500000     141.076 seconds

1000000     585.868 seconds

3. Safety:

GARBAGE Collector:

Go prefers to allocate memory on the stack, so most memory allocations will end up there. This means that it has a stack per goroutine and when possible it will assign variables to this stack.

Golang mark and sweep garbage collector has two phases: Mark, and Sweep. First, it will mark all unused and used variables, then sweep unused ones.

The statistics and the description above suggest why one should work with Go. Golang framework that is best for creating APIs also accelerates and facilitates development.

Why do we use Beego Framework?

Be it Go or Beego, both are fantastic for developing high-performance REST APIs. 

Beego is a “battery included” framework, with built-in tools ( bee tool ), ORM, and libraries compared with other frameworks like Gin-gonic which is not a “battery included” type and contains most essential libraries and features not good for server-side features.

Beego uses a typical Model-View-Controller (MVC) framework which has turned out to be good for people (like us) who work on Python-Django before and Beego is quite similar.

Why do we use Beego Framework?

Conclusion: 

That’s how we started our application with Golang and Beego. We worked on PDF, Image handling with ImageMagick, AWS-SNS, AWS-SES SMTP, IVR calls, Fax, Digital signatures, Reports generation with ORM, and many more. And we haven’t found any blockage while working with third-party features like Twilio or AWS. It is really simple to write code on Golang as mentioned by their creators. There are certain challenges in using this framework but there are solutions as well. We really enjoyed it working on this framework. BEST OF LUCK for your upcoming Golang applications.

About the Author

Piyush Raj graduated from IIT Kharagpur in Chemical Dept. He started his career with ML and AI, and now works at Mantra Labs as a software developer. In his free time, he likes to explore new paths in the real world or on paper through traveling and painting.

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Smart Manufacturing Dashboards: A Real-Time Guide for Data-Driven Ops

Smart Manufacturing starts with real-time visibility.

Manufacturing companies today generate data by the second through sensors, machines, ERP systems, and MES platforms. But without real-time insights, even the most advanced production lines are essentially flying blind.

Manufacturers are implementing real-time dashboards that serve as control towers for their daily operations, enabling them to shift from reactive to proactive decision-making. These tools are essential to the evolution of Smart Manufacturing, where connected systems, automation, and intelligent analytics come together to drive measurable impact.

Data is available, but what’s missing is timely action.

For many plant leaders and COOs, one challenge persists: operational data is dispersed throughout systems, delayed, or hidden in spreadsheets. And this delay turns into a liability.

Real-time dashboards help uncover critical answers:

  • What caused downtime during last night’s shift?
  • Was there a delay in maintenance response?
  • Did a specific inventory threshold trigger a quality issue?

By converting raw inputs into real-time manufacturing analytics, dashboards make operational intelligence accessible to operators, supervisors, and leadership alike, enabling teams to anticipate problems rather than react to them.

1. Why Static Reports Fall Short

  • Reports often arrive late—after downtime, delays, or defects have occurred.
  • Disconnected data across ERP, MES, and sensors limits cross-functional insights.
  • Static formats lack embedded logic for proactive decision support.

2. What Real-Time Dashboards Enable

Line performance and downtime trends
Track OEE in real time and identify underperforming lines.

Predictive maintenance alerts
Utilize historical and sensor data to identify potential part failures in advance.

Inventory heat maps & reorder thresholds
Anticipate stockouts or overstocks based on dynamic reorder points.

Quality metrics linked to operator actions
Isolate shifts or procedures correlated with spikes in defects or rework.

These insights allow production teams to drive day-to-day operations in line with Smart Manufacturing principles.

3. Dashboards That Drive Action

Role-based dashboards
Dashboards can be configured for machine operators, shift supervisors, and plant managers, each with a tailored view of KPIs.

Embedded alerts and nudges
Real-time prompts, like “Line 4 below efficiency threshold for 15+ minutes,” reduce response times and minimize disruptions.

Cross-functional drill-downs
Teams can identify root causes more quickly because users can move from plant-wide overviews to detailed machine-level data in seconds.

4. What Powers These Dashboards

Data lakehouse integration
Unified access to ERP, MES, IoT sensor, and QA systems—ensuring reliable and timely manufacturing analytics.

ETL pipelines
Real-time data ingestion from high-frequency sources with minimal latency.

Visualization tools
Custom builds using Power BI, or customized solutions designed for frontline usability and operational impact.

Smart Manufacturing in Action: Reducing Market Response Time from 48 Hours to 30 Minutes

Mantra Labs partnered with a North American die-casting manufacturer to unify its operational data into a real-time dashboard. Fragmented data, manual reporting, delayed pricing decisions, and inconsistent data quality hindered operational efficiency and strategic decision-making.

Tech Enablement:

  • Centralized Data Hub with real-time access to critical business insights.
  • Automated report generation with data ingestion and processing.
  • Accurate price modeling with real-time visibility into metal price trends, cost impacts, and customer-specific pricing scenarios. 
  • Proactive market analysis with intuitive Power BI dashboards and reports.

Business Outcomes:

  • Faster response to machine alerts
  • Quality incidents traced to specific operator workflows
  • 4X faster access to insights led to improved inventory optimization.

As this case shows, real-time dashboards are not just operational tools—they’re strategic enablers. 

(Learn More: Powering the Future of Metal Manufacturing with Data Engineering)

Key Takeaways: Smart Manufacturing Dashboards at a Glance

AspectWhat You Should Know
1. Why Static Reports Fall ShortDelayed insights after issues occur
Disconnected systems (ERP, MES, sensors)
No real-time alerts or embedded decision logic
2. What Real-Time Dashboards EnableTrack OEE and downtime in real-time
Predictive maintenance using sensor data
Dynamic inventory heat maps
Quality linked to operators
3. Dashboards That Drive ActionRole-based views (operator to CEO)
Embedded alerts like “Line 4 down for 15+ mins”
Drilldowns from plant-level to machine-level
4. What Powers These DashboardsUnified Data Lakehouse (ERP + IoT + MES)
Real-time ETL pipelines
Power BI or custom dashboards built for frontline usability

Conclusion

Smart Manufacturing dashboards aren’t just analytics tools—they’re productivity engines. Dashboards that deliver real-time insight empower frontline teams to make faster, better decisions—whether it’s adjusting production schedules, triggering preventive maintenance, or responding to inventory fluctuations.

Explore how Mantra Labs can help you unlock operations intelligence that’s actually usable.

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