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Monitoring vs Observability: What’s the difference?

Fri, 23 Aug 2024 02:23:02 +0000

In this article, we are discussing how so similar-sounding terms like monitoring and observability differ of Monitoring vs Observability

How is monitoring different from observability?

Bugs and errors are inevitable even with the most experienced developers and tools on the team. Finding and resolving these bugs is a major part of the development process. But the problem comes when customers find these bugs first. Because then they might give a bad ‘public’ review or worse, switch to a competitor never to return again.

However, this unfortunate situation can be avoided if the developers find these bugs first and fix them before the customers face them. But how?

Here comes monitoring and observability tools that help developers detect and fix errors and anomalies across the system. They may sound similar, but monitoring and observability are quite different based on their functionality and scope of work.

Let’s first understand what is monitoring…

What is monitoring?

Monitoring refers to continuously collecting and analyzing data on performance, availability, and system health in general. It measures specific metrics like CPU usage, uptime/downtime, response time, error rates, etc.

SRE(Site Reliability Engineering) developed by Google is the practice of combining software engineering and operations to automate tasks and ensure system reliability. Dynatrace describes it as “As a discipline, SRE focuses on improving software system reliability across key categories including availability, performance, latency, efficiency, capacity, and incident response.” SRE practices involve monitoring of some the most important metrics known as the 4 golden signals. They are described as follows:

Latency: It is the time taken by a system to respond to a request. For example, the time it takes for a website to load when the user clicks on its link. If the latency is more i.e. the website takes more time to load, it annoys the user who might abandon it. Thus, the lower the latency, the better the user experience.
Traffic: Traffic measures the amount of load a service or system is handling at a time. A higher traffic indicates higher user demand for the service.
Error: The error rate is the rate of unsuccessful or failed requests. It is important to track both the overall error rate and the service-specific error rate. Categorizing errors into critical and non-critical also helps the development team deal with the most disruptive errors first.
Saturation: Saturation tells you about the overall capacity of a service or how close a service is to reaching its limits. It is measured in terms of how much CPU, memory, or network bandwidth the service is utilizing.

Application performance monitoring or APM is another term related to monitoring that focuses on tracking the performance of applications in particular. Some popular monitoring tools include Nagios, Zabbix and Prometheus.

What is observability?

Strongdm defines observability, also known as O11Y, as “the ability to assess an internal system’s state based on the data it produces.”

Unlike monitoring, which barely collects data as fixed metrics, observability involves analyzing current and historical data and helps to diagnose the root cause of errors in the system. That said, although observability has a wider scope than monitoring, it cannot function without it.

The 3 pillars of observability through which it determines system health are – logs, metrics, and traces…

Metrics: Metrics are quantitative (or numerical) measures of data that indicate the system’s health and performance. It is the place where observability overlaps with monitoring. Error rate, CPU utilization, and network throughput are a few examples of metrics.
Logs: SolarWinds defines logs as “…detailed records of events from every piece of software, user action, and network activity.” They include time-stamped and chronological data on all the processes that occur within a service. That said, while metrics raise alerts during a problem, logs tell about what events happened and are happening around the problem. This helps the developers find the cause of the problem.
Traces: Traces record the flow of a request from one end to the other. They help developers see the flow of requests and identify errors in their path.

Observability tools help to gather and analyze data like metrics, logs, and traces to diagnose issues in the system. AppDynamics, Datadog and Dynatrace are a few of the leading observability tools in the market.

Monitoring vs observability

Here’s a table for a quick summary of the difference between monitoring and observability…

Conclusion

Monitoring and observability perform similar functions but differ in terms of the depth of work. Monitoring provides surface-level insights like what the problem is while observability provides deeper insights into what caused the problem and how it is affecting the system. Moreover, observability tools can include monitoring features but it is not so the other way around. Hence, monitoring can be said as a part of a larger and advanced field which is observability.

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An Ultimate Guide On Low-Code, No-Code Platform (LCNC)

Shiwani Sharma — Wed, 15 May 2024 07:50:00 +0000

Low Code, No Code Development Platform(LCNC)

Introduction

This LCNC platform is designed for people with limited coding knowledge and for those who know coding but have no time to do it. They may be familiar with real coding languages such as Java, Python, etc. Both platforms aim to speed up the application development process. Nowadays, many organizations prefer LCNC because it accelerates the software development process, reducing dependence on traditional coding.

Both low-code and no-code have become popular and important because they significantly reduce the time required to create software applications and make them available to the audience. LCNC techniques are beneficial for constructing internal tools and implementing applications of various sizes. This is best for those organization and people who are making tools online, and now anybody can make the website and application by their own with the help of LCNC including content creators, designers and owner of any organization.

Let’s understand these two terms separately, as defined below:

Low-Code Development

In traditional development, developers create applications manually, which is time-consuming and error-prone. With low-code development, developers can create applications quickly by avoiding manual coding. They use custom code when necessary and rely on templates and pre-built components to build applications. An important role of low-code development is to address business problems without writing comprehensive code.

Definition of Low-Code Development Platform (LCDP) as per Wikipedia: A low-code development platform (LCDP) provides a development environment used to create application software, generally through a graphical user interface (as opposed to only writing code, though some coding is possible and may be required). A low-coded platform may produce entirely operational applications, or require additional coding for specific situations.

No-Code Development

No-code takes the concept of low-code further by providing a platform where users with little or no coding knowledge can easily and quickly create applications. The targeted audience of the no-code platform includes citizen developers, business users, and individuals without a programming background but possess domain expertise and can identify automation opportunities to streamline processes.

Definition of No-Code Development (NCDPs) as per Wikipedia: No-code development platforms (NCDPs) allow creating application software through graphical user interfaces and configuration instead of traditional computer programming based on writing code.

Image Credit:https://gradientflow.com/ranking-low-code-development-platforms/

The above images shows you the common types of low-code, No-code development tools.

Advantages of LCNC

Here is a list of advantages of LCNC:

Faster Development: LCNC importantly speeds up the development process, reducing the overall development time and enabling faster application development.
Easy to Supervise: LCNC makes it easier to manage the development process and minimizes struggles related to coding.
Easy to Use: Unlike complex code that can be difficult to understand and execute, LCNC is easy to comprehend, making it simple to identify configurations.
Cost Saving: LCNC eliminates the need for a large development team, resulting in indirect cost savings for the organization.
Bridging the Knowledge Gap: LCNC allows non-developers to create applications without coding knowledge, bridging the gap between business requirements and IT.

Disadvantages of LCNC

However, LCNC also has some disadvantages:

Less Customization: LCNC does not support highly customized solutions, limiting the building of high-level applications. Unlike traditional coding, developers may have fewer options for customization.
Less Security: Since LCNC platforms have no control over the source code, applications created by users with limited coding knowledge may have higher security risks.
Performance Issues: Applications built with LCNC may have lower performance compared to those developed using manual methods.
Deliberation Of Cost: With the help of LCNC, the cost of decreasing the requirement of skilled developers is low, organization has to be strictly follow the overall cost of ownership, that consists training cost, potential vendor lock-in, to secure cost-effectiveness over the long term.
Depending on Third-Party Services: Organization should be think of potential changes to those integration which could impact the application functionality and performance.

Difference Between Low-Code And No-Code

S.No.	Low-Code	No- Code
01	Low-code is used for complex application	It is used for tracking the application, for reporting and analytics.
02	In low-code, it’s important to have basis technical knowledge.	In no-code there is no limitations required.
03	It is a advance application.	It is a simple and easy application.
04	It is used by developer.	It is used by business users only.
05	Coding is requires in little basis.	No coding requires in no-code.

Conclusion

This LCNC platform is designed for people with limited coding knowledge and for those who know coding but have no time to do it. Both Low-Code, No-Code platforms aim to speed up the application development process. Both low-code and no-code have become popular and important because they significantly reduce the time required to create software applications and make them available to the audience.

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R Programming Language

Shiwani Sharma — Tue, 07 May 2024 07:43:19 +0000

What is R Programming Language?

An open-source programming language, formulated by two professors, Robert Gentleman and Ross Ihaka, in 1993 and developed by the R Development team, was released in 1995. This programming language is used on various platforms such as Linux, Windows, macOS, etc., for statistical computing and graphical presentation. One of the most important functions of the R language is data visualization and analysis. The R language is an open-source and free language that is released under the General Public License (GPL) project. The open source nature means anyone can utilize, modify, and distribute the software as per their requirements.

The name “R” is derived from the initials of its two developers, Robert and Ross. The R language is instrumental in classifications, non-linear modeling, linear modeling, classical statistics, and more. Due to its unique and robust features, it has gained popularity in academic circles. Additionally, its availability for free download from the Free Software Foundation contributes to its widespread use. Users adopt the R language to read data, load it, implement operations, and even create their own functions. Another useful and famous feature of R is looping, which allows users to perform actions repeatedly, such as extracting samples from large datasets.

This is the premier language for data analysis and statistical computing. Its rising popularity can be attributed to several factors. Data scientists, researchers, and statisticians are drawn to it due to its extensive package ecosystem, robust data manipulation capabilities, strong community support, and powerful visualization tools.”

Why Choose the R Language?

One might wonder why they should use the R language. Well, here are some benefits:

Free of cost and open-source: R programing language is an open source that means it is free of cost, you can easily installed as well update it on your system. The other main reason is you don’t ha0ve to buy any license mandatory as well you can shared out and buy it again.
It boasts an extensive community: R language has a very huge number of active community such as numbers of Data Scientists, Developers & Coders. Whenever you need any help you can take it from R, because as you now they have numbers of community that work together. So, you can take any idea with other professionals.
It is platform-independent: In a number of operating system, R has the capacity to run in numbers of these operating system with the identification of software and hardware. You can observe or had observed somewhere that some language like R or Window, Linux, Mac can run properly on the operating systems. and also capable to import the data from MySQL, Microsoft Excel & SQLite.
Incorporates cutting-edge technology.
Segregated from other languages: R language is segregated from other language
A trending language: As you have seen the image in the above article that how R language is utilizing in several industries such as healthcare, insurance, academic etc. the other reason of becoming popular of R language is, it has many community as Data scientists, Developers and Coders. So, everyone wants to learn R because in any industry you are struck in any problem you can take help from them.
A vast resource for data science, machine learning, data visualization, and data analysis.

Features of R Programming:

Data Analysis: R offers a vast collection of tools for data analysis. R programming language is best for data analysis. As we know many data scientists are involved in R language so this is the best programming language in terms of data analysis.
Data Visualization: It provides robust visualization capabilities through packages like base graphics and lattice, enabling users to create graphs and charts.
Open Source: R is freely available for installation as an open-source platform.
Community: With a vast community of data analysts, statisticians, and researchers, it fosters a rich ecosystem of packages.
Data Manipulation: R is a reliable source for various data manipulation tools, including transformation, filtering, summarization, and data cleaning practices.

Industries Using R Language

R language finds applications in numerous industries, including eRtail, Education (Edtech), Fintech, manufacturing, healthcare, data journalism, social media, and more.

Image credit: https://www.guru99.com/r-programming-introduction-basics.html

With a glance at the image above, you will gain an understanding of how R language is being used in various industries. Every sector, including academia, healthcare, government, and insurance, utilizes the R language. Although this data or report is from the year 2017, you can still observe the impact R language has had on multiple industries.

Comparison Between R and Python

Let’s look at the comparison between two popular language that is R and Python.

S.No.	R	Python
01	R language is used for data visualization.	It is used for deep learning.
02	The important package and library of R is tidyverse,ggplot2,etc.	The important package & library are pandas,numpy, Tensor-flow etc.
03	The objective of R is analysis of data as well statistical modeling.	Python objective is web development & in data science.
04	It is formulated for statistical and mathematical computing.	It is created for basis purpose of programming language with the function of code readability.
05	It is open source and free.	It is also free and open source language.

Conclusion

As we know, there are numerous programming languages available in the market. If you want to become a programmer or developer and are confused about which is the best language to learn to brighten your career, R language is one of the best and trending languages, alongside Python, Java, C++, and others. You can choose R language fearlessly; it will undoubtedly help you in your career.

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Service ,All about CAAS (Container-as-a-Service)

Shiwani Sharma — Mon, 22 Apr 2024 04:52:59 +0000

Introduction TO CAAS (Container As A Service)

CAAS is a cloud service that permit and provide the platform to software developers and IT department to organize, run, deploying and scaling containerized applications. The weight of these containers are very light, movable that give a summary of software and its outpost, permits for efficient and smooth flow of deployment across several computing environments. Users can concentrate on deploying, developing application without dealing with the difficulties of infrastructure management.

This CAAS platform also consists scaling, networking, orchestration and monitoring. One of the most important examples of the CAAS platform are Docker Swarm, Amazon ECS (Elastic Container Service) & Kubernetes.

In the other ways you can also says a cloud service model that clarify the management and deployment of containerized applications.

Wikipedia explain the concept of CAAS as a service-oriented model, where the service provider delivers the content on demand to the service consumer via web services that are licensed under subscription. The term “Content as a service” (CaaS) is considered to be part of the nomenclature of cloud computing service models & Service-oriented architecture along with Software as a service (SaaS), Infrastructure as a service (IaaS), and Platform as a service (PaaS).

Benefit Of CAAS

The person or the users who are using container as a service, they only have to pay for those resources they are using as like scheduling capabilities, load balancing and compute instances etc. It permits the efficient utilization of resources by automatically adjusting container placement based on accessible capacity.
It has high availability: This platform consists of the features for permits high availability, as like automated load balancing and failover and support the maintain the availability of applications even at the time when find some problems or arising the failures of hardware.
Resources Orchestration: This platform permits for the structured using resources by automatically regulate container that is based on accessible space. The platform of CAAS has automate the deployment, scaling and organized the containerized applications. Orchestration tool help in many difficult tasks as like rolling updates, decrease the operational overhead, load balancing etc. and one of the famous orchestration tool are Kubernetes.
Cost- Efficiency: CAAS platform optimized the utilization of resources and automating management tasks and that’s the reasons of cost saving. When we uses this platform we can save the resources for the containers it is required, and one of the important features of CAAS is it has automated scaling that will support and assure you that they are not over provisioning resources.
Increased Application Performance: When we compared CAAS platform with others such as virtual machines, some others startup times and all around performance of applications, it have lower overhead. This will help to provide good compassion and experience.

Why CAAS is important?

One of the main benefit or the importance of CAAS is you can shift from one cloud to another and even you can go back to the server system that is physical. It also permits you to build multi and hybrid cloud system for the organizations. Now we will explain some of the other important reasons or points why CAAS (Container As A Service) is important is as follow:

Users can use the resources as per their requirement and they can only pay for the resources they are using. This is the best benefit because it help to save the time and money of the users, they can select or choose the resources they want to work in.
Developers gets the benefits of CAAS just by developing a application deployment and scalable container and this benefit is important to the IT teams, the reasons of its importance is it provide a container deployment service in a safe environment.

Challenges Of Using Container-as – a – Service (CAAS)

There are some challenges that CAAS has to faces and some of these are ( Networking, Security, Data Management, Cost Management, Skill Set Challenges) as follow:

Networking: It is difficult to manage network for containers and in case of large-scale deployments. In a large container or for small as well it is complex to make a communication or coordination between containers and assure for proper network segmentation and that become also challenging to handle outer access.

Security: It may arises the security risks, if the OS Kernel host is not properly isolated. It important to execute the security best practices as like utilizing reliable images, sufficient container privileges etc. It is also difficult for the security challenges to access control and privilege escalation.

Data Management: This platform container are manufactured to be stateless, but there are many application that need to stateful data and that become challenges for Container-as-a-services. In the other word you can also says that managing data backups and assuring data consistency is also become challenge.

Cost Management: It is very difficult to handle and monitor cost effectively due to the lack of visibility into the container resources. There is also a problem arises in terms of network-related cost and data transfer cost is increases mostly while serving the micro services architectures where containers are interact with each other.

Conclusion: As we journeyed through the world of Container as a Service (CAAS), this CAAS platform also consists scaling, networking, orchestration and monitoring. This platform consists of the features for permits high availability, as like automated load balancing and failover and support the maintain the availability of applications even at the time when find some problems or arising the failures of hardware.

As we conclude our exploration of CAAS, it is very clear that success lies in a holistic approach and it consist of some orchestration tools, that secure the containerized applications and navigate the cost management.

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