Why remote culture has increased the need for cybersecurity?

Remote work is now a standard option for most professionals, but the rising popularity of work from anywhere has driven a corresponding rise in cybersecurity incidents.

Remote work during the COVID-19 pandemic drove a 238% increase in cyber attacks, according to a March 2022 report by Alliance Virtual Offices, which provides services to the remote workforce. And Gartner’s “7 top trends in cybersecurity for 2022” called the expansion of the attack surface that came with remote work and the increasing use of public cloud a major area of cybersecurity concern. Trends such as these have made security improvements for remote employees and risk-based vulnerability management the “most urgent projects” in 2022 for 78% of CISOs surveyed by security software provider Lumu Technologies.

Let us discuss some of the upcoming trends in the cybersecurity domain for the year 2023:

Changes to the cybersecurity workforce

First and foremost, the demand for security professionals has definitely increased exponentially over the past couple of years. 

  • There’s a shortage of qualified cybersecurity professionals, so you can expect to see more security specialists and fewer generalists.
  • Heightened(or Higher) Focus on technical abilities, compared to before when leadership and management were prioritized over technical proficiency.

Growing connection between adaptive security architecture and endpoint devices.

As the number of connected devices increases, it is difficult to manage them. This can be especially true regarding the endpoint and edge devices connected via networked infrastructure and generating massive amounts of data.

Organizations must have an intelligent system that can detect or prevent potential threats in order to act quickly and wisely. They need tools that allow swift action to stop threats before they harm their network, without having to shut down the entire system due to one infected device (which could take days).

What are some of the articles/solutions that they should look at?

Zero trust model.

The zero trust model is the latest security trend to be introduced into the cybersecurity world. This model treats users, devices, and resources as untrusted. A zero-trust model requires a more granular approach to security than previous approaches. Still, it can also be used in various applications such as

  • Healthcare
  • Manufacturing
  • Transportation

AIOps to manage security operations.

The AIOps platform is a software platform that uses machine learning, AI, and big data to automate security operations. It can help with threat hunting, incident response, and cyber forensics.

AIOps help organizations to detect threats more quickly than they could before. This will help them respond faster when a security incident occurs so they can take action to prevent further damage or loss of data beyond what was caused by the initial attack.

XDR (Extended detection and response) will become mainstream.

XDR is a hybrid approach that combines the best of both worlds. It combines traditional NIDS and NIPS with AI to detect threats and automatically respond to them.

XDR uses machine learning to identify malicious activity and automatically responds by blocking it before it can disrupt your network. This is beneficial, allowing for swift response without manual labor, allowing for more focus on the core of the business rather than security matters.

More sophisticated threat intelligence.

Threat intelligence is collecting, analyzing, and sharing information about cyber threats. Gaining insight into your network’s data assists in defending against malicious actors.

The importance of threat intelligence has grown over time as attacks have become more sophisticated. Additionally, many new threats are emerging every day around the world. So companies everywhere need to stay up-to-date with what’s happening in this space to protect themselves from becoming victims!

Adaptive authentication to integrate AI into authentication policies.

AI is a powerful tool that can be used to enhance the user experience and security. By using AI, companies can improve their authentication process by scanning data and identifying anomalies to identify users trying to access protected areas of their network without authorization. This will allow for more accurate identification of potential threats and reduce false positives that may otherwise result from traditional methods such as facial recognition technology or voice printing software.*

Microsegmentation will take off.

Microsegmentation secures networks by dividing them into smaller, more manageable segments that can be assigned different security requirements and policies. It can be done at the network level or at the host level. Microsegmentation can also be done using virtualization, containerization, and software-defined networking (SDN).

AI-driven cyberattacks.

The next generation of malware will be created by artificial intelligence and advanced machine learning techniques. These new attacks will be more challenging to detect because they will have no human fingerprints or signatures associated with them.

AI can also be used in a defensive capacity; for example, it can analyze large amounts of data to identify patterns that indicate an attack is underway. It can then automatically block traffic and remove infected computers from networks before any damage has been done (or even noticed).

AI cyberattacks are harder to spot than traditional ones because they function internally, not online. They will likely improve, so intrusion detection systems, which detect suspicious activities without knowing the source, are essential.

Automated attack remediation

Automated attack remediation is a new approach to security that provides automatic threat detection and mitigation.You can concentrate on your main tasks, while the system detects and eliminates any potential harm.

The benefits of automated attack remediation include the following:

  • Saving time and money by removing manual processes from your organization’s infrastructure.
  • Reducing risk by immediately responding to attacks with minimal human involvement to avoid critical impacts on operations or reputation.

Advanced threat hunting

Threat hunting is integral to cybersecurity, especially for organizations hit by a cyberattack. It uses tools and techniques to find and stop cyberattacks before they get too far along their life cycle. The term “threat hunter” has been around since the early 2000s, but security professionals only recently embraced it as a legitimate role within their organizations.

Threat hunters utilize threat intelligence to identify threats before they can cause harm, and collaborate with other teams (i.e. IT or legal) to decide the most effective course of action when a threat is uncovered. A good example of this type of collaboration is when Microsoft released its latest Windows version following reports that hackers were targeting users running older versions of OS X (OS X 11).

Updated ICS security 

The ICS security landscape is transforming. ICS will add more security features to make it more sophisticated and proactive. It will also become more automated, integrated with other systems and technologies, and adaptive based on user activity patterns.

These changes result in your cybersecurity posture needing to evolve if you want to remain competitive in this new world order.

IoT devices are more secure by default.

The IoT has been around for a while, but it’s only recently that we’ve seen the rise of more secure devices. This is because IoT devices are more accessible to secure than traditional ones and are becoming more so because of their design and deployment.

For example, in the past, hackers would need physical access to your device or an internal network connection before gaining control. But now, with the rise of cloud-based services such as AWS, hackers don’t even have access points to install malware on your device. They simply hack into AWS servers instead and then use them for their own purposes!

Development of more advanced anti-malware programs

In the following year, we will see more advanced anti-malware programs being developed. This is because there is a need for these types of programs as cyberattacks have become more common, and companies are adopting more AI technology to prevent them.

Security is an integral part of business strategy for all companies, big or small.

Security is an integral part of business strategy for all companies, big or small. Whether a large enterprise or a small startup, your data can be valuable to hackers and other threat actors.

Small businesses can benefit from security practices and learn from their larger counterparts while keeping costs down by implementing more cost-effective solutions tailored to their needs and budget constraints.

Security is not just about protecting your customers’ information. It is also about protecting yourself from criminals who want to steal data from your network or server room (or both!).


Cybersecurity isn’t going away, but it will continue to evolve. It will continue to be a key component of business strategy. Cybersecurity is a permanent necessity for any contemporary business, regardless of whether you possess delicate details or interact with customers on social media.

As we move into the new year, we must understand how cybersecurity evolves and what trends will likely occur next year (and beyond). 

Want to stay up to date with the latest news and trends in cybersecurity? Make sure you follow Skedler on LinkedIn and Twitter!

How to configure auto-restart of Skedler service in Windows using NSSM


Application crashes are the worst! You lose your progress and also your data. And you might think that the only way to get it up and running would be to restart it again. There could be many reasons behind an app crashing on Windows, right from a bad Windows update to a critical system error.

If you, too, are tired of performing the usual solutions, such as updating or restarting apps and services.

NSSM is a service helper, which doesn’t suck. Many other service helper programs suck because they don’t handle the failure of the application running as a service. If you use such a program, you might notice the service listed as started even when, in fact, the application has died. NSSM monitors the running service and will restart it if it dies. With NSSM, you know that if a service says it’s running, it really is. 

Alternatively, even if your application is well-behaved, you can configure NSSM to absolve all responsibility for restarting it and let Windows take care of recovery actions. In addition, it also logs its progress to the system Event Log so the user can get an idea of why an application isn’t behaving as it should.

NSSM has comprehensive use cases and global acceptability when it comes to Managing a service that should run constantly. If your application fails or crashes, NSSM will attempt to start it up again. This is very helpful while using an application that requires services to be up and running to work smoothly. Integrating NSSM with Skelder, where Skedler service must be up and running to Create and generate Operational intelligence reports, has been a significant safety measure to observe and direct execution to restart services in case of a crash.

Configure Nssm as a service manager

Step 1 – To integrate Skedler with NSSM, you can start by downloading NSSM using the Download link.

Once the download is complete open the .zip file and choose the folder as per your system OS (windows 64-bit or 32-bit)

Step 2 – Open in PowerShell (Recommended: As an Administrator) and Enter the following command line .\nssm.exe install skedler

Step 3 – Now, you can proceed to configure the settings in the NSSM service installer. Start by selecting the Application path, and you’ll notice that the Startup directory is automatically filled once you select the application path.

Note, Make sure to remove \bin from the end of the Startup directory path.


Path:  C:\Users\Administrator\Downloads\skedler-xg\skedler-xg\bin

Startup Directory: C:\Users\Administrator\Downloads\skedler-xg\skedler-xg

Step 4 – The next step is to configure the ‘Details” tab. You can simply switch to the details tab in the NSSM service installer, enter the details below, and click on the Install Service button.

Display name: Skedler
Description: Skedler – Reporting tool

You’ll notice a success pop-up message once the installation completes.


With the NSSM service installed, Skedler will automatically handle the failure of the application running as a service. Providing Zero Application Downtime & saving time on the DevOps side by eliminating the need to invest time in performing the usual solutions, such as updating or restarting apps and services in case of a crash, restart, or critical system error.

If you face any challenges or have questions, feel free to email us at (support@skedler.com).

Everything You Need to know about Data Observability

Data derived from multiple sources in your arsenal affects the business decision. It may be data that your marketing team needs or you need to share some statistics with your customer; you need reliable data. Data engineers process the source data from tools and applications before it reaches the end consumer. 

But what if the data does not show the expected values? These are some of the questions related to bad data that we often hear:

  1. Why does this data size look off?
  2. Why are there so many nulls?
  3. Why are there 0’s where it should be 100’s?

Bad data can waste time and resources, reduce customer trust, and affect revenue. Your business suffers from the consequences of data downtime—a period when your data is missing, stale, erroneous, or otherwise compromised. 

It is not acceptable that the data teams are the last to know about data problems. To prevent this, companies need complete visibility of the data lifecycle across every platform. The principles of software observability have been applied to the data teams to resolve and prevent data downtime. This new approach is called data observability.

What is Data Observability?

Data observability is the process of understanding and managing data health at any stage in your pipeline. This process allows you to identify bad data early before it affects any business decision—the earlier the detection, the faster the resolution. With data observability, it is even possible to reduce the occurrence of data downtime.

Data observability has proved to be a reliable way of improving data quality. It creates healthier pipelines, more productive teams, and happier customers.

DataOps teams can detect situations they wouldn’t think to look for and prevent issues before they seriously affect the business. It also allows data teams to provide context and relevant information for analysis and resolution during data downtime. 

Pillars of Data Observability

Data observability tools evaluate specific data-related issues to ensure better data quality. Collectively, these issues are termed the five pillars of data observability. 

  • Freshness
  • Distribution
  • Volume
  • Schema
  • Lineage

These individual components provide valuable insights into the data quality and reliability. 


Freshness answers the following questions:

  • Is my data up-to-date?
  • What is its recency?
  • Are there gaps in time when the data has not been updated?

With automated monitoring of data intake, you can detect immediately when specific data is not updated in your table. 


Distribution allows us to understand the field-level health of data, i.e., is your data within the accepted range? If the accepted and actual data values for any particular field don’t match, there may be a problem with the data pipeline.


Volume is one of the most critical measurements as it can confirm healthy data intake in the pipeline. It refers to the amount of data assets in a file or database. If the data intake is not meeting the expected threshold, there might be an anomaly at the data source. 


Schema can be described as data organization in the database management system. Schema changes are often the culprits of data downtime incidents. These can be caused by any unauthorized changes in the data structure. Thus, it is crucial to monitor who makes changes to the fields or tables and when to have a sound data observability framework.


During a data downtime, the first question is, “where did the data break”? With a detailed lineage record, you can tell exactly where. 

Data lineage can be referred to as the history of a data set. You can track every data path step, including data sources, transformations, and downstream destinations. Fix the bad data by identifying the teams generating and accessing the data.

Benefits of using a Data Observability Solution

Prevent Data Downtime

Data observability allows organizations to understand, fix and prevent problems in complex data scenarios. It helps you identify situations you aren’t aware of or wouldn’t think about before they have a huge effect on your company. Data observability can track relationships to specific issues and provide context and relevant information for root cause analysis and resolution.

Increased trust in data

Data observability offers a solution for poor data quality, thus enhancing your trust in data. It gives an organization a complete view of its data ecosystem, allowing it to identify and resolve any issues that could disrupt its data pipeline. Data observability also helps the timely delivery of quality data for business workloads.

Better data-driven business decisions

Data scientists rely on data to train and deploy machine learning models for the product recommendation engine. If one of the data sources is out of sync or incorrect, it could harm the different aspects of the business. Data observability helps monitor and track situations quickly and efficiently, enabling organizations to become more confident when making decisions based on data.

Data observability vs. data monitoring

Data observability and data monitoring are often interchangeable; however, they differ.

Data monitoring alerts teams when the actual data set differs from the expected value. It works with predefined metrics and parameters to identify incorrect data. However, it fails to answer certain questions, such as what data was affected, what changes resulted in the data downtime, or which downstream could be impacted. 

This is where data observability comes in. 

DataOps teams become more efficient with data observability tools in their arsenal to handle such scenarios. 

Data observability vs. data quality

Six dimensions of measuring data quality include accuracy, completeness, consistency, timeliness, uniqueness, and validity. 

Data quality deals with the accuracy and reliability of data, while data observability handles the efficiency of the system that delivers the data. Data observability enables DataOps to identify and fix the underlying causes of data issues rather than just addressing individual data errors. 

Data observability can improve the data quality in the long run by identifying and fixing patterns inside the pipelines that lead to data downtime. With more reliable data pipelines, cleaner data comes in, and fewer errors get introduced into the pipelines. The result is higher quality data and less downtime because of data issues.

Signs you need a data observability platform

Source: What is Observability by Barr Moses

  • Your data platform has recently migrated to the cloud
  • Your data team stacks are scaling with more data sources
  • Your data team is growing
  • Your team is spending at least 30% of its time resolving data quality issues. 
  • Your team has more data consumers than you did 1 year ago
  • Your company is moving to a self-service analytics model
  • Data is a key part of the customer value proposition

How to choose the right data observability platform for your business?

The key metrics to look for in a data observability platform include:

  1. Seamless integration with existing data stack and does not require modifying data pipelines.
  2. Monitors data at rest without having to extract data. It allows you to ensure security and compliance requirements.
  3. It uses machine learning to automatically learn your data and the environment without configuring any rules.
  4. It does not require prior mapping to monitor data and can deliver a detailed view of key resources, dependencies, and invariants with little effort.
  5. Prevents data downtime by providing insightful information about breaking patterns to change and fix faulty pipelines.


Every company is now a data company. They handle huge volumes of data every day. But without the right tools, you will waste money and resources on managing the data. It is time to find and invest in a solution that can streamline and automate end-to-end data management for analytics, compliance, and security needs. 

Data observability enables teams to be agile and iterate on their products. Without a data observability solution, DataOps teams cannot rely on its infrastructure or tools because they cannot track errors quickly enough. So, data observability is the way to achieve data governance and data standardization and deliver rapid insights in real time. 

Do you need both SIEM and SOAR?

Since 2005, Security Incident and Event Management (SIEM) tools have been integral to any Security Operations Center (SOC). However, Security Orchestration, Automation, and Response (SOAR) have quickly become one of the most sought-after tools for cybersecurity. 

You might be thinking:

  1. What’s the difference between SOAR and SIEM?
  2. Do I need SOAR if I have a SIEM?
  3. Can I use SOAR to improve the effectiveness of a SIEM? How?

Let’s discuss in detail both of the tools to answer these questions:

What is SIEM?

SIEM is a security solution that offers complete real-time visibility to an organization’s cybersecurity through log management, event correlation, and threat intelligence.

SIEM aggregates logs from the firewalls, network appliances, and intrusion detection systems and generates alerts when a potential threat is detected. Security personnel further investigate the alerts, determine if it is a genuine incident, and take necessary actions.

With the increasing number of attacks, the SecOps team fails to interpret all SIEM alerts before a data breach occurs. 

This is where SOAR comes in. 

What is SOAR?

SOAR offers orchestration and automation of the manual workflow of security teams after a SIEM alert is received. It combines Security Orchestration and Automation (SOA), Security Incident Response Platforms (SIRP), and Threat Intelligence Platforms (TIP)

SOAR tool delivers more value from the company’s existing security solutions by automating the incident response processes. SOAR can overcome the challenges of a SIEM tool, such as – alert fatigue, human error, and even a skill set shortage. Security Operations that do not need constant human insight can be performed via workflows or SOAR playbooks.

How do SOAR and SIEM work together?

How does SIEM and SOAR improve SecOps ?

Let’s say you get a brute-force correlation alert from SIEM. What are the next steps for incident response?

Logs show 10 login attempts in less than one minute and login failure in this case. An alert is triggered as it violates an existing SIEM rule. A security analyst now needs to investigate the alert and take action. But, as mentioned above, the number of such daily alerts is heavier than the SOC team can handle. 

SOAR is the solution to this problem. With a SOAR in place, the user can be disabled automatically without manual intervention. You can also include further steps per your incident response strategy to streamline the workflow and reduce human intervention. 

User And Entity Behavior Analytics Ueba (UEBA) is a security solution that detects threats by identifying unusual traffic patterns, unauthorized data access, movement, or suspicious or malicious activity on a computer network or endpoints. If the SIEM supports SOAR and UEBA, you can group similar alerts to create an incident. You can assign this incident to a dedicated technician for further investigation and prevention.

The situation could have led to a security incident without a SOAR solution initiating a quick fix. 

Top SIEM tools with SOAR capabilities:

Elastic (ELK) Stack is one of the popular SIEM tools that can also be configured as a SOAR solution. If you use ELK as a SIEM/SOAR solution, you must send daily, weekly or monthly reports to your clients and stakeholders. Not everyone will have access or willingness to sit in front of a dashboard and interpret the metrics. So, you need a reporting solution to share the data with clients in an actionable format.

Are you spending your time writing code to send out these periodic reports? What if there was a much easier way?

Skedler is an affordable, easy-to-use report automation tool that converts Kibana dashboards into branded reports with zero coding. Set up Skedler and send out reports in an hour. 

Some other SIEM tools with SOAR capabilities are:

  • SolarWinds SIEM Security and Monitoring
  • Splunk Enterprise SIEM
  • LogRhythm
  • IBM QRadar
  • Insight IDR

Skedler can also be used to automate LogRhythm reports. 

Can SOAR replace SIEM?

The need for a SIEM arises because an organization generates thousands of daily security information and events. SOAR improves the security program’s incident response and vulnerability management using artificial intelligence and machine learning.

SIEM provides the alerts from the logs collected from various data sources. SOAR gathers the alerts, correlates them, and automatically takes the appropriate actions. So, both are crucial for an organization’s incident management architecture. 

They are no longer considered to be independent of one another. A SIEM solution is now expected to provide SOAR capabilities or the ability to integrate seamlessly with a SOAR solution.

Do I need a SOAR if I have a SIEM?

SIEM lacks incident response, investigation, and case management tools and workflows to manage threats efficiently. A security analyst must review and investigate each SIEM alert to determine if the event is a false positive. Only then can they initiate the necessary actions.

SOAR can improve the process by determining if the alert is genuine and automating further investigation and remediation.

SIEM is an ideal alert source with its threat detection ability from log and event data. Alerts escalated to an integrated SOAR platform save resources by reducing constant manual intervention. SOAR combined with a SIEM solution constitutes an efficient and responsive security program.


As discussed above, SIEM and SOAR are not alternatives but complement each other. To build a robust security solution for your organization, you must look for a SIEM solution with SOAR capabilities. 

SIEM and SOAR are effective in improving an organization’s security operations.

With information and event management, SIEM produces more alerts than SecOps teams can effectively respond to. SOAR enables the security team to handle the alert load quickly and efficiently. Using automated response, SOAR can mitigate vulnerability to the organization. 

LDAP integration with Skedler – authentication made simpler!


Businesses today rely heavily on professional applications for every daily task or critical operation. These applications should be set up with secure user authentication. Solutions such as LDAP will allow the users to save more time on manual management of critical information and avoid its risks. LDAP integration is one of the features to look for while selecting an application for your daily tasks.


LDAP or Lightweight Directory Access Protocol is a software protocol that allows individual users and applications to find and verify whatever information they need within their organization. It has been used as a database of information, primarily storing information like:

  • Users
  • Attributes about those users
  • Group membership privileges

Organizations then used this information to enable authentication to IT resources such as applications or servers. The LDAP database validates whether users would have access to the applications by verifying the user’s credentials.

LDAP authentication

A user cannot access information stored within an LDAP database or directory without first authenticating (proving they are who they say they are). The database typically contains user, group, and permission information and delivers requested information to connected applications.

LDAP authentication involves verifying provided usernames and passwords by connecting with a directory service that uses the LDAP protocol. Some LDAP directory servers are OpenLDAP, MS Active Directory, and OpenDJ.

LDAP Integration with Skedler

Skedler is a report-automation tool created to reduce the amount of time and money invested daily in a cumbersome task in data analytics such as reporting. Generating and distributing reports from Security Onion, Kibana and Grafana has never been easier. With Skedler, MSSPs can generate compliance reports (e.g., PCI, ASV reports) quickly and efficiently to save countless man-hours, deliver reports 10x faster, and enable their customers to mitigate vulnerabilities more quickly.

If you have not already, download Skedler to check out how easy it is to automate your reports. You will be shocked at the amount of time saved every day!


Like any other LDAP-integrated application, Skedler also uses the integration to authenticate users based on their LDAP credentials. Once the LDAP integration is completed in the Skedler Admin account, any user with correct credentials from the LDAP database can log in to Skedker without creating a separate Skedler account.

When a new user attempts to log in to Skedler, the integration checks to see if this user has an existing Skedler account. If not, it automatically queries the LDAP server for the entered username and password. If a matching LDAP account is found, Skedler creates a new account for the user and logs the user into the organization. 

Steps for LDAP integration with Skedler

New User

Before going through the steps to integrate Skedler with LDAP authentication, please refer to this documentation to install Skedler and activate the license. Once Skedler is installed on your machine, follow these steps to integrate LDAP authentication.

  1. Add LDAP configuration to the Skedler reporting.yml file.
  2. Sign in with LDAP user credentials

Skedler then validates the entered credentials with the LDAP server. Based on the reporting.yml configuration, Skedler will map the user to the respective roles and organizations.

We have created this support documentation with detailed steps. Take a quick look.

Existing User 

If you are an existing Skedler user, you can follow these steps to incorporate LDAP authentication:

  1. Upgrade to the Skedler’s latest version
  2. Configure the reporting.yml file
  3. Restart the server 
  4. Login using LDAP credentials

Note that if you are adding new roles or organizations to your LDAP server, you have to add the same in Skedler reporting.yml file as well.

Future of Skedler with LDAP integration

This is an opportunity to dedicate your time to areas of innovation and remediation! Skedler is here to help you bring more value to your product, customers, and other stakeholders by automating your cumbersome daily task of reporting.

MSSPs commonly lack visibility into user accounts and activity. They manually manage their resource access, accounting for a decentralized and unorganized Identity and Access Management model filled with redundancies, friction, and security risk. With the new LDAP integration, Skedler can easily identify new or existing users and log them in securely in no time, without ever going to the Admin to ask for credentials or permission.

With Skedler, you can save time, secure your business and provide a seamless employee and customer experience.

Connect Skedler with Kibana, Grafana, and Security Onion in seconds. Automate your reports on hourly, daily, weekly, monthly, and yearly schedules and put them on auto-pilot! Click on this button to get 250 reports free for 15 days!

Three Pillars of Observability – Metrics ( Part 2)


Distributed systems mean services and servers are spread out over multiple clouds. The individual users who consume the services increase their number, device of choice, and location. Having visibility into the client’s experience while using the application – i.e., observability – is now a vital part of the metrics required to operate the applications in your infrastructure.

What is Metrics?

A metric is a quantifiable value measured over a while and includes specific characteristics like timestamp, name, KPIs, and value. Unlike logs, metrics are structured by default, making it easier to query and optimize for storage giving you the ability to retain them for more extended periods.

Metrics help uncover some of the most primary queries of the IT department. Is there a performance issue that’s affecting customers? Are employees having trouble accessing? Is there high traffic volume? Is the rate of customer churn going up?

Standard metrics include

  1. System metrics such as CPU usage, memory usage, disk I/O,
  2. App metrics such as rate, number of errors, time,
  3. Business metrics such as revenue, signups, bounce rate, cart abandonment, etc.

Different Components of Metrics

Metrics is the most valuable of the three pillars because they’re generated very often and by every module, from operating systems to applications. Associating them can give you a complete view of an issue, but associating them is a huge and tedious task for human operators.

Data Collection

The most significant part of metrics is small and does not consume too much space. You can gather them cheaply and store them for an extended period. These give you a general overview of the whole system without insights.

So, metrics answer the question, “How does my system performance change through time?”

Data Storage

Most people used statsd along with graphite as the storage backend. Some people now prefer Prometheus, an open-source, metrics-based monitoring system. It does one thing pretty well, with a simple yet powerful data model and a query language, it lets you analyze how your applications and infrastructure perform.

Visualization and Reporting

I would also consider visualization a part of metrics, as it goes hand in hand with metrics.

Grafana is used to visualize the data scraped by sources like Prometheus, a  data source to grafana, which works on a pull model. You can also use Kibana as your visulaization tool, primarily supporting elastic stack.

And you can use Skedler to generate reports from these visualizations to share with your stakeholders.

There is a simple and effective way to add reporting for your Elasticsearch Kibana (including Open Distro for Elasticsearch) or Grafana applications that are deployed to Kubernetes using Skedler.

You can deploy Skedler on air-gapped, private, or public cloud environments with docker or VM on various flavors of Linux.

Skedler is easy to install, configure and use with Kibana or Grafana. Skedler’s no-code Drag-n-drop UI generates PDF, CSV, Excel Kibana, or Grafana reports in minutes and saves up to 10 hours per week.

Try our new and improved Skedler for custom generated Grafana or Kibana reports for free!

Download Skedler


Metrics are the entry point to all monitoring platforms based on the data collection from CPU, memory, disk, networks, etc. And so, they no longer belong only to operations —  metrics can be created by anyone and any system in the distributed network. For instance, a developer may opt to showcase application-specific data such as the number of tasks performed, the time required to complete the tasks, and the status. Their objective is to link these data to different levels of systems and define an application profile to identify the necessary architecture for the distributed system itself. This adds to improved performance, reliability, and better security system-wide.

Metrics used by development teams to identify points in the source code that need improvement can also be used by operators to assess the system requirements and plan needed to support user demand and the team to control and enhance the adoption and use of the application.

Three Pillars of Observability – Logs


Observability evaluates what’s happening in your software from the outside. The term describes one cohesive capability. The goal of observability is to help you see the condition of your entire system.

Observability needs information about metrics, traces, and logs – the three pillars. When you combine these three “pillars,” a remarkable ability to understand the whole state of your system also emerges. This information might go unnoticed within the pillars on their own. Some observability solutions will put all this information together. They do that as different capabilities, and it’s up to the observer to determine the differences. Observability isn’t just about monitoring each of these pillars at a time; it’s also the ability to see the whole picture and to see how these pieces combine to fit in a puzzle and show you the actual state of your system.

The Three Pillars of Observability

As mentioned earlier, there are three pillars of observability: Logs, Metrics, and Traces.

Logs are the archival records of your system functions and errors. They are always time-stamped and come in either binary or plain text and a structured format that combines text and metadata. Logs allow you to look through and see what went wrong and where within a system.

Metrics can be a wide range of values monitored over some time. Metrics are often vital performance indicators such as CPU capacity, memory usage, latency, or anything else that provides insights into the health and performance of your system. The changes in these metrics allow teams to understand the system’s end performance better. Metrics offer modern businesses a measurable means to improve the user experience.

Traces are a method to follow a user’s journey through your application. Trace documents the user’s interaction and requests within the system, starting from the user interface to the backend systems and then back to the user once their request is processed. 

This is a three-part blog series on these 3 pillars of observability.  In this first part, we will dive into logs.

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The First Pillar – Logs

In this part of the blog, we will go through the first pillar of Observability – Logs. 

Logs consist of the system’s structured and unstructured data when specific programs run. Overall, you can think of a log as a database of events within an application. Logs help solve unpredictable and irregular behaviors of the components in a system.

They are relatively easy to generate. Almost all application frameworks, libraries, and languages support logging. In a distributed system, every component generates logs of actions and events at any point.

Log files entail complete system details, like fault and the specific time when the fault occurred. By examining the logs,  you can troubleshoot your program and identify where and why the error occurred. Logs are also helpful for troubleshooting security incidents in load balancers, caches, and databases.

Logs play a crucial role in understanding your system’s performance and health. Good logging practice is essential to power a good observability platform across your system design. Monitoring involves the collection and analysis of logs and system metrics. Log analysis is the process of deriving information from these logs. To conduct a proper log analysis, you first need to generate the logs, collect them, and store them. Two things that developers need to get better at logging are: what and how to log.

But one problem with logging is the sheer amount of logged data and the inability to search through it all efficiently. Storing and analyzing logs is expensive, so it’s essential to log only the necessary information to help you identify issues and manage them. It also helps to categorize log messages into priority buckets called logging levels. It’s vital to divide logs into various logging levels, such as Error, Warn, Info, Debug, and Trace. Logging helps us understand the system better and help set up necessary monitoring alerts. 

Insights from Logs

You need to know what happened in the software to troubleshoot system or software level issues. Logs give information about what happened before, during, and after an error occurred.

A trained eye monitoring log can tell what went wrong during a specific time segment within a particular piece of software.

Logs offer analysis at the granular level of the three pillars. You can use logs to discover the primary causes for your system’s errors and find why they occurred. There are many tools available for logs management like

You can then monitor logs using Grafana or Kibana or any other visualization tool.

The Logs app in Kibana helps you to search, filter, and follow all your logs present in Elasticsearch. Also, Log panels in Grafana are very useful when you want to see the correlations between visualized data and logs at a given time. You can also filter your logs for a specific term, label, or time period.

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Limitations of Logs

Logs show what is happening in a specific program. For companies running microservices, the issue may not lie within a given service but how different connected functions. Logs alone may show the problem but do not show how often the problem has occurred. Saving logs that go back a long time can increase costs due to the amount of storage required to keep all the information.

Similarly, coming up with new containers or instances to handle client activity means increasing the logging and storage cost. 

To solve this issue, you need to again look to another of the three pillars of observability—the solution for this: metrics. We will cover metrics in the second part of our observability series. Stay tuned to learn more about observability.

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Cultural Side of Supply Chain Security

With cybersecurity & ransomware attacks on the rise, strengthening our defenses towards ensuring the safety & privacy of customer data has assumed paramount importance. One of the major challenges in this endeavor today is to manage the risk associated with integrating open-source software in the products that we develop. This is where Software Supply Chain Security swoops in and, potentially, saves the day.

The sources of attack on a supply chain can be varied & need not be relegated to just the piece of software being shipped or the vulnerabilities therein. For this post, however, we shall be limiting the scope of discussion to the cybersecurity aspect & shall be discussing various efforts towards improving the security in this space.

So what is Software Supply Chain Security exactly?

During the development of any application software, developers piece open source & proprietary libraries together. This software is further deployed on a platform towards making it available for end-user consumption. In this entire chain of design, development, and deployment there are various software packages being used with no means to corroborate their security. This leads to an architecture that is susceptible to attacks not only via traditional exploitation measures but also via indirect means such as utilizing political influence, blackmail, or even threats of violence against the developers who release such libraries[1]

Given the multi-faceted nature of this problem, the approach we use towards securing the product also needs to be holistic. Merely defending the endpoints will no longer suffice. Right from the design & build stage, security considerations must be incorporated into the process towards ensuring extensive mitigation of the aforementioned attacks. This implies that anything affecting your code – libraries, operating systems, etc, as it passes from development to production will be accurately recorded & tracked so that appropriate monitoring & mitigation processes can be put into place. 

A cultural shift?

While a quick Google search can list down the many efforts being taken towards developing appropriate tooling for this purpose, as with everything else, this calls for a cultural shift along with a technological one. Merely integrating available technology in a software supply chain will achieve very minimal results since this will always be an evolving space due to the nature of attacks & the scope involved. A shift in the mindset, as well as the ways of working, needs to accompany the ongoing advancements in tooling & technology.

Assuming shared responsibility

Much like DevOps, the onus of ensuring a secure supply chain doesn’t lie on one team or person alone. It is a shared responsibility and everybody in an organization should collaboratively work towards the end goal. Rather than security as an afterthought, it must be the focal point for every decision an individual/team makes throughout the cycle. Yes, that also includes tooling!

Automated tooling

Every single package matters! This is also why every release iteration will require the packages involved to be recorded, analyzed, & monitored for any vulnerabilities. In the event of a vulnerability, there also needs to be a way to assess the impact and mitigate it as soon & effectively as possible. Doing this ad infinitum in a manual manner would be effort & resource intensive which is why there is a requirement for intelligent and automated tooling to be in place. 

Embracing failures

As the discipline of Chaos Engineering evolves, there is hope for sophistication in the sphere of supply chain attack simulation. Simulations help us discover further vulnerabilities within the existing processes/tooling in place & help us improve, should they occur in real-time because let’s face it; everything fails! How we deal with failure is what ultimately matters. Planning ahead for mitigation and remediation measures as an outcome of such simulations will only help make our supply chains more reliable.

What are the odds?

A four-fold increase in supply chain attacks is expected by the end of this year. Per the report published by the European Union Agency for Cybersecurity titled, Threat Landscape for Supply Chain Attacks[2], the sophistication and complexity of the attacks were only going to improve with time, thereby requiring equally intelligent & holistic measures towards securing them.

What are our options?

Glad you asked! There is a lot of work underway currently in various areas as detailed extensively in this document by Aeva Black. With efforts such as standardization frameworks, open-source projects, and companies like Chainguard Inc. towards revolutionizing the available tools, this is one space that will be seeing a rapid transformation in the coming years.

OpenTelemetry 101

With a mindset shift in most organizations adopting DevOps & Agile practices, one of the usual starting points in their transformation journey was to break down their monolith into several microservices. This, not only, helped with the continuous integration, delivery, and testing tenet but also expedited changes that previously used to take weeks or even months to execute. However, this transformation in architecture presented its own set of challenges with respect to monitoring. For traditional architectures, monitoring was relegated to understanding a known set of failures based on usage thresholds & parsing content off logs. With the architectural shift, monitoring alone no longer served the purpose of understanding the state of the system given that failures within the newer systems were never linear. 

Observability & Telemetry

Thus was born observability as a discipline to complement monitoring with its data-driven approach. In short, monitoring systems didn’t die out but were supplemented with more data to understand the internal state of the architecture & navigate from effect to cause more easily with the discipline of observability. Founded on the three pillars of logs, metrics, and tracing, commonly known as telemetry data, observability systems enabled us to understand our systems & their failures better.

However, with an increasing demand for observability systems, there also was another challenge on the rise – the lack of standardization in the offerings. In addition to this, the ones that were adopted lacked portability across languages. A combination of the above two challenges resulted in the overhead of implementations being maintained by the developer/SRE staff within the organization contributing to an increase in complexity & workload.

Thus was born OpenTelemetry: Built-in, high-quality telemetry for all

In 2019, the maintainers of OpenTracing (a CNCF vendor-agnostic tracing project) & OpenCensus (a vendor-agnostic tracing & metrics library led by Google) merged towards solving some of these challenges and standardizing the telemetry ecosystem with OpenTelemetry. As outlined in this excellent announcement post, the vision of the project was to provide a unified set of instrumentation libraries and specifications towards providing built-in high-quality telemetry for all. 

With an open, vendor-agnostic standard that was backward-compatible with both of its founding projects, OpenTelemetry’s aim was to allow for cross-platform & streamlined observability that would allow for more focus on delivering reliable software without getting mired down by the various available options. Because in the end, isn’t that the end goal of every business?

The nitty-gritty

A CNCF incubating project as of writing this post, OpenTelemetry is composed of the following main components as of v 0.11.0 released on 8th October 2021

  1. Proto files to define language independent interface types such as collectors, instrumentation libraries etc. 
  2. Specifications to describe the cross-language requirements for all implementations. 
  3. APIs containing the interfaces & implementations of the specifications
  4. SDKs implementing the APIs with processing & exporting capabilities
  5. Collectors to receive, process, and export the telemetry data in a vendor-agnostic manner
  6. Instrumentation libraries towards enabling observability for other libraries via manual & automatic instrumentation

As aforementioned, both manual & automatic instrumentation are supported; automatic instrumentation,  being the simpler of the two, involves only the addition of dependencies and configuration via environment variables or language-specific means such as system properties in Java. Manual instrumentation, on the other hand, would involve significant code dependencies on the API & SDK components in addition to the actual creation & exporting of the telemetry data. While extremely useful, a significant drawback is that manual instrumentation can lead to redundancies & inconsistencies in the way we treat observability data along with being a massive expenditure of manual efforts.

So where are we headed?

As of today, 14 vendors support OpenTelemetry. With a focus on developing the project on a signal-by-signal basis, the project aims to stabilize & improve LTS for instrumentation. With support for over 11 languages, there are also efforts expended towards expanding & improving the instrumentation across a wider variety of libraries as also incorporating testing & CI/CD tooling towards writing & verifying the quality of instrumentation offered.

With a vibrant community & extensive documentation around the project, there has never been a better time to get involved in the efforts towards standardizing the efforts for built-in high-quality telemetry.

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Observability 101 – How is it Different from Monitoring

Monitoring IT infrastructure was, in the past, a fairly complicated thing, because it required constant vigilance: software continuously scanned a network, looking for outages, inefficiencies, and other potential problems, and then logged them. Each of these logs would then have to be checked by a qualified SOC team, which would then identify any issues. This led to several common problems, such as alert fatigue and false flags – both of which we’ll discuss more later – and burnout was prevalent. In fact, these three issues (fatigue, flags, and burnout) have only increased as our interconnectivity has increased. Much like the pitfalls that have befallen the airline industry (such as increased security risks and tougher identification and authorization measures), our increasing connectivity is also presenting increased security risks, risks that require more stringent identification and authorization measures, adding to the workload of SOC teams.

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What does monitoring do? It lets us know if there are latency issues; it lets us know if we’ve had a jump in TCP connections. And while these are important notifications, they are no longer enough. Secure systems do not remain secure unless they are also maintained. Security teams need a system that can monitor all of these interconnected components. This is where observability comes in.

What is monitoring?

Observability is the capacity to deduce a system’s internal states. Monitoring is the actions involved in observability: perceiving the quality of system performance over a time duration. The tools and processes which support monitoring can deduce the performance, health, and other relevant criteria of a system’s internal states. Monitoring specifically refers to the process of analyzing infrastructure log metrics data.

A system’s observability lets you know how well the infrastructure log metrics can extract the performance criteria connected with critical components. Monitoring helps to analyze the infra log metrics to take actions and deliver insights.

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What is Observability?

Observability is the capacity to deduce the internal states of a system based on the system’s external outputs. In control theory, observability is a mathematical dual to controllability, which is the ability to control the internal states of a system by influencing external inputs. 

Infrastructure components that are distributed operate in multiple conceptual layers of software and virtualization. Therefore it is not feasible and challenging to analyze and compute system controllability.

Observability has three basic pillars:  metrics, logs, and tracing. As we noted a moment ago, observability employs all three of these to create a more holistic, end-to-end look at an entire system, using multiple-point tools to accomplish this. 

Comparing observability and monitoring

People are always curious about observability and its difference from monitoring. Let’s take a large, complex data center infrastructure system that is monitored using log analysis, monitoring, and ITSM tools. Monitoring multiple data points continuously will create a large number of unnecessary alerts, data, and red flags. Unless the correct metrics are evaluated and the redundant noise is carefully filtered monitoring solutions, the infrastructure may have low observability characteristics.

A single server machine can be easily monitored using metrics and parameters like energy consumption, temperature,  transfer rates, and speed. The health of internal system components is highly correlated with these parameters. Therefore, the system has high observability. Considering some basic monitoring criteria, such as energy and temperature measurement, the performance, life expectancy, and risk of potential performance incidents can be evaluated.

Observability in DevOps

The concept of observability is very important in DevOps methodologies. In earlier frameworks like waterfall and agile, developers created new features and product lines while separate teams worked on testing and operations for software dependability. This compartmentalized approach meant that operations and monitoring activities were outside the development’s scope. Projects were aimed for success and not for failure i.e debugging of the code was rarely a primary consideration. There was no proper understanding of infrastructure dependencies and application semantics by the developers. Apps and services were built with low dependability. 

Monitoring ultimately failed to give sufficient information of the distributed infrastructure system about the familiar unknowns, let alone the unfamiliar unknown.

The popularity of DevOps has transformed SDLC. Monitoring is no longer limited to just collecting and processing log data, metrics, and event traces but is now used to make the system more transparent I.e observable. 

The scope of observability encapsulates the development segment which is also aided by people, processes, and technologies operating across the pipeline.


Collaboration of cross-functional teams such as Devs, ITOps, and QA personnel is very important when designing a dependable system. Communication and feedback between developers and operations teams are necessary to achieve observability targets of the system that will help QA yield correct and insightful monitoring during the testing phase. In turn, DevOps teams can test systems and solutions for true real-world performance. Constant iteration based on feedback can further enhance IT’s ability to identify potential issues in the systems before the impact reaches end-users.

Observability has a strong human component involved, similar to DevOps. It’s not limited to technologies but also covers the approach, organizational culture, and priorities in reaching appropriate observability targets, and hence, the value of monitoring initiatives.

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