How to defeat downtime with Observability?

Introduction

In today’s world, the essential ingredient for the success of an organization is the ability to reduce downtime. If not handled properly, it interrupts the company’s growth, impacts customer satisfaction, and could result in significant monetary losses. Resolutions can also be difficult when the correct data is unavailable, thus prolonging the downtime. This affects the SLA and decreases the product’s reliability in the market.

The best way to deal with downtime is to avoid its occurrence. Data teams should have access to tools and measures to prevent such an incident by detecting it even before it happens. This kind of transparency can be achieved using Observability. By implementing Observability, teams can manage the health of their data pipeline and dramatically reduce downtime and resolution time.

What is Observability? 

Introduction to Observability

Observability is the ability to measure the internal status of a system by examining its outputs. A system is highly observable if it does not require additional coding and services to assess and analyze what’s going on. During downtime, it is of utmost importance to determine which part of the system is faulty at the earliest possible time. 

Three Pillars of Observability

The three pillars that must be considered simultaneously to obtain Observability are logs, metrics, and traces. When you combine these three “pillars,” a remarkable ability to understand the whole state of your system emerges. Let us learn more about these pillars:

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. 

A system’s overall performance can be maintained and enhanced by implementing the three pillars of Observability, i.e., logs, metrics, and traces. As distributed systems become more complex, these three pillars give IT, DevSecOps, and SRE teams the ability to access real-time insight into the system’s health. Areas of degrading health can be prioritized for troubleshooting before impacting the system’s performance. 

What are the benefits of Observability?

Observability tools are not only a requirement but a necessity in this fast-paced data-driven world. Key benefits of Observability are:

1. Detecting an anomaly before it impacts the business, thus preventing monetary losses.
2. Speed up resolution time and meet customer SLAs
3. Reduce repeat incidents
4. Reduce escalations
5. Improve collaboration between data teams (engineers, analysts, etc.)
6. Increase trust or reliability in data
7. Quicker decision making

Observability Use-cases

Observability is essential because it gives you greater control over complex systems. Simple systems have fewer moving parts, making them easier to manage. Monitoring CPU, memory, databases, and networking conditions are usually enough to understand these systems and apply the appropriate fix.

Distributed systems have a far higher number of interconnected parts, so the number and types of failure are also higher. Additionally, distributed systems are constantly updated, and every change can create a new kind of failure. Understanding a current problem is an enormous challenge in a distributed environment, mainly because it produces more “unknown unknowns” than simpler systems. Because monitoring requires “known unknowns,” it often fails to address problems in these complex environments adequately.

Observability is better suited for the unpredictability of distributed systems, mainly because it allows you to ask questions about your system’s behavior as issues arise. “Why is X broken?” or “What is causing latency right now?” are a few questions that Observability can answer.

SREs often waste valuable time combing through heaps of data and identifying what matters and requires action. Rather than slowing down all operations with tedious, manual processes, Observability provides automation to identify which data is critical so SREs can quickly take action, dramatically improving productivity and efficiency, rather than slowing down all operations with tedious, manual processes.

Best practices to implement Observability

• Monitor what matters most to your business to not overload your teams with alerts.
• Collect and explore all of your telemetry data in a unified platform.
• Determine the root cause of your application’s immediate, long-term, or gradual degradations.
• Validate service delivery expectations and find hot spots that need focus.
• Optimize the feedback loop between issue detection and resolution.

Observability tools

Features to consider while choosing the right tool

Observability tools have become critical to meeting operational challenges at scale. To get the best out of Observability implementation, you will need a reliable tool that enables your teams to minimize toil and maximize automation. Some of the key features to consider while choosing an application are:

• Core features offered
• Initial set-up experience
• Ease of use 
• Pricing
• Third-party integrations
• After-sales support and maintenance

List of tools

Considering the above factors, we have compiled a list of effective observability tools that can offer you the best results:

• ContainIQ
• SigNoz
• Grafana Labs
• DataDog
• Dynatrace
• Splunk
• Honeycomb
• LightStep
• LogicMonitor
• New Relic

Reporting for Observability

Skedler Reports helps Observability and SOC teams automate stakeholder reports in a snap without breaking the budget.

Try Skedler for Free !

Reporting for Observability

With effective observability tools, you also need a reliable reporting tool that can deliver professional reports from these tools to your stakeholders regularly on time. If you use Grafana for Observability or Elastic Stack for SIEM, check out Skedler Reports. 

Skedler Reports helps Observability and SOC teams automate stakeholder reports in a snap without breaking the budget. You can test-drive Skedler for free and experience its value for your team. Click here to download Skedler Reports.

Is observability the future of systems monitoring?

As the pressure increases to resolve issues faster and understand the underlying cause of the problem, IT and DevOps teams need to go beyond reactive application and system monitoring.

They will need to dig deeper into the tiniest technical details of every application, system, and endpoint to witness the real-time performance and previous anomalies to correct repeat incidents.

A mature observability strategy can give you an insight into previous unknowns and help you more quickly understand why incidents occur. And as you continue on your observability journey and understand what and why things break, you’ll be able to implement increasingly automated and effective performance improvements that impact your company’s bottom line.

Three Pillars of Observability – Traces ( Part 3)

Introduction

The ability to measure a system’s internal state is observability. It helps us understand what’s happening within the system by looking at specific outputs or data points. It is essential, especially when considering complex, distributed systems that power many apps and services today. Its benefits are better workflow, improved visibility, faster debugs and fixes, and agility.

Observability depends on three pillars: logs, metrics, and traces. Hence, the term also refers to multiple tools, processes, and technologies that make it possible. We have already touched upon logs and metrics, and this article will cover the last pillar, traces.

Understanding Traces

The word ‘Trace’ refers to discovery by investigation or to finding a source, for example, tracing the origin of a call. Here too, the term refers to something similar. It is the ability to track user requests fully through a complex system. It differs from a log. A log may only tell us something went wrong at a certain point. However, a trace goes back through all the steps to track the exact instance of error or exception. 

It is more granular than a log and a great tool to understand and sorting bottlenecks in a distributed system. Traces are ‘spans’ that track user activity through a distributed system (microservices). It does this with the help of a unique universal identifier that travels with the data to keep track of it.

Multiple spans form a trace that can be represented pictorially as a graph. One of the most common frameworks used for Traces is OpenTelemetry, created from OpenCensus and OpenTracing.

Why do we need to use Traces?

Traces help us correct failures provided we are using the right tools. Tracks are life-savers for admin and DevOps teams responsible for monitoring and maintaining a system. They can understand the path the user request takes to see where the bottlenecks happened and why to decide what corrective actions need to be taken. 

While metrics and logs provide adequate information, traces go a step better to give us context to better understand and utilize these pillars.

Traces provide crucial visibility to the information that makes it more decipherable.

They are better suited for debugging complex systems and answering many essential questions related to their health. For example, to identify which logs are relevant, which metrics are most valuable, which services need to be optimized, and so on.

Software tracing has been around for quite some time. However, distributed tracing is the buzzword in the IT industry these days. It works across complex systems that span over Cloud-based environments that provide microservices.

Therefore, we cannot pick one over the other from the three observability pillars. Traces work well along with metrics and logs, providing much-needed overall efficiency. That is what observability is all about, to keep our systems running smoothly and efficiently.

Limitations

Implementing traces in systems is a complex and tedious task, especially considering most are distributed. It might involve codes across many places, which could be challenging for DevOps personnel. Every piece of data in a user request must be traced through and through. Implementing it across multiple frameworks, languages, etc., makes the task more challenging.

Also, tracing can be an issue if you have many third-party apps as part of your distributed system. However, proper planning, usage of compatible tools that support custom traces, monitoring the right metrics, etc., can go a long way in overcoming these.

The Skedler advantage

As we have already seen, if we have to make good use of the three pillars of observability, we need to rely on some good tools. We need a reliable reporting tool if we need good visualization from traces based on the information it has access to. That’s where Skedler comes in. 

Skedler works with many components in the DevOps ecosystem, such as the ELK stack and Grafana, making it easier to achieve observability. The Skedler 5.7.2 release supports distributed tracing, the need of the hour. It performs with a new panel editor and a unified data model.

Skedler gives an edge by leveraging the best from the underlying tools to provide you with incredible visualized outputs. These reports help you make sense of the multitude of logs, metrics, traces, and more. They give you enriched insights into your system to keep you ahead. Thus, it helps ensure a stable, high-availability system that renders a great customer experience.

Conclusion

In conclusion, we could say that observability is a key aspect of maintaining distributed systems. Keeping track of the three pillars of observability is critical – logs, metrics, and traces. Together, they form the pivotal backbone of a healthy system and a crucial monitoring technique for all system stakeholders.

While multiple tools are available for this purpose, a crucial one would be to provide you with unmissable clarity on the system’s health. A good observability tool should generate, process, and output telemetry data with a sound storage system that enables fast retrieval and long-term retention. Using Skedler can help you deliver automated periodic visualized reports to distributed stakeholders prompting them to take necessary action.

What’s New in Skedler

The release of Skedler in November came with many improvements, such as auto-scaling support for Grafana dashboard layout reports and an updated user interface. In the December release, we came up with more features like Autoscaling support for charts in Kibana and the option to configure proxy URL. We are very proud of these releases, but the team is always looking forward to new ways of making Skedler better for you. We are already improving our product further and wanted you to know about our newly added features and UI.So, before we end the year, we want to update you on the features we released and go through some of the important ones in this blog.

Halt your reporting schedules for Specific Days

Want to make sure you are not sending your reports on a holiday? We got you covered! You can now choose the days you do not wish to schedule reports with our new Weekday feature.

Autoscaling support for charts in Kibana

Skedler now supports autoscaling of charts in Kibana. You do not have to worry about your reports being messy or missing out on important information when you add more data to your chart because Skedler will automatically take care of that.

Added an auto-scaling support for Grafana dashboard layout reports 

You can now stop worrying about your graphs and modules getting distorted in your reports as Skedler has added auto-scaling support for generating reports from Grafana Dashboard.

 Added a privilege to super admin users to change their email id

Super Admins can now update their email ID in their profile. You can add a new Mail ID instead of the one you used when you opened your account.

 Generate reports using Grafana dashboard timezone

You can now generate reports in Skedler as per your Grafana time window by selecting “use dashboard time” in Skedler. You do not have to worry about missing or skipping any reports.

Support for fiscal year time window in Grafana dashboards. 

Grafana 8.2  has the option of the configurable fiscal year in the time picker. This option enables fiscal quarters as time ranges for business-focused and executive dashboards. Skedler now supports this feature too!

Added support for Outlook SMTP

Skedler now supports Outlook. So you can set up Outlook as your notification channel in your Skedler account.

These are just some of the new features of Skedler. For more details on these features, do check out our release notes.

If you would like to stay updated on the latest release news or know about upcoming features, please feel free to reach out to the team and keep an eye out for our monthly newsletters.

Installing, configuring Skedler Reports as Kibana Plugin with Elasticsearch and Kibana Environment using Docker Compose

Introduction

If you are using ELK stack, you can now install Skedler as a Kibana plugin. Skedler Reports plugin is available for Kibana versions from 6.5.x to 7.6.x.

Let’s take a look at the steps to Install Skedler Reports as a Kibana plugin.

Prerequisites:

1. A Linux machine
2. Docker Installed
3. Docker Compose Installed

Let’s get started!

Login to your Linux machine and update the repository and install Docker and Docker Compose. Then follow the below steps to update the Repository:

Setting Up Skedler Reports

Create a Directory, say skedlerplugin

ubuntu@guidanz:~$ mkdir skedlerplugin

ubuntu@guidanz:~$ cd skedlerplugin/

ubuntu@guidanz:~$ vim docker-compose.yml

Now, create a Docker Compose file for Skedler Reports. You also need to create a Skedler Reports configuration file, reporting.yml, and a Docker Compose file for Skedler as below,

—

version: “2.4”

services:

#  Skedler Reports container

  reports:

    image: skedler/reports:latest

    container_name: reports

    privileged: true

    cap_add:

      – SYS_ADMIN

    volumes:

      – /sys/fs/cgroup:/sys/fs/cgroup:ro

      – reportdata:/var/lib/skedler

      – ./reporting.yml:/opt/skedler/config/reporting.yml

    command: /opt/skedler/bin/skedler

    depends_on:

      elasticsearch: { condition: service_healthy }

    ports:

      – 3000:3000

    healthcheck:

      test: [“CMD”, “curl”, “-s”, “-f”, “http://localhost:3000”]

    networks: [‘stack’]

volumes:

  reportdata:

    driver: local

networks: {stack: {}}

Create an Elasticsearch configuration file – reporting.yml and paste the config as below.

ubuntu@guidanz:~$ mkdir skedlerplugin

ubuntu@guidanz:~$ cd skedlerplugin/

ubuntu@guidanz:~$ vim reporting.yml

Download the reporting.yml file found here

Setting Up Elasticsearch

You also need to create an Elasticsearch configuration file, elasticsearch.yml. Docker Compose file for Elasticsearch is below,

#Elasticsearch container

  elasticsearch:

    container_name: elasticsearch

    hostname: elasticsearch

    image: “docker.elastic.co/elasticsearch/elasticsearch:7.6.0”

    logging:

      options:

        max-file: “3”

        max-size: “50m”

    environment:

      – http.host=0.0.0.0

      – transport.host=127.0.0.1

      – bootstrap.memory_lock=true

      – “ES_JAVA_OPTS=-Xms${ES_JVM_HEAP} -Xmx${ES_JVM_HEAP}”

    mem_limit: 1g

    ulimits:

      memlock:

        soft: -1

        hard: -1

    volumes:

      – ./elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml

      – esdata:/usr/share/elasticsearch/data

    ports: [‘9200:9200’]

    healthcheck:

      test: [“CMD”, “curl”,”-s” ,”-f”, “http://localhost:9200/_cat/health”]

    networks: [‘stack’]

volumes:

  esdata:

    driver: local

networks: guidanz

Create an Elasticsearch configuration file elasticsearch.yml and paste the config as below.

cluster.name: guidanz-stack-cluster

node.name: node-1

network.host: 0.0.0.0

path.data: /usr/share/elasticsearch/data

http.port: 9200

xpack.monitoring.enabled: true

http.cors.enabled: true

http.cors.allow-origin: “*”

http.max_header_size: 16kb

Setting Up Skedler Reports as Kibana Plugin

Create a Directory inside skedlerplugin, say kibanaconfig

ubuntu@guidanz:~$ mkdir kibanaconfig

ubuntu@guidanz:~$ cd kibanaconfig/

ubuntu@guidanz:~$ vim Dockerfile

Now, create a Docker file for Kibana and check the Docker file for Kibana as below,

FROM docker.elastic.co/kibana/kibana:7.6.0

RUN ./bin/kibana-plugin install https://www.skedler.com/plugins/skedler-reports-plugin/4.10.0/skedler-reports-kibana-plugin-7.6.0-4.10.0.zip

Then, copy the URL of the Skedler Reports plugin matching your exact Kibana version from here.

You also need to create a Docker Compose file for Kibana is below,

#Kibana container

  kibana:

    container_name: kibana

    hostname: kibana

    build:

      context: ./kibanaconfig

      dockerfile: Dockerfile

    image: kibanaconfig

    logging:

      options:

        max-file: “3”

        max-size: “50m”

    volumes:

      – ./kibanaconfig/kibana.yml:/usr/share/kibana/config/kibana.yml

      – ./kibanaconfig/skedler_reports.yml:/usr/share/kibana/plugins/skedler/config/skedler_reports.yml

    ports: [‘5601:5601’]

    networks: [‘stack’]

    depends_on:

      elasticsearch: { condition: service_healthy }

    restart: on-failure

    healthcheck:

      test: [“CMD”, “curl”, “-s”, “-f”, “http://localhost:5601/”]

      retries: 6

Create a Kibana configuration file kibana.yml inside the kibanaconfig folder and paste the config as below.

ubuntu@guidanz:~$ cd kibanaconfig/

ubuntu@guidanz:~$ vim kibana.yml

server.port: 127.0.0.1:5601

elasticsearch.url: “http://elasticsearch:9200”

server.name: “full-stack-example”

xpack.monitoring.enabled: true

Create a Skedler Reports as Kibana Plugin configuration file skedler_reports.yml inside the kibanaconfig folder and paste the config as below.

ubuntu@guidanz:~$ cd kibanaconfig/

ubuntu@guidanz:~$ vim skedler_reports.yml

#/*********** Skedler Access URL *************************/

skedler_reports_url: “http://ip_address:3000”

#/*********************** Basic Authentication *********************/

# If Skedler Reports uses any username and password

#skedler_username: user

#skedler_password: password

Configure the Skedler Reports server URL in the skedler_reports_url variable. By default, the variable is set as shown below,

If the Skedler Reports server URL requires basic authentication, for example, Nginx, uncomment and configure the skedler_username and skedler_password with the basic authentication credentials as shown below: Now run the docker-compose.

ubuntu@guidanz:~/skedlerplugin$ docker-compose up -d

Access Skedler Reports the IP and Port and you will see the Skedler Reports UI.

| http://ip_address:3000

Access Elasticsearch the IP and Port and you will see the Elasticsearch UI.

| http://ip_address:9200

Access Kibana using the IP and Port and you will see the Kibana UI.

| http://ip_address:5601

So now the Composite docker-compose file will look like below,

You can Simply do compose up and down.

—

version: “2.4”

services:

#  Skedler Reports container

  reports:

    image: skedler/reports:latest

    container_name: reports

    privileged: true

    cap_add:

      – SYS_ADMIN

    volumes:

      – /sys/fs/cgroup:/sys/fs/cgroup:ro

      – reportdata:/var/lib/skedler

      – ./reporting.yml:/opt/skedler/config/reporting.yml

    command: /opt/skedler/bin/skedler

    depends_on:

      elasticsearch: { condition: service_healthy }

    ports:

      – 3000:3000

    healthcheck:

      test: [“CMD”, “curl”, “-s”, “-f”, “http://localhost:3000”]

    networks: [‘stack’]

#  Elasticsearch container

  elasticsearch:

    container_name: elasticsearch

    hostname: elasticsearch

    image: “docker.elastic.co/elasticsearch/elasticsearch:7.1.1”

    logging:

      options:

        max-file: “3”

        max-size: “50m”

    environment:

      – http.host=0.0.0.0

      – transport.host=127.0.0.1

      – bootstrap.memory_lock=true

      – “ES_JAVA_OPTS=-Xms${ES_JVM_HEAP} -Xmx${ES_JVM_HEAP}”

    mem_limit: ${ES_MEM_LIMIT}

    ulimits:

      memlock:

        soft: -1

        hard: -1

    volumes:

      – ./config/elasticsearch/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml

      – esdata:/usr/share/elasticsearch/data

    ports: [‘9200:9200’]

    healthcheck:

      test: [“CMD”, “curl”,”-s” ,”-f”, “http://localhost:9200/_cat/health”]

    networks: [‘stack’]

 #Kibana container

  kibana:

    container_name: kibana

    hostname: kibana

    build:

      context: ./kibanaconfig

      dockerfile: Dockerfile

    image: kibanaconfig

    logging:

      options:

        max-file: “3”

        max-size: “50m”

    volumes:

      – ./kibanaconfig/kibana.yml:/usr/share/kibana/config/kibana.yml

      – ./kibanaconfig/skedler_reports.yml:/usr/share/kibana/plugins/skedler/config/skedler_reports.yml

    ports: [‘5601:5601’]

    networks: [‘stack’]

    depends_on:

      elasticsearch: { condition: service_healthy }

    restart: on-failure

    healthcheck:

      test: [“CMD”, “curl”, “-s”, “-f”, “http://localhost:5601/”]

      retries: 6

volumes:

  esdata:

    driver: local

  reportdata:

    driver: local

networks: {stack: {}}

You can Simply do compose up and down.

ubuntu@guidanz:~/skedlerplugin$ docker-compose down 

ubuntu@guidanz:~/skedlerplugin$ docker-compose up -d

Summary

Docker compose is a useful tool to manage container stacks for your client. And manage all related containers with one single command.

The Best Tools for Exporting Elasticsearch Data from Kibana

As a tool for visualizing elasticsearch data, Kibana is a perfect choice. Its UI interface allows creating a dashboard, search, and visualizations in minutes and analyzing the data with its help.

Despite having tons of visualizations, the open source version of Kibana does not have advanced reporting capability. Automating export of data into CSV, Excel, or PDF requires additional plugins.  

We wrote an honest and unbiased review of the following tools that are available for exporting data directly from Elasticsearch.

1. Flexmonster Pivot plugin for Kibana 
2. Sentinl (for Kibana)
3. Skedler Reports

1. Flexmonster Pivot plugin for Kibana

https://github.com/flexmonster/pivot-kibana

Flexmonster Pivot covers the need in summarizing business data and displaying results in a cross-table format interactively & fast. All these Excel-like features, to which so many of you are used to, and its extended API will multiply your analytics results remarkably.

Though initially created as a pivot table component that can be incorporated into any app that uses JavaScript, it can serve as a part of Kibana as well. You can connect it to the Elasticsearch index, fetch the documents from it and start exploring the data.

Pros of Flexmonster Pivot plugin for Kibana

• Flexmonster is in line with the concept of Kibana
• Simply embeddable Pivot for Kibana

Cons of Flexmonster Pivot plugin for Kibana

• To automate the exporting of data on a periodic basis, you need to write your own cron job.
• Flexmonster Pivot plugin installation is a bit tricky. 

2. Sentinl (for Kibana)

https://github.com/sirensolutions/sentinl

SENTINL extends Kibana with Alerting and Reporting functionality to monitor, notify and report on data series changes using standard queries, programmable validators and a variety of configurable actions – Think of it as a free and independent “Watcher” which also has scheduled “Reporting”.

SENTINL is also designed to simplify the process of creating and managing alerts and reports in Siren Investigate/Kibana 6.x via its native App Interface, or by using native watcher tools in Kibana 6.x+.

Pros of Sentinl

• It’s simple to install and configure
• Added as a Kibana plugin.

Cons of Sentinl

• This tool supports only 6x versions of Elasticsearch.  It does not support 7.x.
• For non-technical users, it’s difficult to use 
• Automation requires scripting which makes it laborious

3. Skedler Reports

https://www.skedler.com/

Disclosure: Skedler Reports is one of our products.

Skedler offers a simple and easy to add reporting and alerting solution for Elastic Stack and Grafana.  There is also a plugin for Kibana that is easy to install and use with the Elasticsearch data. It’s called Skedler Reports as Kibana Plugin. 

Pros of Skedler Reports

• Simple to install, configure, and use
• Send HTML, PDF, XLS, CSV reports on-demand or periodically via email or #slack
• Report setup takes less than 5 minute
• Easy to use, no coding required

Cons of Skedler Reports

• It requires a paid license which includes software and also enterprise support
• Installation is difficult for users who are not fully familiar with Elastic Stack or Grafana

What tools do you use?

Do you have to regularly export data from Kibana for external analysis or reporting purposes? Do you use any other third-party plugins?   Email us about the tool at hello at skedler.com.

Episode 1 – AI Usage in Cybersecurity – is it hype/real? The Infralytics Show interview with Bharat Kandanoor, Head of Technology for Security and Cloud at Blue Ally

Shankar Radhakrishnan, Founder of Skedler, recently sat down with Bharat Kandanoor to discuss the use of Artificial Intelligence (AI) in cybersecurity. Bharat, who is the Technology Head for cybersecurity and cloud at Blue Ally, a managed service provider, was able to shed light on the intricacies of AI’s usage in cybersecurity processes. Let’s dive deep into understanding whether AI is an overhyped cybersecurity solution, how it is being used to tackle network security problems, and how AI may be able to create a better cybersecurity future for the end user.

See and listen to the Infralytics Show  interview with Bharat Kandanoor

[video_embed video=”L9i4ESNEFpM” parameters=”” mp4=”” ogv=”” placeholder=”” width=”700″ height=”400″]

Is AI in Cybersecurity Overhyped or Not?

69% of enterprises believe AI will be necessary to respond to cyberattacks, with U.S.-based enterprises placing a more than 15% higher priority on AI-based cybersecurity applications and platforms than the global average when measured on a country basis. Is this level of AI adoption a response to measurable cyber threats that AI can help to remediate or is it merely an overhyped reach by firms around the world? Bharat Kandanoor tells us in our exclusive one-on-one video podcast that “Artificial Intelligence is being used as an overhyped terminology in general.” Bharat goes on to explain that “everyone expects using AI can solve lots of problems, but not necessarily can it do that.”

All in all, these AI tools will always have big drawbacks due to it being an overhyped solution. Bharat explains that “AI can give valuable actionable information, but at the end of the day, it is a human who can decide if the data is an anomaly or not.” It is with this human interaction that data anomalies can be found and analyzed by a human operator who is focused on the end goal of long-term data and network protection at all times.

Using AI to Tackle Cybersecurity Problems

AI has the ability to weed through the plethora of incident response data and find a solution exponentially faster than humans are able to. With AI, you can drill deeper into your data to pull out actionable insights that can help your team work more efficiently and effectively to detect anomalies using behavior analytics, network traffic analysis, and email scanning solutions for phishing/spear phishing attacks.

Small-to-Medium Enterprises (SMEs) struggling with cybersecurity have more to lose than their data and potential profits; the loss could stretch to their customers. AI-enabled technologies allow organizations of all sizes to implement a healthy security posture, from network monitoring and risk control to detecting rising cyber threats and recognizing the scam.  With more SMEs looking to AI as their silver bullet solution in the face of a current shortage of more than 3 million cybersecurity experts globally, SMEs can use AI to react to existing cyber threats and head off new ones.

Incorporating AI Into Your SME’s Cybersecurity Strategy

Even though SMEs believe AI will positively affect their business, uptake of AI solutions within SMEs has been slow, with just a 4% adoption rate per a 2019 report. No matter what the level of maturity is for an enterprise, it is vital that C-suite, IT, and security teams rationalize their existing technologies with solutions that can support their initiatives for a strong return on investment (ROI). Bharat explains that “It’s more of what fits into your use case and how you can make it work” when it comes to incorporating AI solutions into your cybersecurity plans. One AI solution may work for one SME where another may not. It’s just a matter of researching, testing, and finding the right solution for you.

Don’t forget to subscribe to the Infralytics Show Channel and review us because we want to help others like you improve their IT operations, security operations and streamline business operations. If you want to learn more about Skedler and how we can help you just go to Skedler.com where you’ll find tons of information on Kibana, Grafana, and Elastic Stack reporting. You can also download a free trial with us, so you can see how it all works at skedler.com/download. Thanks for joining and we’ll see you next episode.

Tabular Reports from Elastic Stack – New in Skedler Reports v4.4

We are excited to announce the release of Skedler Reports v4.4. As always, it’s packed with capabilities to help you meet compliance, audit, and snapshot reporting requirements.

Tabular PDF, Excel, CSV Reports from Kibana Data Table

If you are a security analyst or network admin looking for the list of unauthorized IP addresses connecting to your machines, Skedler can deliver the data to you in the form of PDF or Excel. With just a couple of clicks, schedule a PDF and/or Excel report that uses the Kibana data table as a source, sit back and have the reports delivered to your stakeholders automatically!

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Schedule Reports with Custom Time Ranges

If your customer needs a daily report that summarizes the top security events during the work hours of 9 AM – 5 PM, you can send it to them right away. Simply create a custom time range in Kibana and customize your dashboard to use this time range.  In Skedler, schedule a daily report with the dashboard as a data source and you’re all set!

Here is the list of additional features in the new release:

• You can use the latest features in Elastic Stack 7.3 and Grafana 6.3 and generate reports with Skedler.
• Users do not need administrator privileges to configure Grafana as a data source in Skedler.

Go Ahead and Try it Out

Test out the data table reports with custom time ranges in ELK 7.3 or Grafana 6.3 environment! Start now below by doing the following:

1. Download Skedler Reports
2. Follow the simple steps in our documentation and start generating reports.

Skedler v4.1: Next Generation Reporting for Elasticsearch Kibana 7.0 and Grafana 6.1 is here

We are excited to announce that we have just released version 4.1 of Skedler Reports!  

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Self Service Reporting Solution for Elasticsearch Kibana 7.0 and Grafana 6.1

We understand that your stakeholders and customers need intuitive and flexible options to save time in receiving the data that matters to them and we’ve achieved exactly that with the release of Skedler 4.1.  The newly enhanced UI offers a delightful user experience for creating and scheduling reports from your Elasticsearch Kibana 7.0 and Grafana 6.1 .

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Multi-Tenancy Capabilities

If you are a service provider, you need a simple and automated way to provide different groups of users (i.e. “tenants”) with access to different sets of data. Skedler 4.1’s powerful and secure multi-tenancy capabilities will now allow you to send reports to your customers from your multi-tenant analytics application within minutes.  Supported with Search Guard, Open Distro & X-Pack.

Intuitive and Mobile Ready Reports

Skedler 4.1 will now allow you to produce high-resolution HTML reports from Elasticsearch Kibana and Grafana that will make it easy and convenient for your end users to access to critical data through their mobile devices and email clients. No more cumbersome and large PDF attachments.

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The latest release also includes:

• Support for the latest and greatest version of Elastic Stack and Grafana. Skedler 4.1 supports the following versions:
  • Elastic stack 6.7 and 7.0
  • Grafana 6.1.x
  • Open distro for Elasticsearch 6.7 and 7.0.  

Please continue to send us feedback for what new capabilities you’d like to see in the future by reaching out to us at hello@skedler.com

Webinar: Save Time and Money With Automated Reports & Alerts

How do you stay up to date on the critical events in your log analytics platform? Do you spend tens of thousands of dollars and countless hours to create reports and alerts from your Elastic Stack or Grafana application?

Whatever critical scenario arises, receiving the right information at the right time can ultimately be the difference between success and failure. Therefore, it’s important to be constantly aware of every situation, whether it be business partners, operations, customers, or employees, is crucial. The faster a possible issue is identified the faster it can be solved.

Benefits of Automation

Join us in the upcoming webinar on Tuesday, December 18th, 2018 @10AM PST to learn how Skedler, which installs in minutes, can help you save time & money with automated reports and alerts for Elastic Stack & Grafana.

Watch Our Webinar Here

You’ll learn how to quickly add reporting and alerting for Elastic Stack and Grafana while seeing how Skedler can provide a flexible framework to meet your complex monitoring requirements. Be ready with your questions and we’ll be more than happy to discuss them in the webinar Q&A session.

Watch Our Webinar Here

Graph Source: https://www.statista.com/chart/10659/risks-and-advantages-to-automation-at-work/

Skedler Update: Version 3.9 Released

Skedler Update: Version 3.9 Released

Here’s everything you need to know about the new Skedler v3.9. Download the update now to take advantage of its new features for both Skedler Reports and Alerts.

What’s New With Skedler Reports v3.9

• Support for:
  • ReadOnlyRest Elasticsearch/Kibana Security Plugin.
  • Chromium web browser for Skedler report generation.
  • Report bursting in Grafana reports if the Grafana dashboard is set with Template Variables.
  • Elasticsearch version 6.4.0 and Kibana version 6.4.0.
• Ability to install Skedler Reports through Debian and RPM packages.
• Simplified installation levels of Skedler Reports here.
• Upgraded license module
  • NOTE: License reactivation is required when you upgrade Skedler Reports from the older version to the latest v3.8. Refer to this URL to reactivate the Skedler Reports license key.
  • Deactivation of Skedler license key in UI

What’s New With Skedler Alerts v3.9

• Support for:
  • Installing Skedler Alerts via Debian and RPM packages.
  • GET method type in Webhook.
  • Elasticsearch 6.4.0.
• Simplified installation levels of Skedler. Refer to this URL for installation guides.
• Upgraded license module:
  • NOTE: License reactivation is required when you upgrade Skedler Alerts from the older version to the latest v3.8. Refer to this URL to reactivate the Skedler Alerts license key.
• Deactivation of Skedler Alerts license key in UI

Get Skedler Reports

Get Skedler Alerts