“Cloud Native DevOps with Kubernetes: Building, Deploying, and Scaling Modern Applications in the Cloud” by John Arundel and Justin Domingus

This book covers the best practices for developing and deploying cloud-native applications using Kubernetes and DevOps principles.

Reason to read: Learn how to deploy, scale, and manage containerized applications in the cloud using Kubernetes.

Read: 5 Best Kubernetes Books for Beginners

“Site Reliability Engineering: How Google Runs Production Systems” by Betsy Beyer, Chris Jones, Jennifer Petoff, and Niall Richard Murphy

This book provides an insight into how Google manages its large-scale production systems and the techniques and practices they use to achieve high reliability.

Reason to read: Learn the best practices for managing large-scale systems and improving reliability.

“The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win” by Gene Kim, Kevin Behr, and George Spafford

This book is a must-read for anyone interested in understanding the principles of DevOps and how they can be applied in real-world scenarios.

Reason to read: Learn how DevOps principles can be used to improve IT operations and business outcomes.

“Infrastructure as Code: Managing Servers in the Cloud” by Kief Morris

This book covers the concept of Infrastructure as Code (IaC) and how it can be used to manage infrastructure in the cloud.

Reason to read: Learn how to manage infrastructure as code and automate the provisioning and deployment of cloud resources.

“The Docker Book: Containerization is the New Virtualization” by James Turnbull

This book provides a comprehensive guide to Docker and containerization and how they can be used to improve application deployment and management.

Reason to read: Learn how containerization can simplify application deployment and management and improve application portability.

“Effective DevOps: Building a Culture of Collaboration, Affinity, and Tooling at Scale” by Jennifer Davis and Katherine Daniels

This book covers the practices and techniques that organizations can use to build an effective DevOps culture.

Reason to read: Learn how to build a DevOps culture and improve collaboration, communication, and tooling.

“Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation” by Jez Humble and David Farley

This book provides an overview of the continuous delivery approach and how it can be used to achieve faster and more reliable software releases.

Reason to read: Learn how to improve software delivery and reliability through automation and continuous integration and deployment.

“Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations” by Nicole Forsgren, Jez Humble, and Gene Kim

This book provides insights into the practices and techniques used by high-performing organizations to achieve faster software delivery and better business outcomes.

Reason to read: Learn the best practices and techniques used by high-performing organizations to improve software delivery and business outcomes.

“The Art of Monitoring” by James Turnbull

This book covers the principles and best practices of monitoring applications and infrastructure in the cloud and how it can be used to improve reliability and performance.

Reason to read: Learn how to monitor applications and infrastructure in the cloud to improve reliability and performance.

“DevOps for the Modern Enterprise: Winning Practices to Transform Legacy IT Organizations” by Mirco Hering

This book provides practical advice and strategies for transforming legacy IT organizations to adopt DevOps principles and practices.

Reason to read: Learn how to transform legacy IT organizations and adopt DevOps practices to improve software delivery and business outcomes.

***

By reading these 10 must-read cloud technology books recommended by DevOps experts, you can gain a fresh perspective on essential and emerging technologies, learn the latest best practices, and stay ahead of the curve in the fast-paced world of IT. So, what are you waiting for? Start reading and enhance your cloud technology skills today. Don’t forget to subscribe to our newsletter to stay up-to-date with the latest tech trends and insights. Happy reading!

Сообщение 10 Must-Read Cloud Technology Books in 2023: A DevOps Perspective появились сначала на Acure AIOps Platform.

The use of data observability is becoming increasingly important as organizations strive to gain analytical insights from their data. By proactively looking at the data they have available, companies are able to identify trends and issues that could be critical in making decisions and shaping strategies. With accurate and timely observations based on collected data, organizations can quickly detect problems before they become bigger issues, minimizing risk and potential costs.

Additionally, organizations can also use observability techniques to observe how existing systems perform and make necessary adjustments, ensuring that processes are always running smoothly and efficiently. Data observability tools give an organization the ability to make quick adjustments to provide better services for customers or develop more products and services for new markets. Ultimately, investing in a good data observability toolset pays off by allowing organizations to optimize their performance in the long run.

In one of our previous articles, we compared the concepts of observability and monitoring. Although they have some differences, they also share some similarities – for example, the instruments of realization.

How to Choose the Right Observability Tool

Choosing the right observability tools can be an overwhelming task You need to assess different factors such as cost, ease of use, security and compliance issues, data retention length and customizations.

Does the tool provide a generous free plan and pricing based on usage? Is it easy to set up and learn? What integrations are available with existing tools? You should also consider if the tool provides scalability in order to handle larger datasets. Lastly, you will want to think about how much data you want to retain and for how long. Assessing each of these features is key when selecting an observational platform for data.

We hope this article will help you with your choice, because in it we have collected the best full-stack observability tools that you should pay attention to in the new year, based on their main advantages and features.

Best Observability Tools

Datadog

Splunk Observability

Acure.io

Dynatrace

New Relic

Grafana Cloud

Elastic Observability

Lightstep

AppDynamics by Cisco

Chronosphere

Datadog

Datadog is an application performance monitoring solution that helps organizations monitor and troubleshoot their systems. It collects data from applications, servers and other infrastructure components to provide real-time insight into the health of the system. Datadog also provides tools for creating alerting rules, custom dashboards and automated reports. With these features, customers can quickly identify issues before they become problems and take corrective action in a timely manner. Additionally, Datadog allows customers to customize their setup with plug-ins or scripts written in Python or Golang. This makes it easy to extend the platform’s functionality to capture data not already supported by Datadog out of the box.

Overall, Datadog is a comprehensive monitoring and troubleshooting solution for organizations of all sizes. Its breadth of features makes it an excellent choice for both small businesses and large enterprises. Datadog’s ability to collect data from multiple sources, its robust alerting capabilities and its ability to be extended with custom scripts make it a great choice for those looking to maximize performance while minimizing operational costs.

 Most liked features:

• Unlimited integrations
• Frequent releases and stability
• Dashboards available from the get-go

Splunk Observability

Splunk Observability provides an end-to-end observability platform that helps you quickly identify, investigate and troubleshoot issues with your applications. With powerful data search and analysis capabilities, it enables teams to gain real-time insights and visibility into the performance of their systems. The platform comes with various tools for building custom dashboards, visualizations, alerting mechanisms and more for proactive monitoring of system health and performance. It also features built-in ML models to help identify potential areas of improvement or detect anomalies in your data.

Splunk Observability’s intuitive user interface makes it easy to navigate through the platform so you can focus on quickly diagnosing any issues. Additionally, its robust security model helps ensure that all your data is protected and private, reducing the risk of unauthorized access.

Furthermore, Splunk’s global support network helps ensure that technical issues are resolved in a timely manner. All in all, Splunk Observability is the perfect tool for any team looking to gain real-time insights into their application performance.

Most liked features:

• Works well with high volumes of data
• Built-in dashboards
• Customized reports

Acure.io

Acure.io is a self-hosted topology-based AIOps platform for observability and automated remediation. It is a fully SaaS solution with a flexible and open architecture that includes quick and easy tools to find the root cause by topology, time and context with business impact and to aggregate and process any data from any system in a single place. Acure allows you to build and manage CMDB with the low-code engine, visualize the state of the entire IT, run automation from one system for all purposes and quickly and cost-effectively put any application on performance monitoring.

Acure aggregates, normalizes and enriches events collected from various monitoring tools You can connect and extract data from various sources including other popular monitoring systems using ready-made configuration templates and plugins or your own tasks.

Acure uses low-code scenarios to correlate alerts into actionable insights – Signals.  IT operation teams can detect incidents before they become failures.

Acure provides rapid identification of the root cause of an incident. This includes mapping the impact of various technical resources on business services, identifying service and infrastructure changes that cause incidents and highlighting possible bottlenecks.

The dependency map is built automatically based on data from your existing monitoring systems and other tools. This is vital for dynamic environments, such as modern cloud ecosystems and microservices on Kubernetes.

Acure optimizes incident response through the automation of grouping incidents into Signals, two-way ticketing, notifications and chat creation.  Running built-in scripted automation tools with low-code and external runbooks allows workflows to be automated for faster incident response.

Most liked features:

• Ready-made templates for different integrations
• Single dependency map of the whole IT infrastructure, event correlation and noise reduction
• Automation engine
• Rich functionality of the free version

Dynatrace

Dynatrace is a comprehensive, full-stack monitoring platform that enables DevOps and IT operations teams to rapidly detect and triage performance issues. It offers services such as application performance management (APM), infrastructure performance monitoring, log analytics, AI-powered automation and more. The platform helps organizations reduce costs, improve customer experience, streamline processes and stay ahead of the competition.

The platform uses artificial intelligence (AI) and machine learning (ML) to automatically detect issues in your environment before they become major problems. Dynatrace also provides an automated root cause analysis engine which quickly points out the source of these problems so you can minimize downtime and get back on track faster.

Its strong observability capabilities come from its distributed tracing technology that helps you monitor your applications across multiple environments and technologies. Having this visibility, Dynatrace can quickly detect issues in complex architectures to keep your infrastructure running smoothly.

Dynatrace also offers advanced analytics tools that provide insights into customer journeys, application performance optimization opportunities and more. This data can be used to make informed decisions about how to optimize the user’s experience and improve overall efficiency. Furthermore, Dynatrace uses AI-assisted automation to streamline manual processes such as incident management; this optimizes resolution time so you can spend less time troubleshooting and more time innovating.

Most liked features:

• Synthetic monitoring
• AI engine
• Real-time alerts

New Relic

New Relic is a SaaS platform that provides users with the tools and insights to monitor their applications, websites, and digital operations. The platform offers customers real-time data analytics, alerting and monitoring capabilities to ensure the optimal performance of their systems. Additionally, New Relic provides deep visibility into customer architectures to identify root cause issues quickly and accurately.

This allows organizations of all sizes to gain valuable insights into application health as well as user experience metrics such as response time, errors per minute, throughput rates, and more. This can be used to provide feedback on how well an organization’s products perform or detect potential problems before they become a problem for customers.

Moreover, New Relic simplifies the process of managing and monitoring large distributed applications across different cloud environments. It also provides an integrated platform for operations teams to quickly identify, fix and prevent incidents within their environments. This gives organizations the visibility and control they need to improve service availability, thereby boosting customer satisfaction. Additionally, New Relic integrates with other popular business applications such as Terraform, Ansible, and Kubernetes to provide a comprehensive toolkit for automation and analytics.

Most liked features:

• Based on OpenTelemetry standards
• Over 470 available integrations
• AI for incident detection and alerting

Grafana Cloud

Grafana Cloud is a platform for monitoring cloud-based applications and ensuring optimal performance. It includes a query editor, dashboard builder and alert system to ensure the right information is available at the right time.

Grafana Cloud also offers advanced alerting capabilities that monitor metrics and send alerts when something is out of the ordinary. Users can set up alerts for specific conditions such as anomalies, thresholds or other issues that might occur in their environment. Teams can quickly set up dashboards and alerts from their data sources to get insight into their systems. This includes monitoring common metrics such as system health, log analysis for troubleshooting and performance optimization. With Grafana Cloud’s query editor, users can access a wide range of queries to help them easily visualize their data.

Additionally, Grafana Cloud includes integration with popular services such as PagerDuty, Slack and VictorOps to ensure teams are notified quickly when an issue occurs.

The platform also enables secure collaboration between teams by allowing them to easily share insights with colleagues.

Most liked features:

• Free-tier with easy setup
• Fast building and delivering new features
• Informative dashboards
• Perfect for time-series graphs

Elastic Observability

Elastic Observability is an open-source platform for monitoring and managing application performance, resource utilization, security threats and other system metrics. It enables organizations to observe their entire application or environment and provides visibility into the health of their systems. The platform collects data from multiple sources such as application logs, metrics, traces, audit logs and other services to give users a holistic view of their infrastructure. By providing insight into system performance in real-time, Elastic Observability allows users to quickly identify problems before they become costly outages.

The platform includes a range of features that make it easy to monitor your environment and application performance. Its intuitive user interface makes the process of setting up and configuring Elastic Observability simple. Additionally, the platform uses distributed tracing and anomaly detection to help users identify issues quickly. It also offers detailed analytics, alerting capabilities, custom dashboards, and reporting tools to provide visibility into application performance.

Most liked features:

• Quick search
• The possibility to link logs and traces
• APM and log correlation

Lightstep

Lightstep is a monitoring and observability platform designed to help software teams discover, diagnose and resolve issues in real-time. With its powerful distributed tracing capabilities, Lightstep can trace transactions across multiple services, provide insights into system performance and user experience, and quickly detect anomalies that may indicate potential problems. This helps software teams stay informed of the health and performance of their applications as they continuously release new products or features. The platform also provides a unified view of system-level metrics alongside custom application data, allowing developers to easily troubleshoot errors and identify performance bottlenecks.

Lightstep’s modern architecture is built for scalability and resilience, with multi-tenancy support for large-scale deployments. Its open-source agent and cloud SDKs are lightweight and easy to use, enabling customers to quickly implement distributed tracing across their infrastructure. Lightstep is also compatible with popular third-party services such as Kubernetes, New Relic Insights, and Splunk. This allows customers to combine data from multiple sources into a single unified view for deeper insights into their operations.

Most liked features:

• Simple and intuitive interface
• High standard of service support, clear documentation
• Contribution to OpenTelemetry

AppDynamics by Cisco

AppDynamics by Cisco provides an agent-based platform for monitoring and optimizing business applications. It helps identify performance issues, diagnose root causes of outages, and ensure that application code is running smoothly. AppDynamics’ features include real-time analytics, automatic diagnostics, and flexibility to customize the deployment across cloud environments.

With this solution, organizations can track every transaction from end-to-end across distributed systems using automatic tracing technology called “Business Transactions”. This feature enables quick identification of potential problems while providing insights into user experience based on snapshot views of data at any given time. In addition, AppDynamics also offers a range of products such as Server Visibility Tools to help monitor application infrastructure, and Business iQ which provides business-level application performance metrics.

Using AppDynamics’ agentless architecture, detailed data can be collected from applications running in public clouds as well as private on-premise systems. This enables a unified monitoring approach that can identify anomalies and detect problems across different application components. The platform also includes advanced analytics such as anomaly detection to quickly pinpoint issues, code-level diagnostics for identifying root cause of the issue, and machine learning algorithms for automating issue resolution. These capabilities make it easier for businesses to proactively manage their application performance and availability.

Finally, AppDynamics by Cisco comes with integrated security features such as user authentication and authorization so organizations can protect their IT environment while also keeping their performance data secure.

Most liked features:

• Integrating business and technology metrics
• Consolidated observability, anomaly detection and root cause analysis
• Alerts with useful custom actions

Chronosphere

Chronosphere is a powerful tool for managing large-scale distributed systems. It provides an intuitive visual interface that simplifies the deployment, operation, and monitoring of multi-node systems. By leveraging the power of cloud computing and container orchestration technologies, Chronosphere enables organizations to quickly deploy highly available infrastructure with minimal effort.

Chronosphere is designed to provide scalability and fault tolerance across multiple nodes and data centers. For example, it can be used to efficiently scale up or down resources based on service demand while maintaining high availability in production environments. The platform also includes sophisticated alerting features to ensure rapid response when problems arise. This helps reduce downtime and ensures that services remain responsive despite heavy workloads or unexpected outages.

In addition to its scalability and fault tolerance features, Chronosphere also provides a range of other powerful tools for managing distributed systems. These include cost optimization tools to reduce operational costs, as well as monitoring tools for tracking system performance. The platform’s analytics capabilities make it easy to identify areas of improvement and uncover potential issues before they become major problems.

Most liked features:

• PromQL function suggestions
• Solving the Prometheus scaling problem
• Customer support and onboarding process

***

Observability is a key pillar of modern data management and selecting the right tools to ensure the highest levels of performance is an important decision. With the rise of cloud-native technologies, the number of observability tools available has grown exponentially.

Utilizing the correct observability tools can have a tangible impact on key business metrics and make downtimes easier to manage. Many of these observability tools offer free or low-cost plans that bring tremendous value with minimal effort. Therefore, it is worthwhile to look closer at the observability stack when deciding which options would be best for each organization. Determining the proper observability tools can be dependent on various factors like technology used and scope of issues, as well as practical matters such as budget and size. We believe this article then provides information to assess needs accurately and select suitable observability tools that could benefit any company.

Сообщение Top 10 Observability Tools to Pay Attention to in 2023 появились сначала на Acure AIOps Platform.

Big data refers to the enormous, complicated volumes of data that might be either structured or unstructured. However, what organizations do with the data impacts more than just the nature or volume of data. 

Big data, notably from new data sources, is simply a term for bigger, more intricate data collections. These data compilations are so large that already existing data processing software face difficulties handling it. However, these great data compilations can be taken advantage of to solve issues that were nearly impossible to solve earlier. 

Big data analysis will help generate information that will eventually help with the decision-making stages and provide support while making critical business implementations.

The emergence of big data depended on the creation of open-source frameworks, making massive data more manageable and less expensive to keep.

The Popular V’s of Big Data

Industry analyst Doug Laney introduced the three V’s in the early 2000s that defined big data in easy-to-understand statements. Let’s look at the V’s that together give meaning to big data. 

Initially, only volume, velocity, and variety were the V’s that were introduced but later on, veracity and value were also included in the list.

1. Volume 

The initial of the important V’s of big data, volume, refers to the quantity of data that is available to us. Volume, or the original size and quantity of information obtained, can be thought of as creating and giving basis to the foundation of big data. Big data can be used to describe a sufficiently massive set of data. 

2. Velocity 

Velocity refers to the high speed at which big data is accumulated. The speed at which data is created and managed to satisfy demands influences the data’s potential where a significant and constant flow of data is present. 

Data comes from various sources, including social media platforms, databases, computers, smartphones, etc. Dealing with problems like “velocity” might be easier compared to other difficult sampling problems.

3. Variety 

The range and diversity of data kinds are referred to as variety. An organization may collect data from various sources, the value of which may differ. Data might originate both inside and outside of an organization. Unstructured, semi-structured, or structured data can be collected. The standardization and dissemination of all the data being gathered pose a problem in terms of variety.

4. Veracity 

Veracity relates to sampling errors and uncertainty that is, readily available data can occasionally become disorganized, and both quality and precision are challenging to manage. 

The gathered information can be incomplete, erroneous, or unable to offer any useful, insightful information. Veracity, in general, refers to the degree of confidence in the data that has been gathered. Because there are so many different data dimensions arising from several dissimilar data types and sources, big data is also unpredictable.  

5. Value 

This is a reference to the benefits using big data can offer, and it has a direct bearing on what businesses can do with the information they gather. Data by itself is useless and unimportant; information must be extracted from it by transforming it into something worthwhile. Therefore, value can be identified as the most significant of the five virtues.

It is necessary to be able to extract value from big data because the importance of big data greatly depends on the conclusions that can be obtained from them.

Interesting Trends Emerging in the Big Data Industry

Data Quality Testing

Data quality checking protects your company from inaccurate data. It’s time to think about a remedy if the quality of the company’s information assets is affecting sales.

For businesses across all industries, accurate data is crucial for statistics and data-driven solutions. Without this information, businesses struggle to remain effective, profitable, and competitive in their market.

Preserving data quality is essential to creating a successful company, especially given how many aspects of daily operations it now supports. Better company decisions are made as a result of high-quality data, which also benefits customers.

Writing test strategies for certain products and initiatives that concentrate on the project’s objectives will be necessary. Before creating and running tests, data sources need to be reviewed after such criteria have been determined.

To provide IT and data owners with an audit record of all access instances, live monitoring, mapping, and alerting of exactly who is receiving and accessing which type of data when and from where helps keep them aware as to how sensitive data is being utilized. The procedure guards against data breaches and misuse.

Anomaly Detection

Data mining’s anomaly detection process, also known as outlier analysis, seeks out data points, occasions, and/or observations that differ from a dataset’s typical pattern of activity. 

Unusual data can point to serious occurrences, like a technological malfunction, or promising opportunities, like a shift in consumer behavior. Automated anomaly detection is increasingly being done using machine learning.

You may use your resources more wisely because monitoring systems will keep hold of your infrastructure, including many other things. You will become more productive as a result, which is undoubtedly essential in your decision to utilize a monitoring system. 

With the knowledge that you will be notified as soon as an issue occurs, your employees will have more time to focus on other activities.

However, with data monitoring, the data is automatically verified to make sure it is accurate at every stage. If a discrepancy is discovered, the data is flagged so that it may be reviewed and any necessary modifications can be made. Your analytics and reporting become more reliable as a result.

The analytics reports may become biased if any data is altered. These reports are designed to assess the company’s performance and identify areas for improvement. But you can’t choose the best course of action for your company and customers if you don’t have the appropriate information.

Shift from Data Monitoring to Data Observability

Teams from DevOps and ITOps must advance from monitoring to observability. Software and systems that can be seen and used to provide answers to inquiries about their behavior are said to be observable.

By releasing data from compartmentalized log analytics programs, observable systems encourage research as opposed to monitoring, which depends on fixed views of static resources. 

You can engage with customers better the more precise the data is. Monitoring data along with observability enhances connections in a variety of ways. Furthermore, accurate information reveals any potential areas for improvement. You can concentrate on customer retention rather than gaining new customers if your analytics suggest that you frequently acquire new customers but rarely see those same customers return.

Correct data monitoring and observability reveals the demographics of your clients. This data can be utilized to target your consumer base more accurately, saving you money on marketing to uninterested parties.

You can read more about the differences between Data Monitoring and Data Observability in our blog.

Low-Code and No-Code

More and more companies have recently been trying to make their solutions more flexible, allowing data engineers to independently adjust systems for themselves and customize functionality without having deep knowledge in programming. Low-code and no-code come to the rescue in this case, when you can create entire scripts without writing long lines of code.

We talked about this approach in more detail in the article.

This is a very promising direction in light of the data decentralization trends and skill shortage.

New Trends – New Stars

Obviously, new trends also impact big data solutions and bring new players to the market that match these trends. In the following research, new “stars” are collected, which currently show promising growth in data monitoring, data observability and data quality testing

Download research on new data observability and monitoring solutions:

A Cure for Big Data

Acure.io is one of the fresh and promising solutions for working with big data. It is not just another monitoring system, it is an AIOps platform that integrates all other log and alert monitoring systems, takes data from there, puts it on a single dynamic link map, and automates the processes of monitoring it.

The dependency map is built automatically based on data from existing monitoring systems and other tools. This is vital for dynamic environments, such as modern cloud ecosystems and microservices on Kubernetes. It not only improves data observability but also provides rapid identification of the root cause of an incident and the impact of various technical resources on business services.

In order to avoid information noise, a solution called Signals is used. It aggregates, normalizes and enriches events collected from various monitoring tools and automatically correlates alerts into actionable insights.

As for automation, here Acure also follows trends and uses a Low-code scripting engine (including auto-building and auto-correlation). Running built-in scripted automation tools with low-code and external runbooks allows workflows to be automated for faster incident response.

Can’t wait to try all these features in action? Create Userspace in Acure.io!

Wrapping Up

The final analysis and the implemented choices can only be foolproof if the data has been handled well and can be trusted. Being dynamic has several advantages. Organizations that are data-driven perform better, have more predictable operations and are significantly profitable.  

Businesses must fully take advantage of the benefits provided by big data to remain at the top of the pop league. They must also go for a more data-driven path so that they can base their choices more on the facts provided by big data rather than just relying on intuition.

Businesses use big data analytics to reap benefits and understanding from large quantities of data. Big data is being used to fuel modern advanced analytics projects like AI and machine learning.

Сообщение The New Trends and New Stars in the Big Data появились сначала на Acure AIOps Platform.

Low-code is a development method that minimizes manual programming. Instead of hard coding, visual constructors are used for application modeling and ready-made scripts are used to solve typical tasks. For low-code development, the process involves moving blocks with ready-made code using the drag-and-drop principle and getting a product with the desired functionality. Ready-made modules in low-code speed up work with typical tasks and eliminate repetitive actions but code can be used for individual solutions, settings and personalization. Development in the platform takes place according to ready-made templates or freely. Integrations and built-in services are also supported.

The main value of low-coding is the ability to do without programmers when you need to create or change some kind of application, module or even product. To carry out the necessary work, the competencies of the platform administrator will be more than enough.

Benefits of Low-coding

Low-code platforms require less development time and give more flexibility in setting up processes. There is no need to plan the architecture, create prototypes, analyze and develop the UI since it is assumed that this is all implemented in the low-code platform itself.

Such platforms integrate with a wide range of systems and allow you to add new features to any application. In addition, manufacturers of low-code platforms talk about their greater security for other applications and stability compared to self-written elements.

The main elements of low-code platforms are:

1. Visual modeling

2. Ready-made components, built-in services

3. Rapid deployment of applications, focus on DevOps

4. Pattern development or abstract development

Since the company’s IT specialists in this case no longer have to write a lot of code, the need for these competencies is reduced and, in turn, the ability of staff to build solutions from ready-made components is prioritized.

Low-code Automation in Acure

Acure Automation Service is a high-performance environment for launching and executing custom scripts. Scenarios can be both custom and supplied by the developers themselves as full-fledged services.

Automation scripts allow you to automatically discover new configuration items, the relationships between them and update the service map in real-time without any manual manipulation.

With the help of low-code scenarios, you can also create signals – special dynamic objects that allow you to correlate and deduplicate incoming events and alerts. Read more about this functionality in the article discussing Acure 2.0.

The low-code engine is used to create automatic scripts. Automation scripts in Acure help significantly expand the functionality of the system and create arbitrary event processing scenarios using visual blocks and establishing links between them.

Of course, low-code, as described above, means a significant reduction in the use of complex hard coding but does not at all relieve you of the need to learn the logic of building scripts and memorizing functions and variables. And if you are now holding your breath in anticipation of a ton of complex information, calmly exhale. Acure ‘s low-сode instruments are no more complicated than the cheat code in your favorite games, and also as a result make life just as easy. Further, you will be convinced of it. 

Low-code Instruments in Acure

Start events

Any script must start with a “startup”. Startup events are responsible for this – blocks that initiate the launch of the script and contain the event model. If a script contains multiple start blocks, it can run on any of them. The composition of the starting blocks is determined by the route map settings.

When the script is running, it is time for variables and functions.

Functions

There are two types of functions in Acure.

Impure functions

• The function is executed every time the input is called from the previous block
• The ArrayAddElement function requests all the data passed to it as input
• This function only works once. To use the result of a function, it is not necessary to call it again

Pure functions

• The function is executed each time its result is requested. Accordingly, to use the result, it will need to be called every time, like Batman.

Variables

Variables are divided into two types.

Local

• Initialized within the current scenario or manually by the user, or using the SET block
• Can be called or initialized anywhere in the current script

System

• Are providers of information about the script, owner, or current space
• Not initialized by the user. They act only as a source

Now you will ask: what about data types supported in Acure? Acure supports multiple types of data but you can only link to pins of the same type.

• The following values are possible for types:

• Boolean: True / False ;
• Byte: Integers from 1 to 255;
• Char: A single Unicode character ;
• Double: ±5.0 × 10 ⁻³²⁴ to ±1.7 × ¹⁰³⁰⁸
• Dynamic: any object;
• GUID: Format value 00000000-0000-0000-0000-000000000000;
• Integer: -2 147 483 648 to 2 147 483 647;
• Integer64: -9 223 372 036 854 775 808 to 9 223 372 036 854 775 807;
• String: Unicode character string

• The type can be either Single or Array.

Wildcard Pins And Connections

It is also worth noting that some functions can work with different data types. For convenience, in such cases, Wildcard pins are used. For Wildcard pins the type of connection is set either manually or when establishing a connection.

For WC pins, there are also requirements in the context of each function. More about this is written in the documentation when describing each type of function.

There are also certain requirements for establishing links. For example, when pinning a function call, one-to-many communications are prohibited, but many-to-one are allowed. With a data transfer pin, the opposite is true.

Function Categories

The main low functions are presented in the table below and divided into several categories.

You can find more information about every function in the corresponding section of the Acure documentation.

In this article, let’s walk through building a simple Autodiscovery scenario.

Creating A Simple Autodiscovery Scenario

As mentioned above, automation scripts in Acure allow you to minimize manual actions, which is especially important when monitoring dynamic environments. After writing several scripts on the low-code engine, you no longer need to think about making changes to the service model yourself. A dynamic map of IT infrastructure links with all configuration items and links will be built and updated automatically.

No shaman tambourines – all the magic happens on the scenario builder page.

By default, there is a start block that runs the script every time the corresponding event arrives.

First, you need to create a rule so that the sequence is executed on specific events. To do this, you need some functions in the form of blocks. You can add them from the context menu by right-clicking on an empty space.

Let’s build a simple rule that will receive only those events that came from a specific stream.

For that, add the FiltredByStreamId function and connect the sequence in such a way that when an event arrives in the system, the script checks the ID of the stream from which it came and, if the filtering is successful, the script will continue to run.

The sequence of execution of script functions is indicated by blue arrows — exact pins.

Note that in addition to exact pins, there are data pins. If the former is responsible for the sequence, then the latter is responsible for transmitting and receiving data.

Now let’s analyze our function. For it to be executed, it must be provided with input data. In our case, the function requests an incoming stream model and filtering parameters (stream id).

We must get the initial data from the primary event, i.e., take away the stream model. To do this, we decompose the original structure using the base function and establish a connection with our filter.

Now we need to specify the required parameter (we take it from the previously created stream) and copy-paste it into the FiltredByStreamId block.

Done! The simple rule is ready. Now, further actions will be executed only if the event came from the stream we specified.

Let’s look at the other tools that are available in the editor as well.

The left panel contains the objects of the current scenario. Here you can create and manage local variables, structures and entire functions. From here, they can be added to the screen for use in a script or selected for further customization.

The settings are available in the right panel where we fill in all the required fields and, in the case of a local function, write the executable code.

To show off your awesome script or make it easier for your team, you can export the script and share it with others. The recipient, using the import tool, creates an exact copy of this script.

Is Low-code the Future? 

The numbers say yes. The 2022 Mendix State of Low-Code study showed a rise in low-code adoption from 77% in 2021 to 94% IN 2022, with four out of 10 companies now using low-code for mission-critical decisions. The study argues that the spread of low-code may soon lead to the overthrow of more “traditional” forms of operations. This report cites Gartner’s forecast that by 2025, low-code solutions will account for 70% of apps, up from 25% in 2020.

At the same time, the scope of low-code products will also be constantly expanding. This technology has already become a trend and subsequently, the entire market will be rebuilt under it.

All this suggests that the market will increasingly be oriented toward simple solutions when any mass user will be able to automate the solution of routine tasks and satisfy needs without deep programming knowledge. At the same time, the growing needs of users stimulate low-code technology to develop faster and improve functions. Thus, low-code systems will be able to solve more and more complex problems as they develop.

Want to experience the benefits of low-code? Register in Acure and write your own automation scenarios.

Сообщение Low-code as a Future of Development and Its Realization in Acure появились сначала на Acure AIOps Platform.

This term was first introduced by Gartner. AIOps aims to provide IT operations with the agility and precision of Artificial Intelligence.

Now Gartner publish an annual rating for top AIOps platfroms. These platforms analyze telemetry and events, and identify meaningful patterns that provide insights to support proactive responses.

It is said the most of AIOps platforms have the particular 5 characteristics:

• Cross-domain data ingestion and analytics;
• Topology assembly from implicit and explicit sources of asset relationship and dependency;
• Correlation between related or redundant events associated with an incident;
• Pattern recognition to detect incidents, their leading indicators or probable root cause;
• Association of probable remediation.

We`d like to add 3 more features. Here they are…

Features of AIOps 

Data Correlation

The capacity of AIOps platforms to absorb, organize, and analyze data from various sources with quality and speed that would be unsurpassed by the efforts put in by humans to analyze is one of their distinguishing features. This gives IT teams a great chance to overcome some of the obstacles they frequently encounter when handling IT crises manually.

Data Mapping

AIOps can be utilized to map out dependencies across various domains using intelligence concerning systems, applications, and various service resources. This is a major help for the processes involved in change and configuration management which have usually struggled in the past due to a lack of knowledge about current configurations and the underlying dependencies. It is also essential for a variety of management output, including incident management. Incident management tasks include change management and other tasks that call for profound awareness of the entire IT configuration.

Automated Incident Management

The projects that leverage AIOps insights to support the automatic remediation of problems brought on by both existing incidents and anticipated problems are established by organizations looking to get the most out of AIOps.

Since they make use of AI’s analytics and learning models, these kinds of capabilities are quite alluring for AIOps. They finally deliver on automation which has hovered over IT operations for years.

Read our blog to learn more about other IT operations advantages.  

Why is AIOps Going to Become the Future of IT Operations?

1. Reduction in Volumes of Noise

An IT operations team may get overburdened by the volume of services, resources, and alerts which may result from a single incident. Vast quantities of additional information are difficult for old IT management methods to keep up with. Key signals are sometimes unable to be distinguished from noise and get lost within them. This amount of noise may lead to prolonged slowdowns and diminished user functioning. 

Workflow automation can be increased with the help of AIOps tools. Teams dealing with IT operations can benefit from this process of detecting and analyzing problems and difficulties to improve security, develop a foolproof plan, or perform an automated patch by examining tools and data. 

AIOps products can connect and separate events to produce meaningful and valuable insights, pinpoint and detect the problem’s location and offer automated solutions for quicker problem-solving.

2. Transparency Across Systems

Having access to data related to operations enables teams deployed for IT operations to see issues even before they arise. By using real-time detection methods, businesses may become proactive in the way they solve problems and act faster and more efficiently. Enterprises must have this detection accuracy and must gain complete insights into operations data. 

These insights can be made available to Enterprises via AIOps big data platforms. An AIOps platform can be used by IT managers to obtain more in-depth analytics about the lifespan of an application.

3. Strengthening Security

By integrating Artificial Intelligence into security systems, systems can detect data theft and violations. By gathering and combining internal logs, such as software and application data, networking records, and malicious sources, we may use AIOps algorithms to detect dangerous and suspicious behaviors. Businesses can also utilize the technology to find potential hazards hiding in their networks.

Security is one of the most significant specialized cases of anomaly detection. Strengthening the security of the IT infrastructure is one of AIOps’ features. 

4. Boost Customer Experience

IT incidents that might affect the user experience must be addressed immediately and managed effectively. Many times, AIOps can foresee future occurrences and stop them from happening with the aid of automation and actionable insights. AIOps advises using knowledge base articles for self-service resolutions so they don’t have to wait for IT professionals, which will reduce resolution time.

The rate of technological changes has made IT an ongoing business collaborator, and expectations have automatically grown for IT to provide experiences comparable to those of other technologically advanced sectors. AIOps can assist in resolving unanticipated occurrences swiftly, even if they do happen. The result is a better user experience.

5. A Boon for Businesses

AIOps anticipates resource usage and productivity problems. By using probable cause analysis, it concentrates on the most probable cause of an issue. Using grouping and detection techniques, it is possible to pinpoint the underlying issues that are responsible for events. 

AIOps aid in maximizing your team’s overall capacity while lowering costs and increasing output. Your desk team’s workload can be reduced by using Artificial Intelligence and automation to analyze the usage patterns, user interaction data, and support ticket patterns.

Do you want to know which AIOps tools meet these criteria? Check out our review of the 20 best AIOps platforms.

AIOps Use Cases

AIOps can be applied to a variety of IT operations use cases to help organizations automate, streamline, and optimize their IT operations. Here are some examples of AIOps use cases:

Incident Management

AIOps can help organizations automate incident detection and response, reducing the time required to identify and resolve incidents. By analyzing log files, performance metrics, and other data sources, AIOps platforms can identify potential incidents and alert IT teams, enabling them to respond quickly and effectively.

Root Cause Analysis

AIOps can help organizations identify the root cause of IT incidents by analyzing data from multiple sources and identifying patterns and correlations. This can help organizations identify the underlying issues that are causing problems and take steps to address them.

Capacity Planning

AIOps can help organizations optimize their IT infrastructure by providing insights into capacity usage and trends. By analyzing performance metrics and other data sources, AIOps platforms can help organizations plan for future capacity needs, ensuring that they have the resources they need to support their business operations.

Security

AIOps can be used to improve security by detecting and responding to security threats in real-time. By analyzing network traffic data and other security-related data sources, AIOps platforms can identify potential security threats and take action to prevent them.

Change Management

AIOps can help organizations manage changes to their IT infrastructure by providing insights into the potential impact of changes. By analyzing data from multiple sources, AIOps platforms can help organizations understand the potential risks and benefits of changes, enabling them to make more informed decisions.

These are just a few examples of the many use cases for AIOps. As organizations continue to adopt digital technologies and seek to optimize their IT operations, AIOps is likely to become an increasingly important tool for IT teams.

Wrapping Up  

In addition to benefiting from autonomic computing and relieving workers from dealing with continuing complexity, IT operations settings can establish a personalized knowledge and understanding over the age that exceeds what individuals can accomplish on their own with the correct technical tools, support, and integration.

A system that uses advanced AI models may continuously learn from its data about its surroundings, improve itself, and provide better recommendations while adjusting to changes.

Read more: “Gartner’s Vision for AIOps in 2022 and Beyond,” presented by Lead Gartner Analyst Pankaj Prasad.

Сообщение 5 Ways Why AIOps is the Future of ITOps (Gartner) появились сначала на Acure AIOps Platform.

Before we start talking about current DevOps trends…

DevOps is the combination of software developers (dev) and operations (ops). Its purpose is to improve the efficiency, speed, security of software development, product delivery and IT services in the context of complex applications.

Read our blog post: Top 15 Skills for DevOps

A common goal of DevOps is to transcend traditional IT operating models. An effective DevOps implementation can improve the customer experience, product quality, and agility of customer interactions.

DevOps aims at building real-time business value in a continuous-delivery environment through automation and continuous integration tools. 

According to a recent market study DevOps industry will reach $20 billion by 2026, and will expand at a CAGR of 24.7% from 2019 to 2026.

The holistic approach that DevOps necessitates, which includes system thinking and the building of a positive culture, can change how traditional software development methods are done. Modern DevOps trends emphasize utilizing design systems to speed up value development.

DevOps Trends For 2022

1. Automation

The term refers to the addition of technology that performs tasks with reduced human assistance to processes such as code review, testing, and configuration management.

Automation is the utmost requirement for DevOps practice, and the guiding philosophy of DevOps is to “automate everything”.

Automation in DevOps begins with the generation of code on the developer’s machine and continues through pushing the code to the code and, even after that, monitoring the application and system in production.

DevOps automation seeks to simplify the manual effort in the DevOps lifecycle.

According to the 2021 State of DevOps report, highly evolved companies have implemented extensive automation modes in their processes.

2. Site Reliability Engineering (SRE) And DevOps

SRE is a type of DevOps that can be applied. SRE is all about relationships and team dynamics. 

To deliver services more quickly, SRE and DevOps both aim to close the gap between development and operations teams.

DevOps teams who need someone with more specialized operations expertise and whose developers are overburdened with operations responsibilities can benefit from SRE.

3. DevOps Security

DevOps security is the science and art of using strategies, policies, procedures, and technology to protect the entire DevOps ecosystem.

DevOps security should support an effective DevOps environment while assisting in the early detection and correction of operational and code issues. 

Early adoption of DevOps security guarantees that security is a fundamental component of all application and system development processes. As a result, uptime is improved, the likelihood of data breaches is decreased, and strong technology is developed and made available to suit business objectives.

4. Application Performance Monitoring (APM) Software

Monitoring and controlling an application’s performance and availability are referred to as application performance management.

APM is a method that takes into account every element of a software application to comprehend it and continuously enhance it for a better user experience.

APM is now more widely available to everyone and is no longer just for the DevOps team and system administrators.

5. The Rise Of DevSecOps

DevSecOps (development, security, and operations) is the dynamic approach to software development that integrates security as a crucial step in the delivery of applications from design to production.

Automating the software delivery process with integration of security initiatives is the core of DevSecOps. It necessitates a thorough understanding of the most recent automation, AI, and machine learning techniques, as well as DevOps tools and technologies. 

Businesses can automate the compliance process with the aid of DevSecOps. Replacing manual compliance processes with automated ones helps save time and resources.

6. Continued Cloud Adoption

Because of the centralized structure of the cloud and the availability of a common, centralized platform for testing, deployment, and production, DevOps and cloud computing work well together. Even while they can coexist, they work best together to deliver significant IT transformation that directly advances corporate objectives.

As cloud computing providers enable DevOps on their platforms, which is less expensive than on-premises automation technology, DevOps automation continues to become increasingly cloud-centric.

By utilizing user-based accounting to track resource usage, cloud-based DevOps facilitates the tracking of development resource expenses.

7. Autonomous IT Ops

In IT operations, the first pillar is automation. Automation is a process and it takes time to build not only the necessary skills but the necessary confidence in AI/ML technology. 

The second pillar is proactive, where an operator can manually take action, but at any time in the future, automation will allow AI to fix the problem without human intervention.

Achieving full autonomy of IT operations is the democratization of AI. It’s about making relevant information available to everyone, when and where they need it, in an easy-to-use and practical way. This democratization can be achieved by simplifying AIOps platforms and making them accessible to everyone, from administrators to users.

8. AI and ML Integration

Automation of repetitive work and the elimination of inefficiencies throughout the SDLC are two ways that artificial intelligence (AI) and machine learning (ML) assist DevOps teams perform better. 

A significant change in its evolution will result from the combination of ML and AI with DevOps. It establishes DevOps as a critical pillar for the organization’s objective of digital transformation.

9. Kubernetes as an Evergreen DevOps Trend

Kubernetes allows organizations to leverage more computing power when running software applications. This allows the engineer to share dependencies with her IT operations. 

One of the main reasons to use Kubernetes for DevOps is to reduce your workload. It also resolves conflicts between different environments. This allows engineers to meet customer demands while relying on the cloud for various work apps.

Kubernetes simplifies container tasks. It simplifies activities such as canary deployments, rolling updates, and horizontal autoscaling.

10. Observability in Application

Not only is observability critical for DevOps, but also the entire organization.

Replacing the static data of legacy monitoring solutions, observability provides a full-spectrum view of application infrastructure.

Observability helps companies monitor the performance of the application or system. It helps in speeding up the Mean Time to Detection.

Also, the management of dependencies is a crucial responsibility for DevOps managers. You may automatically map all application and infrastructure dependencies using dynamic service modeling.

***

Significant changes are being made to the key facets of DevOps. The perfect catalyst for accelerating the adoption of these DevOps trends would be the unexpected increase in the requirement for digital transformation. Although security will likely rank among the top concerns. 

DevOps trends will emphasize constant advancements in several fields. No matter what the future of IT organizations holds, DevOps will continue to change and adapt. Businesses should apply these DevOps approaches to spearhead big IT transformations that directly support their goals and ambitions. 

The aforementioned developments will aid firms in quickly moving past automation whileconcentrating on steadily bettering results. The establishment of a reliable release pipeline and improved communication between the business, IT, and development teams are sparked by these trends.

Сообщение Top 10 DevOps Trends That Could Become Mainstream появились сначала на Acure AIOps Platform.