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Category: DevOps Onramp

DevOps Download: What to Know About Life in the Cloud(s)
According to the 2022 Flexera State of the Cloud Report, 89% of cloud customers now use multiple cloud hosting providers. Nearly nine out of 10 might sound high, but it tracks with the anecdotal evidence we’ve seen. For varied and good reasons, companies are spreading their usage across multiple hyperscalers rather than putting all their eggs in one basket.

In this article, I’ll take a look at what life in the cloud(s) looks like today. Along the way, I’ll examine the value that companies and their DevOps teams can derive from this approach, the challenges to be aware of, use cases for making the multi-cloud move and more.

What is Multi-Cloud?

Multi-cloud is a type of cloud strategy approach where an organization uses computing services from at least two cloud providers to run their applications. Instead of a single-cloud stack, these environments include two or more public or private clouds, or a combination of both. With multi-cloud, you can have an application running on Google Cloud Platform (GCP), another in Amazon Web Services (AWS), and maybe one on premise for hard-to-move applications.

There are three kinds of cloud setups. Single cloud is when workloads run solely on one environment. Hybrid cloud entails overlapping workloads across multiple clouds, such as operating the same programs on different providers for business continuity purposes. The final setup is planned multi-cloud, which disperses applications and usage across providers along specific lines, like having compute intensive applications with one provider and data analysis applications with another.

Got a Preference?

Make no mistake, using multiple cloud providers is more complex than a single host, but as its popularity shows, the benefits can carry more weight. Foremost, that’s because an organization gains the flexibility to create a best of all worlds situation, based on factors like the following:
- Operational preference: Users may like different features from different providers, and splitting types of use can yield the ideal setup. For instance, a customer may run compute on AWS but want their business intelligence and machine learning on GCP. In this deployment, the front-end app could gather the needed information, then send the data to the other provider for analysis and ML.
- Regulatory requirements: Certain workloads may require data storage in specific regions, but not all cloud vendors have multiple “regions” in each country. Companies that want to use the cloud can set up a “multi-region” environment by having one region on AWS and a second region on GCP within the same country.
- Cost savings: Cloud providers offer different savings plans. For a savvy user, structuring a flexible program can take advantage of each to significantly reduce costs. It’s also a great way to avoid the pitfalls of lock-in and can make it easier to go elsewhere should a provider lose your trust.
- Failsafe Options: Outages happen, so companies needing 24/7 access to apps will sometimes run portions redundantly on two hosts to ensure smooth operations. For example, a retail app may run on GCP and AWS with live failover between them. That way, if one goes down the other can kick in without noticeable downtime.
Not Without its Challenges

The benefits to running a multi-cloud setup are clear, but there are some challenges as well. A carefully planned multi-cloud setup can save money, but a careless approach will duplicate operations and cost. It also leaves companies vulnerable to mistakes that can lead to significant overrun, especially for hybrid cloud operations, which can be extremely complex. Specialists must be well versed in FinOps principles and experienced at writing modern applications that can scale and work across regions and clouds, to ensure success in a multicloud approach.

Coming to Terms

You’ll undoubtedly come across a lot of new ideas and terminology when researching cloud setups. Here’s a few of the more crucial ones to become familiar with as you plan strategy.
- Lift and shift: This refers to cloud-to-cloud migration, in other words, taking what you have in one cloud and putting it in another, whether it is private to public or public to public.
- Move and improve: This usually refers to migrating while making a small change to take advantage of a public cloud feature. For instance, you could be using open source Kubernetes on premise but want to move to a managed version. Eliminating management of Kubernetes would require a minor change but it improves operations greatly.
- Cloudbursting: On premise offers hard limits to capacity. For companies able to anticipate business peaks, cloudbursting can scale workloads to an on-demand public cloud for a short period. For example, a retailer who operates on premise may cloudburst on Black Friday in order to handle a volume increase.
- High Availability: The goal of this is to eliminate single points of failure by enabling users to continue working even if one of the IT components that an applications depends upon fails.
- Disaster Recovery: Clouds offer solutions to help an organization restore critical systems and data after a disaster strikes. Recovery comes in three “heat levels”: Cold is for data backup only, warm restores operations in several hours, hot is for near-instant resumption of cloud operations.
People and Processes

The most important considerations for operationalizing multi-cloud are people and processes. Providers pump out new services fast, and not only can it be difficult for staff to keep up, it can be demoralizing when they’re unable to. Allowing time to succeed, fail and learn is key, and larger companies might consider designated specialists for each platform.

When it comes to processes, management and security are highly customizable, but it requires deep knowledge to get them right. Operating via infrastructure as code rather than ClickOps is a must. The latter is a time consuming approach that requires selecting options in a provider menu and introduces both operational and security risks in the form of configuration drift.

Life in the Clouds

Multi-cloud brings strong benefits to DevOps. Organizations can get the right infrastructure balance based on their needs. Distributing workloads can reduce the impact of outages and bolster resiliency. There’s greater opportunity for cost savings, and scaling is simple. Ultimately, multi-cloud operational enhancements can result in more reliable and innovative solutions, which delivers on the mantra of DevOps itself.
August 10, 2023
Automation in DevOps: Advancements, Platforms and Examples

Automation is the backbone of DevOps. By facilitating seamless integration and continuous delivery, it revolutionizes the ways organizations deliver software, products and services. When applied correctly, automation can significantly enhance productivity, reduce time-to-market, improve software quality and minimize the risk of human error.

A range of automation tools support these goals, each tailored to a specific aspect of the software development life cycle (SDLC).

From code integration to testing and deployment, these tools optimize processes and enable the rapid and reliable release of software. In this article, we will explore the role of automation in the DevOps landscape.

The Importance of Automation in DevOps

Automation in DevOps is the process of designing, developing and deploying software and systems with minimal human intervention. The goal is to streamline the software development life cycle from design, development and testing to deployment and management.

Automation provides several benefits:

1. Speed and efficiency: Automation enables quick development and deployment of software, which results in faster delivery of features and fixes to customers.
2. Reliability: Automated systems are less prone to human error, resulting in more reliable code deployment and management.
3. Consistency: Automation guarantees that routine tasks are performed the same way every time, ensuring consistent results.
4. Reduced cost: Automation eliminates the need for manual intervention in routine tasks, leading to a reduction in operating costs.
5. Increased productivity: By taking over routine tasks, automation frees up the team to focus on more critical, high-level tasks.

Automation in DevOps: An Overview

Automation in DevOps encapsulates various stages, from development and testing to integration, deployment, and monitoring. The process is often referred to as a DevOps pipeline, a set of practices aimed at reducing development times and improving software quality.

● Development: Automation begins at the development phase with tools for source control (like Git), which help developers manage and track code changes.
● Integration: The next step is continuous integration, where tools like Jenkins automate the build process, allowing teams to identify issues early in the development life cycle.
● Testing: Automated testing tools (like Selenium) streamline the testing process, ensuring that the code is error-free and ready for deployment.
● Deployment: Continuous deployment tools (like Kubernetes) automate the deployment process, providing fast, consistent and repeatable deployments.
● Documenting: Technical documentation is automated with tools like Scriversi to ensure updated, error-free documentation through each version and release.
● Monitoring: Finally, automated monitoring tools (like Prometheus) offer real-time insights into the application’s performance, facilitating proactive troubleshooting.

Automation’s main objective is to minimize manual intervention, thus reducing the risk of errors, improving efficiency and fostering a culture of shared responsibility.

Examples of Automation in DevOps

1. Continuous Integration/Continuous Deployment (CI/CD): CI/CD is a critical element of DevOps automation. With CI, developers merge their changes back to the main branch as often as possible. The CI/CD server automatically builds and tests every change. For example, Jenkins, a popular open source automation server, allows developers to automate various stages of their build pipeline.

2. Infrastructure-as-Code (IaC): This practice involves managing and provisioning computing infrastructure with machine-readable script files rather than physical hardware configuration or interactive configuration tools. For instance, Terraform, an open source IaC software tool, provides a consistent CLI workflow to manage hundreds of cloud services.

3. Configuration Management: This is the process of maintaining a system’s configuration in a known, consistent state. For example, Ansible, a configuration management tool, helps to automate software provisioning, configuration management and application deployment.

4. Monitoring and Logging: Automated monitoring and logging systems gather data on how software and hardware are performing and alert the team to any problems. One example is Prometheus, a popular open source monitoring and alerting toolkit.

5. Technical Documentation: Proper documentation is crucial in the software development process. Yet, it is often overlooked or not updated frequently due to time constraints or the tedious nature of the work. When automated, it simplifies and automates the process of creating and maintaining technical documents.

Final Thoughts

In the world of DevOps, automation is not a luxury but a necessity. By automating repetitive and error-prone tasks, teams can focus on what they do best: creating innovative solutions that meet customer needs. From CI/CD, IaC and configuration management to monitoring and logging, automation simplifies the software development process, reduces human error and enhances speed and efficiency. Tools like Jenkins, Terraform, Ansible, Prometheus and Scriversi support this trend, providing various automation capabilities that make the DevOps approach more efficient and effective.

July 28, 2023
Syslogs in Linux: Understanding Facilities and Levels

Recently, I found myself in the position of investigating the root cause of a booting failure in an embedded device. To diagnose the problem effectively, I relied on syslogs, which proved to be invaluable. Syslogs, or system logs, are a crucial element of Linux systems, as they capture and retain important data about different events and actions. In this article, we will emphasize the importance of syslogs in Linux and examine the various facilities and levels utilized to classify and prioritize log messages.

Understanding Facilities

Facilities in syslogs are used to categorize log messages based on their sources or purposes. They provide a way to differentiate between various components of a Linux system.

Photo from RFC5424

Understanding Levels

Syslog levels indicate the severity or importance of log messages. They help prioritize and filter log entries based on their significance.

Photo from RFC5424

An Example

For easier debugging, you can clear out the syslog before running the user application for investigation and to get fewer lines of logs. To clear out the syslogs, you can either truncate or delete them.

Remember that clearing syslogs removes log history, so ensure you don’t require the information for troubleshooting, analysis, or compliance purposes before clearing the logs. Additionally, clearing logs may require administrative privileges, so you may need to use the command or run the commands as the root user.

After clearing the syslog, remember to restart the rsyslog. Otherwise, your application won’t be able to log your user-level messages. Below is an example in Ubuntu on how to truncate, remove and restart the service.

This is a simple C++ code that generates a log message for the facility user that indicates an informational message.

In this example, we include the <syslog.h> header file, which provides the necessary functions and constants for syslog logging. The <openlog() function is used to open a connection to the syslog service, specifying a custom identifier (“SyslogSampleApp”) for our application, the logging options (LOG_PID to include process ID), and the facility (LOG_USER for user-level messages).

We then use the syslog() function to generate log messages of different levels. In this case, we create a user-level message using LOG_INFO. You can adjust the log levels based on your specific needs.

Finally, we close the syslog connection closelog() to release any resources associated with the syslog service.

Viewing your syslog depends on the Linux distribution that you’re using. /var/log/syslog is used for Debian and Ubuntu while /var/log/messages is used for Red Hat and CentOS.

Conclusion

Syslogs are an integral aspect of Linux systems, serving as a foundation for efficient monitoring, troubleshooting and analysis. They play a crucial role in capturing and organizing log messages across various facilities and levels. This categorization allows for valuable insights into system behavior, promotes effective issue resolution, and contributes to the overall maintenance of system health and security. It is essential to utilize the appropriate log level, such as LOG_INFO, to ensure that the syslog remains uncluttered and focused on the relevant scenario, avoiding the unnecessary inclusion of LOG_ERR entries.

July 21, 2023
Complete Guide to AWS Savings Plans
AWS Savings Plans are a flexible and cost-effective way to save on your Amazon Web Services (AWS) usage. These plans allow you to commit to a specific amount of compute capacity over a one- or three-year term in exchange for a significant discount on your AWS usage. Savings plans can help you reduce your AWS bill by up to 72%, depending on the type of plan and your level of commitment.

Why Do You Need AWS Savings Plans?

With the growing reliance on cloud infrastructure in modern IT, it is crucial for organizations to optimize cloud computing costs without compromising on performance or scalability. AWS Savings Plans address this need by providing a flexible and cost-effective solution that allows you to commit to a certain level of usage at a discounted rate.

With AWS Savings Plans, by committing to a specific amount of compute capacity over a set time period, you can save up to 72% on your AWS usage. This can result in significant cost savings for your organization, freeing up resources to invest in other business-critical initiatives.

Another benefit of AWS Savings Plans is the flexibility they offer. Unlike Reserved Instances, which require you to specify instance type, operating system and region, Savings Plans are more flexible and can be applied across different instance types, regions and operating systems. This flexibility makes it easier for you to optimize your cloud computing costs while still meeting your business needs.

Difference Between AWS Savings Plans and Reserved Instances

AWS Savings Plans and Reserved Instances are both designed to help you save on your AWS usage, but they offer different levels of flexibility and cost savings.

Reserved Instances require you to commit to a specific instance type, operating system and region for a one- or three-year term in exchange for a discount on your AWS usage. While this can result in significant cost savings, it also limits your ability to optimize your cloud computing costs based on changing business needs.

AWS Savings Plans offer a more flexible solution that allows you to commit to a certain level of usage across different instance types, regions and operating systems. Additionally, in most scenarios, Savings Plans can provide a higher level of cost savings compared to Reserved Instances.

Types of AWS Savings Plans

There are two types of AWS Savings Plans: Compute Savings Plans and EC2 Instance Savings Plans.

Compute Savings Plans

Compute Savings Plans are the most flexible type of Savings Plans, providing a significant discount on your AWS usage across any instance type, region or operating system. This flexibility makes it easy for you to optimize your cloud computing costs based on your changing business needs.

Compute Savings Plans offer a discount of up to 66% compared to on-demand pricing, depending on the level of commitment you make. They can be used across all AWS services that are powered by Amazon EC2 instances, including Amazon EMR, AWS Lambda and Amazon ECS.

EC2 Instance Savings Plans

EC2 Instance Savings Plans are a more specific type of Savings Plan, offering a discount on your AWS usage for a specific instance type within a region. These plans provide a higher level of cost savings compared to Compute Savings Plans, with discounts of up to 72%.

EC2 Instance Savings Plans are ideal for organizations that have a predictable workload and require specific instance types within a region. However, they offer less flexibility than Compute Savings Plans, as they cannot be applied across different instance types, regions or operating systems.

AWS Savings Plans Pricing Models

AWS Savings Plans offer three different payment options to suit your organization’s budget and commitment level: All Upfront, Partial Upfront and No Upfront.

All Upfront

With the All Upfront payment option, you pay for the entire term of your Savings Plan upfront, resulting in the highest discount compared to on-demand pricing. This option is ideal for organizations that have the budget available to make a significant upfront payment in exchange for maximum cost savings.

Partial Upfront

The Partial Upfront payment option allows you to pay a portion of your Savings Plan upfront and the remaining balance on a monthly basis. This option provides a balance between upfront commitment and ongoing flexibility, with a lower discount compared to the All Upfront option.

No Upfront

With the No Upfront payment option, you pay for your Savings Plan on a monthly basis, with no upfront payment required. This option provides the lowest discount compared to on-demand pricing but offers the greatest flexibility for organizations with limited budgets or uncertain future usage requirements.

How to Monitor and Manage AWS Savings Plans

Monitoring and managing your AWS Savings Plans, as part of your overall cloud operations strategy, is critical to ensuring that you are optimizing your cloud computing costs and maximizing your savings. AWS provides several tools and services to help you monitor and manage your Savings Plans effectively:
- AWS Cost Explorer: A powerful tool that allows you to analyze your Savings Plans usage and costs, helping you identify opportunities to optimize your cloud computing costs. With Cost Explorer, you can view your Savings Plans coverage, utilization and savings, as well as forecast your future usage and costs.
- AWS Budgets: This service allows you to set custom cost and usage budgets for your organization, helping you track your Savings Plans usage and ensure that you are staying within your budget.
- Savings Plans Recommendations: This feature analyzes your past usage and recommends the optimal Savings Plans for your organization. It can help you make informed decisions about which Savings Plans to purchase and how much commitment to make.
Conclusion

AWS Savings Plans are an excellent solution for organizations looking to optimize their cloud computing costs and save on their AWS usage. With their flexibility, cost savings and easy monitoring and management options, AWS Savings Plans are a valuable tool for any organization using AWS services.

To unlock the full potential of AWS Savings Plans, it’s crucial to understand the different types of plans, their pricing models and how to effectively monitor and manage your usage. By doing so, you can maximize your savings and ensure that your organization is getting the most out of its investment in AWS.
July 21, 2023
Shift Left With DAST: Dynamic Testing in the CI/CD Pipeline

Dynamic application security testing, or DAST, is a process used to evaluate an application’s security while it’s running rather than while it’s in a static state. This method involves simulating attacks on an application, identifying vulnerabilities and analyzing the responses. DAST focuses on the exposed HTTP and HTML interfaces of web-enabled applications, making it an excellent tool for spotting security vulnerabilities that may occur during an application’s active runtime.

DAST is a ‘black-box’ testing method, meaning it tests the application from the outside in—much like a potential attacker might. It doesn’t have access to the source code and instead focuses on the application as a whole rather than its individual components. This external perspective enables DAST to discover potential security threats that static testing methods might miss.

While DAST is an effective security testing tool, it becomes even more potent when paired with a shift left approach.

Understanding the Concept of ‘Shift Left’ in Software Testing

Shift left is a practice in software development where testing is conducted earlier in the life cycle—that is, it’s ‘shifted left’ on the project timeline. Traditionally, testing was seen as the last phase before the release of a software product. However, with the shift left approach, testing becomes an integral part of every stage of the development process from planning to deployment.

This approach has several advantages. It allows for the identification and resolution of issues earlier in the development process, reducing the cost and effort required to fix them later. It also encourages collaboration between developers and testers, fostering a more cohesive, efficient development environment.

Shift left is not just a testing strategy but a philosophy that promotes preventive measures over reactive solutions. It’s about building quality and security into software from the get-go, rather than tacking it on at the end.

DAST in the Context of Shift Left

Importance of DAST in Early Stages of Development

The integration of DAST in the early stages of development is crucial for several reasons. First, by conducting dynamic security testing from the onset, teams can identify vulnerabilities earlier, making them easier and less costly to fix. This proactive approach helps to prevent security issues from becoming ingrained in the code, which can lead to significant problems down the line.

Second, early integration of DAST encourages a security-focused mindset from the beginning of the project, promoting a culture of security within the team. This cultural shift is crucial in today’s cybersecurity climate, where threats are increasingly sophisticated, and the stakes are higher than ever.

How DAST Complements Other Testing Methods

DAST doesn’t replace other testing methods; rather, it complements them. While DAST focuses on external vulnerabilities, other testing methods like static application security testing (SAST) can target internal issues. By combining these methods, teams can achieve a more comprehensive view of their application’s security.

In a shift left approach, this combination of testing methods can be very powerful. By conducting these tests early and often, teams can ensure that both the external and internal aspects of their application are secure. This layered approach to security testing can help to catch any vulnerabilities that might otherwise slip through the cracks.

Incorporating DAST Into the CI/CD Pipeline

1. Understanding the Application Structure

Before we can integrate DAST into the CI/CD pipeline, it’s crucial to understand the structure of the application we’re working with. This involves understanding the layers of the application, the technologies used and the data flows. By analyzing the application’s architecture, you can identify the critical components that need more attention during the security testing phase.

Understanding the application structure is not just about knowing the technology stack. It’s also crucial to understand the business logic and the functionality of the application. This will allow you to create better test cases during the DAST process. Additionally, understanding the application structure will enable you to make better decisions when configuring the DAST tool, leading to more efficient and effective security testing.

Finally, understanding the application structure will help you prioritize the vulnerabilities found during the DAST process. Not all vulnerabilities are created equal; some might be more critical due to the data they expose or the potential damage they could cause to the application.

2. Configure the DAST Tool

The next step is to configure the DAST tool. This involves setting up the scanning parameters, defining the test cases, and tuning the tool to reduce false positives.

When configuring the DAST tool, it’s important to strike a balance between thoroughness and speed. You want the tool to be able to identify all potential vulnerabilities, but you also want the scanning process to be fast enough not to slow down the development process. This balance can be achieved by carefully selecting the scanning parameters and continuously tuning the tool based on the results of the scans.

Additionally, when configuring the DAST tool, you should consider the unique characteristics of your application. For example, if your application uses a specific technology or framework that is known to have certain vulnerabilities, you should ensure that the DAST tool is configured to test for these vulnerabilities. Similarly, if your application handles sensitive data, you should prioritize testing the components that process and store this data.

3. Integrating DAST Into the CI Pipeline

The next step is to integrate DAST into the CI pipeline. The CI pipeline is a sequence of steps that developers follow to merge their changes into the main codebase. By integrating DAST into this pipeline, we can ensure that every code change is automatically tested for security vulnerabilities.

Integrating DAST into the CI pipeline can be achieved using various tools and plugins. Most DAST tools provide APIs that allow you to automate the testing process. By using these APIs, you can trigger a DAST scan every time a new code change is pushed to the repository. This allows you to catch vulnerabilities early in the development process, reducing the cost and time needed to fix them.

However, integrating DAST into the CI pipeline is not a one-time task. It requires continuous monitoring and tuning to ensure that the testing process remains effective. You will need to update your DAST configurations as the application evolves, add new test cases as new features are developed, and fine-tune the testing process to reduce false positives.

4. Scan for Vulnerabilities

After integrating the DAST tool, you can start scanning for vulnerabilities. The scanning process involves executing the test cases defined during the configuration phase and identifying potential security issues in the application. Depending on the size and complexity of your application, this process can take from a few minutes to several hours.

During the scanning process, the DAST tool will interact with the application just like a real user would. It will send inputs to the application, observe the responses and analyze them for signs of security vulnerabilities. This dynamic approach allows the tool to identify vulnerabilities that static analysis tools might miss, such as runtime vulnerabilities and issues related to the application’s business logic.

5. Analyze and Prioritize Findings

Once the scanning process is complete, the next step is to analyze and prioritize the findings. The DAST tool will provide a report of the identified vulnerabilities, including their type, location and potential impact. This information should be used to prioritize the vulnerabilities based on their risk to the application and the business.

Analyzing and prioritizing the findings is not an easy task. It requires a deep understanding of the application, the business, and the threat landscape. You need to consider various factors, such as the potential impact of the vulnerability, the likelihood of exploitation, the sensitivity of the affected data, and the complexity of the remediation process. By considering these factors, you can prioritize the vulnerabilities and focus your efforts on those that pose the highest risk.

In addition, analyzing and prioritizing the findings should involve all relevant stakeholders. Developers, security teams, and business leaders should all participate in this process, as they each bring unique perspectives and insights that can help make better prioritization decisions.

6. Remediate and Retest

After analyzing and prioritizing the findings, the next step is to remediate the vulnerabilities and retest the application. Remediation involves modifying the application’s code or configuration to eliminate the identified vulnerabilities. Depending on the complexity of the vulnerabilities, this process can involve simple code changes or significant architectural modifications.

After remediation, it’s crucial to retest the application to ensure that the vulnerabilities have been effectively addressed. This involves running the DAST tool again and verifying that the previously identified vulnerabilities are no longer present. Additionally, retesting allows you to check for new vulnerabilities that might have been introduced during the remediation process.

Remediation and retesting is a continuous process. As new vulnerabilities are discovered and new features are developed, you will need to repeat this process to ensure the ongoing security of your application. This continuous effort is a critical component of the shift left with DAST strategy.

7. Continuous Monitoring and Updates

The final step in incorporating DAST into the CI/CD pipeline is continuous monitoring and updates. This involves regularly checking the effectiveness of the DAST tool, updating its configurations as the application evolves, and tuning it to reduce false positives.

Continuous monitoring and updates are crucial to maintaining the effectiveness of your shift left with DAST strategy. As the application evolves and the threat landscape changes, the DAST tool needs to be updated to remain effective.

Conclusion

In conclusion, incorporating DAST into the CI/CD pipeline is a powerful strategy to enhance the security of your software. By understanding the application structure, integrating DAST into the CI pipeline, configuring the DAST tool, scanning for vulnerabilities, analyzing and prioritizing findings, remediating and re-testing, and continuously monitoring and updating, you can unleash the power of shift left with DAST. This comprehensive approach will allow you to identify and address security vulnerabilities early in the development process, leading to more secure and reliable software.

July 18, 2023
A DevOps Guide to IoT Technology

Internet-of-things (IoT) is a term used to describe the network of physical devices—from everyday household items to sophisticated industrial tools—connected to the internet, sharing and collecting data. With the advent of cheap computer chips and the ubiquity of wireless networks, it’s possible to make anything a part of the IoT. Add to that the exponential growth in artificial intelligence and machine learning, and the IoT is a truly significant part of our lives.

The IoT allows devices to communicate and interact over the internet, and they can be remotely monitored and controlled using technologies many of us have never heard of, such as MQTT, QUIC and AMQP. The proliferation of these devices has led to an exponential increase in the amount of data we can gather, analyze and use to make more informed decisions. We’ve already seen the transformative potential of IoT in our homes, workplaces and cities, and it’s only set to increase as more and more devices come online.

At the heart of this technology is the concept of seamless connectivity and communication. However, the rapid development and deployment of IoT devices pose significant challenges. This is where the DevOps approach comes in, offering a solution to these challenges by improving efficiency and effectiveness in the development and operation of IoT systems.

Importance of DevOps in IoT Development and Deployment

In the context of IoT, DevOps is crucial. IoT devices are often deployed in the field, in homes or in other environments where physical access is difficult or impossible. As such, the ability to update and manage these devices remotely is a critical necessity. DevOps methodologies enable this remote management, providing mechanisms for continuous integration and continuous delivery (CI/CD) that make it possible to push updates to devices regularly and reliably.

Moreover, given the pace at which IoT technology is advancing, it’s crucial to be able to adapt quickly to changes and new developments. In this regard, DevOps is invaluable. By bringing together development and operations, it allows for more agile response to changes, faster issue resolution and overall better performance.

The Role of DevOps in IoT

Speeding Up IoT Development Cycles

One of the key benefits of using a DevOps approach in IoT development is the ability to speed up development cycles. With traditional development practices, the time between identifying a requirement or an issue and implementing a solution can be lengthy. In contrast, DevOps practices, such as continuous integration and continuous delivery, enable rapid prototyping, development, testing and deployment of new features and updates.

With the DevOps approach, developers and operations teams work together from the beginning of a project, collaborating closely to ensure that the software being developed is robust, reliable and meets the needs of the business. This cooperative approach, combined with automated testing and deployment processes, significantly reduces the time to market for new features and updates.

Ensuring High-Quality, Reliable IoT Applications

Another significant advantage of utilizing DevOps in IoT development is the increased quality and reliability of the applications being developed. With traditional software development methods, testing and quality assurance often occur after the development process. This can lead to issues being identified late in the cycle, which can be costly and time-consuming to fix.

On the other hand, DevOps practices emphasize continuous testing throughout the development process. This means that issues can be identified and addressed much earlier, leading to higher-quality, more reliable software. In the context of IoT, where devices may be deployed in critical applications such as health care or transportation, the importance of this cannot be overstated.

Enabling Scalability and Flexibility in IoT Systems

Finally, DevOps plays a crucial role in enabling the scalability and flexibility of IoT systems. As the number of IoT devices continues to grow exponentially, the ability to manage and monitor these devices efficiently is becoming increasingly important. DevOps practices, such as infrastructure as code (IaC) and the use of containers and microservices, provide the tools necessary to scale and adapt IoT systems as needed.

With IaC, the infrastructure required to run an IoT application is defined and managed as code, making it easy to replicate and scale. Containers and microservices, on the other hand, allow applications to be broken down into smaller, independent components that can be scaled individually, providing a high level of flexibility and efficiency.

Implementing DevOps in IoT Development

Integrating CI/CD in IoT Application Development

Continuous integration/continuous delivery (CI/CD) is a fundamental DevOps practice that plays a vital role in IoT development. It involves automating the processes of software delivery and infrastructure changes. With CI, developers frequently merge their code changes into a central repository where automated builds and tests are run. CD takes this a step further by automating the release of these changes to the production environment.

For IoT, integrating CI/CD can help maintain the quality of software, increase efficiency, and speed up the process of delivering software updates or new features to devices. It also ensures that any changes made can be easily rolled back, reducing the impact of any potential issues.

Applying IaC for IoT Infrastructure Management

Infrastructure-as-code (IaC) is another key DevOps practice that is highly relevant to IoT. IaC is the process of managing and provisioning computing infrastructure through machine-readable definition files rather than physical hardware configuration or interactive configuration tools.

In the context of IoT, IaC can simplify the process of setting up and managing the complex infrastructure required for IoT applications. With IaC, developers can quickly and reliably replicate infrastructure setups, making it easier to scale IoT applications as the number of connected devices grows.

Using Containers and Microservices in IoT Application Architecture

Containers and microservices are two more DevOps practices that can significantly benefit IoT development. A container is a standalone executable package that includes everything needed to run an application, including the code, runtime, system tools, system libraries and settings. Microservices is an architectural style that structures an application as a collection of small, loosely coupled services.

In the context of IoT, using containers and microservices can greatly simplify the process of developing, testing and deploying IoT applications. Moreover, they can enhance the scalability and flexibility of IoT systems, making it easier to adapt to changing requirements and scale as the number of connected devices grows.

Challenges of Implementing DevOps for IoT and How to Overcome Them

Dealing with Device and Platform Diversity

One of the main challenges in implementing DevOps for IoT is dealing with the diversity of devices and platforms. IoT devices can range from simple sensors to complex machines, each with its own capabilities, requirements and operating systems. Moreover, these devices may communicate using different protocols and have different connectivity requirements.

The key to addressing this diversity is to adopt a platform-agnostic approach. This involves developing software that can run on any device, regardless of its hardware or operating system. Containerization can help achieve this by providing a consistent and reproducible environment for running applications.

Ensuring IoT Security and Privacy

Security and privacy are significant concerns in IoT, and they can be particularly challenging in a DevOps context. The increased frequency of updates and changes that come with the DevOps approach can potentially introduce new vulnerabilities. Moreover, the interconnected nature of IoT devices means that a security breach can have far-reaching consequences.

To address these challenges, it’s essential to integrate security practices into the DevOps process. This is often referred to as DevSecOps. This approach involves continuously monitoring and testing for security vulnerabilities throughout the development process rather than treating security as an afterthought.

Managing IoT Data Volume and Complexity

The sheer volume and complexity of data generated by IoT devices can also pose a challenge in a DevOps context. Traditional data management and analysis tools may not be able to handle this volume and complexity.

To tackle this, it’s important to utilize modern data management solutions that can handle large volumes of data. Additionally, employing advanced analytics and machine learning techniques can help make sense of complex data and extract valuable insights.

Coordinating IoT System Updates and Maintenance

Finally, coordinating updates and maintenance for a large number of IoT devices can be a significant challenge. This is particularly true in a DevOps context, where updates and changes are frequent.

To address this, it’s crucial to have a robust system for managing and tracking updates. Automated deployment tools can be invaluable in this regard, allowing updates to be pushed out to devices in a controlled and coordinated manner.

Conclusion

In conclusion, implementing DevOps in IoT development can bring significant benefits, including faster development cycles, higher-quality, reliable applications and improved scalability and flexibility. However, it also presents unique challenges that need to be effectively addressed. By understanding and properly implementing the principles and practices of DevOps, we can harness the full potential of IoT technology and drive forward innovation.

July 14, 2023
Is ChatGPT Helping or Hurting Your Developers’ Productivity?

ChatGPT’s ability to translate natural language into working code has sparked tremendous interest in the programming community. Developers are exploring ways to use ChatGPT to reduce redundant tasks, from creating code snippets to analyzing and debugging programs. This also allows them to focus on more complex tasks like data analysis and monitoring with the help of a strong full-stack observability platform. Despite ChatGPT’s impressive capabilities, developers and their employers are split on whether the AI chatbot significantly boosts developer productivity.

We will explore how to use ChatGPT to benefit your developers’ productivity and go over the advantages and disadvantages of ChatGPT to help you make the right decision for your business.

How to use ChatGPT for Better Productivity

ChatGPT has quickly become the most popular intelligence chatbot, with over a million user signups in its first five days and over 100 million in the first two months. Here are a few ways ChatGPT, developed by OpenAI, can help boost productivity in your organization.

Improve Their Code
Sometimes manually written code snippets consume additional CPU resources. Using ChatGPT, developers can optimize their functions for better performance. Additionally, ChatGPT can also write test cases and spot security vulnerabilities in their code, helping developers work faster.

Simplify Documentation
ChatGPT can give a simple explanation for the layman, avoiding technical jargon to a large extent. For instance, a developer at Twitter fed ChatGPT their code and it responded with a pretty detailed explanation. Using ChatGPT to generate the first draft of a company’s internal documentation would make knowledge transfers easier. New hires could quickly get up to speed on the company’s codebase. Further, ChatGPT could also be used for external-facing documents where other developers can learn how to use the company’s APIs and frameworks.

Test and Understand Code
ChatGPT can write automated tests for code that is fed into it. Although many developers consider testing boring and stressful—and sometimes a waste of time—it’s a critical step in ensuring that published applications are bug-free.

With ChatGPT, developers can output reliable, well-tested code much more quickly and easily. ChatGPT can also give high-level and line-by-line explanations of code and even teach developers how to use external modules and APIs in their own code, helping them build much faster.

Debug Code
ChatGPT makes a great debugging tool for developers. It can take in error messages and output information on the likely cause of the error and how to fix it. This is typically a lot more time efficient than browsing forums and blogs for a solution.

The tool can also suggest helpful code alternatives. Many developers claimed that ChatGPT can be much faster and less mentally challenging than the conventional approach of searching the web for a solution.

As the CEO of Replit, Amjad Masad, put it, “ChatGPT could be a good debugging companion; it not only explains the bug but fixes it and explains the fix.”

Code Comprehension
ChatGPT’s terabytes of training data come from various sources, including common crawl and WebText2. This training data makes it easy for ChatGPT to simplify explanations of complex pieces of code for developers. Therefore, developers can save time deciphering any code’s underlying logic and structure.

For instance, Stanford instructor and well-known developer Matt Harrison used ChatGPT to build a simple graphic design tool. He explained, “I’m using ChatGPT to create a GUI program for drawing lines. I haven’t used QT [a development framework] in years, yet I can throw this together really quickly.”

Generate Documentation
Writing code documentation is often considered a mundane task by developers. With ChatGPT, developers can significantly cut down on the amount of time needed for documentation. For instance, they can expedite the creation of troubleshooting guides, tool user manuals and test cases by asking ChatGPT to perform code analysis and generate detailed technical reports. Accelerating this documentation process allows for easier onboarding of new developers and faster product release times.

Writing Tests
ChatGPT can analyze code snippets and automate testing for them, saving developers lots of time and helping them output reliable code. Test coverage is an essential metric for most enterprises, and ChatGPT could help streamline the process.

ChatGPT Disadvantages

While many developers have praised ChatGPT for increasing developer productivity, others have criticized it as unreliable. Let’s understand some key areas where the tool falls behind.

Outputting Buggy or Unusable Code
Developers have complained that the chatbot would sometimes output plausible-looking code that doesn’t work. This might result from misunderstanding the user’s requirements or simply being unable to come up with a correct answer—then faking it.

Programming Q&A forum Stack Overflow even issued a temporary ban on ChatGPT-generated answers. The ban was because the chatbot generated an influx of answers whose average accuracy was pretty low. ChatGPT sometimes uses nonexistent commands and outdated modules in its code, so while they theoretically might make sense, they don’t work in practice.

Writing Outdated Code (and Forgetting Previously Written Code)
Since ChatGPT’s training data cut off in September 2021, the chatbot has no knowledge of the recent developments in programming languages and modules after that. As a result, ChatGPT often needs to use outdated commands, modules and paradigms in its code.

ChatGPT also occasionally forgets the context within which it is working, affecting code output. Some users have reported that ChatGPT sometimes forgets about the task at hand and begins generating incompatible code.

Security Concerns
Security concerns are the biggest drawback to having developers work with ChatGPT. Open AI uses the data users input into ChatGPT to train the underlying AI model.

Research by cybersecurity firm Cyberhaven has shown that 11% of the data employees input into ChatGPT is confidential. This presents a significant risk, as such data can be used in the chatbot’s next responses. Corporations like JPMorgan and Verizon have even gone as far as to block employees’ access to ChatGPT because of these concerns.

Lacks Problem-Solving Skills
A developer’s job is to understand the business problem and develop customized solutions to address it. Although AI/ML tools like ChatGPT help developers code faster, they can’t help in problem-solving or implementing a software application.

Furthermore, when developers become overly reliant on AI-generated code, they’re discouraged from upskilling on new programming frameworks and languages. This can hurt the organization in ways that might not be evident immediately.

To ChatGPT or not to ChatGPT

As Google’s head of developer experience Bret McGowen said, “While AI will transform software development in many ways, human judgment, intuition and creativity will remain essential to building great products.”

While ChatGPT may not be a perfect solution for every aspect of software development, it can be a useful addition when combined with human problem-solving skills. However, companies should be careful while allowing the usage of AI in day-to-day processes. Guidelines educating in-house developers on the fair usage of AI are a must to protect sensitive information and maintain a high-quality control process.

July 13, 2023
Addressing Skills Gaps in Your DevOps Team

Skills gaps refer to the disparity between the skills that employers need and the skills that employees possess. These gaps can occur at an individual level, in a certain organization or across an entire industry or sector. Either way, they hinder productivity, stifle innovation and lead to missed opportunities.

Understanding skills gaps is crucial for anyone looking to advance their career or improve their organization’s performance. By identifying skills gaps, organizations can take steps to close them by helping their teams become more proficient and versatile.

Skills gaps are not always negative. They can also present opportunities for learning and growth. If an organization knows its skills gaps, it can proactively give employees the necessary skills and knowledge. This can result in increased employability and job satisfaction and foster a culture of continuous learning and improvement.

The Importance of Having a Skilled DevOps Team

One area where skills gaps can have a significant impact is DevOps. The DevOps model requires a broad range of technical, operational and soft skills, making it particularly susceptible to gaps.

Having a skilled DevOps team is vital for several reasons. First, it enables faster, more efficient software delivery, which can give your organization a competitive edge. Second, it promotes a culture of collaboration and continuous improvement, leading to higher-quality products and services. Third, it increases job satisfaction among team members who can see the direct impact of their work on the organization’s success.

Unfortunately, skills gaps in DevOps teams are common and can hinder these benefits. They can delay software delivery, create friction between team members and lead to subpar products. Therefore, identifying and addressing these gaps should be a top priority for any organization using the DevOps model.

Common Skills Gaps in DevOps Teams

Technical

When it comes to DevOps, technical skills gaps are often the most noticeable. These can include a lack of proficiency in coding, system administration, cloud computing and other technical areas.

Gaps in this area can have a significant impact on a DevOps team’s performance. They can slow down software delivery, lead to more bugs and errors and make it harder to implement new technologies or practices. Moreover, they can create a divide between team members, with some feeling overwhelmed or underqualified while others feel overburdened.

Operational

Operational skills gaps in a DevOps team can be just as detrimental, if not more so, than technical skills gaps. These can include a lack of understanding of the software development life cycle, project management or the ability to monitor and troubleshoot systems effectively.

Gaps in this area can lead to inefficient processes, missed deadlines and poor-quality products. They can also create tension within the team as members may struggle to understand their roles or how their work contributes to the overall goals of the project.

Soft Skills

Finally, let’s talk about soft skills gaps. These are often overlooked in the tech industry, but they are crucial for a successful DevOps team. Soft skills include communication, teamwork, problem-solving and emotional intelligence, among others.

Gaps in this area can lead to miscommunication, misunderstandings and conflict within the team. They can also hinder collaboration and continuous improvement, which are at the heart of the DevOps philosophy. Therefore, addressing these should be an integral part of any DevOps skills gap strategy.

Identifying Skills Gaps in a DevOps Team

Define What’s Required

The first step is to outline what’s necessary for the DevOps team. These could range from specific technical skills such as coding or system administration to soft skills like problem-solving and communication. A clear understanding of the skills required for each role within the team is crucial. This should not be a static list but rather a dynamic one that evolves with the changing needs of the industry and the organization.

Evaluate Current Skill Levels

Once the required skills have been defined, the next step is to evaluate the current skill levels of the team members. This can be done through performance reviews, one-on-one meetings or skills assessments. It’s important to be objective in this evaluation and focus on the skills rather than the individual. This process will help identify where the team stands in terms of what is necessary and which areas need improvement.

Identify the Gaps

The comparison of the required versus current skills will help identify the gaps. These are the areas where the team’s skills do not meet the requirements. Identifying these gaps is not an indictment of the team’s abilities but rather an opportunity for growth and development. It is crucial to communicate this to the team to maintain morale and motivation.

Prioritize the Gaps

Not all skills gaps are created equal. Some may have a more significant impact on the team’s performance and the organization’s goals. Therefore, it’s important to prioritize the gaps. Consider factors such as the impact of the gap on the team’s productivity, the complexity of the skill and the resources required to address the gap.

Create a Skills Matrix

A skills matrix is a visual tool that maps out the team’s skills against the requirements. It provides a clear picture of where the skills gaps lie and can help in planning how to address them. A well-designed matrix can also identify hidden talents within the team and highlight areas for cross-training.

Four Strategies to Address the Skills Gaps in Your DevOps Team

1. Internal Training and Development Programs

One of the most effective ways to address skills gaps is through internal training and development programs. These can be tailored to the specific needs of the team and can provide in-depth knowledge and hands-on experience. They also provide an opportunity for team members to learn from each other and strengthen their collaboration skills.

2. Hiring and Onboarding Skilled Professionals

While training existing team members is important, sometimes the skill gaps may be too large or the need for the skill too urgent. In such cases, hiring new team members with the required skills can be a viable option. A well-structured onboarding program can help these new members integrate into the team and share their skills.

3. Leveraging External Training Resources and Certifications

External training resources and certifications can also be an effective way to address skills gaps. These can provide industry-recognized validation and can often be tailored to the specific needs of the team. In addition, they offer an opportunity for team members to expand their network and learn from industry experts.

4. Cultivating a Learning Culture within the Team

Finally, this is not a one-time task but an ongoing process. Cultivating a learning culture within the team can help in this regard. Encourage continuous learning and make resources available for team members to upgrade their skills. Recognize and reward efforts to learn and grow. This will not only help address the current gaps but also prepare the team for future challenges.

Conclusion

In conclusion, skills gaps in a DevOps team can be a challenge but also an opportunity. By identifying and addressing these gaps, we can ensure that our team is equipped to deliver high-quality results. As professionals, it’s our responsibility to lead this process and create a culture that values continuous learning and skill development.

July 10, 2023
Cloud Orchestration and its Impact on DevOps Teams
Cloud orchestration refers to the coordination and management of various interconnected cloud services and resources to automate and streamline complex IT processes. In simple terms, it is the process of integrating, synchronizing, and automating various cloud-based components to create a seamless and unified workflow.

Cloud orchestration aims to optimize the deployment, management, and use of resources in a cloud environment, improving efficiency, reducing costs, and ensuring that cloud services are delivered in a timely and effective manner. It is related to the concept of CloudOps (cloud operations), which uses cloud orchestration concepts to allow more efficient management of cloud resources. Cloud orchestration is a critical aspect of modern IT infrastructure management, as it enables organizations to manage and control their cloud resources more effectively.

Difference between Cloud Orchestration and Cloud Automation

Though the terms cloud orchestration and cloud automation are often used interchangeably, they have distinct differences.

Cloud automation refers to the process of automating individual tasks and processes within the cloud environment. It involves the use of scripts, templates and other tools to automate repetitive tasks, such as provisioning resources, deploying applications and configuring environments. The primary goal of cloud automation is to reduce the time and effort needed to manage cloud resources.

Cloud orchestration is a broader concept that encompasses not only the automation of tasks but also the coordination and management of the entire cloud ecosystem. It involves the integration of various cloud services, tools and resources to create a unified, streamlined workflow. Cloud orchestration ensures that all components in the cloud environment work together seamlessly, automating complex processes and managing dependencies between tasks.

In essence, cloud automation is a subset of cloud orchestration. While cloud automation focuses on automating individual tasks, cloud orchestration takes a more holistic approach, orchestrating multiple tasks and resources to achieve a higher level of automation and efficiency.

How Cloud Orchestration Enhances DevOps Practices

Increased Efficiency and Speed

One of the main benefits of cloud orchestration in a DevOps context is the ability to increase efficiency and speed in the software development and deployment process. By automating and streamlining various tasks, cloud orchestration eliminates manual processes, reduces the potential for human error and accelerates the overall development cycle.

Improved Collaboration and Communication

Cloud orchestration fosters better collaboration and communication between development and operations teams by providing a unified platform for managing cloud resources. This shared platform enables teams to work together more effectively, breaking down traditional silos and ensuring that all stakeholders have visibility into the development and deployment process.

Enhanced Infrastructure and Application Management

Cloud orchestration simplifies the management of complex cloud infrastructure by providing a single, unified platform for cloud optimization—improving the management of resources, applications and services in the cloud.

This centralized management approach enables organizations to more easily monitor, control and optimize their cloud environment, ensuring that resources are used effectively and that applications are running smoothly. With cloud orchestration, organizations can more easily scale their infrastructure to meet changing demands, ensuring that they have the resources they need when they need them.

Improved Monitoring and Troubleshooting

Cloud orchestration provides improved monitoring and troubleshooting capabilities, enabling organizations to more effectively identify and address issues in their cloud environment. With comprehensive monitoring tools and dashboards, teams can gain insight into the performance of their applications, identify bottlenecks and pinpoint potential issues before they become critical.

Challenges of Integrating Cloud Orchestration With DevOps

Despite the numerous benefits of cloud orchestration, integrating it into DevOps practices can present several challenges. Some of the common challenges include:
- Complexity: Cloud orchestration involves the integration of various cloud services, tools and resources, which can be complex and time-consuming. Organizations may struggle with complexity, particularly if they lack the necessary expertise or resources to effectively implement and manage the solution.
- Resistance to change: Implementing cloud orchestration requires a change in mindset and approach to software development and deployment. As your organization migrates more systems to the cloud, some team members may be resistant to adopting new processes and tools, making it difficult to fully embrace and realize the benefits of cloud orchestration.
- Security concerns: As with any cloud-based solution, security is a critical concern when integrating cloud orchestration into DevOps practices. Organizations must ensure that their solution is secure and that they have the necessary policies and procedures in place to protect their sensitive data and resources.
- Cost and resource constraints: Implementing and managing a Cloud Orchestration solution can be resource-intensive, requiring significant investment in infrastructure, tools, and personnel. Organizations must carefully consider the costs and resources required to implement cloud orchestration and determine whether the benefits outweigh the investment.
Best Practices to Integrate Cloud Orchestration Into DevOps

To successfully integrate cloud orchestration into DevOps practices and overcome the challenges mentioned above, organizations should consider the following best practices:
- Assess your organization’s readiness: Before implementation, evaluate your organization’s readiness for change. This includes assessing your existing infrastructure, processes and tools, as well as your team’s willingness and ability to adopt new practices and technologies.
- Develop a clear strategy: Develop a clear strategy for integrating cloud orchestration into your DevOps practices. This should include defining your goals and objectives, identifying the tools and resources you will need and establishing a timeline for implementation.
- Choose the right tools and technologies: Select the appropriate tools and technologies that best meet your organization’s needs and requirements. Consider factors such as ease of integration, scalability, security and cost when evaluating potential solutions.
- Train and educate your team: Ensure that your team has the necessary knowledge and skills to effectively implement and manage cloud orchestration. This may involve providing training and education on new tools, processes and best practices, as well as fostering a culture of continuous learning and improvement.
- Monitor and measure success: Continuously monitor and measure the success of your implementation. This includes tracking key performance indicators (KPIs), such as deployment speed, resource utilization and application performance, to ensure that your solution is delivering the desired results.
Conclusion

Cloud orchestration is an essential tool for organizations looking to streamline and optimize their IT infrastructure, particularly in the context of DevOps practices. By automating and coordinating complex cloud processes, organizations can improve efficiency, speed, collaboration and overall performance.

However, successfully integrating with DevOps can be challenging, requiring a clear strategy, the right tools and technologies and a commitment to continuous improvement. By following the best practices outlined in this guide, organizations can overcome these challenges and fully realize the benefits.
July 6, 2023
Why Your Data Lake Strategy is Broken for Observability

Someone once said, “Life is better at the lake.” That may be true in the physical world, but when it comes to using data lakes for observability, that’s not typically the case.

Just about every organization in every industry has a digital presence, whether it’s a website, an app or another digital service. This makes observability a universal need; it helps teams monitor software performance, optimize performance, detect potential security issues and quickly monitor for bugs if and when they occur. These responsibilities typically fall on engineering teams and require as close to real-time response as possible.

Some organizations also have business intelligence (BI) needs, aimed at generating insights from multiple data sources for business decision-making. These responsibilities typically fall on business decision-makers; they are not real-time decisions, but rather made by looking back over long time periods.

All-In on a Data Lake Strategy

Companies that go all-in on a data lake strategy may ignore the needs of the engineering teams working on the real-time mission-critical aspects of the business and force all of the company’s data, queries, dashboards, etc. into a data lake solution. That doesn’t actually meet the needs of the engineering teams. The result? Engineering teams use unsanctioned solutions, fracturing the company’s data strategy from the ground up.

By using a standards-based observability solution built on open source InfluxDB 3.0, companies can give their engineers the observability tools they need while preserving their data lake strategy.

Here are three main challenges that teams come across when using data lakes for time-series, and how to solve the problem with InfluxDB 3.0.

Problem One: Inefficient and Slow Ingest
Data sources generate time-series data at an incredibly high rate, easily producing hundreds or thousands of data points every second; some sources even do so as quickly as every nanosecond. Data lakes can store massive volumes of data, but they aren’t optimized to quickly ingest and make it readable on a millisecond time scale.

Data lake ingest jobs can be measured in minutes or even hours, which is far too late for any meaningful interventions that can prevent catastrophes such as downtime. Customers will notice the outage before the data is even ingested into the data lake, much less acted upon.

Problem Two: Time-Series Life Cycle
Full-fidelity data is necessary for real-time analytics and observability, but the value of the full-fidelity data fades over time. Storing the full-fidelity data in a data lake may be convenient, but it is costly and not necessary for typical BI purposes.

InfluxDB is designed for the time-series data life cycle. This life cycle entails:

1. The leading edge of data. That is, data written in the last two hours or so. This is the data that is most likely to be queried, so the full fidelity data is kept in memory ready to query instantly.
2. Compacted data. That is, data older than the leading edge that is still likely to be queried and requires full fidelity is compacted off to disk in object store, which still permits fast querying but does not incur the costs of storing data in memory.
3. Retention policies. That is, data that has lost its value in its full fidelity form. For example, for observability data, data older than a few weeks can be aggregated into a much smaller data footprint. Then, this aggregated data is ingested into the company’s data lake while the full fidelity data is automatically dropped from InfluxDB 3.0.

Without such a system, it is necessary to store the full fidelity data in the data lake and incur those massive costs without a reasonable way to manage the life cycle of the data. With such a system, the correct amount of observability data can be ingested into your data lake to support your BI needs.

Problem Three: Slower Query Performance
Engineers can experience a high degree of frustration when they are troubleshooting an issue in real-time, but their dashboards won’t load or their queries won’t return in a reasonable amount of time. Data lakes simply are not designed to produce fast results for observability, and so query performance is a primary complaint of engineers trying to use a data lake for observability. Waiting minutes for a query to return when you are running a BI report may be annoying, but it could spell extended downtime for an engineering team.

Fortunately, InfluxDB is built on the Apache Arrow standards, which means that it can be directly queried by your engineering team. Via protocols such as Delta Sharing and Iceberg, anyone can query into InfluxDB 3.0 from their data lake and join the real-time observability data stored in InfluxDB 3.0 with the data lake data. This is akin to having your cake and eating it too.

The Bottom Line

If you are trying to use a data lake strategy to fulfill your engineering team’s observability needs, you are going to frustrate those teams because the data lake is not designed for data ingestion, the data life cycle, or the query needs of those teams. This will cause those teams to create rogue solutions that are inconsistent with your company’s data lake strategy.

By providing those teams with open source InfluxDB 3.0, engineering teams will have a first-class observability platform that is designed to interoperate sensibly with your company’s data lake. This enables the critical mission of your observability platform while maintaining your overall data lake strategy for your BI needs.

June 30, 2023