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Category: Enterprise DevOps

No, You Don’t Want to be a Prompt Engineer

Prompt engineering became a thing in 2023 and will stop being a thing in 2024.

We’re already seeing signs of its decline, but let me set the stage for those only vaguely familiar.

A prompt engineer is someone who knows how to get an AI (Normally a chatbot, but honestly, any of the raft of generative AI solutions out there for art, literature, code generation, etc.,) to do what is necessary. While at first, no one was really thinking about this, it quickly became clear that getting a quality result required some massaging in the same sense that getting a quality result from a search engine requires a specific ordering of keywords and limiting of possible results. So, as time went on, those who were better at it started being referred to as ‘prompt engineers.’ I am not certain of the genesis of the phrase, but it honestly doesn’t matter for this discussion. The name became a title, and it got popular; there were some crazy claims about exorbitant salaries for a wordsmith that made it attractive to many.

AI Tools

For some of my work, we use subcontractors to do technical research. Normally, we use technical people and have them turn out results we know will be topically close, even if they didn’t understand the specific technology when they started researching for us. On occasion, we have tried people with less technical backgrounds, and the results are definitively lower quality. With the advent of decent AI tools, we have done some research on using them for this work and have discovered that they allowed less technical people to generate roughly the same quality of results as the more skilled folks.

What this approach doesn’t do is raise either of these researcher types to the level of being able to do something with the results or understand the market well enough to determine results. And we see this across the board. In our development/scripting work, we use generative AI to help, but it doesn’t write finished code complete with the context of our tools/environment. We do that afterward, along with validating the code it does produce. My other business is in publishing and uses a lot of art, so we have contacts with a fair number of artists. Our art director uses generative AI for inspiration but not creation. She asked the other artists we use to follow that example at least until the courts work out IP law about generative AI, and across the board they came to the same conclusion. Some artists are using it for personal/learning purposes, but most AI-generated art is not being generated by professional artists. That has the usual implications for professional artists, but they’re used to being under-appreciated and being underpaid for their work … whether it’s an AI depressing costs versus an amateur doesn’t matter much in context.

Learn it and Use It

Prompt engineering will steadily become less necessary due to simple forces. Just like people learned the ins and outs of search engine prompts, they will learn to do so with AI in their space. As the AI gets more training/specialization, it will get easier to use, and at the same time, it will get more accurate. So, my advice is the same as with all automation tools. Learn it, see how it fits into your organization and use it to your advantage. But it’s not a career; it’s an automation tool. As time goes on, it will be able to do more and more, and maybe one day we’ll be able to say, “Hey Google, write me a CRM that works with our billing system.” But for the foreseeable future, focus on coding and use it to do some of the grunt work.

The only caveat I have also offered before. Go out of your way to hire and train good entry-level people. The weakness of AI doing a lot of the grunt work is that the “learning period” is shorter, so you do need to consciously say, “What is necessary to debug and/or manage our systems?” and make certain your entry-level people get that – because the day-to-day that they have traditionally trained in is far smaller.

And use it to create and maintain even better systems in the new year! I am stoked about the possibilities it opens up, and you should be, too. If I don’t have to write and debug that for loop, just generate, review and test, I’m thrilled. We should all be happy to use that time on more productive things than entry-level code.

January 17, 2024
Running MongoDB on AWS: A Practical Guide
MongoDB is a source-available document-oriented database program. MongoDB is a a NoSQL database solution that uses JSON-like documents with optional schemas. Many organizations are opting to run MongoDB in the Amazon Web Services (AWS) cloud to gain improved scalability and reliability for their MongoDB deployment.

AWS offers a range of services that can be integrated with MongoDB, including computing power and multiple storage options. Running MongoDB on AWS can be done in a few different ways:
- You can manually install and manage MongoDB on an AWS instance.
- You can use MongoDB Atlas, a fully-managed database-as-a-service from the makers of MongoDB, that automatically takes care of your database operations.
In this article, we will cover the benefits of running MongoDB on AWS and show how to get started with MongoDB Atlas on AWS.

Benefits of Running MongoDB on AWS

Infrastructure Flexibility

With MongoDB on AWS, you can choose the instance that best fits your workload. For example, if you need more computing power, you can select a compute-optimized instance. If your workload requires more memory, you can opt for a memory-optimized instance. This flexibility extends to storage as well, with AWS offering both SSD and HDD storage options for your MongoDB database.

Scalability

Whether you’re dealing with a small application or a large enterprise-level operation, AWS allows you to quickly scale up or down to handle changes in requirements or spikes in popularity. AWS’s auto-scaling feature, combined with MongoDB’s sharding capabilities, provides you with a database solution that can grow with your business.

Integrated Security

Running MongoDB on AWS also offers robust security measures. AWS provides various security features, such as AWS Identity and Access Management (IAM), which allows you to set user permissions and policies. This means that you can have granular control over who can access your MongoDB databases and what actions they can perform.

Backup and Disaster Recovery

Finally, the combination of MongoDB and AWS provides robust options for backup and disaster recovery. AWS offers Amazon S3 for object storage, which is known for its durability and is well-suited to NoSQL workloads. You can also use AWS Snapshots to create backups of your MongoDB databases. In the event of a disaster, you can quickly launch more resources in AWS to ensure business continuity.

What is MongoDB Atlas?

MongoDB Atlas is a cloud-based, fully managed database service offered by MongoDB Inc. It provides an integrated suite of advanced features such as automated backups, monitoring, scaling, and security to run MongoDB in the cloud.

MongoDB Atlas handles the operational aspects of running MongoDB, eliminating the need for manual interventions. Some primary characteristics of MongoDB Atlas include:
- Managed Service: MongoDB Atlas offloads the tasks of installation, configuration, maintenance, and scaling from the user, making it a go-to option for those who want to focus more on database operations and less on administration.
- Platform-Agnostic: While MongoDB Atlas operates seamlessly on AWS, it is also available on other cloud platforms, including Google Cloud Platform (GCP) and Microsoft Azure. Users can choose their preferred cloud provider and region.
- Security: MongoDB Atlas comes with built-in security measures, including end-to-end encryption, VPC peering, and enterprise-grade authentication mechanisms.
- Automated Backups: Atlas provides continuous backups with point-in-time recovery. This means you can restore your data to any second in the past without significant downtime.
- Performance Insights: MongoDB Atlas offers real-time monitoring and alerts. Users can gain insights into queries, resource utilization, and other metrics directly from the Atlas dashboard.
Migrating MongoDB to Atlas on AWS

Here are the high-level steps involved in migrating a self-hosted MongoDB database to MongoDB Atlas on AWS Cloud. For more details, see Amazon’s prescriptive guidance.

Step One: Discovery and Assessment

The first phase involves understanding your current MongoDB setup and preparing for the migration. Begin by evaluating your existing MongoDB environment, including the database size, configuration, and performance metrics. This assessment will help you determine the right size and configuration for your MongoDB Atlas cluster on AWS. It’s also crucial to analyze your application’s dependencies on the database to understand how the migration will impact your application.

During this phase, you should also identify any potential challenges or limitations that might arise during migration. These could include compatibility issues, data integrity concerns, or performance implications. By addressing these issues upfront, you can mitigate risks and plan for a smoother migration.

Step Two: Configure Security and Compliance

Security and compliance are paramount in any migration process. Start by configuring the network settings in MongoDB Atlas to ensure secure connectivity. This typically involves setting up VPC peering or AWS Direct Connect between your AWS environment and MongoDB Atlas.

Next, focus on configuring the security features in MongoDB Atlas. These include setting up encryption at rest and in transit, identity and access management, and auditing and monitoring features. Ensure that these settings align with your organization’s security policies and compliance requirements.

It’s also vital to understand the shared responsibility model in AWS and MongoDB Atlas. While MongoDB Atlas manages the security of the cloud, you are responsible for security in the cloud, which includes securing your data and controlling access.

Step Three: Migrate Data

With the preparations complete, you can start migrating your data to MongoDB Atlas. MongoDB Atlas offers a live migration service, which allows you to migrate your data with minimal downtime. To use this service, you will need to provide details about your source MongoDB environment and initiate the migration through the MongoDB Atlas interface.

Monitor the migration process closely to ensure that the data is transferred accurately and efficiently. It’s advisable to perform validation checks during and after the migration to ensure data integrity. Also, be prepared to address any issues that arise during this phase promptly.

Step Four: Configure Operational Integration

Once your data is migrated, the next step is to integrate MongoDB Atlas into your operational processes. This involves updating your application configuration to point to the new MongoDB Atlas cluster. You will need to update your application’s database connection strings and test the application thoroughly to ensure it is interacting correctly with MongoDB Atlas.

Additionally, configure monitoring and alerting in MongoDB Atlas to keep track of your database’s performance and health. MongoDB Atlas provides various tools and metrics that can help you monitor your database effectively.

Finally, consider implementing any necessary changes to your backup and recovery procedures. MongoDB Atlas offers automated backup solutions, but you should align these with your data protection policies and test your backup and recovery process to ensure it meets your requirements.

Conclusion

In conclusion, running MongoDB on AWS offers a combination of flexibility, scalability, security and robust backup and disaster recovery options. By leveraging AWS’s diverse infrastructure, you can tailor your MongoDB environment to meet specific workload demands, be it compute, memory, or storage-centric requirements.

The migration process to MongoDB Atlas on AWS involves discovery and assessment, configuring security and compliance, data migration and operational integration. By following this process, organizations can effectively migrate and optimize their MongoDB deployments in the AWS Cloud, reaping the benefits of a fully managed, scalable and secure database environment.
January 12, 2024
AI a Key Driver Behind HPE’s $14 Billion Deal for Juniper

Hewlett Packard Enterprise is looking to become a more significant player in the networking space through its planned $14 billion acquisition of Juniper, a deal that it hopes will make it a more formidable rival to longtime market leader Cisco Systems.

The deal, announced Tuesday after the markets closed, is a big deal in the early days of the new year for a networking industry that has become central in an IT sector that is becoming more distributed and more cloud-native.

During a virtual briefing with analysts and journalists this morning, HPE CEO Antonio Neri described an HPE centered around its networking business that has AI capabilities and its GreenLake edge-to-cloud platform of IT services at its foundation.

“HPE will be a new company where networking will be the core foundation of everything we do,” Neri said. “We’re going to accelerate what we call an AI-driven agenda, and that will allow us to capture this massive inflection point.”

Even when the deal closes – which is expected to happen later this year or in early 2025 – HPE will still likely be in third place in the global networking space behind Cisco and Huawei, but will have a stronger portfolio that will not only include greater AI capabilities but also a stronger presence in both the enterprise and telecom spaces.

Once it closes, Juniper CEO Rami Rahim will lead the combined HPE networking business and report to Neri.

Juniper’s Mist AI is at the Center

Unsurprisingly, AI was a key component of the deal. In a research note, Will Townsend and Patrick Moorhead, analysts with Moor Insights and Strategy, wrote that their thinking after initial news reports about a possible deal circulated was that HPE likely was looking for a “strong AI anchor” for its portfolio of hardware, software, and GreenLake IT consumption services.

“AI is hot, ignited by the attention being directed toward generative AI, the underlying large language models, and many promising use cases,” Townsend and Moorhead wrote. “One could argue that beyond the AIOps capability found in the HPE Aruba Networking portfolio today, HPE needs further AI depth to remain competitive and continue to grow its top-line revenue and profitability. Juniper could deliver on that front.”

Rahim called AI “the biggest inflection since the dawn of the internet itself” and added that the combination of HPE and Juniper “will be able to bring the depth and the breadth of the portfolios necessary to capture the full market opportunity that AI presents in front of us.”

AI in networking is a strength for Juniper, which in 2019 bought Mist Systems and its AI technologies, including the Mavis virtual network assistant, which the analyst wrote serves “as the tip of the spear for Juniper’s reinvigorated efforts within the enterprise for WLAN, LAN, WAN, and SD-WAN solutions.”

“By all measures, the Mist acquisition has been a success, with Juniper growing its enterprise install base at a faster rate than its service provider business over the last 12 to 18 months,” Townsend and Moorhead wrote.

AI and Networking

AI will play an increasingly important role in networking going forward, from dynamically adjusting bandwidth and self-correcting in the network for maximum uptime to quickly finding root causes for problems and deploying virtual network assistants.

In a blog post last month, Liz Centoni, executive vice president, chief strategy officer, and general manager for applications at Cisco, wrote that according to the company’s AI Readiness Index, 95% of survey respondents have an AI strategy in place or being developed but that only 14% are ready to integrate it into their business.

That will change in 2024, as AI becomes central in everything from networks to infrastructure to applications.

“[Generative AI] will offer APIs, interfaces, and services to access, analyze, and visualize data and insights, becoming pervasive across areas such as project management, software quality and testing, compliance assessments, and recruitment efforts,” Centoni wrote. “As a result, observability for AI will grow.”

With Juniper, HPE is looking to grow its AI capabilities. According to Juniper’s Rahim, his company has spent the past seven years building out its AIOps capabilities, with marked results, including seeing revenue from products that use AI growing almost 100% year-over-year for the last two quarters.

“And we’re starting to see early momentum from our investments in enabling and automating AI data centers, including those handling emerging AI training and inference models,” he wrote in a blog post. “This combination [with HPE] will supercharge our offerings in AI-native networking. Together, we will accelerate innovation at every layer: compute, storage and networking; silicon, systems and software; campus and branch, data center and the wide area network.”

GreenLake is a Foundational Part

Neri, in 2019, declared that within three years, HPE would be offering its entire portfolio as a service through the GreenLake platform, which has expanded significantly over the past several years. During the briefing, Neri and Rahim focused on GreenLake as the base for building out a layered AI-driven architecture at a massive scale and what the HPE CEO called the most complete networking portfolio.

“What we’re doing today with this proposed acquisition is to create a new core,” he said. “The core is the network fabric by which we deliver hybrid cloud and AI-native services with the rest of the portfolio we have. We deliver all this unified experience to the same platform called HPE Greenlake, which is a true edge-to-cloud platform. … The core business inside GreenLake – by which we deliver the entire set of services, whether it’s cloud-native or AI-native – is networking.”

The Juniper deal will give HPE the ability to deliver a better experience to users, which is important given that the most difficult part of the architecture is the network, Neri said, adding, “That’s why we believe this changes dramatically who we are and it will increase our relevancy with customers and partners to ways we couldn’t imagine before.”

There’s More to the Deal

Beyond AI, acquiring Juniper will enable HPE to play a greater role in a networking space that has long been dominated by Cisco. According to reports, Neri told HPE employees in a letter that the deal would enable his company to essentially double the size of its networking business and expand significantly in such areas as the enterprise, service providers, cloud environments and – again – AI.

HPE has been growing its networking business for a number of years, in large part through acquisitions, including such companies as ConteXtream and Plexxi. It also acquired the high-performance Slingshot interconnect through its 2019 acquisition of supercomputer maker Cray. In the data center, its most significant purchases were Aruba Networks in 2015 – which has become the core of its networking business – and Silver Peak five years later to bolster its software-defined networking (SDN) chops.

That said, HPE’s networking revenue is about $3.7 billion – a fraction of Cisco’s $24.1 billion – according to Zeus Kerravala, principal analyst with ZK Research. Bringing Juniper – and its 11,0000 employees – into the fold will bolster that to about $5.8 billion, making HPE the number-three vendor in terms of revenue. Still a long way off from Cisco, but HPE would become a stronger rival with more capabilities.

Moor’s Townsend and Moorhead wrote that while there are significant benefits in the merger, there also is a large amount of product overlap that will need to be addressed.

“Serious roadmap rationalization will need to occur, and without a doubt, some solutions will be sunsetted,” they wrote. “There will also likely be a reduction in force as enterprise and service provider networking product teams and internal channel sales and marketing personnel are blended.”

January 10, 2024
Broadcom Survey Surfaces Raft of IT Automation Challenges

A survey of 501 IT leaders published today by Broadcom suggested that islands of automation that operate independently of each other are major contributing factors to organizations failing to meet service level agreements (SLAs).

The survey found that 98% of respondents encountered at least one automation issue, with 61% noting that SLA issues are a monthly occurrence. More than a third (35%) said these issues occur weekly. A full 88% indicated SLA breaches directly impacted customers in a significant way.

The survey also found nearly three-quarters (74%) lacked visibility across their data center and cloud environments, with an equal number lacking end-to-end visibility of their automated business processes. Only 28% possessed tools that could predict automation issues, and more than two-thirds (68%) were unable to model or predict whether a new or updated automation job would actually meet SLAs. A full 69% said they are unable to properly size automation workload resources, with 61% having no way to simulate automation workloads before implementation, the survey found.

Only slightly more than half (55%) of respondents, however, could provide SLAs and key metrics in a format that is appropriate for business stakeholders.

Aline Gerew, head of automation for the Agile Operations Division at Broadcom, said that while it’s routine for IT teams to leverage a range of automation tools and platforms to manage IT, it’s clear they are challenged to manage those platforms in a comprehensive fashion. As a result, whenever there is an issue with one tool or platform, it creates a bottleneck that impacts customer experience in a way that ultimately adversely impacts revenue, she noted.

In fact, many IT teams are now starting to identify automation architects within their IT teams to enable DevOps teams in a more holistic manner, added Gerew.

Overall, the survey found that 80% of respondents are using three or more automation platforms, with just over a quarter (27%) using six or more.

Nearly three quarters (74%) are also relying on three or more observability solutions, with just under a quarter (24%) using six or more. Just under three-quarters of respondents noted they are consequently subjected to alert storms that ultimately serve to make it more challenging to resolve issues. More than half (56%) of respondents are blind to the downstream effects of an initial alert and, as a result, do not inform response teams of which systems, jobs, applications and business processes are affected.

In some ways, IT automation has become too much of a good thing. Every provider of an IT platform makes automation tools available, but as they are invoked, each one creates yet another island that IT teams are expected to manage in isolation. However, all it takes for an SLA not to be met is for an issue to arise that IT teams can’t easily determine the root cause of because one or more automation platforms obscure the underlying issue in the absence of any standardization, noted Gerew.

Given the complexity of modern IT environments, automation is now a requirement. The issue, as always, is finding a way to apply it all in the most transparent way possible.

January 9, 2024
Best of 2023: ‘Scrum == Cancer’ ¦ Plus: Linux 6.5 Ships

As we close out 2023, we at devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: Scrum sucks, sources say; and here comes the Linux 6.5 kernel. (more…)

December 29, 2023
Best of 2023: Microservices Sucks — Amazon Goes Back to Basics

As we close out 2023, we at devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: Amazon Prime Video has ditched its use of microservices-cum-serverless, reverting to a traditional, monolithic architecture. It vastly improved the workload’s cost and scalability. (more…)

December 28, 2023
DevOps 2024: DevOps Isn’t Dead; Long Live DevOps

Well, it has been quite the year in general and quite the year as it relates to DevOps. As I sit here on the cusp of the 10th year of publishing devopsy.kinsta.cloud, I look back and recognize how much DevOps has grown, evolved, matured and shifted over time. As was the case 10 years ago, there are those who say DevOps does not work or insist that DevOps is dead. My response:

“They stab it with their steely knives
But they just can’t kill the beast.”
~ Eagles, Hotel California

No, it seems you can’t kill DevOps. Much like Obi-Wan Kenobi being struck down by Darth Vader, DevOps, it seems, is more powerful than ever. It has seeped into the very fabric of IT.

While cloud native has become the dominant form of computing, the fact is you really would not have cloud-native stacks without DevOps fundamentals enabling them. As cloud-native continues to grow, so does DevOps.

One of the interesting things about DevOps is the fact that it really has become an enterprise toolset. While initially scaling DevOps across an organization was challenging (and still is for many), it has become much more common to see large enterprises—or ‘horses,’ as Gene Kim would call them—adopting DevOps methodologies across significant parts of their IT teams.

Speaking of Gene Kim, some have pointed to his recent announcement that he’s changing the name of his DevOps Enterprise Summit (DOES) to Enterprise Technology Leadership Summit as evidence of DevOps’ demise. We will be interviewing Gene on Techstrong.TV the first week in January 2024 and will address this with him, so stay tuned. But without giving out too many spoilers, Gene felt that DOES had expanded beyond just DevOps to encompass a general technology discussion (hey, AI will be in there for sure) aimed at technology leaders (C-suite). But rest assured that Gene Kim does not think DevOps is dead.

Looking ahead to 2024, I see DevOps making broad inroads into the SMB and mid-enterprise market. The DevOps toolset and methodology have matured to the point where it can be adopted and used successfully in IT environments not as resource-rich or process-driven as large enterprises generally are.

Another continuing story in DevOps is DevSecOps. Security and/or quality continues to be a key driver for DevOps pipelines, with the software supply chain and SBOM security still driving lots of adoption and adaptation.

Did I mention AI? You can’t discuss much today without mentioning it, or so it seems. AI is a catalyst for automation. Generally, in software development and deployment, where there is automation, you guessed it—there is DevOps.

But here is one out of left field for you: ITSM. It seems like IT service management is making a big comeback (some say it never left). Today’s ITSM incorporates many DevOps fundamentals to increase scale and agility. On this front, stay tuned; in partnership with PeopleCert, the folks behind ITIL, Prince 2 and the DevOps Institute, we will roll out a series of virtual events in 2024, tying all three of these key technologies together.

So, there’s lots going on in the DevOps world for 2024. You can count on us here at Techstrong Group to keep you in the know.
On that note, this will close out our coverage for 2023. To all of our readers, watchers, listeners, friends, sponsors and contributors, a Merry Christmas, Happy New Year, and here is to a healthy and prosperous 2024.

December 26, 2023
Hybrid Cloud in 2024: Trends and Predictions

Hybrid cloud environments are becoming increasingly pivotal for DevOps teams, primarily due to their ability to bridge the gap between the rapid deployment of public cloud resources and the security and control of private cloud infrastructure. This blending of environments provides DevOps teams with flexibility and scalability, which is crucial for operating continuous integration and continuous deployment (CI/CD) in large enterprises.

In a hybrid cloud setup, DevOps teams can swiftly deploy and test new applications in a cost-effective and scalable public cloud environment. Once the applications are ready for production, they can be seamlessly transitioned to a private cloud or on-premises environment, where they benefit from enhanced security and control. This allows DevOps teams to optimize their workflows, reduce time to market, and respond more effectively to business needs.

The hybrid cloud also enables a more collaborative and agile working environment. DevOps teams can leverage various tools and platforms available in the public cloud while ensuring that sensitive data and critical applications are securely managed in the private cloud. This both enhances operational efficiency and improves compliance with data governance and regulatory requirements. These benefits will make hybrid cloud an indispensable asset for DevOps teams in 2024 and beyond.

What is a Hybrid Cloud?

Let’s start with a brief definition. A hybrid cloud is a computing environment that combines a public cloud and a private cloud by allowing data and applications to be shared between them. This combination allows businesses to enjoy the best of both worlds, leveraging the scalability and cost efficiencies of public cloud platforms while enjoying the security and control offered by private cloud infrastructure.

This hybrid cloud model provides businesses with more deployment options and greater flexibility to scale computing resources. It also helps in reducing capital expenditure and improving security by allowing sensitive data to be stored on a private cloud. A hybrid cloud strategy enables businesses to allocate resources efficiently, keeping less critical applications on the public cloud, while mission-critical applications run on the private cloud.

Current State of Hybrid Cloud Computing

The hybrid cloud market is in a state of rapid growth and evolution. As businesses increasingly recognize the benefits of a hybrid approach, the adoption of hybrid cloud strategies is on the rise.

In a world where digital transformation is the norm, hybrid cloud computing has emerged as a key enabler. It offers the flexibility needed to adapt quickly to changing business conditions, the scalability to handle sudden increases in workload, and the security to protect sensitive data. These benefits have made hybrid cloud a cornerstone of digital transformation strategies.

However, the journey to hybrid cloud is not without its challenges. Businesses are grappling with issues related to data security, compliance, integration, and management. There are also complexities related to workload portability, interoperability, and the need for skilled resources. Despite these challenges, the adoption of hybrid cloud continues to grow as businesses seek to leverage its benefits to drive innovation and competitive advantage.

Emerging Trends in Hybrid Cloud for 2024

Increased Adoption Across Industries

Many industries, including the banking and finance sector, healthcare, retail, manufacturing, and even the government sector, are seeing increased adoption of hybrid cloud strategies. This trend is driven by the need for greater agility and flexibility, better cost management, and improved security and compliance.

In the healthcare sector, for example, hybrid cloud is enabling the integration of disparate systems, facilitating the sharing of patient data, and supporting telehealth initiatives. In the banking sector, it is helping to manage risk, improve customer experiences, and drive innovation. In retail, hybrid cloud is supporting omnichannel strategies, personalizing customer experiences, and improving supply chain efficiency.

Advancements in Cloud Technology

The hybrid cloud landscape in 2024 will be shaped by advancements in cloud technologies. Technologies like containers and Kubernetes are simplifying the deployment and management of applications, making it easier for businesses to adopt a hybrid cloud strategy. The rise of serverless computing is also contributing to the growth of hybrid cloud by eliminating the need to manage servers and allowing developers to focus on writing code.

Furthermore, advancements in networking technologies are making it easier to connect and integrate public and private clouds. The emergence of 5G is expected to further accelerate the adoption of hybrid cloud by providing the high-speed connectivity needed to support real-time applications.

Integration of AI and Machine Learning

The integration of artificial intelligence (AI) and machine learning (ML) into hybrid cloud environments is a trend that is rapidly gaining momentum. One key aspect of this integration is the ability to use AI for better resource management within the hybrid cloud. AI algorithms can analyze patterns in data usage and application performance, allowing for more efficient allocation of resources across public and private cloud environments.

Furthermore, AI and ML are also facilitating better data management and analytics. Cloud-based services are making AI/ML more accessible to businesses of all sizes. By integrating these technologies, businesses can unlock insights from vast amounts of data stored across different cloud environments.

Predictions for Hybrid Cloud in 2024

Growth in Edge Computing Integration

By 2024, the integration of edge computing with hybrid cloud architectures is expected to grow significantly. Edge computing brings data processing closer to the location where it is needed, optimizing response times and saving bandwidth.

In a hybrid cloud context, this means that organizations can process critical data at the edge, in real-time, while still leveraging the cloud for less time-sensitive tasks. This integration is particularly beneficial for industries like manufacturing and autonomous vehicles, where real-time data processing is crucial. The combination of edge and hybrid cloud computing enables businesses to harness the power of cloud computing while maintaining the speed and efficiency of edge processing.

Improved Security and Compliance Measures

In 2024, we anticipate significant advancements in the security and compliance aspects of hybrid cloud environments. As these environments become more complex, the need for robust security measures that span both public and private clouds is crucial. Enhanced encryption techniques, more sophisticated identity and access management systems, and advanced threat detection tools will become standard.

Additionally, we expect to see the development of more streamlined compliance benchmarks and frameworks designed specifically for hybrid cloud architectures, simplifying the process of adhering to various regulatory standards and ensuring data privacy and protection across multiple platforms.

Innovations in Cloud Management Platforms

The year 2024 is likely to witness substantial innovations in cloud management platforms, driven by the need to manage complex hybrid cloud environments more efficiently. These platforms will offer enhanced capabilities in terms of automation, orchestration, and unified management across multiple cloud services.

The use of AI and machine learning for predictive analysis, resource optimization, and proactive problem resolution will become more prevalent. These platforms will also provide better tools for cost management and analytics, giving businesses a more granular understanding of their cloud usage and enabling more cost-effective decision-making.

Customizable and Flexible Cloud Solutions

As hybrid cloud environments continue to evolve, 2024 will likely see a rise in more customizable and flexible cloud solutions tailored to specific business needs. Providers will offer a range of options that allow businesses to mix and match services from both public and private clouds, creating highly personalized cloud environments.

This customization will extend to aspects like security, performance, and compliance, allowing businesses to fine-tune their cloud infrastructure to their specific requirements. Such flexibility will be particularly beneficial for businesses in niche markets or those with unique operational needs, enabling them to leverage the power of cloud computing without compromising on their specific requirements.

Conclusion

By 2024, hybrid cloud computing will have significantly evolved, becoming a critical component in the digital transformation strategies of various industries. This technology offers a blend of public and private cloud benefits, crucial for DevOps teams in terms of flexibility, scalability, and security. The integration of advanced technologies like AI, machine learning, and edge computing will enhance resource management, data analytics, and real-time data processing. Additionally, the hybrid cloud’s role in ensuring agility, better cost management, and compliance is expected to drive its wider adoption across sectors like healthcare, banking, retail, and government.

The landscape will also see advancements in security and compliance, with sophisticated measures spanning both cloud types and cloud management platforms will evolve to include AI and machine learning for more efficient operations. Customizable and flexible cloud solutions will become prevalent, allowing businesses to tailor their cloud environments according to specific needs. Overall, hybrid cloud in 2024 will be a strategic enabler, equipping businesses with the necessary tools and capabilities to thrive in an increasingly digital world.

December 20, 2023
Shared Responsibility? Yeah.

One of the things that the last decade or two of enhancements has brought about is an increasing reliance on everyone across the organization to be more secure. While users have long since thrown up their hands and looked for ways to minimize the fallout when a vendor they frequent is compromised, enterprises have to lock down their own things. And given the ever-increasing volume and sophistication of attacks, the demands are growing.

Cloud brought us the concept of a ‘shared responsibility‘ model. It is necessary in a world where an org does not control the physical servers and supporting infrastructure that applications ultimately run on. The fact is, the shared responsibility model was really cloud vendors reminding us that we still have to protect our apps, no matter where they’re deployed.

This model and DevOps together have driven us to demand more of developers and DevOps teams with regard to security. It has also driven ops to have to care more, as more is exposed and security demands are up while security staff isn’t.

And I’ll just remind you of the obvious. If you are responsible for security at the organization, you can get help from everyone, but those people’s job and direct responsibility is not security. They’re lending a hand because everyone cares, and that is the current state of most enterprises – DevOps made everyone involved in the SDLC a bit more responsible for all the things that aren’t their jobs (often a very good thing, often … not), but they’re not specially trained for it. That’s what security teams are for. And high-quality development security/policy management tools offer Dev and Ops and everyone in between a bit of guidance to good security practices along the way. I’ve praised putting security feedback right in the IDE half a dozen times here and elsewhere, and as a security-conscious developer, will continue to do so until it is so pervasive that people start to worry I’m losing it by going on about it. That’s a great example of helping everyone be more secure without requiring a ton of security staff hours in training. Developers don’t purposely write insecure code any more than they set out to make buggy spaghetti code. Giving tools to help them make the code more secure is a huge bonus – to them and to the corporate security team. So, if you’re not using them, start. And look into all of the other options to help non-security staff lock down their bit. The number of available security people on the market is perhaps the largest it has ever been (It’s so bad I actually know a security researcher who took months to find a new gig – that is pretty much unheard of or a unique case before recent days), but budgets to grow the security team aren’t there – any more than they are currently there to grow most teams. So tools to help are still imperative because you’re not going to get double the security staff in 2024 unless there’s only one of you … and you threatened to quit if they don’t get you help, which no one recommends as a negotiation tactic, so even if you’re solo, no doubling for you.

Just remember who is responsible for overall infosec. Make use of the specialist and their knowledge in their space by finding tools to help them secure in a manner that matches workflow. Then verify because the organization’s security posture is security’s job.

And keep rocking it. We’ve got so much cool stuff going on at the moment in IT in general that there is plenty of opportunity to improve architectures that are even relatively recent. Check what you have, consider it from a security perspective, improve what you have and keep rolling along.

But first, relax, take a breather, and enjoy the holiday period.

December 20, 2023
KubeCon 2023: Elevating Observability

Speaker 1: This is Techstrong TV.

Alan Shimel: Hey everyone. We’re live back here at KubeCon. I hope you’ve been enjoying our coverage today, Mitchell Ashley, our CTO and a GM of Techstrong Research. And I have been kind of splitting the duties. Just we’re that booked with people. I don’t know, there’s over 10,000 people here. I’m going to guess there’s 12 to 15,000 people.

Alex Burkhardt: Yeah, there’s a lot of people.

Alan Shimel: … At the event. Maybe more I’ll know more. I’ll find out tonight. But anyway, let me introduce you to our next guest. His name is Alex Burkart. Alex is with Observe Inc. Alex, welcome to Techstrong TV. Thanks for being here with us at KubeCon.

Alex Burkhardt: Yeah, thanks Alan. I appreciate you guys taking some time to talk to me today.

Alan Shimel: Absolutely. So let’s start with this. Let’s start with Alex.

Alex Burkhardt: Yeah.

Alan Shimel: What do you do at Observe?

Alex Burkhardt: Yeah, so at Observe I run our technical customer facing team. We call it field engineering. And we work with customers from the first interaction when they’re in the sales cycle to figure out if observes a great solution for them, all the way to success and renewal and helping them realize value from their investment in our partnership.

Alan Shimel: Okay. I mean, so you’re at that role, but let’s tell a little bit about more your personal journey.

Alex Burkhardt: Oh yeah. So I started as a computer scientist actually in the Navy.

Alan Shimel: Really?

Alex Burkhardt: Yeah. I went to the Naval Academy, got a computer science degree, and they said, that’s great, go drive some ships.

Alan Shimel: Such is life in the Navy, what do you expect?

Alex Burkhardt: And then my journey brought me to Silicon Valley after I did some cybersecurity work for the Navy. And what I love more importantly than anything else is helping people find a way to use technology to solve a problem that makes their life better.

Alan Shimel: Excellent. Now, when did you come to Observe?

Alex Burkhardt: I’ve been at Observe for more than three years now. Yeah, I was employee 28. I think if my numbers are right, pre customers, in fact.

Alan Shimel: Really early.

Alex Burkhardt: I think when I started, we signed our first customer.

Alan Shimel: Good for, good for you. Good for them. So we don’t have to assume, I know for a fact there are people out here who don’t know, Observe from, have never observed Observe. Tell them. How would you describe what Observe is and does to our audience?

Alex Burkhardt: That’s a great question, Alan. So at Observe, what we realized is observability is a data problem. So we started by building a data company. What we wanted to figure out is can we bring a solution to market that allows customers to send any data they’d like to correlate it and interact with it in a way that allows them to get more value out of it more quickly. And what we’ve done is we’ve brought a solution to market that unifies the observability tooling into one platform that allows you to send logs, metrics, traces, and any other type of data structure or unstructured that you’d like to keep in your observable universe into one tool, one data store.

Alan Shimel: Love it. And before we jump into news and stuff, for people who want to just say, hey, I’d like to just see what their website is, what’s the URL?

Alex Burkhardt: Yeah, our website is www.observeinc.com.

Alan Shimel: So it’s observeinc.com.

Alex Burkhardt: Observeinc.com. We are Observe Inc technically. So observeinc.com.

Alan Shimel: Makes sense to me. All right, we’ve got all that out of the way, Alex, let’s jump in. Now, you guys recently announced some new product feature releases. Why don’t we jump in right there and see what’s that about?

Alex Burkhardt: Our last release was called Hubble, and Hubble is significant for a couple of reasons. One, naming after the Telescope, one of the things that we want to do is help people elevate their observability. And that came in two specific forms.

Alan Shimel: I get it. I just got to ask, is the next release called Web?

Alex Burkhardt: We have considered other satellites, and in fact, we had Erica on our launch. She was actually a NASA scientist.

Alan Shimel: Very cool. All right.

Alex Burkhardt: One of the things that we know is very important to our customers is if we bring a solution to market and it’s very, very powerful, is it easy to use and are we taking advantage of the latest and greatest technology that’s emerging in the market? On the ease of use side, one of the things that this Hubble launch and we did, we call it a launch included, was the recognition that when we introduced our logs in our metrics and our tracing explorer, we’ve seen an enormous increase in users engaging in the platform. And we supercharge that by adding one additional layer, which is how we leverage GPT or AI in our platform. And that happens in four specific locations. We have a chat bot that allows you to get guidance on how to use the tool, will actually allow the AI to write any of our query language for you if you’d like to do that. We call that copilot.

We also have two cool functionalities in terms of making your data more actionable. You can click on a field and observe and ask Ollie or Ollie GPT specifically to explain what an error means and it will actually return an answer that helps you stop having to copy and paste into Google and do outside investigations. The last thing that’s most interesting is we leverage GPT to allow users to separate out their data and do those hard parsing and Reg X challenges that no one likes writing Reg Xs and anyone that has written them knows that regx101.com is their friend, and we wanted to kind of bring that into the product and make that easy for customers to use.

Alan Shimel: Very cool. Very cool. Now this was released not here at the show, it was released earlier in the month.

Alex Burkhardt: October 4th.

Alan Shimel: What’s been the feedback?

Alex Burkhardt: Well, we’ve more than doubled our monthly active users since we’ve released that.

Alan Shimel: Really?

Alex Burkhardt: Yeah.

Alan Shimel: Since October 4th?

Alex Burkhardt: Technically since late September. But yes.

Alan Shimel: Wow.

Alex Burkhardt: It’s really simple. We listen to our customers and we really care about what they say and how they experience ourselves on our platform. And when we say we’re focusing on ease of use, we mean it.

Alan Shimel: So the entrepreneur in me just has to ask you.

Alex Burkhardt: Okay.

Alan Shimel: What were you waiting for?

Alex Burkhardt: It’s very easy to make very quick, bad decisions. It’s a lot harder to take a data-driven approach to how to build a product better. And when we approach any problem at Observe, and it’s built into our culture as we first of all figure out this is an important problem, how can I measure the impact I can make on this problem? And that’s how we measured usability. Now for AI, we kind of knew that it was going to be something special and it could help. We’re still figuring out exactly how to measure the impact there, but every decision we make at Observe is grounded in what’s best for our customers and how do we help make the market better.

Alan Shimel: So how much of this phenomenal growth in such a short period of time do you think is just attributed to riding the AI wave, if you will?

Alex Burkhardt: That increase in active user growth actually wasn’t tied to the AI release. It was more to the ease of use functionality that we brought. And I think that’s important for any product, but more specifically, one as powerful as ours. When you’re solving big data problems, people get exposed to big data. And one of the things we thought very deeply about is how do we take the experience that a user has to have to get to the outcome they want and narrow that down and give them an obvious path to go through so that they don’t have to deal with all of the problems if they just want to come in and check their logs and see what’s not working. Sometimes it’s the simplest question of, hey, is it easy to do this in the tool? And when we decided ourselves that it wasn’t easy enough, then we started making some really serious changes and that was backed by every single one of our customers agreeing with us back in the active user growth.

Alan Shimel: Sure. That’s fine. That’s phenomenal, man. Alex, I can’t look for those out here. This is impressive. Let’s talk a little bit about KubeCon here now. I mean, it’s only day one of the expo floor open and everything. What are you hearing from the crowd, from the attendees?

Alex Burkhardt: Yeah, that’s a great question. This is my second KubeCon. I was new to it last year when we were in Detroit, and I really love this conference, Alan.

Alan Shimel: Yeah, it’s great. It’s a great community.

Alex Burkhardt: It’s a great community. It’s a great conference. And frankly, when people come up to our booth and want to talk to us, they want to talk about what we do. There’s not a lot of tire kickers. Everyone that’s here seems to be operators, people that are experienced and steepen the problem. And I love whenever someone comes up and talks to us. I think the other thing I’ve seen in this particular venue specifically, the room is packed. It’s crowded, so crowded that I think that we’re going to have to have a bigger footprint next year.

Alan Shimel: Observers or the whole?

Alex Burkhardt: Both.

Alan Shimel: Yeah. Well, the problem is there’s a pretty big convention center. I mean, short of breaking into sort of a two different exhibit halls, which RSA used to do that. Now it’s one big continuous one. It’s hard. The other thing I will tell you, so we do the North American and the European. So in Amsterdam in the spring, they actually said, look, I think it was 12,000 was the number. At 12,000 they stopped selling tickets. They did a waiting list and a lot of people didn’t get to the show. They were on the waiting list. As anti-business as that sounds, I wouldn’t be surprised if they did something similar.

Alex Burkhardt: Yeah, that makes sense. I mean, the quality of conversations and the quality of attendee keep the quality super high. And you asked about some of the conversations we’re having when people come by. It’s a variety of things and a couple of themes that I see. I’m curious if other people that have come on the show have said the same, but people continue to talk to us about saving money. They want to do more with less.

Alan Shimel: That’s the big thing today.

Alex Burkhardt: [inaudible 00:09:23].

And I think they also want to figure out, do we need all of the tools? Is there a way we can both get some hard dollar savings and some soft dollar savings. By compressing some tooling and aligning teams we’re also seeing a lot of push and the DevOps teams are getting more involved in security conversations as well. Which I guess-

Alan Shimel: No, no, that there’s no doubt.

Alex Burkhardt: As a former security practitioner, if you had asked me five years ago if I thought these two markets would converge, I would not have said yes. Now I think it’s inevitable.

Alan Shimel: I think it’s already happened, right? So there’s a lot of convergence going on. There’s security with DevOps into what we call DevSecOps. But there’s also, there was a time, and I’ve been going to these conferences now before Covid, so I don’t know, four or five years. There was a time where cloud native was very developer-centric, not as much centric, but it was very distinct from DevOps. There was DevOps and there was cloud native and then there was security. So you look at a company like ours, right? We have devopsy.kinsta.cloud, cloud native now, security boulevard.

And I always knew there was an inherent overlap between them, but we’ve seen much more than an overlap now. We’ve seen a conversion around this with security being a primary concern of people. And you don’t do cloud native today without doing DevOps.

Alex Burkhardt: Agreed.

Alan Shimel: And everything getting pushed out today, especially Greenfield, not Brownfield stuff. It’s predominantly probably 85% or better cloud native.

Alex Burkhardt: Yeah, I think that’s another thing I’ve seen too as well. Many, many customers are adopting the cloud native terminology, setting goals in those languages and words.

Alan Shimel: Now, multi-threaded, no doubt. Container based, the whole cloud native thing. And now what I think we’re starting to see and we see, look, you’re naming your company’s Observe. It’s not just the architecture and development being cloud native, but the cloud native aspect of the operations of ops. Forget dev, forget dev ops, ops.

Alex Burkhardt: It even touches revenue. So one of the things that we see very often is do the nature of how our platform can process data. We take technical telemetry from a customer’s application. We also take business data. Sometimes customers ask us, how can you Observe, help us give our customers a better experience? Well, one of the ways is we can tell you when a particular customer is hurting, when a customer has a challenge or help you reframe all these very deep technical jargon and things that are happening into the context of how does this impact the customer’s experience? How am I able to get proactive? Is what I’m doing in my infrastructure plan having a net positive impact on my customers, on my users?

And those are questions that previously companies I don’t think they thought they could answer. And when they start working with us, they start seeing a path to that. And I love that. We have some customers even completely away from the DevOps side, that send us shipping data just because they want to know, can I make a support dashboard for my low level support employees so that they can be more efficient with their time and our data? It’s such a cool convergence. I agree with you. I love that word convergence.

Alan Shimel: It really is. Let’s talk a little bit about observability though too. So over the last two years, so there was a KubeCon in LA during around Covid time that was a little dicey. Then there was the one in Europe, then got canceled. Then there was Detroit, then Amsterdam here. We’ve really seen the observability market just explode, right?

Alex Burkhardt: Yeah.

Alan Shimel: Prometheus. What’s the other, I’m drawing a blank. The other big observability open source. OpenTelemetry.

Alex Burkhardt: OpenTelemetry. Yes. Everyone-

Alan Shimel: I think of the second and third biggest projects of the gazillion projects in CNCF now. Right?

Alex Burkhardt: That’s such a good point. I remember when OpenTelemetry was OpenTracing and it was still kind of a fractured project. If you asked us three years ago if we thought that open telemetry would be the way, we weren’t super sure a year into that. So two years ago we were like, yeah, this is the direction. It’s solid.

Alan Shimel: No doubt about it. You look and you can’t see this at home guys because all you’re seeing is here, but there’s a-

Alex Burkhardt: All our beautiful faces.

Alan Shimel: … Big floor out there, but so much of it is based now on this. Really good stuff, really good stuff.

Alex Burkhardt: I think the other thing, observability as a vernacular is kind of emerging even at the higher level in the business. By definition observability is, can I tell something about a system by looking at its outputs? And one of the things as we’re moving closer to cloud native is more and more systems have outputs and that kind of builds on that data problem I mentioned and why we were founded. So I look forward to seeing how this problem space changes in the next year.

Alan Shimel: Oh, I think we’re just, look, when you start, you guys are already doing it with gen AI and stuff. You start adding gen AI to this stuff, we start putting more focus on it. We’re in for an interesting times. What’s the Irish proverb? May you live in interesting times.

Alex Burkhardt: Yes.

Alan Shimel: Definitely interesting times. Anyway, Alex, thanks for joining us here live at KubeCon. This is Observe Inc. So let me again observeinc.com. Check it out. Hey, they’re doing amazing things. This new release, something that gets twice as many new visitors right? In a couple of weeks, a month. It’s got to doing something right.

Alex Burkhardt: Yeah.

Alan Shimel: All right, we’re going to take a break. We’re live at KubeCon in Chicago. I think we have one or two more videos before we wrap for today. We’ll be right back.

December 18, 2023