devopsy.kinsta.cloud

Tag: devops

  • Avoiding Mistakes in Multi-Cloud Configurations

    Avoiding Mistakes in Multi-Cloud Configurations

    The digital sky is bursting with clouds of every kind, and organizations are reaching out to grasp several at once. They seize the opportunities offered by diverse cloud providers, breaking free from the confines of a single vendor. Their approach is a powerful strategic move. Its promise—slashed costs, bolstered resilience and unmatched data back-ups; its bottleneck—configuration issues. And the tightrope between reaping multi-cloud benefits and stumbling into cybersecurity risks is thin.

    “The foremost area where IT leaders have to adapt is cybersecurity,” said Aleksandr Morozov, a cloud security researcher and CTO at Oasis Defender. “As digital interactions multiply, so does our vulnerability to cybersecurity threats. We have to defend against attacks, safeguard data privacy and adhere to international regulations. The adequacy of cybersecurity measures we take directly impacts business integrity and consumer trust.”

    Given these stakes, multi-cloud configuration seems to be an even more tricky aspect to stay on top of. Any slip-ups can spiral into escalating costs, performance hitches and glaring security vulnerabilities. Knowing how to configure multiple clouds mistake-free is essential to keep the promise of multi-cloud alive.

    Multi-Cloud Adoption is Escalating

    On the brink of adopting multiple clouds, a question arises: Why pay more when you can pay less and get more? Organizations steer towards the most cost-effective cloud services, making sure each application finds its optimal cloud match to optimize performance and costs.

    The global march toward digital transformation results in a whopping 92% of organizations already using a multi-cloud approach or in the process of adopting one. Statistics suggest an average usage of 2.6 public clouds and 2.7 private clouds among organizations.

    The multi-cloud setup beckons organizations to distribute their workload across a spectrum of cloud services, each boasting unique benefits. This distribution not only fuels innovation but also provides organizations with robust data recovery mechanisms to use in the event of breaches.

    Multi-cloud also flings open the gates to escape vendor lock-in. No longer shackled to the offerings of a single cloud provider, organizations go for cherry-picking cloud features, explore diverse pricing options and choose geographical locations for sensitive data storage.

    In many regions worldwide, stringent local data regulations dictate the storage of sensitive data within that region’s borders and within specified cloud environments. By leveraging multiple clouds, global organizations can stay compliant with relevant regulations and data governance policies.

    With digital transformation beckoning, organizations are just beginning to harness the benefits of multi-cloud. However, with multiple clouds come multiple risks. Configuring and orchestrating several clouds typically pose challenges, and businesses across industries require robust multi-cloud tools and strategies in place.

    These Industries Need Multiple Clouds More Than Others

    The shift towards a multi-cloud paradigm among businesses isn’t merely a trend chase. It’s a strategic move to elevate operational processes.

    Although the use of a multi-cloud approach is bound with configuration and data breach risks, employing multiple cloud services from different providers brings game-changing benefits. This approach allows businesses to enhance flexibility and improve service monitoring and cost-efficiency—all while helping them better adapt to market changes and technological advances.

    On top of that, a multi-cloud strategy lays a solid groundwork for implementing advanced analytics and AI solutions. Businesses handling vast data, requiring high computational power, or having strict regulatory compliance needs stand to benefit most from this approach.

    “IT leaders must prioritize the infrastructure for remote work. It’s essential that this infrastructure supports seamless collaboration, regardless of geographical boundaries,” Morozov says.

    The global insurance sector is clearly betting big on multi-cloud, with statistics indicating an average use of 11.4 clouds in 2023. Insurance businesses handling massive amounts of sensitive customer data employ multiple clouds as the key to operational efficiency and compliance with data regulations.

    Banking and finance organizations stick to strict industry regulations and use multi-cloud environments to save costs, improve data security, and recover data easily. They seek to use the best features offered by multiple cloud providers to meet data storage rules and avoid reliance on a single provider.

    Besides, financial service organizations often experience rising costs and growing operational issues. That urged them to shift to a multi-cloud approach — their great strategic move. With this transition, they can see a 40% drop in spending by having a clear view of cloud activities across various regions.

    Similarly, health care businesses embrace multi-cloud to distribute workloads across different cloud services and keep their critical and sensitive data secure. They need a reliable backup in case of system failures to improve and maintain high standards of patient care and operations.

    The telecommunication sector is no less in need of a multi-cloud approach. In this field, escaping vendor lock-in and optimizing costs by combining a variety of vendors serve as the propelling forces.

    Retailers typically use around eight clouds from multiple vendors. Their multi-cloud strategies are cost-efficient, help improve scalability, and enhance customer experience by tailoring different cloud services to specific workloads and geographical locations.

    Configuration Mistakes: Why They Occur While Managing Multiple Clouds

    Managing many clouds means juggling a set of rules and systems from various cloud providers. Different settings in each cloud platform can lead to errors, especially when getting clouds to work together. Even a minor mistake can cause issues affecting performance, security, and costs.

    Besides, handling multiple clouds requires special talent. To err is human; common errors involve setting up access controls incorrectly, mismanaging user permissions or incorrectly configuring workloads. Small oversights lead to huge security and operational problems, and without the right training or experience, the chances of making errors increase.

    Configuration mistakes often come from the complex nature of multi-cloud setups. Moving into many clouds means dealing with different infrastructures, APIs, databases, networks, and security systems daily.

    The complexity of multi-cloud makes getting a full view of the whole setup tricky. This lack of clarity can hide configuration mistakes, letting them linger undetected or delaying fixing processes.

    Last but not least, different policies across cloud environments also complicate things. Each cloud setup might have its specific policies and configurations. Without reliable tools, clear rules and ongoing learning, configuration mistakes are imminent.

    How to Avoid Multi-Cloud Configuration Mistakes With AI

    Navigating through multiple clouds can feel like sailing in stormy seas, and organizations need robust tools and reliable solutions to mitigate performance issues and stay safe.

    AI-driven software allows for unified visualization and configuration of multi-cloud security. Let’s see how it aids in the journey.

    Unified Dashboard

    Multi-cloud management solutions bring together data from different cloud providers onto a single dashboard. This makes it easier to see and manage security across an organization’s cloud setup, making the process less complicated.

    Smooth Integration

    A unified interface helps different cloud systems work together smoothly. That means fewer errors and better cooperation between cloud environments.

    Better Visibility

    Having data put together, organizations get a better view of security setup across different clouds. They can see and fix issues faster while keeping multiple clouds secure.

    AI and Machine Learning

    Solutions hinge on using intelligent technology to spot potential threats quickly. Fast actions help reduce the negative impact of any security issues and keep multi-cloud setups shielded from breaches.

    Compliance Monitoring

    The cloud setup has to align with industry rules and standards, which becomes more complicated while using multiple clouds. Using a unified system, organizations can stay compliant and avoid legal issues.

    AI-driven solutions offer an intelligent way to untangle the complex knots of multi-cloud configurations while assuring a safer and more efficient multi-cloud experience.

    Final Thoughts

    The multi-cloud trend is on the rise, offering both opportunities and challenges for organizations. It allows businesses to pick cloud features from multiple providers, easily comply with regional data rules and avoid being tied to just one vendor.

    Different sectors like finance, health care and retail are adjusting their cloud strategies to adopt multi-cloud. Yet, configuring multiple clouds is a tough nut to crack. Diverse policies, different settings across cloud platforms and possible human error complicate multi-cloud configuration.

    However, AI-driven tools can offer a unified dashboard for a better view and management of security across cloud platforms, simplifying multi-cloud setups. Using AI and machine learning for managing clouds leads to quicker threat detection and resolution and helps organizations keep their strategies within legal limits.

    In the multi-cloud discourse, careful configuration supported by AI is a key to receiving multi-cloud benefits while managing the risks efficiently.

  • 4 Things to Consider When Integrating Generative AI

    4 Things to Consider When Integrating Generative AI

    Generative AI has emerged as one of the most influential innovations of the past two decades. ChatGPT gained 100 million users in record time, and new generative AI-powered point solutions emerge every day.

    Considering the current state of VC funding, the surge of investment dollars in AI companies is remarkable. Microsoft’s $13 billion investment in OpenAI and AWS’ $4 billion investment in Anthropic (who themselves just raised a $850 million Series C round) demonstrate the tremendous excitement around Generative AI and how it will shape the future of tech.

    As newer companies like Adept and Cohere are emerging, existing SaaS companies are integrating generative AI capabilities into their products and services. However, in the race to join the hype, many companies are building first and strategizing later. Before diving headfirst into building a generative AI solution, companies should carefully consider a few crucial factors, including model selection, usage tracking, monetization and customer transparency.

    1. AI Model Selection: To Build or to Buy?

    Companies must first consider whether to build a generative AI model from scratch, or whether to use a pre-existing model from one of the major AI vendors. This decision depends on the level of customization and control desired, as well as the resources and expertise available.

    Building a from-scratch model is likely infeasible due to the inaccessibility of the necessary hardware. The GPUs needed for cutting-edge model training and inference are currently only built by NVIDIA, and there is stiff competition and long lead times for any company looking to acquire the product. What’s more, large cloud and AI companies like OpenAI, Google, Meta and Tesla already have built systems with up to 100,000 of these chips. New companies looking to enter the scene would face a steep climb to assemble the infrastructure needed to even begin to compete with the current classes of models.

    Small or new companies do not have the funding or time to invest in building their own infrastructure or generative AI models, so a key business decision is to select the best vendor and model for the use case. While ChatGPT is the most well-known, there are other specialized models that can be better suited for certain use cases. For example, OpenAI’s DALL-E is built for image generation, while Anthropic’s Claude 2 is built to perform better with specialized tasks such as legal and math and to handle long-form inputs.

    Regardless of the AI vendor you choose, you will be charged based on your usage with them, which, by extension, is your customers’ usage. Tracking this usage is critical to your generative AI solution becoming profitable.

    2. Metering AI Service Usage: The Key to Success

    Implementing usage metering for generative AI services is crucial at both the individual user level and at the global or system-wide level. This is because usage tracking is valuable to all business functions and is a critical element of any modern PLG strategy.

    For example, customer-facing teams can leverage usage data to proactively track account health and to contextually recommend new solutions and approaches. Usage history provides valuable intelligence to account teams for up-selling, cross-selling, and contract renewal. Product teams can identify usage patterns and customer segments, as well as areas of friction or improvement for new feature development.

    Usage data also plays a major role in developing pricing plans that align with customer expectations and overall market trends. It can be used to inform pricing plan development and optimization to ensure the rates are sensible for the level of consumption and value being realized. Without tracking usage, this becomes a guessing game or a futile exercise in “gut feel.”

    Usage data is imperative to calculating your internal costs. Vendors commonly charge according to a usage-based model for these generative services–per word, per token or per generation are some common models. In any case, having accurate, granular usage data is a must-have for calculating internal costs and, in turn, customer-facing pricing.

    3. Monetizing Generative AI Services: Usage-Based Pricing Strategy

    In the rush to beat competitors to market and offer generative AI services to customers, do not neglect to lay the groundwork for monetization from day zero. It is much easier and more efficient to build with monetization in mind from the start than it is to backtrack and try to monetize a service once it has been offered, particularly when usage-based pricing is involved.

    Usage-based pricing has emerged as the optimal pricing strategy in the AI domain. This is because service usage varies over time for each customer. These services are priced by the vendors according to a usage-based pricing model. Since the backend costs of the service are elastic, the frontend, customer-facing pricing should also be elastic.

    As a roadmap to monetization, follow these steps:

    -Meter service usage to understand segments and patterns

    -Calculate internal costs of usage using vendor pricing

    -Using the information from steps 1 and 2, build customer-facing pricing

    -Track revenue and margins and continue to iterate with pricing as needed

    4. Customer Transparency: On-Demand Access to Usage and Billing Data

    Prioritizing transparency and clear communication with customers is non-negotiable. Ensuring that usage and billing data can be seen at any point in the billing cycle helps foster trust between the company and its users and eliminates billing surprises when the invoice is delivered.

    Adopting Transparent Communication Builds Trust With Customers

    Invest in creating a customer-facing application, portal or experience that allows for on-demand access to usage and billing data. The beauty of combining this level of visibility with usage-based pricing is that customers are empowered to pay for only what they use while viewing usage statistics on demand and modulating usage as needed in real-time to keep the spend within an acceptable range.

    In practice, usage data is available on a per-query level from the vendor’s APIs. For example, for any generation action, OpenAI will show the precise number of tokens consumed for both the prompt and response, as well as all details around the model type and context needed to identify the per-token rate from the vendor pricing. It is important to collect this data and build a user-friendly way to display it back to customers in an app, portal, or website that allows for on-demand access and browsing.

    Conclusion

    While planning to build a product with generative AI, take all of the above factors into account. Do not make the mistake of racing to deliver a technology solution without also considering the business implications and monetization strategy. With all the hype, excitement and investment around generative AI in 2023, it is akin to a modern-day gold rush. Don’t show up to the gold rush without a shovel.

  • What is Just-In-Time (JIT) Permission Management, and Why is it Essential?

    What is Just-In-Time (JIT) Permission Management, and Why is it Essential?

    Have you ever experienced the frustration of discovering an unexpected charge on your bank statement for a forgotten gym membership? Perhaps you kept it active “just in case” you wanted to exercise someday. Similarly, always-on privileged access, also known as “standing privileges,” can lead to similar challenges. 

    Surprisingly, 77% of organizations provide unrestricted access to employees who do not require it. However, maintaining an always-on approach does not necessarily facilitate their work; instead, it creates opportunities for security breaches that can go unnoticed. In fact, in 2022 alone, 55% of organizations fell victim to cyberattacks where hackers exploited privileged credentials, a critical attack vector as identified by Verizon. 

    This article explores the concept of just-in-time (JIT) permission management and its significance in providing the necessary security and efficiency for access control in organizations. We will delve into various types of JIT management, including automated JIT, and discuss the best practices for its implementation within your business. 

    Understanding Just-In-Time (JIT) Permission Management

    JIT, also known as just-in-time access, is a cybersecurity practice that adheres to the principle of least privilege. It grants users access to resources, such as applications and systems, only for a limited period when it is specifically needed. Once the designated time expires, users lose access to these resources. This methodology not only limits a user’s access timeframe but also reduces the chances of attackers infiltrating your cloud security perimeter. 

    With the increasing number of applications, services, users and resources in the cloud, 45% of breaches in 2022 were cloud-based, making JIT an essential requirement. While traditional privileged access management (PAM) processes, such as session management, are effective for on-premises environments, JIT is particularly suitable for controlling access across cloud resources. 

    Types of JIT Permission Management

    JIT permission management can be implemented in different ways, including: 

    Ephemeral: This approach involves creating a one-time account to fulfill the user’s access requirements, which is then disabled or deleted. 

    Temporary: Users can request elevated privileges on their existing accounts when necessary. 

    Justification-based: Users must provide a valid justification for requiring privileged access, based on predetermined policies. A privileged account and credentials are created and rotated using a central vault in this “broker and remove” access model. 

    The Importance of Automated Just-In-Time Access 

    Managing JIT permissions manually can be akin to playing a never-ending game of whack-a-mole, with constant access requests popping up across your organization. Can you realistically allocate sufficient time and personnel to grant and revoke access throughout the day, especially considering the resource constraints faced by 59% of organizations that struggle to deploy zero-trust? 

    In contrast, automated JIT platforms alleviate the challenges posed by manual permission management. These platforms validate, monitor and revoke access without requiring human intervention. By offering features such as auto-expiring permissions and comprehensive reporting capabilities, automated JIT platforms empower users to self-serve permission requests while maintaining your organization’s security posture. Entrusting permission management to an automated JIT platform minimizes human error, reduces the attack surface, eliminates bottlenecks and ultimately preserves productivity. 

    Benefits of Automated JIT

    Automated JIT not only eases the burden on your IT and security teams but also provides several other advantages: 

    Enhanced security posture: Compromised or stolen credentials were the most common attack vector in 2022, according to IBM’s Cost of a Data Breach report. By eliminating the need for standing privileges, automated JIT significantly reduces the risk of privilege abuse and breached identities. 

    Improved business productivity: With an automated JIT approval workflow, you can bid farewell to manual review cycles, waiting times and human error. You can grant access at scale, tailored to specific tasks, significantly improving operational efficiency. 

    Compliance adherence: Automated JIT enables you to meet compliance and customer requirements, such as SOC2, by enforcing zero trust and least privilege access. These platforms include auditing and reporting features that provide visibility into all sessions and privileges, ensuring compliance with regulations like SOC2 and PCI-DSS. 

    Enabling JIT Access: Best Practices 

    To successfully enable and implement JIT access, consider the following best practices: 

    Identify and inventory: Begin by identifying the accounts and assets with the most privileges, particularly those belonging to administrators, which pose the highest risk. Implement JIT access control for these accounts first and then gradually extend it throughout the organization. 

    Use RBAC and ABAC: Utilize role-based access control (RBAC) and attribute-based access control (ABAC) as supplementary solutions to define granular policies and circumstances for elevated access. Categorize accounts, differentiate their rights, and create control policies that users must satisfy to gain access. 

    Define and enable temporary access: Apart from justification-based access, establish criteria for users requesting temporary access, including which accounts are eligible and the duration of access. Implement time-based controls, such as granting access to specific resources during predefined days and times. 

    Record and audit activity: An automated access management solution enables you to log all access activities, receive alerts for suspicious behavior, and record JIT-privileged access. Maintaining a comprehensive digital paper trail is essential for auditing, governance, and compliance with regulations such as SOC2 and PCI-DSS. 

    Assign responsibility: Delegate responsibilities to employees and determine who will review permission requests. Properly training employees on granting and revoking access, especially during critical incidents like “break glass” and “on-call” situations, minimizes the risk of incidents. Automated JIT facilitates configuring access flows for these scenarios, helping resolve incidents promptly and eliminating bottlenecks. 

    Use short-lived (ephemeral) credentials: Regularly rotate credentials manually to invalidate them, preventing hackers from exploiting stolen passwords. Employ a centralized vault with the highest level of security clearance to manage these credentials effectively. 

    Employ an automated access management tool: Simplify cloud access management by leveraging solutions like Apono, which enforces an automated JIT approach. By using such tools, you can streamline processes, reduce over-privileges, and ensure that permissions are never overlooked, safeguarding your organization from unnecessary risks. 

  • Maximizing Data Insights: How OLAP Empowers Modern Data Stacks

    Maximizing Data Insights: How OLAP Empowers Modern Data Stacks

    In the era of big data and cloud computing, online analytical processing (OLAP) has fallen out of favor. As modern data warehouses evolved to accommodate larger volumes of data and claims faster processing capabilities, OLAP fell out of fashion as a notion emerged that it would struggle to manage the combinatorial explosion. The focus shifted toward runtime querying engines. Instead of using predefined or pre-aggregated OLAP cubes, many business intelligence (BI) and analytics tools used their own data modeling applications that worked directly with raw data.

    However, in recent times, there has been a realization that OLAP, when implemented intelligently, can be highly beneficial even in the context of a massive scale of data and cloud computing. Modern OLAP leverages advanced technologies and optimizations that overcome any previous limitations. Besides being cost-effective, especially for cloud implementations, it delivers real performance advantages.

    Modern OLAP systems are designed to handle large datasets efficiently. One of the key strengths of OLAP lies in its ability to provide real-time or near-real-time insights, enabling businesses to make data-driven decisions with greater agility. The multidimensional data model of OLAP cubes facilitates instant query response, empowering users to derive valuable insights from complex data sets without compromising on query performance.

    Consider These Advantages of Using OLAP in Modern Data Stacks

    Performance: As data volumes continue to grow, OLAP remains a powerful technology to extract valuable insights and unleash the true potential of modern data stacks and cloud environments. OLAP’s multidimensional data model with pre-aggregation cubes enables efficient data processing and analysis, delivering faster insights and sub-second query responses. Modern algorithms further enhance performance, allowing organizations to handle large-scale data workloads without worrying about data explosion problems.

    Unique OLAP Capabilities: Some domains, like finance, have an inherent need to preserve crucial OLAP capabilities even with the growing data storage and compute requirements for which they make a move to modern data stacks and cloud environments. Moving to the cloud enables data scalability, addressing the challenges of handling vast data volumes. However, they cannot afford to compromise on essential OLAP functionalities such as advanced hierarchies, like parent-child relationships, and alternate hierarchies with weighted rollups.

    These capabilities are indispensable, especially in the world of financial analytics, regardless of how modern and advanced the new data stack may be. By leveraging OLAP in the cloud, organizations can continue to analyze and explore data with complex hierarchies efficiently, ensuring accurate and in-depth financial insights without sacrificing the flexibility and analytical power that OLAP provides.

    Cloud Architecture and Cost Effectiveness: OLAP in cloud architecture presents a powerful and forward-looking solution that combines the benefits of OLAP technology with the scalability and cost-effectiveness of cloud computing. By harnessing the cloud’s resources, OLAP can efficiently process and analyze large volumes of data, thus meeting the growing demands of modern businesses.

    Once OLAP cubes are constructed and populated, they provide a highly efficient and aggregated representation of the data. These pre-aggregated structures accelerate query performance, enabling teams to access insights in real-time or near-real-time. BI teams can seamlessly integrate existing tools with the OLAP model, fostering a cohesive and familiar analytics environment. It empowers decision-makers to explore data interactively, drill down into details, and gain valuable insights.

    One of the primary advantages of OLAP in the cloud is its cost-effectiveness. OLAP technology, being primarily based on pre-aggregation, drastically reduces runtime querying compute cost, as answers to business queries are already pre-computed and stored in an aggregated structure. At querying time, OLAP tools have to look for right answers from the aggregated structure—not the cloud data storage—and return the query response, which results in much less compute cost as compared to runtime aggregation compute cost. It truly enables build once, query many times culture. Apart from this, businesses can leverage the cloud’s elasticity to scale computing and storage resources up or down as their data requirements fluctuate. This adaptability ensures companies can handle peak workloads without overprovisioning and reducing unnecessary expenses.

    Universal Semantic Layer: A USL approach ensures consistent data definitions and analytics across the organization, reducing data discrepancies and improving data governance. A common understanding of the data enables better communication among teams and results in a smooth implementation of a data-driven decision-making process.

    At the same time, using a USL means that multiple business intelligence tools and reporting platforms can connect to and leverage the same semantic model. Hence, various parts of the organization can continue to work with their familiar BI interfaces while accessing common underlying data.

    OLAP lends itself naturally to delivering on a powerful universal semantic layer. The concept of dimensions, measures, hierarchies and calculations forms the foundation of an OLAP semantic model. OLAP semantic models also accommodate complex calculations, custom expressions or formulas required for complex business-specific logic. These components provide a powerful way to structure and organize data, making it more accessible and intuitive for business users who need to derive insights from the data.

    If we consider typical sales data, product categories, geographic regions and time periods could be dimensioned. Measures, on the other hand, would be sales revenue, profit margins, quantities sold or any other KPIs. Hierarchies allow data to be analyzed at different levels of granularity. Users may roll up or drill down to view data at different time intervals such as year, quarter, month, day, etc.

    USL and OLAP models are designed to shield business users from the underlying complex data modeling and schemas. They can work with filters, dimensions and measures that are business-focused representations of the data using familiar business terms while abstracting technical intricacies. They can view and explore data through intuitive hierarchies, filters and measures aligned with their business processes and KPIs.

    Conclusion

    OLAP in cloud architecture represents a forward-looking, fast and cost-effective solution. It capitalizes on the cloud’s scalability and pay-as-you-go model to process vast amounts of data efficiently, while its pre-aggregated structures and real-time capabilities empower business intelligence teams to gain valuable insights seamlessly. With its agility, OLAP in the cloud is well-positioned to meet the challenges of the future, providing organizations with a competitive edge with data-driven decision-making.

  • DevOps and ITIL Integration: Driving Collaborative Agility in ITSM

    DevOps and ITIL Integration: Driving Collaborative Agility in ITSM

    Businesses face continuous pressure to deliver high-quality IT services efficiently. One emerging integration to meet these goals is between DevOps and IT service management (ITSM), more commonly known as DevOps+.

    DevOps+ is becoming more than a trend and has emerged as a powerful collaborative approach to meet these challenges. By combining the strengths of DevOps’ agile software development practices and ITSM’s structured service management approach, organizations can foster better collaboration, enhance agility and improve overall service delivery efficiency.

    DevOps and ITSM Symbiosis

    DevOps and ITSM traditionally operated as separate frameworks with distinct focuses. DevOps emphasizes rapid software delivery and collaboration between development and operations teams. On the other hand, ITSM-based ITIL (information technology infrastructure library) provides best practices for ensuring service stability, reliability and adherence to governance and compliance.

    Integrating DevOps and ITIL creates a symbiotic relationship, allowing organizations to leverage the best of both worlds. DevOps+ encourages a cultural shift, breaking down silos and promoting cross-functional communication. This collaboration enhances the entire software development lifecycle, leading to faster feedback loops, accelerated innovation, and increased responsiveness to business needs.

    Enhancing Agility and Speed

    DevOps+ integration brings greater agility and speed to IT service management. Organizations can streamline the release process by automating deployment pipelines and implementing continuous integration and continuous delivery (CI/CD) practices, reducing time-to-market for new features and updates. Agile development methodologies enhance the adaptability of ITSM processes to meet evolving business requirements effectively.

    Ensuring Stability and Reliability

    While agility is crucial, it must not compromise service stability and reliability. ITIL’s best practices provide a strong foundation for maintaining service quality. By incorporating ITIL processes such as incident management, change management and problem management, organizations can effectively manage risks, reduce service disruptions and ensure the stability of critical IT services.

    Leveraging Automation and Orchestration

    Automation is central to both DevOps and ITIL. Through integration, organizations can leverage automation and orchestration tools effectively. Automated testing, infrastructure provisioning and configuration management improve efficiency and consistency across the ITSM lifecycle. This enables teams to focus on higher-value tasks, reducing manual errors and operational overhead.

    Emphasizing Customer-Centricity

    DevOps+ puts the end-user experience at the forefront of service delivery. By continuously seeking customer feedback and measuring service performance, organizations can better align their offerings with user expectations. A customer-centric approach fosters higher satisfaction, loyalty, and competitive advantage.

    Breaking Down Barriers to Change

    Change management is vital in ITIL, ensuring that modifications are planned and controlled. In the context of DevOps+, change management becomes more adaptive and responsive, facilitating a culture that embraces change as an integral part of the development process. This promotes experimentation, reduces resistance to change, and accelerates innovation.

    Challenges of DevOps and ITIL Integration

    Despite the numerous benefits of DevOps+ integration, organizations may encounter challenges during implementation:

    Cultural Shift: Merging DevOps and ITIL requires a cultural shift. Overcoming resistance to change and fostering collaboration between traditionally separate teams can be challenging but is crucial for success.

    Process Alignment: DevOps and ITIL have different processes and terminologies. Aligning these methodologies while preserving their individual strengths can be complex.

    Skill Sets: Integrating DevOps and ITIL necessitates upskilling teams in both domains to operate effectively in the integrated environment.

    Tooling and Automation: Adopting suitable tools and technologies that support DevOps+ practices and integration is vital but may require significant investments.

    Metrics and Performance Measurement

    Measuring the success of DevOps+ integration is essential to understand its impact and effectiveness. Key performance indicators (KPIs) can help monitor progress and identify areas for improvement.

    Deployment Frequency: The frequency of software deployments indicates the efficiency of the integrated approach. Higher deployment frequency implies faster and more reliable releases.

    Mean Time to Resolve Incidents (MTTR): MTTR measures how quickly teams can resolve incidents and restore services after disruptions occur. A reduced MTTR demonstrates improved service reliability and faster incident resolution.

    Customer Satisfaction Scores: Measuring customer satisfaction provides insights into how well the integrated approach meets end-users’ needs and expectations.

    Continuous Improvement and Feedback Loops

    Continuous improvement is at the core of DevOps+. Establishing feedback loops and conducting regular retrospectives empowers teams to identify areas for improvement and implement iterative enhancements to processes and practices.

    Implementing DevOps+ integration requires a well-structured roadmap. For example, assessment and planning help leaders evaluate the organization’s current ITSM practices to understand how DevOps and ITIL principles align and define integration vision and goals.

    Additionally, your continuous optimization efforts allow you to continuously monitor performance metrics, gather feedback, and conduct retrospectives to identify improvement areas, leading to better team collaboration and communication. Fostering cross-functional collaboration between development, operations and other relevant teams is easily accomplished. To meet these goals, encourage regular meetings, schedule joint planning sessions and enable shared documentation to ensure everyone is aligned on goals and progress.

    When this happens, leverage automation tools to streamline manual processes and increase efficiency. Implement continuous integration and continuous delivery pipelines to automate software deployment and testing. Automation enhances consistency, reduces the risk of human errors and adds a new layer for measuring and evaluating to continuously measure the performance of the integrated approach using defined KPIs. In this vein, you can regularly review progress against the initial vision and goals to ensure the integration aligns with organizational objectives.

    Next, as the DevOps+ integration proves successful in pilot projects, gradually scale and expand it to other teams and departments. Share the success stories and best practices to encourage broader organizational adoption.

    Conclusion

    In conclusion, the integration of DevOps and ITIL, known as DevOps+, marks a transformative approach to IT service management. By combining the agility and rapid deployment of DevOps with the stability and reliability of ITIL, organizations can drive collaborative agility and achieve efficient service delivery.

    DevOps+ integration requires overcoming cultural barriers, aligning processes and upskilling teams, but the rewards are immense. With clear metrics to measure success, organizations can continually optimize their DevOps+ approach to meet customer needs and adapt to evolving market demands.

    As technology evolves, DevOps+ integration will remain at the forefront of cutting-edge IT service management, empowering organizations to embark on a journey of innovation, efficiency and excellence.

    By embracing DevOps+ as a guiding philosophy, organizations can cultivate a culture of collaboration, continuous improvement and customer-centricity, laying the groundwork for a prosperous future in the dynamic world of IT service management.

  • How to Build a Data Platform for Self-Service, Ad-Hoc Analytics

    How to Build a Data Platform for Self-Service, Ad-Hoc Analytics

    In a digital world, data is often the differentiator between success and failure. Whether it’s defending against cybersecurity threats, improving application performance, or resolving full-blown outages, data is critical to the modern DevOps team.

    In fact, there are plenty of situations where DevOps engineers, SRE teams and other observability-focused departments need to explore reams of data rapidly and flexibly. An SRE trying to figure out the root cause of latency may need to dissect the movement of data across endpoints to find the malfunctioning one. Another engineer may need to compare historical and current performance to find anomalous behavior. Lastly, a DevOps engineer may have to dissect user performance metrics to understand the scope of a slowdown—whether it is global or regional.

    Every use case requires dashboards that can provide detailed data through a wide variety of visualizations, including choropleth maps, stack areas, pie charts and bar graphs. Ideally, a dashboard will enable teams to isolate dimensions, apply filters, and dive into data for deeper insights.

    Unfortunately, not all dashboards can support this type of fast, flexible analysis. Many still utilize outdated technologies, which lack the flexibility, agility and scalability required to seamlessly explore data. Many were also designed without the urgency that today’s data requires—after all, in previous years, most uses for dashboards (such as internal reporting) were not time-sensitive.

    Other dashboards are constrained by templates, which offer a finite array of widgets, tools and drill-down capabilities. These dashboards may feel unwieldy and sluggish, unsuited for a rapidly evolving situation like an application outage.

    Requirements

    To truly fulfill the promise of self-service, ad-hoc analytics across huge datasets, teams need to work in real-time—and so they need a database capable of timely responses.

    When an application goes down, an SRE may not know what to look for and, hence, needs to quickly comb through lots of data. While some dashboards rely on workarounds for faster queries, such as pre-aggregations, precomputing, or rollups, this is not possible in this instance because the SRE simply won’t know what to look for. After all, they can’t necessarily predict what will go wrong, and even if they work on assumptions from previous failures, this current issue could be much different.

    Therefore, dashboards must provide plenty of functionalities and visualizations. Users should be able to filter data by time, isolate variables like location, and zoom into specific time intervals with a few clicks. Dashboards should also accommodate diverse data types, including intricate parent-child relationships and nested columns.

    Further, dashboards must offer a depth of insight. An online streaming media platform may need to assess user metrics (such as latency or load times) across different devices, operating systems, and regions to fine-tune performance. A cloud provider has to monitor their physical hardware for high temperatures, slowed network switches or devices, and other anomalies.

    Because data is now so important to success, many more people across a company, including data scientists, product managers and external users, themselves require data-driven insights. In these situations, dashboards must handle the increased user traffic and query activity, maintaining fast responses even under load. This is especially important considering that a single-user operation (such as a zoom) will require multiple queries on the backend to execute.

    A database also must scale seamlessly. If an organization’s environment generates millions of events an hour, that equates to billions of events per day or week—challenging for any database to ingest, store, analyze and query. In fact, many databases cannot successfully provide fast response times while managing large datasets and high query volume. As an example, transactional databases (OLTP) can often query rapidly but cannot execute analytics at scale, while analytical databases (OLAP) can analyze massive volumes of data but not at speed.

    Apache Druid for Independent, Ad-hoc Data Exploration

    This is where open source Apache Druid comes in. Combining the scale and advanced analytics of an OLAP database with the speed of an OLTP database, Druid offers swift, real-time data exploration.

    Upon ingesting data, Druid makes it immediately available for querying and analysis, removing the need to first batch or aggregate the data in some way. In addition, Druid natively integrates with streaming technologies like Apache Kafka and Amazon Kinesis, removing the need for workarounds or connectors.

    Druid powers interactive visualizations, providing millisecond response times, enabling more versatile exploration, expanding the range of available dimensions and filters, and even maintaining subsecond speeds in the face of surging user and query volumes.

    Druid’s unique architecture also enables easy scaling. By separating key duties among separate node types—data nodes for storage, master nodes for data ingestion and availability, and query nodes for executing queries and returning results via the scatter/gather method—Druid ensures that nodes can be independently scaled based on need. Afterward, Druid also automatically rebalances traffic to ensure consistent performance.

    Salesforce: A Druid Success Story

    Salesforce pioneered the customer relationship management (CRM) space, serves 150,000 customers worldwide and earns billions in annual revenue. 

    The Edge Intelligence Team is the division of Salesforce that tackles the massive task of ingesting, processing, filtering, aggregating, and querying the entirety of their log data — anywhere from billions to trillions of lines daily. Each minute, Salesforce ingests 200 million metrics, while each day, Salesforce processes five billion daily events globally. In total, Salesforce accumulates dozens of petabytes of data in their transactional store, five petabytes of logs in their data centers, and almost 200 petabytes in their Hadoop storage.

    Salesforce teams use Druid to unlock real-time insights into product performance and user experiences, diving into large datasets instantly. Anyone from engineers to account executives can query a wide variety of dimensions, filters and aggregations to better understand trends, troubleshoot any issues that arise and set strategies for the future.

    By using Druid’s compaction abilities, Salesforce also decreased the number of Druid rows by 82%, leading to an overall reduction of their storage footprint by 47% and accelerating their query response times by 30%.