October 2022 Summaries

Elastic Synthetics is a tool that allows users to monitor the performance of Kibana dashboards by simulating user interactions and measuring load times across various global locations without requiring any installations. This capability helps determine if perceived dashboard slowness is due to actual performance issues or external factors like network connections. Users can set up synthetic monitors that execute at specified intervals, providing insights into average loading times and identifying potential bottlenecks. By utilizing Elastic Cloud's Uptime feature, users can easily configure and manage these monitors, select locations of interest, and employ machine learning to detect anomalies in page load times. Additionally, detailed performance breakdowns, including step-by-step execution with screenshots, assist in verifying and analyzing dashboard performance across different regions.

Free software and in-house development tools can initially seem cost-effective for small businesses, but they may not be sustainable for medium to large enterprises, especially those not primarily focused on technology. For such companies, investing in managed cloud services can offer significant advantages, including superior 24/7 support, robust API integrations, cost efficiency, and elimination of the challenges associated with maintaining homegrown solutions. Cloud services provide scalable solutions that ensure optimal performance during peak times and offer top-notch security measures to protect against vulnerabilities. While free software is beneficial for experimentation and learning, enterprises stand to gain more reliable performance and peace of mind by leveraging cloud service providers, which support mission-critical applications and help maintain productivity and revenue.

Elastic RUM (Real User Monitoring) helps developers gain insights into application performance by tracking user interactions, but diagnosing errors in minified JavaScript files can be challenging. Minification, used to improve loading times by reducing file sizes, often compresses code into a single line, making error tracking difficult. Source maps offer a solution by mapping minified code back to its original form, allowing easier debugging. Developers can generate source maps for their applications and upload them to Elastic APM, which translates minified error messages into understandable code. The article provides steps for uploading source maps using cURL or a custom application, ensuring that the service version aligns with the application configuration for effective error diagnostics. This integration enhances the visibility of application errors and improves debugging efficiency, offering a comprehensive view of performance issues.

Personalized search experiences are crucial for enhancing customer engagement and business outcomes, as they tailor digital interactions to individual user preferences based on data analysis. Elastic, a data analytics company, offers a suite of tools to streamline the personalization process, from data ingestion and analysis to the application of personalized search insights. These tools include prebuilt connectors, data visualization through Kibana, and APIs for relevancy model tuning, all designed to provide flexibility and control to developers. The process involves integrating customer data with additional datasets, analyzing it to uncover trends and preferences, and using these insights to create customized search experiences. Elastic's approach emphasizes flexibility, allowing organizations to build personalized recommendations and search experiences that drive customer loyalty and sales.

In the context of ecommerce, the article delves into the intricacies of managing product variants and SKUs to optimize search experiences using Elastic Enterprise Search. It explains the hierarchy in product data modeling, where a product can have multiple variants, each identified by unique attributes such as color or size, and how SKUs serve to track inventory at different levels. The challenge lies in deciding how to display search results—whether at the product, variant, or SKU level—while maintaining an efficient and user-friendly search interface. The article emphasizes the importance of de-normalizing data structures for search indexing and suggests strategies for grouping variants to simplify search results. It also discusses implementing filters, like "in-stock" options, to improve the search functionality for customers, ultimately guiding users on setting up comprehensive ecommerce search solutions.

With the rapid increase in data creation, expected to reach 181 zettabytes by 2025, IT leaders face the challenge of converting this vast, predominantly unstructured data into actionable insights to drive business-critical decisions. A study by Forrester Consulting, commissioned by Elastic, highlights the pivotal role of search-powered technology, which enables organizations to access and analyze data from various sources such as websites, databases, and cloud environments. This technology helps overcome issues related to data silos, enhances cybersecurity by detecting threats quickly, and supports digital transformation efforts by maintaining IT infrastructure stability and improving customer experiences. By employing search-powered tools, businesses aim to become more data-driven, reduce complexity, and transform data insights into tangible business value.

Migrating customer data to the cloud can significantly enhance customer experience, as demonstrated by a U.S.-based expense management firm's transition from an outdated on-premises Elasticsearch to a cloud-based solution on AWS. This migration, assisted by the software development company Softjourn, resulted in faster and more accurate searches, improved system stability, and a more robust infrastructure, thus enhancing customer satisfaction and loyalty. Cloud technology, while not inherently superior to on-prem solutions, offers advantages such as quicker deployment of features, better customer support, and seamless third-party integrations, as noted by industry experts like Boris Jabes and Craig Boyle. A unified cloud-based data platform allows for efficient data analysis, leading to more personalized customer interactions, better decision-making, and higher retention rates. Companies can leverage this unified data to identify high-value customers, optimize marketing strategies, and improve service delivery, ultimately driving revenue and profit growth. However, careful planning and testing are essential for successful cloud migrations, with the option to maintain certain in-house systems for closer management, particularly for sensitive data.

Financial institutions are facing heightened cybersecurity risks due to digital transformation, which expands the attack surface in the sector. The pandemic has accelerated the adoption of digital banking and other services, increasing data complexity and providing more opportunities for cyberattacks, which Jerome Powell, U.S. Federal Reserve Chairman, identifies as the top threat to the global financial system. Remote work and reliance on third-party services further exacerbate these vulnerabilities, while a global shortage of cybersecurity professionals poses additional challenges. To address these issues, financial security leaders are encouraged to focus on modernizing their Security Information and Event Management (SIEM) systems to consolidate tools, enhance integration, and utilize advanced analytics like AI and machine learning. Modern SIEMs should support diverse environments, allow for flexible data management, and offer native protections and automated threat detection to improve efficiency and reduce attack susceptibility. The financial sector's reliance on outdated SIEMs is costly and inefficient, prompting many to consider new solutions that can keep pace with regulatory demands and evolving cybersecurity threats.

Security Information and Event Management (SIEM) systems, essential for intrusion detection and response, face significant challenges in multi-cloud environments due to issues with compatibility, data complexity, and scalability. As organizations shift to cloud-based infrastructures, traditional SIEMs struggle to process diverse, high-velocity cloud data and often lack out-of-the-box capabilities for threat detection across different cloud platforms. This complexity is compounded by the steep learning curve required to interpret cloud security data, which differs markedly from traditional security indicators, often leaving practitioners without sufficient support. Despite claims of supporting multi-cloud environments, many SIEMs fall short in practice, failing to meet the dynamic requirements of rapidly evolving cloud instances. Solutions like Elastic's cloud security-focused SIEM aim to address these challenges by offering tools designed to handle the intricacies of cloud security, emphasizing the need for systems that can effectively normalize and process cloud data in real-time.

Version 7.17.7 of the Elastic Stack has been released, and users are encouraged to upgrade to this latest version over previous 7.17.x patch versions. The release includes various fixes and changes across products like Elasticsearch, Kibana, Beats, Logstash, Elastic Enterprise Search, Elastic Observability, APM, and Elastic Security. For a detailed list of issues addressed and product-specific changes, users are directed to consult the release notes.

Adopting hybrid or public clouds has become a common strategy for IT organizations to achieve scalability, enhancing price performance and enabling advanced tech-driven business capabilities, although it doesn't always build customer trust due to data breaches and system reliability issues. Customer trust is crucial, as a PwC study highlights a significant gap between business executives' perceptions of consumer trust and the reality reported by consumers, with data breaches being a major threat. Companies like WePay, Albert Heijn Technology, and Kroger have improved customer trust and operational efficiency by leveraging real-time insights and observability tools to enhance their IT systems' performance and security. These organizations demonstrate that optimizing customer experiences, reducing downtime, and achieving scalable log ingestion are key to maintaining trust, as they continue to harness cloud-based data analytics to manage cybersecurity, application performance, and customer interactions.

Finding the appropriate security privileges in Elasticsearch can be challenging, and this blog post outlines a method for determining required privileges through examples. The author demonstrates how to create a user with specific access to index-related functions, such as creating and updating documents, by iteratively adjusting privileges based on error messages received during attempted operations. This iterative process involves fine-tuning roles to grant necessary permissions for specific actions, like viewing index metadata and document creation, while highlighting the risks of using overly permissive roles. Additionally, the post provides an example of setting cluster-level permissions for accessing the Elastic homepage and ILM policies. The author emphasizes that there are numerous combinations of permissions, which makes it impractical to cover all scenarios with built-in roles and suggests trying this method in existing environments or Elastic Cloud.

Public sector organizations, including government agencies and educational institutions, are increasingly turning to modern Security Information and Event Management (SIEM) platforms as part of their Zero Trust cybersecurity strategies, to address the rising threat of cybercrime. SIEM systems integrate security information management (SIM) and security event management (SEM) to provide a comprehensive view of data from multiple sources, enabling real-time detection and mitigation of cyber threats. With cybercrime expected to grow significantly, and data breaches costing millions, SIEM's ability to handle vast quantities of structured and unstructured data is crucial for public sector entities, which are often targets due to their sensitive data. As data usage grows, SIEM solutions must offer speed and scalability, while balancing log storage requirements and costs, especially in light of new directives like M-21-31 that demand extended log retention. Cloud-based SIEM solutions are becoming more affordable, allowing even smaller agencies to benefit from these platforms, though considerations around cloud versus on-premise deployments and compliance with mandates such as FedRAMP are important.

Observability solutions are emerging as key enablers of digital transformation by addressing common challenges such as organizational silos, lack of trust between business and IT departments, and insufficient communication. These tools provide enhanced visibility into systems and processes, allowing organizations to preempt issues and effectively communicate the value of technology investments. For instance, Ahold Delhaize's adoption of an observability platform allowed its IT team to track the entire retail order fulfillment journey, improving incident response and operational efficiency. Similarly, Oui.sncf leveraged observability to dismantle data silos and streamline customer experience, significantly reducing incident resolution times. By visualizing business health through real-time dashboards, companies can foster stronger C-suite support and make more informed decisions, ultimately increasing the likelihood of successful digital transformations.

CIOs play a crucial role in fostering business resilience in uncertain times by seamlessly integrating core technology processes to mitigate risks and drive innovation. They are instrumental in digital transformation, cybersecurity, and enhancing customer experience, with their investment strategies shaping organizational trajectories for several years. The COVID-19 pandemic highlighted the importance of CIOs in ensuring remote work capabilities and cloud migration for cost savings and productivity. Despite the increasing recognition of technology's role, many CIOs face challenges in securing organizational support for technology investments, as evidenced by a Gartner report indicating limited collegial relationships between CFOs and CIOs. Organizations are increasingly moving to the cloud to enhance digital resilience, recognizing its advantages over on-premises systems in terms of redundancy, availability, and security. Cybersecurity remains paramount, requiring CIOs to build resilient systems in collaboration with CISOs to counteract growing threats from cyber espionage and warfare. Balancing innovation with governance, CIOs must navigate pressures from various stakeholders while breaking down silos and enhancing governance through advanced enterprise search capabilities, emphasizing the strategic and empathetic nature of modern technology leadership.

Elastic Real User Monitoring (RUM) offers a comprehensive solution for capturing and analyzing user interactions with web applications, providing insights into real user experiences from a performance standpoint. The Elastic RUM Agent, a JavaScript-based tool, integrates with any JavaScript application to identify performance bottlenecks, optimize user experience, and monitor service level agreements (SLAs) through detailed metrics such as latency and throughput. Users can set up Elastic RUM by installing the Elastic Stack and using the APM server, with options for local installation or via Elastic Cloud. The blog demonstrates the setup process using a simple car database application with a React frontend and Spring Boot backend, showcasing both default and custom instrumentation capabilities. By incorporating Elastic's APM, developers can utilize distributed tracing to visualize interactions from the frontend through to backend databases, which is particularly useful for understanding the impact of new features and optimizing application performance across different browsers and regions. Additional configurations, such as cross-origin resource sharing (CORS), are necessary to support distributed tracing across different server origins, enhancing the capability to track and resolve performance issues comprehensively.

Elastic has rapidly established itself as a significant player in the Security Information and Event Management (SIEM) market, achieving over 80% year-over-year growth and securing the fifth-largest market share among more than 20 vendors, according to the IDC report. Recognized by Gartner as a Visionary in the 2022 Magic Quadrant for SIEM, Elastic has been praised for its open, transparent platform and its comprehensive technical capabilities, which include native endpoint detection and response, cloud workload monitoring, and policy enforcement. The platform supports both on-premise and cloud deployments across AWS, GCP, and Azure, and offers features such as security orchestration, automation, response (SOAR), threat intelligence, and advanced security analytics. Elastic's unique multi-cloud architecture allows for federated analysis without centralizing data, enabling fast, cost-efficient searches through years of telemetry. Despite being a relatively new entrant in the SIEM space, Elastic's rapid expansion into Extended Detection and Response (XDR) and cloud security demonstrates its commitment to modernizing security operations and addressing customer needs.

The article highlights the innovative use of the Elastic stack by Isra Sunhachawi, a senior solutions architect at Elastic, to create a tool that assists U.S. veterans in making informed decisions regarding housing, healthcare, and financial benefits. Sunhachawi's project, which won the Academy Health DataJam Challenge in 2022, employs interactive maps and dashboards to present data such as pay grades, housing prices, VA loan statistics, and healthcare information. It leverages Natural Language Processing for semantic searches, enhancing the accessibility and relevance of information for veterans. This initiative demonstrates the significant potential of public-private collaboration in improving health outcomes and addressing disparities, while also highlighting Elastic's commitment to supporting veterans through initiatives like Operation Giving Back, which provides training and community support to military personnel.

Discover in Kibana has evolved significantly, enhancing its usability and functionality for users exploring Elasticsearch data. Initially released as part of Kibana 4 and later updated with various improvements, the recent version 8.x introduces major features like an enhanced document table, field statistics, and search threshold alert creation. The new data table, powered by EuiDataGrid, allows users to adjust column widths, view images in tables through URL formatting, and sort by multiple fields more intuitively. Discover now provides a full-screen mode for focusing on data and offers the ability to manually filter and search documents in a flyout. Additionally, a new Field Statistics tool gives insights into data structure, such as top values and cardinality. Speed improvements have been achieved by splitting requests to Elasticsearch into separate tasks for document fetching and aggregated data, allowing quicker visibility of query results. Alerting capabilities have been expanded to help users receive notifications for important document ingestions, with a variety of alert actions available. Future updates aim to simplify querying by removing the need for predefined data views, allowing users to immediately query data patterns of interest.

The 2022 Results That Matter study, managed by Elastic and Socratic Technologies, highlights how public sector organizations benefit from Elastic’s solutions, including Enterprise Search, Observability, and Security, in their unique environments. The study reveals that Elastic Enterprise Search enhances search efficiency by 70%, improving employee satisfaction and access to essential services. Elastic Observability accelerates decision-making by 62% by offering real-time insights across hybrid and multi-cloud environments, thus enhancing infrastructure resilience and reducing service incidents. Elastic Security reduces the risk of data breaches by 67%, assisting public sector entities in mitigating cybersecurity threats through continuous monitoring and threat protection. The study underscores data's strategic value across organizations, emphasizing the importance of real-time data analysis for situational awareness and cybersecurity compliance. Elastic Cloud, authorized for use on AWS GovCloud, is recommended as the optimal platform for deploying Elastic's solutions, offering speed, scalability, and security.

The automotive industry is undergoing a significant transformation driven by changes in consumer behavior, mobility preferences, and technological advancements in connectivity and AI. This shift is projected to result in a massive increase in revenue from "as-a-service" business models by 2035. To adapt, automakers are enhancing supply chain visibility and adopting software-defined vehicles to meet evolving customer expectations, despite challenges such as supply chain constraints and the need for continuous software upgrades. The proliferation of connected cars, expected to surpass 400 million by 2025, brings increased security risks due to the massive amount of data they generate and the complex software systems they contain. As a response, automakers are prioritizing cybersecurity measures, adhering to international regulations, and incorporating DevSecOps processes to mitigate potential vulnerabilities. Additionally, unifying data across the automotive value chain and leveraging machine learning can help manufacturers optimize operations and accelerate the deployment of new services, ultimately positioning themselves as transformative companies in the industry.

Elastic and Google Cloud have announced an expanded partnership aimed at enhancing data search, observation, and security capabilities for organizations using Google Cloud services. The collaboration will introduce three main integrations: a search experience combining Elasticsearch with BigQuery and BigLake, broader native integrations for Elastic Observability and Security across Google Cloud services, and enhanced visualization through Looker for Elastic insights. These integrations aim to facilitate efficient data management by allowing users to analyze data in place without requiring redundant copies, thereby providing near real-time insights. This partnership also extends support for Google Cloud services, such as Google Compute Engine and Google Kubernetes Engine, to improve cloud security and observability. Additionally, Elastic and Google Cloud are focusing on embedding search capabilities into the Data Cloud Ecosystem to allow seamless data indexing and analysis within existing data lakes, thereby reducing data movement and accelerating time to value.

The text discusses the integration of Elastic Cloud on Kubernetes with Microsoft Azure, emphasizing the advantages of deploying Elastic on Azure Kubernetes Service (AKS). This partnership aims to enhance data utilization and infrastructure efficiency, enabling retailers to improve product development, application monitoring, and security. The collaboration focuses on simplifying the deployment and management of Elastic solutions through the Azure Kubernetes Apps Marketplace, offering benefits like streamlined deployments, integrated billing, and easier scalability. By leveraging Kubernetes, Elastic users can automate administrative tasks and focus on innovation, with deployments contributing to the Microsoft Azure Consumption Commitment. The piece highlights the ongoing efforts to refine this collaboration, inviting users to explore these capabilities via the Azure Marketplace.

In the article, Lionel Palacin discusses how to integrate a store locator feature into an ecommerce storefront using Elastic's tools. The process involves utilizing geographic data in the form of latitude and longitude to map store locations and cities, which are then uploaded into Elastic Cloud's App Search engine for indexing and searchability. The guide outlines the creation of a proof-of-concept application using React, MapBox, and Elastic's Search UI to enable users to search for cities and find nearby stores on an interactive map, leveraging geo-based filtering and sorting capabilities for optimal user experience. The implementation of this functionality can enhance ecommerce platforms by providing a seamless omnichannel experience, encouraging direct customer engagement, and potentially driving business growth.

Joshua Quek's article provides a comprehensive guide on managing data automation in Elasticsearch using Kibana's "Stack Management" feature to efficiently move data through hot, warm, cold, and frozen tiers without coding. It introduces core concepts like high availability, redundancy, and data partitioning, explaining how data can be distributed across different datacenters or "Availability Zones" to ensure reliability and accessibility. The article uses an Excel spreadsheet analogy to illustrate data sharding and rollovers, emphasizing how partitioned data can enhance search efficiency. Quek outlines a step-by-step process to set up and manage data using Elastic "Data Streams," "Index Templates," and "Index Lifecycle Policies" within Kibana, demonstrating how these tools help control index sizes and facilitate the movement of indices through various data tiers. The guide encourages readers to explore Kibana and Elasticsearch, highlighting their capabilities in handling large datasets effectively.

Elastic has been awarded the 2022 CyberSecurity Breakthrough Award for Threat Intelligence Platform of the Year, highlighting its advanced capabilities in threat intelligence through Elastic Security. This recognition underscores Elastic's role in helping organizations better understand and respond to both existing and emerging cyber threats by offering streamlined, actionable intelligence. Elastic Security enhances threat response efficiency by providing turnkey integrations with various threat intelligence sources, allowing for the consolidation of Indicators of Compromise (IoCs) into a unified view. This approach empowers threat intelligence analysts and practitioners to quickly identify, investigate, and act on threats, thereby reducing adversary dwell times. James Johnson, managing director of CyberSecurity Breakthrough, emphasized the importance of understanding both local and widespread attacks, noting that Elastic provides a breakthrough solution for proactive threat response. Elastic expressed gratitude for the recognition and the trust of its customers in tackling complex security challenges.

ElasticON, Elastic's premier annual event, is set to engage its community through a series of events leading up to a global virtual conference from March 7–9, 2023. The event will feature in-person sessions in cities worldwide, including Singapore, New York City, Amsterdam, Tokyo, and Washington, D.C., offering attendees insights into Elastic's product roadmaps, expert advice, and innovative solutions for observability, security, and search applications. Participants can choose between technical breakout sessions and real-world use case discussions, with opportunities to network and learn from Elastic community members. Select cities will also offer a three-day Elasticsearch Engineer training course, providing hands-on experience in managing and scaling clusters. ElasticON aims to inspire and educate by showcasing how Elastic's search-powered solutions empower organizations to tackle critical challenges.

Version 8.4.3 of the Elastic Stack was released on October 5, 2022, addressing a significant bug introduced in the previous 8.4.2 release that affected Elasticsearch's cross_fields type, potentially causing query failures. Users relying on this feature are encouraged to upgrade to the latest version to prevent encountering errors related to term frequency discrepancies. The update is preferred over earlier patch versions in the 8.4.x series, and detailed information about fixed issues and changes across various products within the Elastic Stack, including Elasticsearch, Kibana, Beats, Logstash, and others, can be found in the 8.4.3 release notes.

Madhura Chopda, Director of Engineering at Elastic, offers practical advice on leading remote teams by emphasizing the importance of personal connections, diversity, and transparent communication. Originally from Pune, India, and now based in California, Madhura combines her love for people with her professional journey in engineering. Her experience at Elastic, a globally distributed company, has taught her to value authenticity and growth, both personally and within her teams. She stresses the significance of embracing failure as a learning opportunity, which has bolstered her confidence as a leader. Madhura's approach includes one-on-one interactions, clear communication of expectations, and organizing team-building activities to foster trust and cohesion, even across different time zones. Through various strategies, she encourages an inclusive culture that maximizes the potential of her team, while also highlighting the unique advantages she brings as a woman leader.

Elastic has announced the latest innovations in its Elastic Search Platform, a comprehensive data analytics platform designed to manage the full data lifecycle for any type of data across various environments, including on-premises, hybrid-cloud, and multi-cloud. The platform aims to help organizations handle the growing data challenges in a connected world by providing real-time insights to drive actionable results. It includes three core solutions: Elastic Enterprise Search, which enhances search and discovery experiences using both traditional and machine learning techniques; Elastic Observability, which transforms metrics, logs, and traces into actionable insights with new synthetic monitoring capabilities; and Elastic Security, which integrates various security functions to rapidly address threats. Built on the Elastic Stack, these solutions offer advanced machine learning capabilities and a simplified architecture to streamline data management across major cloud providers. Elastic also offers resources such as Quick Start guides and free trials to assist users in leveraging these new capabilities effectively.

Elastic Stack and Cloud are unveiling new capabilities to enhance user experience, particularly by facilitating faster insights and supporting a range of machine learning models, including vector search, while introducing a vision for a stateless Elasticsearch architecture. This development aims to create a fully-managed serverless Elastic Stack, offering simplified onboarding across popular cloud providers like AWS, Google Cloud, and Microsoft Azure. Elastic's integration with these platforms is designed to streamline data ingestion and analytics, with AWS users now able to initiate Elastic Cloud from the AWS console in just three clicks. The platform also enhances machine learning capabilities, supporting advanced features like vector search and modern natural language processing, thus improving search relevance and security threat detection. Elastic is investing in a new cloud-native architecture called Stateless Elasticsearch, which aims to optimize resource use by decoupling compute and storage, moving cluster state data to cloud storage services like Amazon S3, Azure Blobstore, and Google Cloud Storage. This approach is designed to enhance scalability and resilience while allowing users to independently manage compute resources. Existing and new Elastic Cloud customers can explore these features, with free trials and resources available for new users.

Elastic Observability has evolved from a logging powerhouse into a comprehensive solution for full-stack multi and hybrid-cloud observability, addressing the complexities of cloud-native environments. It focuses on enhancing visibility across applications, services, and infrastructure while managing vast data volumes. Innovations include Universal Profiling™ based on eBPF for deep system insights without instrumentation, and new synthetic monitoring features for better user experience analysis. Elastic also integrates machine learning for smarter operations, supported by an open, extensible platform and native OpenTelemetry support. This integration enables future-proofing, seamless telemetry data management, and improved CI/CD pipeline visibility. Elastic’s AIOps capabilities aim to streamline operations with automated anomaly detection and predictive analytics, supporting diverse data types and facilitating efficient root cause analysis. The platform provides powerful machine learning, mapping, and visualization tools, ensuring robust observability from pipeline to production.

Elastic Enterprise Search offers a comprehensive solution for developing search-powered applications, enhancing relevance and precision through a blend of traditional and machine learning-assisted techniques. Building on its open-source foundation, Elastic provides developers with both out-of-the-box and flexible tools to integrate search functionalities into applications, supported by a vast and innovative developer community. The platform incorporates advanced vector search capabilities, allowing for context-aware and similarity-based search experiences that interpret natural language and utilize non-textual inputs like images. This approach improves search relevance across various use cases, such as retail recommendation engines, support Q&A systems, image search for e-commerce, and academic research. Elastic Enterprise Search is accessible through Elastic Cloud, offering tools and training for new users, with options for a free trial and self-managed downloads.

Elastic Universal Profiling™ is an emerging technology in observability that allows continuous profiling of code in production environments across various programming languages without the need for service restarts or instrumentation. This system, which can function in both containerized and non-containerized settings, uses eBPF to capture essential performance data with minimal resource consumption, ensuring a low overhead of less than 1% CPU and 250MB of RAM. Unlike traditional profiling tools that often require per-app instrumentation and result in high overheads, Elastic Universal Profiling offers a comprehensive view of the entire system, allowing users to efficiently identify optimization opportunities across large fleets of ARM or x86 nodes. With powerful filtering capabilities and a user-friendly interface, it enables detailed analysis through features like flamegraphs, supporting total observability and effective debugging of performance issues, while also contributing to reduced energy consumption and CO2 footprint.

Elastic has announced several initiatives to enhance and modernize security operations for its customers, focusing on reducing mean time to respond (MTTR) and making threat intelligence more actionable. Key advancements include new Security Orchestration, Automation, and Response (SOAR) features that streamline analyst workflows and improve threat intelligence functions to facilitate quicker review and action on threats. Elastic's Extended Detection and Response (XDR) capabilities now include automated endpoint and cloud response actions, further reducing remediation time. Additionally, the platform centralizes threat intelligence from various sources into a unified view, allowing analysts to efficiently search, filter, and investigate Indicators of Compromise (IoCs) directly within Elastic Security's interface. This consolidation aims to decrease cognitive load and expedite response times. Elastic also continues to enhance its cloud security features, providing visibility and protection for cloud workloads. The company emphasizes its commitment to delivering ongoing innovations and improvements in security operations.