March 2023 Summaries

Financial institutions face challenges in achieving observability due to legacy systems, large data volumes, and regulatory demands, prompting technology leaders to invest in AI and machine learning (ML) for enhanced data analysis and cyberattack detection. In a recent panel discussion with Société Générale, Microsoft, and Elastic, experts emphasized the importance of flexible, scalable observability platforms with integrated AI and ML capabilities to address these challenges. Société Générale has implemented Elastic as an "Observability as a Service" to efficiently monitor its IT environment, optimize resources, and manage compliance with regulations like GDPR. The panel also highlighted the significance of cloud strategies, with Microsoft investing in secure, compliant solutions to support financial services institutions in navigating data sovereignty and regional compliance standards. By leveraging platforms like Elastic on Azure, financial services can enhance IT visibility and client outcomes while adapting to evolving cloud strategies.

Elasticsearch 8.7 introduces significant enhancements, including a 70% reduction in metric data storage for time series databases and a simplified vector search process for semantic text searches. This release also improves performance and monitoring capabilities for ingest pipelines and geo-spatial data, offering a more efficient storage solution with features like downsampling and time series data streams (TSDS). The update includes general availability of natural language processing (NLP) capabilities, allowing for tasks such as text classification and question answering, supported by state-of-the-art transformer models. Additionally, Elasticsearch introduces new features like HNSW-based KNN vector search over multiple fields and geohex_grid aggregation for geo_shapes. Ingest pipeline processing has been made 45% faster, and new monitoring dashboards offer enhanced visibility into pipeline performance. The Health API, now generally available, provides comprehensive insights into cluster health, surpassing the previous cluster health API by covering various health facets, such as master election stability and disk space availability, making it a valuable tool for monitoring and troubleshooting Elasticsearch deployments.

Elastic Enterprise Search 8.7 introduces a range of new features aimed at enhancing content ingestion and search experiences, including advanced filtering for the MySQL connector, which allows for more efficient data ingestion from large MySQL databases. The release also upgrades the Elastic Web Crawler with customizable content extraction and programmatic scheduling, enabling precise data extraction from web pages and flexible crawl scheduling. The connector framework has reached beta, supporting popular databases like Postgres, Oracle, and MS SQL, as well as cloud storage formats on GCP and Azure, allowing for customized data source connections using languages like Python. Additionally, the new web and search analytics client helps capture and analyze user behavior to optimize search relevance and website performance. The release is available on Elastic Cloud and for self-managed experiences via Elastic Stack and cloud orchestration tools.

Elastic Observability 8.7 enhances the management and monitoring of synthetic monitoring, serverless applications, and Kubernetes deployments, offering new capabilities such as distributed tracing for Azure Functions and monitoring of Google Kubernetes Engine (GKE) Autopilot clusters. This version introduces a public beta for a new synthetic monitoring UI in Kibana 8.7, which simplifies the setup and management of synthetic monitors, aiming to ensure reliable user experiences. The update supports serverless observability with auto-instrumentation for Azure Functions and provides easy deployment of Elastic Agent on GKE Autopilot clusters with certified collaboration with Google. Elastic Observability 8.7 is available on Elastic Cloud and for self-managed experiences, with existing customers able to access features directly from the Elastic Cloud console.

Elasticsearch 8.7 introduces a new Health API designed to diagnose and resolve cluster health issues by providing root cause analysis and actionable insights. Unlike the existing cluster health API, which focuses on indices and data streams, the new Health API performs comprehensive checks and reports using both high-level and detailed modes, indicated by red, yellow, or green statuses. The API uses various health indicators to track metrics such as shard availability and disk space, offering a diagnosis and step-by-step resolution guide when problems are detected. For example, the API can identify issues like unassigned shards or unsuccessful snapshot policies and suggest corrective actions. Elastic has integrated this Health API into Elastic Cloud to offer users insights into their deployment health, enabling them to address issues proactively. The API's flexibility allows for automatic step execution to resolve diagnosed problems, ensuring the overall health and functionality of Elasticsearch clusters.

Elastic Stack 8.7 introduces several enhancements, including the ability to apply complex filters in Kibana, noise reduction for alerting, and new anomaly detection methods. The update simplifies semantic search implementation with a one-step API, improving data analysis and search experiences. Onboarding guides for various use cases, such as building search-powered applications, SIEM, and Kubernetes monitoring, are also provided. The release supports matured semantic search and NLP capabilities, offering streamlined text embedding and the deployment of trained NLP models. Users can now create complex filter relationships with multiple AND/OR clauses, and the Unified Histogram in Discover allows for further data exploration. Alert flapping detection and alert action summarization reduce notification noise, improving response times. Anomaly detection is enhanced with change point detection and geo data capabilities, while a new geo wizard simplifies anomaly detection job setup. The release also includes improvements in machine learning job management and configuration, offering better visibility and ease of use.

Elastic 8.7 has been officially released, offering enhanced features across Elastic Security, Observability, and Enterprise Search, all built on the Elasticsearch Platform. The update introduces unified solutions for security, including SIEM, endpoint, and cloud security, to swiftly address cyber threats. Elastic Observability now supports serverless distributed tracing for Azure Functions and monitoring for Google Kubernetes Engine Autopilot clusters, along with a new Synthetics Monitoring interface. Elastic Enterprise Search continues to set the standard for search and discovery experiences, with improvements detailed in the release notes. The platform also includes the general availability of a time series database (TSDB), facilitating more efficient data handling. Elastic 8.7 is available on Elastic Cloud, incorporating all these new features to convert data into actionable insights.

Elastic Security 8.7 introduces several enhancements aimed at reducing alert fatigue, lowering mean time to respond (MTTR), and improving security in cloud environments. The release integrates Security Information and Event Management (SIEM), cloud security, and endpoint security, offering new data source integrations and expanded prebuilt detection content. It includes features like automated rules and analytics for threat detection, streamlined workflows for alert triage, and automation in investigation and response processes. The update also introduces one-click Elastic Agent integrations with popular security sources, and new ingest health dashboards to monitor data ingestion. Alert management is improved with visualization and grouping capabilities, while the response console now allows remote file retrieval from compromised hosts. Additionally, the Cloud Security Posture Management (CSPM) feature helps identify and remediate cloud configuration risks, offering a dashboard view and step-by-step remediation guidance. This release is available across various subscription tiers and aims to empower security teams to efficiently manage and secure their environments.

Elastic Security has launched new capabilities to modernize cloud security operations for AWS, enhancing its position as a comprehensive Cloud Native Application Protection Platform (CNAPP). These advancements include Cloud Security Posture Management (CSPM) for AWS, Container Workload Security, and Cloud Vulnerability Management, building on existing features like Kubernetes Security Posture Management (KSPM) and Cloud Workload Protection (CWP). The pressing need for such solutions arises from the misconception that cloud workloads are inherently secure, despite the shared responsibility model of cloud security, which often results in misconfigurations and vulnerabilities. Elastic Security addresses these challenges by providing a unified platform that merges SIEM, endpoint, and cloud security, minimizing risks associated with fragmented security approaches and vendor complexity. This integration is essential as organizations increasingly adopt cloud-first models, with Gartner citing that 99% of cloud failures are customer-related due to errors like misconfigurations. Elastic Security's comprehensive suite aims to provide deep visibility, reduce complexity, and enhance threat detection and remediation across hybrid and multi-cloud environments.

Elasticsearch 8.7.0 has introduced advanced hexagonal spatial analytics through the integration of the Uber H3 library, enhancing the capability to perform geo-spatial aggregations on a hexagonal grid, which offers significant advantages over traditional rectangular grids. Initially, the H3 library was ported from C to Java and integrated into Elasticsearch and Kibana, allowing for geo-point aggregations, but the recent update now supports geo_shape aggregations. This transition required performance enhancements through code refactoring and optimization of trigonometric calculations. The hexagonal grid system offers a more consistent area and distance between tiles globally, providing more accurate statistical results compared to rectangular grids. Kibana also supports these enhancements, enabling users to visualize and compare geohex and geotile aggregations, offering a more robust toolset for geospatial data analysis.

Elastic Observability is a comprehensive solution designed to manage and monitor Kubernetes clusters and cloud-native applications by unifying telemetry data, including metrics, logs, and traces, into a single platform powered by Elasticsearch. It supports various open technologies like Kubernetes, OpenTelemetry, Prometheus, and Istio, allowing operations engineers to deploy and observe applications consistently across different cloud environments without being tied to specific tools. The platform leverages advanced machine learning and analytics to transform data into actionable insights, enhancing the efficiency of site reliability engineers (SREs) by focusing on operations and business performance rather than managing disparate tools. Elastic Observability provides out-of-the-box dashboards and visualization capabilities, enabling engineers to analyze logs, monitor application interactions, and gain granular insights into the behavior of clusters and applications. Its native support for OpenTelemetry eliminates vendor lock-in, offering flexibility in choosing observability tools while providing integration capabilities to ingest data from multiple sources, including Prometheus metrics and Fluentd logs, thereby ensuring a streamlined and efficient observability experience.

KubeCon Europe 2023 in Amsterdam will feature Elastic Observability as a key participant, offering insights and solutions for observability and security in Kubernetes environments. Elastic invites attendees to visit their booth for demonstrations and to engage with their team of engineers and experts. A significant announcement at the event was the acceptance of Elastic's proposal to merge the Elastic Common Schema (ECS) with OpenTelemetry Semantic Conventions, aiming to create a unified open schema under OpenTelemetry, which is expected to influence the future of observability and security deployments. Elastic will also host a User Group meetup on April 18th to foster community engagement and provide further learning opportunities.

Elastic Observability is a comprehensive solution designed to meet the demands of today's always-on applications by providing site reliability engineers (SREs) with tools to manage and analyze vast amounts of telemetry data from applications, infrastructure, and business operations. Built on the Elastic Stack, it integrates machine learning, search capabilities, and a unified data platform to offer end-to-end visibility and insights into cloud-native and distributed systems. Elastic Observability supports a wide range of technologies, including Kubernetes and serverless architectures, and boasts over 350 integrations for seamless data ingestion. It enhances operational efficiency by consolidating metrics, logs, and traces, while its open-source roots and support for open standards like OpenTelemetry allow for easy integration with existing systems. The platform's AIOps capabilities improve predictability and reduce mean time to resolution by automating anomaly detection and providing actionable insights. Additionally, Elastic Observability's Time Series Database optimizes storage costs, and its newly introduced SLO/SLI monitoring helps SREs maintain business performance objectives. The solution's flexibility in supporting custom data models and machine learning frameworks makes it a versatile tool for organizations looking to enhance their observability capabilities and achieve operational excellence.

Kathy Hsu, Elastic’s Federal Sales Director, emphasizes the significance of authenticity in her sales approach, especially within the federal space, where understanding and supporting customer missions is vital. Her journey began in Silicon Valley, working at Apple and a startup, which ignited her passion for tech sales and serving federal agencies like the Department of Defense and the Intelligence Community. At Elastic, she leverages her skills and relationships to deliver mission support, remaining authentic to foster trust with clients. As an Asian American woman in a typically underrepresented field, she has focused on developing a personal brand that stands out, while also balancing her role as a new mother. Hsu’s dedication to authenticity has allowed her to connect deeply with clients and earn their trust, which she views as crucial in the federal business landscape.

The text explores the evolving role of observability in the face of tightening macroeconomic conditions and technological advancements. It highlights the increasing importance of observability for ensuring successful digital transformation and cloud adoption, particularly as organizations strive to optimize operational efficiency and reduce costs. The text identifies key trends, such as the need for integrating operational and business data, consolidating observability tools, and the rise of cloud-native technologies like Kubernetes and serverless. It emphasizes the growing significance of machine learning and analytics for providing actionable insights and automating IT operations, and notes the importance of open standards to avoid vendor lock-in. Additionally, the establishment of Observability Centers of Excellence is recommended to drive best practices and standardize processes across organizations. Finally, the text addresses a shift towards more transparent and value-driven pricing models for observability solutions, which are seen as crucial for navigating economic uncertainty while aligning with long-term strategic goals.

NORBr is a leading global distributor of payment services for digital merchants, offering a platform that connects, manages, and benchmarks payment services through a single API, allowing merchants to optimize payment processing costs and focus on business performance. The platform employs a no-code routing engine for seamless transaction management, addressing the complexities of modern payment ecosystems. By using Elastic, NORBr monitors vital business metrics and sets alerts for potential issues, ensuring high success rates and meeting industry benchmarks. This proactive monitoring system allows NORBr to communicate effectively with merchants and partners, maintaining its service level agreements and maximizing uptime, which helps merchants secure maximum revenue. The successful integration of Elastic has encouraged NORBr to explore further technological advancements, including the development of a new AI algorithm.

Elastic has announced the AWS Service Validation for Amazon Linux 2023, allowing customers to deploy the Elastic Stack, including Elasticsearch, Kibana, Logstash, and the Elastic Agent, on this new version of Amazon Linux. This certification highlights the collaboration between Elastic and AWS, ensuring quality assurance and architecture standards for running Elastic Stack on Amazon Linux 2023, which is designed for general-purpose workloads with features like long-term support, improved security, and faster access to innovations. Amazon Linux 2023 offers a predictable release cadence, enhanced security options like SELinux and OpenSSL 3.0, and the stability needed for managing long project lifecycles, with major versions released every two years and five years of support. Elastic's participation in the AWS Service Ready Program, alongside its existing certifications such as AWS Graviton Ready and AWS PrivateLink Ready, underscores its growing partnership with AWS, offering flexible deployment options both in Elastic Cloud regions and customer-managed environments. Customers can begin with a seven-day free trial via AWS Marketplace, and any new features or functionalities remain subject to Elastic's discretion.

Philipp Kahr's blog post offers an in-depth analysis of Lichess data, highlighting trends and patterns in online chess games. Since its inception in 2013, Lichess has hosted over 4 billion games, with a significant increase in activity beginning in early 2020. The analysis reveals that white pieces have a slight winning edge over black, and Blitz games are the most popular format, comprising nearly 2 billion of the total matches. Rapid games have gained popularity since their introduction in 2016, while Correspondence games remain the least favored. The study also explores various time control settings, noting that the most popular increment is 180 seconds with an additional 2 seconds per move. Kahr employs tools like field statistics and visualization techniques to dissect the data, offering insights into the dynamics of online chess.

Rain Hu, Area Vice President at Elastic, shares insights and advice on building a successful career in tech sales, emphasizing the importance of passion, mentorship, teamwork, feedback, and resilience. Rain's journey began shortly after her MBA when she was drawn to a sales position despite having no prior experience, captivated by the prospect of helping clients and the allure of commissions. Over fifteen years, she has held executive roles, eventually joining Elastic in 2018, attracted by its culture and growth opportunities. Rain manages a diverse team across multiple countries and credits her success to learning from colleagues and fostering connections. Her key advice for those in tech sales includes enjoying your work, seeking mentors, valuing teamwork, embracing feedback, and maintaining resilience, inspired by her son's experiences in competitive chess. Elastic's supportive culture has been instrumental in her career growth, providing the flexibility needed to balance professional and personal life.

The article "Demystifying SIEM migration: Pitfalls to avoid and tips for ensuring success" by James Spiteri discusses the complexities and strategies involved in migrating to a new Security Information and Event Management (SIEM) solution, likening the process to moving house. It emphasizes the importance of thorough planning, familiarizing oneself with the new SIEM's capabilities, and critically assessing existing data sources and use cases to avoid unnecessary migration of outdated elements. The migration process is broken into three phases: Planning and Design, Execution and Implementation, and Operationalize and Refine. During the planning phase, it is crucial to consider team needs and strengths, while the execution phase should leverage vendor tools for efficiency. The operational phase involves integrating and regularly updating procedures to incorporate new features and third-party tools. The article encourages ongoing feedback to vendors to enhance the migration experience and suggests utilizing community resources for problem-solving.

The 2022 Elastic Excellence Awards celebrated innovative and impactful uses of Elastic's data solutions across diverse sectors such as healthcare, finance, defense, and public service. Zuellig Pharma received the Cause Award for its eZRx platform, which facilitated healthcare access during the COVID-19 pandemic. Cisco won the Business Transformation Award in Enterprise Search for enhancing search capabilities across its platforms, while Wells Fargo was recognized in Observability for its Distributed Tracing solution that improved application resiliency. ECS was honored in the Security category for its advanced threat detection capabilities. Police Scotland earned the Solve with Search Award for providing officers quick access to crucial data, and Inwatec received the Innovation Award for automating industrial laundry workflows. Lawrence Livermore National Laboratory was acknowledged in the Public Sector category for its system security and monitoring advancements, and James Pittiglio was named Certified Professional of the Year for his contributions to cybersecurity initiatives across U.S. federal agencies. The awards highlight the Elastic community's resilience and innovation in addressing complex challenges.

Philipp Kahr and Francesco Gualazzi explore the challenges of importing and processing over 4 billion chess games from Lichess, utilizing Elasticsearch and Universal Profiling to address performance issues in their custom Python implementation. The project faced significant hurdles, especially in parsing games using the python-chess library, which was identified as a bottleneck due to its inefficiency in handling large volumes of game data. By employing Elastic APM and Universal Profiling, the authors pinpointed slow areas in their code, leading to the development of a custom parser that significantly improved processing speed. They replaced the python-chess library with a line operator to create game strings and utilized regex for move comparison, resulting in the ability to parse 1.6 million games per minute. This enhancement reduced the time required to process the games dramatically and allowed for advanced data analysis within the Elastic Stack. The post is part of a series that delves into chess game trends and the impact of external factors like YouTube tutorials on chess strategies.

The Continuous Diagnostics and Mitigation (CDM) dashboard, operated by the Cybersecurity and Infrastructure Security Agency (CISA), centralizes data from over 100 civilian agencies to enhance cybersecurity by uncovering and sharing vulnerabilities. Powered by Elastic's technology, the dashboard ingests, indexes, and visualizes petabytes of structured, unstructured, and semi-structured data, allowing comprehensive cross-agency data analysis. This centralized approach enables CISA to detect hidden threats and respond swiftly, reducing the risk of cyber threats like the WannaCry ransomware attack and Log4j vulnerabilities. Elastic's cross-cluster search capability enhances this process by providing seamless visibility across disparate environments, thereby eliminating data silos and facilitating rapid threat detection and remediation. The CDM program, celebrating its 10th anniversary, serves as a model for public sector organizations to consolidate tools, save costs, and develop unified data strategies, demonstrating Elastic's value as a strategic asset in managing government data for security, logging, and actionable insights.