#OpenTelemetry

[govindhtech]

Cassandra To Spanner Proxy Adaptor Eases Yahoo’s Migration

Yahoo’s migration process is made easier with a new Cassandra to Spanner adapter.

A popular key-value NoSQL database for applications like caching, session management, and real-time analytics that demand quick data retrieval and storage is Cassandra. High performance and ease of maintenance are ensured by its straightforward key-value pair structure, particularly for huge datasets.

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However, this simplicity also has drawbacks, such as inadequate support for sophisticated queries, the possibility of data repetition, and challenges when it comes to modeling complicated relationships. In order to position itself for classic Cassandra workloads, Spanner, Google Cloud’s always-on, globally consistent, and nearly infinite-scale database, blends the scalability and availability of NoSQL with the strong consistency and relational nature of traditional databases. With the release of the Cassandra to Spanner Proxy Adapter, an open-source solution allowing plug-and-play migrations of Cassandra workloads to Spanner without requiring modifications to the application logic, switching from Cassandra to Spanner is now simpler than ever.

Spanner for NoSQL workloads

Strong consistency, high availability, nearly infinite scalability, and a well-known relational data model with support for SQL and ACID transactions for data integrity are all features that Spanner offers. Being a fully managed service, it facilitates operational simplification and frees up teams to concentrate on developing applications rather than managing databases. Additionally, by reducing database downtime, Spanner’s high availability even on a vast global scale supports business continuity.

Spanner is always changing to satisfy the demands of contemporary companies. Improved multi-model capabilities including graph, full-text, and vector searches, higher analytical query performance with Spanner Data Boost, and special enterprise features like geo-partitioning and dual-region settings are some of the most recent Spanner capabilities. These potent features, together with Spanner’s alluring price-performance, provide up a world of fascinating new opportunities for Cassandra users.

Yahoo has put the Cassandra to Spanner adapter to the test

“Spanner sounds like a leap forward from Cassandra,” in case you were wondering. How can I begin? The proxy adapter offers a plug-and-play method for sending Cassandra Query Language (CQL) traffic from your client apps to Spanner. The adapter works as the application’s Cassandra client behind the scenes, but it communicates with Spanner internally for all data manipulation operations. The Cassandra to Spanner proxy adapter simply works without requiring you to migrate your application code!

Yahoo benefited from increased performance, scalability, consistency, and operational efficiency after successfully migrating from Cassandra to Spanner. Additionally, the proxy adapter made the migration process simple.

Reltio is another Google Cloud client that has made the switch from Cassandra to Spanner. Reltio gained the advantages of a fully managed, globally distributed, and highly consistent database while minimizing downtime and service disruption through an easy migration process.

These success examples show that companies looking to upgrade their data architecture, uncover new capabilities, and spur creativity may find that switching from Cassandra to Spanner is a game-changer.

How is your migration made easier by the new proxy adapter? The following procedures are involved in a typical database migration:Image credit to Google Cloud

Some of these stages are more complicated than others, such as moving your application (step 4) and moving the data (step 6). Migrating a Cassandra-backed application to point to Spanner is made much easier by the proxy adaptor. A high-level summary of the procedures needed to use the new proxy adapter is provided here:

Assessment: After switching to Spanner, determine which of your Cassandra schema, data model, and query patterns may be made simpler.

Schema design: The documentation thoroughly discusses the similarities and differences between Spanner’s and Cassandra’s table declaration syntax and data types. For optimum efficiency, you can additionally utilize relational features and capabilities with Spanner, such as interleaved tables.

Data migration: To move your data, follow these steps:

Bulk load: Utilizing programs like the Spanner Dataflow connector or BigQuery reverse ETL, export data from Cassandra and import it into Spanner.

Replicate incoming information: Use Cassandra’s Change Data Capture (CDC) to instantly replicate incoming updates to Spanner from your Cassandra cluster.

Updating your application logic to execute dual-writes to Cassandra and Spanner is an additional option. If you want to make as few modifications to your application code as possible, google Cloud do not advise using this method.

Update your Cassandra setup and set up the proxy adapter: The Cassandra to Spanner Proxy Adapter operates as a sidecar next to your application; download and start it. The proxy adapter uses port 9042 by default. Remember to modify your application code to point to the proxy adapter if you choose to use a different port.

Testing: To make sure everything functions as planned, thoroughly test your migrated application and data in a non-production setting.

Cutover: Move your application traffic to Spanner as soon as you are comfortable with the migration. Keep a watchful eye out for any problems and adjust performance as necessary.

What does the new proxy adapter’s internal components look like?

The application sees the new proxy adaptor as a Cassandra client. The Cassandra endpoint’s IP address or hostname has changed to point to the proxy adapter, which is the only discernible change from the application’s point of view. This simplifies the Spanner migration without necessitating significant changes to the application code.Image credit to Google Cloud

To provide a one-to-one mapping between every Cassandra cluster and its matching Spanner database, Google builds the proxy adapter. A multi-listener architecture is used by the proxy instance, and each listener is connected to a different port. This makes it possible to handle several client connections at once, with each listener controlling a separate connection to the designated Spanner database.

The complexities of the Cassandra protocol are managed by the translation layer of the proxy. This layer handles buffers and caches, decodes and encodes messages, and most importantly parses incoming CQL queries and converts them into counterparts that are compatible with Spanner.

To gather and export traces to Cloud Trace, the proxy adapter supports OpenTelemetry.

Taking care of common issues and difficulties

Let’s talk about some issues you might be having with your migrations:

Cost: Take a look at Accenture’s benchmark result, which shows that Spanner guarantees cost effectiveness in addition to consistent latency and throughput. To help you utilize all of Spanner’s features, the company has also introduced a new tiered pricing structure called “Spanner editions,” which offers improved cost transparency and cost-saving options.

Latency increases: When executing the proxy adapter in a Docker container, Google advises running it on the same host as the client application (as a side-car proxy) or on the same Docker network to reduce an increase in query latencies. Additionally, Google advised limiting the proxy adapter host’s CPU usage to less than 80%.

Design flexibility: Spanner’s more rigid relational design gives benefits in terms of data integrity, query capability, and consistency, but Cassandra offers more flexibility.

Learning curve: There are some distinctions between Cassandra’s and Spanner’s data types. Examine this thorough material to help with the transition.

Start now

For companies wishing to take advantage of the cloud’s full potential for NoSQL workloads, Spanner is an appealing choice due to its robust consistency, streamlined operations, improved data integrity, and worldwide scalability. Google Cloud is making it simpler to plan and implement your migration strategy with the new Cassandra to Spanner proxy adapter, allowing your company to enter a new era of data-driven innovation.

Read more on govindhtech.com

[mp3monsterme]

Checking your OpenTelemetry pipeline with Telemetrygen

Testing OpenTelemetry configuration pipelines without resorting to instrumented applications, particularly for traces, can be a bit of a pain. Typically, you just want to validate you can get an exported/generated signal through your pipeline, which may not be the OpenTelemetry Collector (e.g., FluentBit or commercial solutions such as DataDog). This led to the creation of Tracegen, and then the…

[kubernetesframework]

How to Test Service APIs

When you're developing applications, especially when doing so with microservices architecture, API testing is paramount. APIs are an integral part of modern software applications. They provide incredible value, making devices "smart" and ensuring connectivity.

No matter the purpose of an app, it needs reliable APIs to function properly. Service API testing is a process that analyzes multiple endpoints to identify bugs or inconsistencies in the expected behavior. Whether the API connects to databases or web services, issues can render your entire app useless.

Testing is integral to the development process, ensuring all data access goes smoothly. But how do you test service APIs?

Taking Advantage of Kubernetes Local Development

One of the best ways to test service APIs is to use a staging Kubernetes cluster. Local development allows teams to work in isolation in special lightweight environments. These environments mimic real-world operating conditions. However, they're separate from the live application.

Using local testing environments is beneficial for many reasons. One of the biggest is that you can perform all the testing you need before merging, ensuring that your application can continue running smoothly for users. Adding new features and joining code is always a daunting process because there's the risk that issues with the code you add could bring a live application to a screeching halt.

Errors and bugs can have a rippling effect, creating service disruptions that negatively impact the app's performance and the brand's overall reputation.

With Kubernetes local development, your team can work on new features and code changes without affecting what's already available to users. You can create a brand-new testing environment, making it easy to highlight issues that need addressing before the merge. The result is more confident updates and fewer application-crashing problems.

This approach is perfect for testing service APIs. In those lightweight simulated environments, you can perform functionality testing to ensure that the API does what it should, reliability testing to see if it can perform consistently, load testing to check that it can handle a substantial number of calls, security testing to define requirements and more.

Read a similar article about Kubernetes API testing here at this page.

[virtualizationhowto]

SigNoz: Free and Open Source Syslog server with OpenTelemetry

SigNoz: Free and Open Source Syslog server with OpenTelemetry @signozhq #homelab #SigNozOpenSourceAlternative #DatadogVsSigNoz #MonitorApplicationsWithSigNoz #ApplicationPerformanceManagementTools #DistributedTracingWithSigNoz #MetricsAndDashboards

I am always on the lookout for new free and open-source tools in the home lab and production environments. One really excellent tool discovered recently is a tool called SigNoz. SigNoz is a free and open-source syslog server and observability program that provides an open-source alternative to Datadog, Relic, and others. Let’s look at SigNoz and see some of the features it offers. We will also…

[hackernewsrobot]

OpenTelemetry Tracing in < 200 lines of code

https://jeremymorrell.dev/blog/minimal-js-tracing/

[ericvanderburg]

Telemetry Pipelines Workshop: Integrating Fluent Bit With OpenTelemetry, Part 1

http://securitytc.com/TCRLwP

[insurgentepress]

El Radar Tecnológico de Thoughtworks destaca el impacto de la GenAI y los factores clave que moldean el desarrollo y entrega de software en la actualidad

Thoughtworks, consultoría tecnológica global que integra estrategia, diseño e ingeniería para impulsar la innovación digital, presenta hoy la edición número 32 del Radar Tecnológico. Este informe semestral se basa en las experiencias de primera línea de Thoughtworks resolviendo los desafíos empresariales más complejos de sus clientes.

La edición más reciente del Radar Tecnológico de Thoughtworks ofrece una visión completa del mundo tecnológico en constante evolución. Aunque en esta edición se analiza de cerca el impacto significativo de la inteligencia artificial generativa, el Radar mantiene su enfoque en las diversas tecnologías, herramientas y técnicas que están transformando la forma en que construimos y entregamos software. Algunos de los temas clave incluyen la aplicación práctica de la IA, la modernización de las prácticas de observabilidad y la importancia crítica de la gestión de datos en los sistemas modernos.

"El Radar Tecnológico de Thoughtworks siempre ha sido una guía de las tecnologías que moldean nuestra industria," afirma Rachel Laycock, Chief Technology Officer de Thoughtworks. "Esta edición es especialmente reveladora, ya que resalta la necesidad de equilibrar el entusiasmo en torno a la IA generativa con un enfoque pragmático para su adopción. También ponemos énfasis en los elementos fundamentales de la entrega de software, como el pensamiento de producto aplicado a los datos y la evolución de la observabilidad, esenciales para construir sistemas resilientes y efectivos."

El Volumen 32 del Radar Tecnológico vuelve a incluir más de 100 blips, destacando una amplia variedad de herramientas, técnicas, plataformas, lenguajes y frameworks. Además, se identificaron cuatro grandes temas que reflejan las principales narrativas surgidas durante la elaboración del Radar:

Agentes supervisados en asistentes de codificación: Los asistentes de codificación basados en IA son cada vez más capaces de implementar directamente dentro del entorno de desarrollo integrado (IDE), navegando, modificando código e incluso corrigiendo errores. Aunque estas herramientas prometen agilizar el desarrollo, se recomienda cautela para evitar caer en la complacencia con el código generado por IA, subrayando la necesidad continua de supervisión humana. El enfoque sigue siendo supervisado, donde developers guían y supervisan las acciones del agente de IA.

Evolución de la observabilidad: El campo de la observabilidad está evolucionando rápidamente, impulsado por la creciente complejidad de las arquitecturas distribuidas. Se observa un mayor enfoque en herramientas de observabilidad para modelos de lenguaje extensos (LLM) y en la adopción creciente de OpenTelemetry para una observabilidad estandarizada. Esta evolución refleja el reconocimiento cada vez mayor de la importancia de la observabilidad en la industria.

La "R" en RAG: El aspecto de recuperación en la generación aumentada por recuperación (RAG) es un área clave de desarrollo, centrada en personalizar los prompts de LLM para generar respuestas relevantes y útiles. Están surgiendo nuevas técnicas como Corrective RAG, Fusion-RAG y Self-RAG, diseñadas para mejorar la recuperación y optimizar los resultados de los LLM, destacando la rápida evolución de los métodos de recuperación efectivos dentro del ecosistema de IA generativa.

Domando la frontera de los datos: La gestión de datos se está enfocando en manejar datos ricos y complejos, más allá del simple volumen. Garantizar una gestión y empaquetado efectivo de los datos es esencial para su aprovechamiento en aplicaciones de IA y análisis. Este tema también subraya la importancia del "pensamiento de producto de datos", que promueve aplicar principios de gestión de productos a los activos de datos para mejorar su usabilidad y valor.

Visita www.thoughtworks.com/es-es/radar para explorar la versión interactiva del Radar o descargar la versión en PDF.

[infernovm]

Splunk launches inventory tool to simplify OpenTelemetry monitoring

Splunk this week announced a new Service Inventory product that uses OpenTelemetry to offer a comprehensive view into service instrumentation, which the observability company says will solve a critical pain point in modern infrastructure monitoring by providing visibility across cloud and Kubernetes environments. Splunk’s Service Inventory helps organizations identify gaps in their observability…

[govindhtech]

Opentelemetry vs Prometheus: Opentelemetry Overview

Opentelemetry vs Prometheus

Prometheus monitors, stores, and visualises metrics but does not keep logs or support traces for root cause analysis. The application cases of Prometheus are more limited than OpenTelemetry.

Programming language-agnostic integrations allow OpenTelemetry to track more complicated metrics than Prometheus. Automated instrumentation models make OTel more scalable and extensible than the Prometheus. OpenTelemetry requires a back-end infrastructure and no storage solution, unlike Prometheus.

Quick summary Prometheus calculates cumulative measurements as a total, whereas OpenTelemetry uses deltas. Prometheus stores short-term data and metrics, whereas OTel may be used with a storage solution. OpenTelemetry uses a consolidated API to send or pull metrics, logs, and traces and transform them into a single language, unlike Prometheus. Prometheus pulls data from hosts to collect and store time-series metrics. OTel can translate measurements and is language agonistic, providing developers additional options. Data and metrics are aggregated by Prometheus using PromQL. Web-visualized metrics and customisable alarms are provided by Prometheus. Integration with visualisation tools is required for OpenTelemetry. OTel represents metric values as integers instead of floating-point numbers, which are more precise and understandable. Prometheus cannot use integer metrics. Your organization’s demands will determine which option is best. OpenTelemetry may be better for complex contexts with dispersed systems, data holistic comprehension, and flexibility. This also applies to log and trace monitoring.

Prometheus may be suited for monitoring specific systems or processes using alerting, storage, and visualisation models.

Prometheus and OpenTelemetry Application performance monitoring and optimisation are crucial for software developers and companies. Enterprises have more data to collect and analyse as they deploy more applications. Without the correct tools for monitoring, optimising, storing, and contextualising data, it’s useless.

Monitoring and observability solutions can improve application health by discovering issues before they happen, highlighting bottlenecks, dispersing network traffic, and more. These capabilities reduce application downtime, improve performance, and enhance user experience.

App monitoring tools OpenTelemetry and the Prometheus are open-source Cloud Native Computing Foundation (CNCF) initiatives. An organization’s goals and application specifications determine which data and functions need which solutions. Before using OpenTelemetry or Prometheus, you should know their main distinctions and what they offer.

Java Opentelemetry OTel exports these three forms of telemetry data to Prometheus and other back ends. This lets developers chose their analysis tools and avoids vendor or back-end lock-in. OpenTelemetry integrates with many platforms, including Prometheus, to increase observability. Its flexibility increases because OTel supports Java, Python, JavaScript, and Go. Developers and IT staff may monitor performance from any browser or location.

Its ability to gather and export data across multiple applications and standardise the collecting procedure make OpenTelemetry strong. OTel enhances distributed system and the microservice observability.

For application monitoring, OpenTelemetry and Prometheus integrate and operate well together. The DevOps and IT teams can use OpenTelemetry and Prometheus to collect and transform information for performance insights.

Opentelemetry-demo OpenTelemetry (OTel) helps generate, collect, export, and manage telemetry data including logs, metrics, and traces in one place. OTel was founded by OpenCensus and OpenTracing to standardise data gathering through APIs, SDKs, frameworks, and integrations. OTel lets you build monitoring outputs into your code to ease data processing and export data to the right back end.

Telemetry data helps determine system health and performance. Optimised observability speeds up troubleshooting, improves system reliability, reduces latency, and reduces application downtime.

Opentelemetry architecture APIs OpenTelemetry APIs uniformly translate programming languages. This lets APIs capture telemetry data. These APIs help standardise OpenTelemetry measurements.

SDKs Software development tools. Frameworks, code libraries, and debuggers are software development building elements. OTel SDKs implement OpenTelemetry APIs and provide telemetry data generation and collection tools.

OpenTelemetry collector The OpenTelemetry collector accepts, processes, and exports telemetry data. Set OTel collectors to filter specified data types to the back end.

Instrumentation library OTel offers cross-platform instrumentation. The instrumentation libraries let OTel integrate with any programming language.

Opentelemetry collector contrib Telemetry data including metrics, logs, and traces can be collected without modifying code or metadata using the OpenTelemetry protocol (OTLP).

Metrics A high-level overview of system performance and health is provided via metrics. Developers, IT, and business management teams decide what metrics to track to fulfil business goals for application performance. A team may measure network traffic, latency, and CPU storage. You may also track application performance trends with metrics.

Logs Logs record programme or application events. DevOps teams can monitor component properties with logs. Historical data might demonstrate performance, thresholds exceeded, and errors. Logs track application ecosystem health.

Traces Traces provide a broader picture of application performance than logs and aid optimisation. They track a request through the application stack and are more focused than logs. Traces let developers pinpoint when mistakes or bottlenecks occur, how long they remain, and how they effect the user journey. This data improves microservice management and application performance.

What’s Prometheus? Application metrics are collected and organised using Prometheus, a monitoring and alerting toolkit. SoundCloud created the Prometheus server before making it open-source.

End-to-end time-series data monitoring is possible using Prometheus. Time-series metrics capture regular data, such as monthly sales or daily application traffic. Visibility into this data reveals patterns, trends, and business planning projections. Prometheus collects application metrics for dedicated functions that DevOps teams want to monitor after integration with a host.

Using PromQL, Prometheus metrics offer data points with the metric name, label, timestamp, and value. For better visualisation, PromQL lets developers and IT departments aggregate data metrics into histograms, graphs, and dashboards. Enterprise databases and exporters are accessible to Prometheus. Application exporters pull metrics from apps and endpoints.

Prometheus tracks four metrics Counters Counters measure increasing numerical values. Counters count completed tasks, faults, and processes or microservices.

Gauges Gauges measure numerical data that fluctuate due to external variables. They can monitor CPU, memory, temperature, and queue size.

Histograms Events like request duration and answer size are measured via histograms. They split the range of these measurements into buckets and count how many fall into each bucket.

Summaries Summaries assess request durations and response size like histograms, but they also count and total all observed values.

Prometheus’ data-driven dashboards and graphs are also useful.

Benefits of Prometheus Prometheus provides real-time application monitoring for accurate insights and fast troubleshooting. It also permits function-specific thresholds. When certain thresholds are hit or exceeded, warnings might speed up problem resolution. Prometheus stores and provides analytics teams with massive amounts of metrics data. It stores data for instant examination, not long-term storage. Prometheus typically stores data for two to fifteen days.

Prometheus works perfectly with Kubernetes, an open-source container orchestration technology for scheduling, managing, and scaling containerised workloads. Kubernetes lets companies create hybrid and multicloud systems with many services and microservices. These complicated systems gain full-stack observability and oversight with Prometheus and Kubernetes.

Grafana Opentelemetry Grafana, a powerful visualisation tool, works with Prometheus to create dashboards, charts, graphs, and alerts. Grafana can visualise metrics with Prometheus. The compatibility between these platforms makes complex data easier to share between teams.

Integration of OpenTelemetry with Prometheus No need to choose OpenTelemetry and Prometheus are compatible. Prometheus data models support OpenTelemetry metrics and OTel SDKs may gather them. Together, these systems provide the best of both worlds and enhanced monitoring. As an example:

When combined, OTel and Prometheus monitor complex systems and deliver real-time application insights. OTel’s tracing and monitoring technologies work with Prometheus’ alerting. Prometheus handles big data. This capability and OTel’s ability to combine metrics, traces, and logs into one interface improve system and application scalability. PromQL can generate visualisation models using OpenTelemetry data. To provide additional monitoring tools, OpenTelemetry and Prometheus interface with IBM Instana and Turbonomic. Instana’s connection map, upstream/downstream service connection, and full-stack visibility let OTel monitor all services. They give the same wonderful experience with OTel data as with all other data sources, providing you the context you need to swiftly detect and address application problems. Turbonomic automates real-time data-driven resourcing choices using Prometheus’ data monitoring capabilities. These optimised integrations boost application ecosystem health and performance.

Read more on Govindhtech.com

[mp3monsterme]

Observability in Action - Book Review

With the Christmas holidays happening, things slowed down enough to sit and catch up on some reading – which included reading Cloud Observability in Action by Michael Hausenblas from Manning. You could ask – why would I read a book about a domain you’ve written about (Logging In Action with Fluentd) and have an active book in development (Fluent Bit with Kubernetes)? The truth is, it’s good to…

[strategictech]

Grafana’s Annual Report Uncovers Key Insights into the Future of Observability

The architecture of modern systems has reached a tipping point where observability is no longer optional, it’s become existential. With advancements in Kubernetes and OpenTelemetry reshaping operational strategies, organizations are turning to open-source solutions to tackle the challenges of complex and distributed systems.

@tonyshan #techinnovation https://bit.ly/tonyshan https://bit.ly/tonyshan_X

[kyber-vietnam]

Kyber Network is hiring a Site reliability engineering in Hanoi or HCMC!

*** ABOUT KYBER NETWORK

Kyber Network (https://kyber.network) is an industry-leading blockchain company, providing cryptocurrency liquidity for the largest companies in the DeFi (decentralized finance). The company raised US$52M in its token sale in 2017, making it one of the largest cryptocurrency-based fundraising in history.

Two co-founders, Dr. Luu The Loi (Chairman) and Victor Tran Huy Vu (CEO), were honored in Asia’s Forbes 30 under 30 in the ‘Finance - Investment’ category by Forbes magazine in 2017 and have since established the company as a market leader. Dr. Luu The Loi also has a PhD in blockchain security technology, and is one of the 10 most prominent Innovators under 35 in the Technology field in the Asia Pacific region (Innovators Under 35 Asia Pacific) published by the MIT Technology Review.

Kyber has developed a family of products including:

KyberSwap: KyberSwap.com - Trade & Earn at the Best Rates in DeFi with our leading Decentralized Exchange (DEX) Aggregator that does >US$1B in monthly trading volume

KyberDAO: Be part of the community governing Kyber Network to shape the future of DeFi and earn $KNC tokens

And many more stealth developments and ventures that the company has taken stake in.

Kyber Network has offices in Singapore, Hanoi, and Ho Chi Minh City:

Singapore office: 1 Upper Circular Road, #05-01 Singapore 058400

Hanoi office: 7th floor Web3 Tower, 15 Lane 4 Duy Tan Str., Cau Giay Dist., Hanoi, Vietnam

Ho Chi Minh city office: 4th floor, 113 Tran Quoc Toan, Vo Thi Sau Ward, District 3, Ho Chi Minh City, Vietnam

Join our team of brilliant and committed professionals pursuing the goal of creating a “Decentralized Economy for Everyone” based on blockchain technology.

Are you ready to take up the challenge of creating world-changing innovations in the next decade? Apply now.

*** JOB DESCRIPTION

Responsibility

Build and automate tools and platforms to eliminate manual work, enhance system operations, and improve the developer experience.

Own and operate the current Kubernetes infrastructure, ensuring its reliability, scalability, and security.

Collaborate with engineering teams to troubleshoot and optimize infrastructure, resolving performance and availability issues.

Conduct R&D on infrastructure solutions to enable business growth and product development.

Implement and manage observability solutions using open-source tools to enhance monitoring, logging, and alerting.

Ensure infrastructure best practices in GCP (Google Cloud Platform), optimizing cost, security, and performance.

Requirements

Must have

Strong programming skills in Golang and proficiency in scripting languages (e.g., Bash, Python).

Experience managing stateful applications (e.g., PostgreSQL, Redis) in a cloud-native environment, including backup strategies, performance tuning, and high availability configurations.

Deep understanding of major DevOps tools (e.g., Terraform, Helm, ArgoCD, GitOps workflows).

Hands-on experience with open-source observability platforms (e.g., Prometheus, Grafana, OpenTelemetry, Loki).

Proficiency in Kubernetes (GKE) and containerized environments.

Familiarity with Google Cloud Platform (GCP) services and networking.

Nice to have

Experience with the Ethereum ecosystem.

Experience with running blockchain nodes.

*** WHAT WE CAN OFFER

Vietnam - based benefits here

*** HOW TO APPLY

Please send your resume to hr@kyber.network with email subject “Your full name_Job title_kyber.vn”

Or talk to our Recruiters on Telegram/Skype: @anhpt_32

*** CONNECT WITH US

Discord: https://discord.com/invite/NB3vc8J9uv

Twitter EN: https://twitter.com/kybernetwork

Twitter KyberDAO: https://twitter.com/KyberDAO

Twitter Turkish: https://twitter.com/kyberturkish

Twitter Japanese: https://twitter.com/kybernetwork_jp

Forum: https://gov.kyber.org/

Reddit: https://www.reddit.com/r/kybernetwork/

Facebook EN: https://www.facebook.com/kyberswap

Facebook Careers: https://www.facebook.com/KyberCareers

Youtube: https://www.youtube.com/channel/UCQ-8mEqsKM3x9dTT6rrqgJw

Only shortlisted candidates will be contacted.

For other job opportunities at Kyber Group, click here https://kyber.vn/.

[hawkstack]

Training Models and Optimizing with Red Hat OpenShift AI (RHOAI)

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries by enabling automation, predictive analytics, and intelligent decision-making. However, training and optimizing AI models require robust infrastructure, scalable environments, and efficient workflows. Red Hat OpenShift AI (RHOAI) provides a powerful, enterprise-ready platform to streamline AI/ML model development, deployment, and optimization.

What is Red Hat OpenShift AI (RHOAI)?

Red Hat OpenShift AI is an AI/ML platform built on Red Hat OpenShift, providing organizations with an integrated solution for managing AI workloads. It allows data scientists and developers to build, train, and deploy models efficiently while leveraging the scalability, security, and automation capabilities of OpenShift.

Key Features of RHOAI:

Scalability: Scale AI workloads dynamically using Kubernetes-powered infrastructure.

MLOps Integration: Support for CI/CD pipelines for machine learning workflows.

Flexible Deployment: Deploy AI models in cloud, hybrid, or on-prem environments.

Security and Governance: Enterprise-grade security for AI model management.

Optimized Performance: Accelerated AI workloads with GPU and CPU optimizations.

Training Models with RHOAI

1. Data Preparation and Preprocessing

Before training an AI model, data must be cleaned, transformed, and prepared. RHOAI enables seamless integration with data lakes, databases, and storage solutions to process large datasets efficiently.

2. Model Training at Scale

RHOAI supports popular AI/ML frameworks like TensorFlow, PyTorch, and Scikit-learn. By leveraging OpenShift’s Kubernetes-based infrastructure, AI models can be trained in distributed environments, ensuring faster and more efficient processing.

3. Resource Optimization with GPUs

AI training is resource-intensive, but with RHOAI, you can leverage GPUs and AI accelerators for faster computation. OpenShift AI intelligently allocates resources, optimizing training performance.

4. MLOps for Continuous Training

RHOAI integrates MLOps principles to automate model retraining, versioning, and deployment. Using Red Hat OpenShift Pipelines and GitOps, organizations can streamline AI workflows, ensuring model updates and improvements happen seamlessly.

Optimizing AI Models with RHOAI

1. Hyperparameter Tuning

Optimizing AI models requires fine-tuning hyperparameters like learning rates, batch sizes, and network architectures. RHOAI provides tools to automate hyperparameter tuning using frameworks such as Kubeflow and Katib.

2. Model Performance Monitoring

RHOAI includes monitoring and logging capabilities using Prometheus, Grafana, and OpenTelemetry to track model performance, detect drift, and ensure accuracy over time.

3. Scalable Inferencing

Deploying AI models in production requires optimized inferencing. RHOAI supports containerized model serving with KServe (formerly KFServing) to scale inferencing dynamically based on demand.

4. Security and Compliance

RHOAI provides Role-Based Access Control (RBAC), encryption, and compliance tools to ensure AI models and data meet enterprise security standards.

Why Choose Red Hat OpenShift AI?

Enterprise-Ready AI/ML Platform: Secure and scalable AI infrastructure.

Cloud-Native AI Workflows: Kubernetes-powered AI model management.

Seamless DevOps & MLOps Integration: Automate model training, deployment, and monitoring.

Hybrid and Multi-Cloud Support: Deploy AI models across diverse environments.

Conclusion

Red Hat OpenShift AI (RHOAI) provides a comprehensive solution for organizations looking to train, optimize, and deploy AI models efficiently. With its scalable infrastructure, automation capabilities, and enterprise-grade security, RHOAI enables businesses to harness AI's full potential while ensuring operational efficiency.

By integrating AI with OpenShift, organizations can accelerate AI innovation, optimize workflows, and stay ahead in the rapidly evolving AI landscape. For more details www.hawkstack.com 

[ericvanderburg]

A Hands-On Guide to OpenTelemetry: Exploring Telemetry Data With Jaeger

http://securitytc.com/TCJGcp