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Deployments vs StatefulSets vs DaemonSets MOST ASKED INTERVIEW QUESTIONS 👉 Deployments are used for managing stateless applications, which are applications that don't need to maintain their state between pod restarts. 1. Scaling: Deployments are well-suited for horizontal scaling. We can increase or decrease the number of replicas to handle varying workloads. 2. Pod Identity: Pods managed by Deployments do not have stable network identities or unique names. It can be resheduled without regard for identity 3. Data Persistence: Stateless applications typically do not require persistent storage because they can be replaced with new pods at any time. Use Cases: Deployments are commonly used for web servers, microservices, and applications where you can have multiple identical instances that serve incoming requests and don't rely on maintaining local state. 👉 StatefulSets are designed for managing stateful applications that require stable network identities and data persistence. Stateful applications maintain their state, and it's important that each pod maintains a consistent identity across rescheduling. 1. Scaling: While StatefulSets can be scaled, it's typically limited to manual scaling, and the ordinal index of each pod remains constant, providing a consistent identity. 2. Pod Identity: StatefulSets ensure that each pod has a unique and stable network identity. For example, a pod named "web-0" will always be "web-0." 3. Data Persistence: Stateful applications often require persistent storage, and StatefulSets help manage the association of pods with Persistent Volume Claims (PVCs) for data persistence. Use Cases: StatefulSets are used for databases, distributed systems, and applications that rely on maintaining state, need unique identifiers, and require data persistence. 👉 DaemonSet: Use Case: DaemonSets are used for running a single pod on every node in the cluster. They are commonly used for running monitoring agents, log collectors, and other system-level services that should be present on every node. 1. One Pod per Node: DaemonSets guarantee one pod running on each node. If you add a node to your cluster, a pod is automatically scheduled on it. If you remove a node, the corresponding pod is removed as well. 2. System-Level Services: DaemonSets are often used to run infrastructure or daemon-like services that are not part of the application but are necessary for cluster management and monitoring. Shubham Londhe Sandip Das The CloudOps Community #kubernetes #devops #orchaestration Piyush sachdeva Abhishek Veeramalla Praveen Singampalli

Hi Connections Please share your resume at bhavaniprasad@nityainc.com Job Title: Logging Enablement - Hands-on Application Architect / Principal Engineer You'll need to have the following: -5+ years of strong Hands-on experience with Enterprise level Elastic Stack of tools(Elastic Search, Logstash & Kibana) that monitor and manage critical applications and infrastructure running on on-premise and cloud -8+ years of experience in defining and implementing different Logging Architectures at enterprise level -8+ years of experience in Solutions and/or Application Architecture -3+ years of experience in leading cross-functional teams. -Strong architecture, design, and implementation experience with the following: Microservices architectures, Cloud-Native architectures, Event-driven architectures, APIs, Domain Driven Design, Public Cloud (AWS), Serverless, Kubernetes, Docker, DevOps, building scalable, reliable, available solutions, and Performance testing. -Strong knowledge of Systems Administration in Linux(RHEL/CentOS) and experience in troubleshooting applications running on these systems -Configuration of index templates and data life cycle management ILM for data retention -Designing and implementing solutions for centralized logs, infrastructure, health metrics, and distributed tracing for different applications. -Understanding of hardware design, cross-site replication, local and global load balancers, etc -Data ingestion & enrichment from various sources, webhooks, and REST APIs with JSON/YAML/XML payloads & testing POSTMAN, etc. #c2c #c2crequirement #c2cavailable #contact #architecture #elasticsearchengineer #elasticsearch

Interviewer : Imagine you are working on a Kubernetes cluster, and you need to deploy a logging service that collects logs from all nodes in the cluster. This service should always run on every node, even if new nodes are added later. How would you achieve this? To deploy a logging service that runs on every node and automatically includes new nodes, I would use a DaemonSet. A DaemonSet ensures that a pod runs on all nodes in the cluster. When new nodes are added, the DaemonSet automatically adds the logging pod to these new nodes, ensuring logs are collected from everywhere. Interviewer: If i wanted to deploy a database service where each instance requires a stable identity, What deployment strategy you follow ? For the database service, I would use a StatefulSet.  Each pod in a StatefulSet gets a stable, unique name and maintains its data in persistent storage even if it’s deleted or rescheduled. I deployed a video on YouTube about using AWS Developer Tools. Check it out https://lnkd.in/g3FVhyGU Please ignore if you are already aware of developer Tools :) #Kubernetes #Deployment #DaemonSet #StatefulSet #LoggingService #DatabaseService #KubernetesCluster #PersistentStorage #DevOps #ContainerOrchestration #TechInterview #CloudComputing #ITInfrastructure #SoftwareEngineering #TechCareers

The Amazon Aurora PostgreSQL Engine team is looking for leading technical experts in databases, who are excited about building large scale database systems. We build and operate a purpose built hyper-scale database service for the cloud. We are the fastest growing service in all of AWS and have revolutionized the way people manage vast volumes of data in the cloud. I am hiring Principal Engineers who have proven expertise in the Query Processing and Optimization domain. You will have the opportunity to build the architectural vision and lay down the strategy of the Query Processing and Optimization area for the Aurora PostgreSQL engine. You will help drive the evaluation, design, development and enhancement of an already successful service and align the organizational roadmap to deliver on the strategy. In the Principal Engineer role you will be able to influence the organization’s priorities, mentor senior engineers, collaborate with other Principal and Senior Principal Engineers and solve complex business and technical challenges with “future proof” solutions, rolling up your sleeves when necessary to get things done. Click the link below to learn about being a Principal Engineer at Amazon - https://lnkd.in/eKVnYHwE #rdbms #datamigration #sql #technology #nosql #databases #dataprocessing #dbms #cloudnative #cloudcomputing #cloudjourney #bigdata #bigdataanalytics #relationaldatabases #datalakes #datamanagement #datascience #datawarehouse #aws #cloud #ai #machinelearning #data #opensource #opensourcesoftware #opensourcedevelopment #postgresql #mysql #aurora

Hi #connections Please let me know if your have profiles/references for the below mentioned position; Share your resume to vamshi@sharkanalytics.com Title: ELK Architect ( Elastic Search) Location: Bay area, CA, Hybrid Duration: 12 months Special Notes: Need local to Bay area, Hybrid 2 days work from home and three days in office in San Ramon or vice-versa Description 12+ years of experience in Solutions and/or Application Architecture Strong architecture, design, and implementation experience with the following: Microservices architectures, Cloud-Native architectures, Event-driven architectures, APIs, Domain Driven Design, Public Cloud (AWS), Serverless, Kubernetes, Docker, DevOps, building scalable, reliable, available solutions, and Performance testing Experience with operational readiness and Solution failure mode analysis Strong Hands-on experience with Elastic Stack of tools(Elastic Search, Logstash & Kibana) that monitor and manage critical applications and infrastructure Hands-on Scripting & Programming in Python, Ansible, bash, data parsing (regex), etc Experience with Security Hardening & Vulnerability/Compliance, OS patching, SSL/SSO/LDAP Understanding of HA design, cross-site replication, local and global load balancers, etc Data ingestion & enrichment from various sources, webhooks, and REST APIs with JSON/YAML/XML payloads & testing POSTMAN, etc CI/CD - Deployment pipeline experience (Ansible, GIT) Strong knowledge of performance monitoring, metrics, capacity planning, and management Ability to apply a systematic & creative approach to solve problems, out-of-the-box thinking with a sense of ownership and focus. Strong skills in creating documentation - engineering runbooks, support procedures, user onboarding, and support documentation. Experience with application onboarding - capturing requirements, understanding data sources, architecture diagrams, application relationships, etc. Influencing other teams and engineering groups in adopting logging best practices. Effective communication skills with the ability to articulate technical details to a different audience. Create and manage Elasticsearch Clusters on premise, including configuration parameters, indexing, search, and query performance tuning, RBAC security governance, and administration. Familiarity with Confluence and JIRA. #elkarchitect #elk #solutionarchitecture #python #ansible #bash #dataparsing #OSpatching

Backend development is more than just APIs and SQL statements… It’s also: – Caching – Data modeling – Cloud technologies  – Autoscaling infrastructure – Concurrency / Idempotency – Events, Message Queues, and Workers I’ve seen so many awesome engineers struggle during interviewing rounds without good experience / understanding in these 6 key areas. Writing some APIs, and spinning up a database + server is awesome as a junior/mid-level. But it won’t cut it for senior engineer and beyond. You’ll want to develop expertise and experience in these areas if you want to continue to grow into senior+ software engineering positions. Check out my deep dive newsletter article here to read up on these: https://lnkd.in/emWbCPQg (paid, but you can grab a free trial!)

YOU WILL: Be responsible for owning the entire cycle of development, from inception to design, development, testing, and production. Build architecture designs and have an influential role in product roadmaps for the federated privacy preserving query engine Be working on distributed systems for data processing with efficient protocols and communication, schedulers, low latency access to distributed storage & cloud data warehouses. Need to understand and reason about concurrency and parallelization to deliver scalability and performance in a distributed environment. Be using modular design, incremental development to deliver high value user facing feature while making trade-off between time-to-market and long term product vision

20 famous tips to help you pass software engineering job interviews: 1. For searching some text in millions of record: Use inverted index 2. For ACID property: use RDBMS. 3. For Unstructured data: use NoSQL 4. Database Scaling: Horizontal (Preferred for NoSQL) or Vertical Sharding(Preferred for RDBMS) 5. Read Heavy System: use Read through Cache 6. Low Latency Requirement: CDN + Load Balancer + Cache 7. Write-Heavy System: Use ASYNC Processing (kafka message queues) 8. Handle Complex Data like Videos, Images, Files etc: Go for Object storage (ex: Amazon S3) 9. For High Availability, Performance, & Throughput: Use Load Balancer 10. Global Data Delivery: Use CDN. 11. For Fast DB Queries: Database Indexing 12. Load Management on a Component: Use Rate Limiter. 13. Avoid Single Point of Failure: Set up Disaster Recovery Data Centre 14. Fault-Tolerance: Write though cache + Master Slave Architecture. 15. Peer to Peer communication: Use WebSockets. 16. For VideoCall : Use WebRTC 17. For Data Integrity between 2 system: Use Checksum Algorithm. 18. Efficient Managing of the Servers: Use Consistent Hashing. 19. High Availability and Consistency Trade-Off: Eventual Consistency. 20. Cache Eviction Policy: LRU cache (generally preferred, but there are more) For full details about these concept, pls check "Concept && Coding" hashtag #HLD Playlist. 𝐇𝐋𝐃 𝐏𝐥𝐚𝐲𝐥𝐢𝐬𝐭: https://lnkd.in/d8eDwYVA 𝐋𝐋𝐃 𝐏𝐥𝐚𝐲𝐥𝐢𝐬𝐭: https://lnkd.in/dJkgzKxf ------------------------------------------------------------------------------------ I enjoy sharing my real-life experiences and insights regularly. If you find them helpful, feel free to join me on this journey. Follow - Akash Kumar for such updates Hope this helps you too! 😊 Keep sharing , keep learning ! #softwareengineer #systemdesign

Quick: 20 famous tips to help you pass software engineering job interviews Akash Kumar

#c2c #urgentrequirement #urgentopening #c2cvendors #urgenthiring Hi Hope you are doing well Please share the resume at kunal.bhatt@rconsultinginc.com API Data Engineer 12 + Years Location: Charlotte NC or Austin TX :: Local Candidates Only   Job Responsibilities: Develop, deploy, and maintain cloud-based APIs using AWS services. Design and implement RESTful APIs using Lambda functions, Python, and API Gateway. Collaborate with cross-functional teams to gather requirements and define API specifications. Ensure high availability, scalability, and security of APIs in the cloud environment. Implement authentication, authorization, and tokenization mechanisms for API security. Optimize API performance and latency by leveraging AWS infrastructure and services. Integrate APIs with various backend systems and third-party services. Develop API documentation and provide technical support to internal and external stakeholders. Monitor, troubleshoot, and resolve issues related to API functionality and performance. Stay updated with AWS best practices and emerging technologies in cloud development. Demonstrate a strong understanding of AWS big data architecture and its application in cloud development projects. Implement security measures and tokenization techniques for APIs. Enable API-to-API and API-to-application communication. Create and maintain API documentation. Implement alerting and monitoring systems for APIs. Utilize step functions and Terraform for infrastructure and API development.