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1. What are the difficulties in deploying and managing the life cycle of data-heavy application 2. Review of kubernetes landscape w.r.t data-heavy applications 3. Robin approach to orchestrating data-heavy applications
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You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights.
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- Treating “data as a product” and how to drive business value for all consumers.
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Use this Data Architecture PowerPoint Presentation Slides to explain important technologies of data architecture. Principles of data architecture can be well explained using these PPT slides. There are many templates provided in this PowerPoint complete deck such as NoSQL databases, real-time streaming platforms, dockers and containers, containers repositories, container orchestration, microservices, functions as a service, principles of data architecture, view data as a shared asset, ensure security and access controls, data architecture, big data architecture, etc. All the templates are designed by our team of experts after an in-depth study of the topic. These templates are completely editable. The presenter can change font, text, and color. It also contains additional slides like mission, puzzle, timeline, target, Venn, idea pie chart, bar graph, area chart helps you to illustrate the concept in a professional manner. Download this data system presentation graphics to present your work smarty and precisely. Ideas acquire a definite form due to our Data Architecture Powerpoint Presentation Slides. It will all begin to jell.
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This document discusses the economics of cloud computing. While cost reduction is often seen as a primary benefit of cloud adoption, public cloud is not always cheaper than traditional hosting. There are factors to consider like total cost of ownership, transformation costs, and an organization's position in the IT lifecycle. Public cloud costs more per usage unit due to short commitments, but exploiting variable demand patterns through scaling can reduce costs. The optimal approach depends on an application's demand graph shape - periodic, spiked, or cyclical usage may benefit most from a hybrid cloud model.
Power BI Advanced Data Modeling Virtual Workshop
Join CCG and Microsoft for a virtual workshop, hosted by Solution Architect, Doug McClurg, to learn how to create professional, frustration-free data models that engage your customers.
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Integrating data across systems has been a perpetual challenge. Unfortunately, the current technology-focused solutions have not helped IT to improve its dismal project success statistics. Data warehouses, BI implementations, and general analytical efforts achieve the same levels of success as other IT projects – approximately 1/3rd are considered successes when measured against price, schedule, or functionality objectives. The first step is determining the appropriate analysis approach to the data system integration challenge. The second step is understanding the strengths and weaknesses of various approaches. Turns out that proper analysis at this stage makes actual technology selection far more accurate. Only when these are accomplished can proper matching between problem and capabilities be achieved as the third step and true business value be delivered. This webinar will illustrate that good systems development more often depends on at least three data management disciplines in order to provide a solid foundation.
Takeaways:
Data system integration challenge analysis
Understanding of a range of data system-integration technologies including
Problem space (BI, Analytics, Big Data), Data (Warehousing, Vault, Cube) and alternative approaches (Virtualization, Linked Data, Portals, Meta-models)
Understanding foundational data warehousing & BI concepts based on the Data Management Body of Knowledge (DMBOK)
How to utilize data warehousing & BI in support of business strategy
Future of Analytics: Drivers of Change
Scott Fairbanks, Senior BI Consultant at CCG, demonstrates the key differentiators between traditional warehouse architectures and new cloud technologies. Learn the key competitors in the cloud space, and what elements separates them in terms of linking analytic solutions.
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The Business Data Lake is a new approach to information management, analytics and reporting that better matches the culture of business and better enables organizations to truly leverage the value of their information.
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Data lakes & data warehouses, whether on-premises or in the cloud promise to provide a centralized, cost-effective and scalable foundation for modern analytics. However, organisations continue to struggle to deliver accurate, current and analytics-ready data sets in a timely fashion. Traditional ingestion tools weren’t designed to handle hundreds or even thousands of data sources and the lack of lineage forces data consumers to manually aggregate information from sources they trust. In this session, you’ll learn how to future-proof your modern data environment to meet the needs of the business for the long term. We'll examine how to overcome common challenges, the related must-have technology solutions in the data lake/ data warehousing world, using real-world success stories and even a few architecture tips from industry experts.
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Big Data Analytics on the Cloud
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
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- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
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For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Slides: Migrate BI Dashboards to Run Directly on a Cloud Data Lake in Five Ea...
While BI dashboards are great at democratizing analytics in organizations, the architecture that traditionally powers them has hidden consequences that have serious impacts on the business.
This architecture is based on a 30-year-old paradigm that requires many different systems, ETL jobs, and copies of data in data marts, data warehouses, and BI extracts. One downside of many is that it takes many days if not weeks to answer a different business question with this architecture. The negative consequences are further multiplied by the tens, hundreds, or even thousands of dashboards needed to run a data-driven organization.
Now, there’s a straightforward way to overcome these challenges that many organizations are already taking advantage of, an open cloud data lake architecture and Dremio
Join Jason Hughes, Technical Director at Dremio, for this webinar to learn how you can migrate BI dashboards to Dremio to quickly provide interactive dashboards to data consumers without the issues of the traditional architecture — and finally deliver the benefits always promised by BI.
What you’ll learn:
• Why BI dashboards’ traditional architecture implemented at scale causes many issues, which hinder the very insights it promises.
• How a Dremio-powered cloud data lake architecture eliminates or mitigates the negative consequences of the traditional approach.
• Step-by-step instructions for migrating a BI dashboard to run directly on a cloud data lake, both a self-contained example and your own dashboards.
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一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(sdsu毕业证书)圣地亚哥州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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◇面对父母的压力，希望尽快拿到；
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◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
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颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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【业务选择办理准则】
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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https://www.penify.dev/
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Deliver Big Data, Database and AI/ML as-a-Service anywhere
- 1. Ravikumar Alluboyina, Tushar Doshi Robin Systems Deliver Big Data, Database and AI/ML as-a-Service anywhere
- 2. Who are we? SAMPLE CUSTOMER DEPLOYMENTS 11 billion security events ingested and analyzed a day (Elasticsearch, Logstash, Kibana, Kafka) 6 Petabytes under active management in a single Robin cluster (Cloudera, Impala, Kafka, Druid) 400 Oracle RAC databases managed by a single Robin cluster (Oracle, Oracle RAC) We have solved some fundamental problems to enable containers and Kubernetes for running complex Big Data, NoSQL, Database and AI/ML workloads Robin is The Kubernetes platform for big data, databases and AI/ML
- 3. What are the challenges with deployment of Big Data, NoSQL and Databases?
- 5. DN1 DN2 DN3 Node fault tolerance Compute anti-affinity
- 6. DN1 DN2DN3 Rack fault tolerance Compute anti-affinity Location Awareness Rack / DC
- 8. DN1 DN2DN3 Storage fault tolerance Compute anti-affinity Location Awareness Rack / DC Storage & Compute Affinity
- 9. DN1 DN2DN3 ZK2ZK1 ZK3 Storage performance Compute anti-affinity Location Awareness Rack / DC Storage & Compute Affinity
- 10. DN1 DN2DN3 ZK2ZK1 ZK3 Workload types and QoS enforcement Compute anti-affinity Location Awareness Rack / DC Storage & Compute Affinity IO patterns QoS
- 11. DN1 DN2DN3 ZK2ZK1 ZK3 CM Unprotected components Compute anti-affinity Location Awareness Rack / DC Storage & Compute Affinity IO patterns QoS
- 12. Compute anti-affinity DN1 DN2DN3 Location Awareness Rack / DC Storage & Compute Affinity ZK2ZK1 ZK3 IO patterns QoS CM High Availability Storage replication and failover
- 13. Compute anti-affinity DN1 DN2DN3 Location Awareness Rack / DC Storage & Compute Affinity ZK2ZK1 ZK3 IO patterns QoS CM High Availability Complete deployment NM NMGW GW HBase Hive Kudu KuduKudu KuduM KuduM KuduM Solr
- 14. Big data deployment and management challenges Storage & Compute Affinity Location Awareness Rack / DC Compute anti-affinity Scale-out compute and storageStorage workload types (IO patterns / QoS) High Availability Data Protection (Backup / DR) Snapshot / Rollback
- 16. Storage and Networking challenges › Latest 2018 CNCF: 48% say Storage is a big challenge, 44% say Networking is a challenge in Kubernetes › There are 27 Storage vendors and 21 Network vendors providing Storage & Networking solutions for containers and Kubernetes1 1 https://github.com/cncf/landscape Despite so many vendor solutions, why is it still a challenge for so many people? Storage vendors Network vendors
- 17. Challenges with containers Incomplete cgroups virtualization causes many Big Data and Databases to misbehave CPU › Contiguous core IDs, CPU ID mapping (Kudu), accurate threads:cores mapping (DB) › NUMA aware assignment (HANA) Memory: › JVM sees entire host memory even if you cap the memory for container (Any JVM app) › Memory allocation inconsistencies (hugepages, shared page cache) (Oracle) Storage › Apps that need raw block devices need correct WWNs management (e.g., Oracle, MapR) › blkio cgroups setting is useless to avoid noisy neighbor problems (All apps) Confidential – Restricted Distribution
- 18. Time to reframe our thinking Let applications drive infrastructure to meet user requirements (in this model application workflows configure Kubernetes, Networking and Storage)
- 19. Robin is The Kubernetes platform for big data, databases and AI/ML www.robin.io 1-click Provision 1-click Scale 1-click QoS Control 1-click Snapshots 1-click Clones 1-click Backup 1-click Upgrade 1-click Migrate
- 20. Robin Software Stack Virtual Networking App-aware Storage Robin’s built-in enterprise-grade storage stack Snapshots, Clones, QoS, Replication, Backup, Data rebalancing, Tiering, Thin-provisioning, Encryption, Compression Built-in flexible networking OVS, Calico, VLAN, Overlay networking, Persistent IPs Application Workflow Manager Kubernetes 1-click application Deploy, Snapshot, Clone, Scale, Upgrade, Backup Application workflows configure Kubernetes, Storage & Networking Works anywhere On-Prem (Bare Metal, VM) or Public-CloudCONFIDENTIAL – RESTRICTED DISTRIBUTION
- 21. Converged NodesCompute-only Nodes Storage-only Nodes Robin Software Stack Robin application-aware scale-out storage Robin’s built-in enterprise-grade storage stack Snapshots, Clones, QoS, Replication, Backup, Data rebalancing, Tiering, Thin-provisioning, Encryption, Compression Application-aware Networking Robin Agent Kublet K8S Master Robin RCM Built-in flexible networking OVS, Calico, VLAN, Overlay networking, Persistent IPs kubectl, helmrobin Robin programs Kubernetes StatefulSets, Persistent Volumes, Claims, Services, etc., are auto-created to meet application needs GPUGPU TensorFlowKafkaMongo Spark Hortonworks Oracle RAC Elastic Search Robin Agent Kublet Works any where On-Prem (Bare Metal, VM) or Public-Cloud Install to deploying apps in 15 minutes Kublet CONFIDENTIAL – RESTRICTED DISTRIBUTION
- 22. Enough talk.. Demo time …
- 24. Robin is The Kubernetes platform for big data, databases and AI/ML www.robin.io 1-click Provision 1-click Scale 1-click QoS Control 1-click Snapshots 1-click Clones 1-click Backup 1-click Upgrade 1-click Migrate