Downloaded 47 times
![{
"customer_id": 123,
"first_name": "Fuat",
"last_name": "Sungur",
"email": "fuat.sungur@mongodb.com",
"dob" : ISODate("1987-04-01T05:00:00Z"),
"annual_spend": NumberInt("22141"),
"address": [
{
"address": "Springs 7, No: 123",
"city": "London",
"country": "England",
"location": "work",
"phone_numbers" : [
{"type": "mobile", "number": "+44314124124"},
{"type": "landline", "number": "+446662123"}
]
},
{
"address": "Downtown, Emma Street No:7",
"city": "Dubai",
"country": "United Arab Emirates"
}
],
"interests": [
"football",
"movies",
"swimming"
]
}
Tabular (Relational) Data Model
Related data split across multiple records and
tables
Document Data Model
Related data contained in a single, rich document](https://image.slidesharecdn.com/dimtriyadhslidesforsharing-241023094712-92727474/85/Data-in-Motion-Tour-2024-Riyadh-Saudi-Arabia-100-320.jpg)
Uploaded byconfluent
Data in Motion Tour 2024 Riyadh, Saudi Arabia
The document outlines an agenda for a data and AI event featuring various speakers from Confluent, Google Cloud, and Elastic, focusing on innovations in data streaming, generative AI, and observability. It highlights discussions about real-time data processing, the Kafka platform, and the integration of AI in workflows, as well as customer success stories in the financial sector. The event emphasizes the importance of transforming data into products to enhance organizational efficiency and decision-making.
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Similar to Data in Motion Tour 2024 Riyadh, Saudi Arabia
Data in Motion Tour 2024 Riyadh, Saudi Arabia
- 1. Thank you toour partners
- 2.
- 3. Thank you toour partners
- 4. 10:30 AM Welcome 11:00AM Keynote: Kafka evolved into the world’s leading data streaming platform Will LaForest, Global Field CTO, Confluent 11:30 AM Generative AI with Confluent Fadi Oweis, Automation and Platforms Pre-sales, SBM 11:50 AM Introduction to Elastic Observability Siddharth Dwivedi, Senior Solutions Architect, Elastic 12:20 PM NETWORKING BREAK & PRAYER TIME 12:40 PM Real-time data streaming on Google Cloud Hassan Ahmad, Data Analytics & AI Specialist, Google Cloud KSA Sabri Traifi, Data Analytics & AI Specialist, Google Cloud KSA Agenda
- 5. Agenda 1:00 PM Customer FiresideChat Waleed Al Omran, Integration Manager, Ministry of Human Resources and Social Development 1:30 PM Modernize your Legacy Applications Fuat Sungur, Advisory Solutions Architect, MongoDB 1:50 PM A guide from the field to a successful data in motion journey Nadine Capelle, Staff Solutions Architect, Confluent 2:05 PM How to Excel in Stream Processing: Key Lessons from Flink Dominique Ronde, Senior Solutions Engineer, Confluent 2:20 PM Data Streaming & AI Ala Al-Sharif, Enterprise Sales Engineer, Confluent 2:35 PM CLOSING REMARKS 2:40 PM LUNCH
- 6. Your Feedback isImportant to Us! Complete our quick and easy survey, and as a thank you, we will enter you into our prize draw for a chance to win some Apple Airpods once the EMEA Data in Motion Tour cities are all completed.
- 7. Will LaForest Global FieldCTO, Confluent Kafka evolved into the world’s leading data streaming platform
- 8. FUNDAMENTAL DISCONNECT Data Processingand Movement Happens in Batch. Reality Happens in Real-Time.
- 10. NETWORK COMPUTE AZ AZAZ Cells Cells Cells OBJECT STORAGE CUSTOMERS Multi-Cloud Networking & Routing Tier Metadata Durability Audits METRICS & OBSERVABILITY CONNECT PROCESSING GOVERNANCE Data Balancing Health Checks Real-time feedback data Other Confluent Cloud Services GLOBAL CONTROL PLANE S E C U R I T Y Kora Architecture
- 11. RESILIENT COST EFFECTIVE 10X FASTELASTIC 16X 30X
- 12. SCAN TO READMORE Kora: A Cloud-Native Event Streaming Platform For Kafka 2023 AWARD WINNER BEST INDUSTRY PAPER
- 13. Core Apache KafkaBroker Cluster Linking RBAC Audit Logs ELASTICITY: Self-Balancing Clusters INFINITE RETENTION: Tiered Storage RESILIENCY: Multi-Region Clusters Schema Validation Confluent for Kubernetes Health+ Additional features included in Confluent Server Separate component dependent on Confluent Server Features of Kora Engine in Confluent Server Confluent uses elements of Kora Engine to provide cloud-native benefits on-premise Confluent Server, the underlying broker that powers our self-managed offering, uses elements of Kora Engine
- 14. 90% LESS EXPENSIVE UP TO Introducing FreightClusters NOW IN EARLY ACCESS ON AWS
- 15. 15 Confluent welcomes WarpStream! “Bringyour own cloud” (BYOC) native data streaming service
- 16. Data Streaming is muchmore than Kafka
- 17. 0 250,000 500,000 750,000 1,000,000 2017 2018 20192020 2021 2022 2023 The Kafka Community’s Growth is Accelerating Active Monthly Unique Users
- 19. Solving A HugeProblem
- 20. OPERATIONAL ESTATE Serve theneeds of applications to transact with customers in real-time ANALYTICAL ESTATE Support after-the-fact business analysis and reporting for various stakeholders Merchandising Distribution Sales Productivity Fraud Finance Marketing Supply Chain Lifetime Value Churn Analytics Compliance Revenue Risk Analysis Claims Data Mart Forecasting Cloud Data Warehouses ML/AI Lakehouse Support Segmentation Data Lake Security Billing App Workday Employees Personalization Microservices Telemetry ServiceNow Ticketing Payments Microservice Mainframe Notification Microservice Location Microservice Loyalty Microservice Recommendation Microservice Customer CRM Purchases App Catalog App JIRA Projects Okta Permissions Medallia Sentiments Pricing App Cassandra PostgreSQ L Neo4j Oracle MariaDB AlloyDB MongoDB Redis SQL Server MySQL MongoDB Amazon Aurora Azure CosmosDB Marketo Campaigns Coupa Requisitions As Data Needs Emerged, Data Complexity Escalated
- 21. OPERATIONAL ESTATE Serve theneeds of applications to transact with customers in real-time ANALYTICAL ESTATE Support after-the-fact business analysis and reporting for various stakeholders Merchandising Distribution Sales Productivity Fraud Finance Marketing Supply Chain Lifetime Value Churn Analytics Compliance Revenue Risk Analysis Claims Data Mart Forecasting Cloud Data Warehouses ML/AI Lakehouse Support Segmentation Data Lake Security Billing App Workday Employees Personalization Microservices Telemetry ServiceNow Ticketing Payments Microservice Mainframe Notification Microservice Location Microservice Loyalty Microservice Recommendation Microservice Customer CRM Purchases App Catalog App JIRA Projects Okta Permissions Medallia Sentiments Pricing App Cassandra PostgreSQ L Neo4j Oracle MariaDB AlloyDB MongoDB Redis SQL Server MySQL MongoDB Amazon Aurora Azure CosmosDB Marketo Campaigns Coupa Requisitions As Data Needs Emerged, Data Complexity Escalated
- 23. UNIVERSAL DATA PRODUCTS: KafkaTopic + Schema + Owner Real-time Data Discoverable Contextualized Trustworthy and Reliable Reusable
- 24. Patients Accounts Diagnosis Treatments Prescriptions Drug Stock Providers Insurers HEALTHCARE FINANCIAL SERVICES Customers Accounts WebLogins Web Clickstreams Transactions Payments Branches Claims Traders Server Logs RETAIL & E-COMMERCE Customers Accounts Purchases Web Clickstreams Inventory Stores Employees Shipments MANUFACTURING Telemetry Inventory Orders Shipments Suppliers Assets Equipment Receiving GOVERNMENT Citizens Claims Visas Equipment Vessels Criminals Shipments Laws TRANSPORTATION Vehicle Drivers Weather Deliveries Repairs Road Conditions
- 25. Use Data Productsto Build New Business Applications Companies that treat data like a product can: ● Reduce the time it takes to implement new use cases by as much as 90% ● Decrease total ownership costs by up to 30% (technology, development, and maintenance) ● Reduce their risk and data governance burden Customers Accounts Web Logins Transactions Fraud Detection Customers Accounts Inventory Purchases Web Clickstream Customer Loyalty Vehicle Telemetry Repairs Drivers Customers Delivery Management Data Products Use Cases
- 26. Shift Left -Clean and Govern Data at the Source Grand River plume discharged into Lake Michigan (Photo by Beaver, 2009). Waiting to clean data: ● Increased complexity and cost from redundant processing ● Expensive & inefficient to clean and enrich data multiple times ● Lakehouse usually doesn’t have your operational data and schema ● Data systems & applications built on stale data
- 27. Shift Left -Clean and Govern Data at the Source Grand River plume discharged into Lake Michigan (Photo by Beaver, 2009). Waiting to clean data: ● Increased complexity and cost from redundant processing ● Expensive & inefficient to clean and enrich data multiple times ● Lakehouse usually doesn’t have your operational data and schema ● Data systems & applications built on stale data
- 28. Fill Out theStack EARLY DAYS TODAY Low-level Commit Log Complete Data Streaming Platform
- 29. 29 PROCESS GOVERN CONNECT STREAM The Capabilities ofa Data Streaming Platform
- 30. A New Paradigm: DataStreaming Platforms and Universal Data Products From Data Mess To Data Products To Instant Value Everywhere CONNECT PROCESS GOVERN STREAM Accounts Customer Profile Purchases Shipments Claims Orders Clickstreams CONNECT Data Systems Inventory Replenishment Forecasting … Custom Apps & Microservices Personalization Recommendation Fraud …
- 31. A New Paradigm: DataStreaming Platforms and Universal Data Products From Data Mess To Data Products To Instant Value Everywhere CONNECT PROCESS GOVERN STREAM Accounts Customer Profile Purchases Shipments Claims Orders Clickstreams CONNECT Data Systems Inventory Replenishment Forecasting … Custom Apps & Microservices Personalization Recommendation Fraud …
- 32. 32 PROCESS GOVERN CONNECT STREAM The Capabilities ofa Data Streaming Platform
- 33. CONNECT 120+ Pre-built and70+ fully managed connectors Custom connectors to homegrown systems and apps Connect with Confluent partners Confluent Connector Ecosystem
- 34. 34 Stream Quality Stream Catalog StreamLineage ` GOVERN The Only Fully Managed Governance Suite for Data Streaming
- 35. EMERGING DEFACTO STANDARD PROCESS 35 0 50,000 100,000 150,000 20202021 2022 2016 2017 2018 KAFKA FLINK Two Apache Projects, Born a Few Years Apart Monthly Downloads
- 36. Stream Processing UseCases Applications AI Pipelines Analytics Datastream API Stateful Functions Table API Flink SQL Apache Flink’s Unified Runtime Apache Flink’s APIS Apache Flink: Any Use Case, Any Scale PROCESS
- 37.
- 38. ANALYTICAL ESTATE OPERATIONAL ESTATE ApacheKafka is the standard to connect and organize business data as data streams Apache Iceberg is an open table format for analytic datasets that feed the analytical estate
- 39. 3 9 TABLEFLOW Tableflow Convert Kafka topics,and associated schemas to Iceberg tables in one click to feed any data warehouse, data lake, or analytics engine
- 40. Industry leaders fordata streaming The Forrester Wave(™): Streaming Data Platforms, Q4 2023 The Forrester Wave(™): Cloud Data Pipelines, Q4 2023
- 41.
- 42. Generative AI withConfluent Fadi Oweis
- 43. Generative AI SolutionArchitecture Data Base Data Lake ERP Extracted Data Intelligent Chunking Chunks Embeddings Vector DB Relevant Answer LLM Source Systems User AI Model Symantec Layer Nvidia GPUs Self Managed CUDA/ NGC (NVIDIA) | Self Managed Jupiter Notebooks Extraction Interface 43
- 44.
- 45. Generative AI Use-Cases 45 EliminateErrors AI Document Understanding AI Interviewer Automated Ticket Resolver AI Human Avatar Kiosk Strategic Chatbot Call Center Assistant Cost Savings User Experience Reduce Redundancy Streamline Process
- 46. AI Document Understanding HaveFull Control over your documents How AI Can Help: Challenges in Document Search: • Document Variety and Complexity • Data Accuracy and Consistency • Scalability • Compliance and Security • Time-Intensive Workflows • Document Classification • Data Extraction • Workflow Automation • Compliance Management • Analytics and Insights NLP Docs Useful Info 46
- 47. AI Interviewer Click Herefor Demo Revolutionize Candidate Screening with AI-Powered Interviews How AI Can Help: Challenges: • Traditional scheduling is time-consuming, • Coordinating availabilities & enduring long interview durations. • Long time to find relevant resolution • Limited Candidate Shortlisting Capacity: Human evaluator constraints lead to qualified applicants being overlooked due to high initial applicant volumes. • Automated scheduling allows candidates to participate at their convenience, saving time and resources • AI evaluates a larger volume of applicants, ensuring thorough assessment and fair shortlisting • Implements a uniform scoring system based on predefined criteria, enhancing reliability of candidate selection. Improved Hiring efficiency NLP Supports multiple languages for global operations Real-time speech-to-text and text-to-speech capabilities using open-source models Real Time Analysis Enhance decision making 47
- 48. Automated ticket Resolver ProcessSupport Tickets (Automated Ticket Resolver) How AI Can Help: Challenges: • High Volume of Tickets • Complexity of Issues • Response Time • Analyzes ticket content and provides potential solutions based on documentation • Accelerates ticket resolution process and reduces workload on support staff Ticket Analysis NLP Supports multiple languages for global operations Real-time speech-to-text and text-to-speech capabilities using open-source models Ticket Resolution Acceleration 48
- 49. Call Center Assistant ClickHere for Demo Empower Your Team with AI-Enhanced Real-Time Knowledge Access and Insights How AI Can Help: Challenges: • High Call Volume • Complex Technical Issues • Long Waiting Time • Quick access to company knowledge base • Real-time call analysis with suggested questions and upsell opportunities • Improves call handling efficiency and identifies sales opportunities Improved call handling efficiency and customer satisfaction NLP Supports multiple languages for global operations Real-time speech-to-text and text-to-speech capabilities using open-source models Company Knowledge Base Real Time Analysis Enhanced decision-making 49
- 50. AI Human AvatarKiosk Enhance Customer Interaction with Personalized AI Avatar Kiosks Click Here for Demo Click Here to read the Journal How AI Can Help: • Customized Human Avatar Creation • Real-time Speech-to-Text Conversion • Natural Language Processing for Query Understanding • Multi-modal Interaction (Voice and Text) Enhanced customer satisfaction NLP Supports multiple languages for global operations Real-time speech-to-text and text-to-speech capabilities using open-source models Database Increased engagement Improved efficiency in customer service 50
- 51. Strategic Chabot Click Herefor Demo Empower Financial Decision-Making with AI-Assisted Insights How AI Can Help: Challenges: • Miscommunication • Slow Response Time • Lack of System Integration • Numerous Requests • Financial Analysis and Forecasting • Risk Management • Strategic Planning • Customer Insights • Immediate Response Click Here for Promo Improved Hiring efficiency NLP Supports multiple languages for global operations Real-time speech-to-text and text-to-speech capabilities using open-source models Database Real Time Analysis Enhance decision making 51
- 52. Jeddah P.O. Box 5648Jeddah 21432 T +966 12 6104000 F +966 12 6651163 Riyadh P.O. Box 818, Riyadh 11421 T +966 11 8133333 F +966 11 8133111 Al Khobar P.O. Box 476 Al Khobar 31952 T +966 13 8495000 F +966 13 8829898 Jubail P.O. Box 476, Al-Khobar 31952 T +966 13 3473925 / 3478927 F +966 13 3473947 Hafouf P.O. Box 476, Al-Khobar 31952 T +966 13 849 5000 F +966 13 882 9898
- 53.
- 54. Built on the ElasticSearch Platform for Speed Scale Relevance Manar Adil Sid Dwivedi
- 55. Amazon Web ServicesGoogle Cloud Microsoft Azure Public Cloud Hybrid On-Premises Elastic is Wherever Your Data Lives 50 Cloud Regions Globally
- 56. Converge metrics, logs,traces, and more to deliver unified visibility and actionable insights with the most widely deployed observability solution. Elastic Observability
- 57. Elastic advantage -One platform, single store Observability On Elasticsearch Metrics Logs Traces Integrated Context Everywhere Machine Learning Everywhere High cardinality High dimensionality High scale (keep ALL your data) ● Unlimited metrics - no limitations by time e.g. 30 days, 12 or 15 months; and custom metrics ● Unlimited logging - no rehydration required, always indexed ● Unlimited tracing - Not limited to 7 or 14 days and no sampling Powerful ML and Analytics ● Anomaly detection across all telemetry data ● Out-of-the box and customizable ML capabilities ● Sophisticated supervised and unsupervised ML models ● Ad-hoc analytics - understand “unknown unknownsˮ
- 58. Todayʼs Status Quo Unified Observability Comprehensive Visibility Automated Analytics Proactive Improvement Poor servicereliability Inefficient siloed operations Tool sprawl Traditional monitoring Full stack observability Single platform for operational and business data Context and correlation across all telemetry Tools consolidation End-to-end visibility across hybrid cloud Service dependency mapping Insights into cloud-native technologies ML-based anomaly and outlier detection AIOps – intelligent incident detection and response Flexible and powerful analytics Code and pipeline visibility for optimization and reliability End-to-end user experience measurement Teams need a unified observability platform
- 59. Elastic Observability MULTICLOU D CLOUD HYBRID DATA CENTER WORKSTATIO N Kibana Explore,Visualize,Engage) UnifiedInterface for Developers, SREs, DevOps, IT Admins Elasticsearch Store,Search,Analyze) Machine Learning and Analytics for Search, Correlation, Causation Integrations Connect,Collect,Alert) Telemetry Metrics, Logs, Traces) - Any source, Any data Hybrid Public cloud On-premises Customer Experience,Innovation,Cloud Adoption APM Infrastructure Monitoring Logging Synthetics 200+ integrations RUM Mobile
- 60. Emirates NBD bankson Elastic to secure billions in assets, and increase customer satisfaction and trust PROFILE GOALS SOLUTIONS RESULTS About: A Dubai government-owned bank maintaining $190 billion in assets, serving more than 14 million international banking customers. Industry: Financial Services Location: International, Dubai HQ ● Eliminate siloed data structures across the company ● Ensure regulatory data-retention compliance ● Eliminate the daily constraints that hundreds of bank developers faced when dealing with legacy solutions. ● Deployed Elastic Observability APM to track the health and performance of their digital experiences ● Implementing built in Machine Learning rules provided by Elastic Security SIEM ● Addressing customer facing problems in minutes rather than days ● Increase customer satisfaction by spending less time resolving issues. ● Detecting both external and Internal threats elastic.co | 2021 Elasticsearch B.V. All rights reserved. Read Customer story "Weʼre using Elastic going forward to better serve our customer needs, to drive a better customer experience.ˮ - Ali Rey, Vice President of Cloud and Data Platforms, Emirates NBD
- 61. Elastic Agentʼs New outputto Confluent Endless possibilities for data collection and streaming The Elastic Agent has several advantages over Beats: 1. Easier deployment and management: Elastic Agent is a single agent that downloads, configures, and manages any underlying policy or component required to collect and parse data. 2. Easier configuration: you define a single agent policy that specifies which integration settings to use, and the Elastic Agent generates the configuration required by underlying tools. 3. Central management: Elastic Agents can be managed from a central location in Kibana® called Fleet. 4. Endpoint protection: Elastic Agent enables you to protect your endpoints from security threats. This allows customers to extend their Elastic value with new Security use cases on the data they are collecting with no additional tools or effort.
- 63. Unified visibility across allenvironments ● Insights into cloud native and 3-tier architectures ● 200+ deep and growing integrations on AWS, Azure, and Google Cloud ● Isolate problems quickly across complex architectures
- 64. Log monitoring ● Scalable,centralized log monitoring for hybrid cloud ● Detect patterns and outliers with log categorization and anomaly detection ● Powerful cross cluster search that searches across everything ● Efficiently optimize performance and storage with data tiers
- 65. Application performance monitoring ● Improvecode quality with end-to-end distributed tracing ● Quickly troubleshoot problems with ML-powered health indicators and anomaly detection ● Identify root cause of slowness and errors with on-demand correlations ● Out-of-the box support for OpenTelemetry and native agents
- 66. Infrastructure monitoring ● KPIsfor hosts, pods, containers, and cloud ● Analyze infrastructure performance anomalies in-context with logs, application performance ● Pinpoint full-stack problems with advanced visualizations ● Break down application and infrastructure silos for faster root cause detection
- 67. Synthetic monitoring ● Proactivelymonitor availability and functionality of user journeys ● Track availability of your key services from external endpoints ● Trace mission-critical issues across your application environment ● Track and deliver on your SLIs and SLOs
- 68. Real user monitoring ●Core web vitals provide a report card for your users' experience ● Break down key page load metrics by location, device, OS, and browser ● Data exploration view to compare, contrast, and correlate different cohorts
- 69. Actionable insights ● Zeroconfiguration (built-in) machine learning ● AI-driven anomaly detection and root cause analysis across all observability data ● Automatic APM correlations to find root causes ● Powerful search for "unknown unknowns" ● Reduces MTTD and MTTR
- 70. NETWORKING BREAK &PRAYER TIME Visit our partners
- 71. Proprietary + Confidential Accelerateevery organization’s ability to digitally transform its business
- 72. Commitment to Cybersecurity& Data Compliance Cloud. On KSA’s terms. what groundbreaking technology + Google’s Security Best-Practices how KSA’s cybersecurity and data residency requirements
- 73. Presidency of State Security Compliance& Alignment Highest CSP License Commitment to Local Regulations & Data Compliance
- 74. Proprietary + Confidential Therise of real time data and AI is driving the need for real time intelligence Real time visibility Real time predictions Real time activation Real time app & infrastructure monitoring Optimize system performance Automated system to resolve customer issues Audit & security Improve customer experience Anomaly detection Recommendations Audience segmentation Churn prediction Demand forecasting Prediction using vision analytics Predict customer lifetime value Dynamic product offers Marketing automation Customer 360 Flexible inventory management Reverse ETL Logistics optimization
- 75. Proprietary + Confidential Automaticallyidentify and take action on insights Execute batch analytics on demand against batch data Always run analytics against streaming data Real-time AI Moving towards Continuous Intelligence Right-time Insights Informs Batch Analytics Real-time Analytics Continuous Intelligence Informs Partners like Confluent Dashboards Alerts Actions
- 76. A unified platformfrom data to deployment and for all your predictive, generative, and agentic needs Databases AI & ML Data analytics Insights AlloyDB Looker BigQuery Vertex AI Agent Builder Model Builder Model Garden Governance Dataplex MLOps in Vertex AI Infrastructure AI Hypercomputer (GPUs and TPUs) Cloud Ops Gemini for Google Workspace Gemini for Google Cloud AI Agents
- 77. Proprietary + Confidential BigQueryunified data platform Unified data platform Data integration and orchestration Managed open source analytics Streaming analytics
- 79. A new paradigmto set your data in motion: Confluent Data Streaming Platform CONNECT PROCESS GOVERN SHARE Real-time AI Apps Data Systems STREAM Pricing Inventory Payments Personalization Fraud Supply Chain Recommendations From Data Mess To Data Products To Instant Value Everywhere
- 80. 1 - Acceleratemigrations from on-prem to Google Cloud STORE ksqlDB Schema Registry DATA SERVICES MAINFRAME KAFKA APPS JDBC / CDC connectors On-prem Google Cloud Cloud Storage PROCESS & ANALYZE BigQuery APPS Looker Vertex AI Dataproc Spark Data Fusion INGEST managed connectors Connect Leverage 200+ Confluent pre-built connectors to continuously bring valuable data from existing services on-prem including enterprise data warehouse, databases, and mainframes Modernize Increase agility in getting applications to market and reduce TCO when freeing up resources to focus on value generating activities and not in managing servers Bridge Hybrid cloud streaming with consistent, event-driven architecture for modern apps Cloud Dataflow
- 81. Ingest Capture event streamswith a consistent data structure using Schema Registry and unify real-time events with batch processing using source Connect 2 - Derive insights from more diverse data in real-time Mobile Web IoT Data store Cloud or on-prem Source connectors Events Process & Enrich Develop real-time ETL pipelines using ksqlDB & Connect and schedule batch processing with Dataflow Store & Analyze Utilize the best of Google Cloud to analyze event streams and take business impacting action on high-value, perishable insights. ksqlDB Schema Registry Cloud Storage Sink Cloud Storage Vertex AI Cloud Bigtable Cloud Dataflow BigQuery Sink BigTable Sink BigQuery
- 82. Serverless integration Connect existingand apps and data stores in a repeatable way without having to manage- Apache Kafka, Schema Registry to maintain app compatibility, ksqlDB to develop real-time apps with SQL syntax, and Connect for effortless integrations with Cloud Functions and data stores GCP serverless platform Stop provisioning, maintaining or administering servers for backend components such as compute, databases, and storage so that you can focus on increasing agility and innovation for your developer teams 3 - Increase developer agility and speed of innovation Apps Edge devices ksqlDB Schema Registry COMPUTE Cloud Functions/Run Data stores REST Proxy & Clients Source Connectors Cloud Functions Sink DATA STORES STORAGE Cloud Storage GCS Sink ANALYTICS BigQuery Vertex AI BigTable Cloud SQL Cloud Dataflow
- 83. Real-time event ingestionand continuous processing Powered by Confluent and BigQuery continuous queries Partner integrations Bigtable Pub/Sub BigQuery tables Vertex AI BigQuery continuous queries
- 84. Retail use case:addressing abandoned shopping carts Abandoned online shopping cart Abandoned cart events table BigQuery continuous query SQL Pub/Sub notification with Application Integration email Real-time email & happy customer Gemini recommendations
- 85. Core processing systems Externaldata Unstructured data Systems of Record Browser mobile Telemetry DWH, Data Lake SaaS apps … Infrastructure Data Sources From Data to AI Event-driven Decoupled architecture Immutable Robust security controls Real-time and performant Fully managed cloud-native service Vector Databases Model Building / Fine-tuning GenAI Consumer & Gateway GenAI Agents 120+ pre-built Connectors Stream Processing Stream Governance Create a real-time knowledge base Bring real-time context at query time Build governed, secured, and trusted AI Experiment, scale and innovate faster Vertex AI Vertex AI Vector Search AlloyDB
- 86. Better Together: Confluent+ Google Cloud Generative AI How real time data streaming helps accelerate Google’s GenAI capabilities Google Cloud Blog
- 87.
- 88. November 12, 2024 FourSeasons Hotel, Riyadh Saudi Arabia
- 89.
- 90. Confluent at Ministryof Human Resources and Social Development Will LaForest Global Field CTO Confluent Waleed AlOmran Digital Integration Manager Ministry of Human Resources and Social Development (HRSD)
- 91. Modernize your Legacy Applications byMongoDB Legacy Modernization Fuat Sungur Advisory Solutions Architect fuat.sungur@mongodb.com 22/October/2024
- 92. If you’ve everconsidered modernizing any of your applications…
- 93. The costs ofrunning on legacy systems Sprawling tech landscape and slow development process block new features Being late to market Legacy systems are unable to meet availability and scale that customers demand Poor user experience Modern apps create semi & unstructured data that is a poor fit for rigid, tabular data models Missed opportunities Expensive hardware, punitive licensing, intrusive audits increases TCO Lower technology ROI
- 94. The Main Challengesof Modernization Finding the best technology Risks and Cost of Migration Manual Activities Data migration Application Refactoring Missing feature parity Not mature technology
- 95. The Main Challengesof Modernization Finding the best technology Missing feature parity Not mature technology
- 96.
- 97. It’s Mission Criticaland General Purpose Laptop, your data center, multi-cloud Additional NoSQL and Big Data Strengths and Innovations Native data structures & semi-structured/ unstructured data Scale & Distribution Enterprise Security & Management Traditional Relational Strengths ACID Transactions Expressive Query Language & Secondary Indexes
- 98. Three Main Pillars DocumentData Model Native Distributed Architecture Freedom to Run Anywhere
- 99.
- 100. { "customer_id": 123, "first_name": "Fuat", "last_name":"Sungur", "email": "fuat.sungur@mongodb.com", "dob" : ISODate("1987-04-01T05:00:00Z"), "annual_spend": NumberInt("22141"), "address": [ { "address": "Springs 7, No: 123", "city": "London", "country": "England", "location": "work", "phone_numbers" : [ {"type": "mobile", "number": "+44314124124"}, {"type": "landline", "number": "+446662123"} ] }, { "address": "Downtown, Emma Street No:7", "city": "Dubai", "country": "United Arab Emirates" } ], "interests": [ "football", "movies", "swimming" ] } Tabular (Relational) Data Model Related data split across multiple records and tables Document Data Model Related data contained in a single, rich document
