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![4 RDBMS vs. Flexible Data Model Relational MongoDB { customer_id : 3, first_name : ”Danilo", last_name : ”Nobrega", city : ”Stockholm", phones: [ { number : “46-64-223-9828”, dnc : true, type : “home” },{ number : “46-173-555- 12144”, type : “cell” }] } Customer ID First Name Last Name City 0 Jonah Rosenboom Hamburg 1 Tim Schojohann München 2 Boris Bialek Zürich 3 Danilo Nobrega Stockholm 4 Ingo Marienfeld Düsseldorf Phone Number Type DNC Customer ID 49-69-223-9828 home T 3 49-69-143-45986 home T 0 49-173-555-12144 cell (null) 3 49-69-777-1212 home T 1 49-175-698-1213 cell (null) 1 49-162-767-444 cell F 2](https://image.slidesharecdn.com/ashnik-webinar-whatismongodb-201026060703/75/Build-robust-streaming-data-pipelines-with-MongoDB-and-Kafka-P2-4-2048.jpg)
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Build robust streaming data pipelines with MongoDB and Kafka P2
MongoDB is a flexible, scalable database that has experienced significant adoption, with over 90 million downloads and 75,000 clusters on MongoDB Atlas. It supports a rich set of data models and query functionality, allowing users to store, manage, and access structured and unstructured data across multiple cloud environments. The platform is designed for operational, transactional, and analytical workloads, emphasizing a unified developer experience with strong capabilities in data synchronization and privacy controls.
Build robust streaming data pipelines with MongoDB and Kafka P2
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- 2. MongoDB Adoption 90,000,000+ MongoDB Downloads 1500,000+ Online Education Students 1m+ MongoDB Atlas Clusters 75,000+ MongoDB User Group Members 1,000+ Technology and Services Partners 17,500+ Customers Across All Industries DB-Engines Rankings Fastest Growing Database over the past decade Worldwide Activations
- 3. 3 Data which isaccessed together, is stored together... Customer Product Contact Pricing Phone Phone Objects Tables isolated in several applications and domains Lead NameNameParts ARR Address Contact Roles SummaryCustomer Detail Specs How to build a flexible and scalable operational data store??
- 4. 4 RDBMS vs. FlexibleData Model Relational MongoDB { customer_id : 3, first_name : ”Danilo", last_name : ”Nobrega", city : ”Stockholm", phones: [ { number : “46-64-223-9828”, dnc : true, type : “home” },{ number : “46-173-555- 12144”, type : “cell” }] } Customer ID First Name Last Name City 0 Jonah Rosenboom Hamburg 1 Tim Schojohann München 2 Boris Bialek Zürich 3 Danilo Nobrega Stockholm 4 Ingo Marienfeld Düsseldorf Phone Number Type DNC Customer ID 49-69-223-9828 home T 3 49-69-143-45986 home T 0 49-173-555-12144 cell (null) 3 49-69-777-1212 home T 1 49-175-698-1213 cell (null) 1 49-162-767-444 cell F 2
- 5. 5 Multiple data models,rich query functionality... Rich Queries Point | Range | Geospatial | Faceted Search | Aggregations | JOINs | Graph Traversals JSON Documents Tabular Key-Value Text GraphGeospatial
- 6. Broadest Reach: Private,Hybrid, Public Self-Managed: Ops Mgr • Automation • Monitoring Alerting • Backup/Restore • Patching • Scaling • Performance Advice • Kubernetes Operator Laptop Mainframe Server Database as a Service Self-managed in the cloud Database as a Service • Self-service & fully automated • Global, elastic clusters • Highly available and secure out of the box • Operational tooling built in: monitoring, backups • On AWS, Azure, GCP, 70+ regions total Convenience: same codebase, same APIs, same tools, wherever you run
- 7. Multi-Cloud Clusters Deploy asingle cluster across multiple clouds simultaneously. This unique capability gives you unparalleled flexibility when it comes to where your data is stored and what cloud services you can use with Atlas. ● Take advantage of best-of-breed technology across multiple clouds ● Seamlessly migrate your cluster from one cloud to another ● Improve high availability with cross- cloud redundancy ● Reach more users by distributing your database across more regions
- 8. Complete Data Platform Unifieddeveloper experience that provides — ● One API across database, search, and data lake ● Supports transactional, operational, and analytical workloads ● Fully elastic infrastructure and serverless ● Declarative data access and privacy controls ● Global reach ● Mobile/Edge data persistence ● Data synchronization Atlas Database Atlas Search Atlas Data Lake Data Foundation with MongoDB Cloud TransactionalOperationalAnalytical MongoDB Cloud DEVELOPER SERVICES Mobile database Edge to cloud sync SDKs Data access & privacy Triggers & Functions Native Visualization Document Model & MQL DATA FOUNDATION MongoDB Atlas Atlas Search Atlas Data Lake Multi-Cloud | Multi-Platform | Secure
- 9. 4.4 Headline Features Custom AggregationExpressions Union Refinable Shard Keys Compound Hashed Shard Keys Mirrored Reads Hedged Reads Resumable Initial Sync Rust and Swift Drivers GA
- 10. 10 Data Access API Change Data Capture (CDC) Extract Transform Load (ETL) MongoDB Cluster DocumentData Model Distributed Systems Architecture Cloud | On-Premises Operational Apps and Systems of Record Producers Operational Data Store Consumers Legacy Systems CRM ERP Order Management Supply Chain Mgmt Data Lake Marketing Automation Website Social Media Reference Data Third-Party APIs Etc. Batch Load API CallsBatch File Exports Real-Time Data Changes Delta Load MongoDB Change Streams Write Back to Producer Systems (Optional) MongoDB Native Drivers Consuming Operational Apps and Services Internal apps, customer-facing services, and APIs for third-party consumption – across any channel Business Intelligence (BI) and Advanced Analytics Visualization and reporting, data analysis, artificial intelligence, machine learning and more Human Capital Mgmt MongoDB Connectors Building an ODS