MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart

MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart

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  @ #MDBlocal Ken W. Alger DeveloperAdvocate, MongoDB kenwalger SAN FRANCISCO
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  #MDBLocal Safe Harbor Statement Thispresentation contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Such forward-looking statements are subject to a number of risks, uncertainties, assumptions and other factors that could cause actual results and the timing of certain events to differ materially from future results expressed or implied by the forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited to, those identified our filings with the Securities and Exchange Commission. You should not rely upon forward-looking statements as predictions of future events. Furthermore, such forward-looking statements speak only as of the date of this presentation. In particular, the development, release, and timing of any features or functionality described for MongoDB products remains at MongoDB’s sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality. Except as required by law, we undertake no obligation to update any forward-looking statements to reflect events or circumstances after the date of such statements.
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  #MDBLocal MongoDB Atlas —Global Cloud Database Self-service & elastic Deploy, modify, and upgrade on demand with best-in-class operational automation Automated database maintenance Database and infrastructure resources as code, optionally via Kubernetes OSB Scale up, out, or down in a few clicks or API calls Global & cloud-agnostic Available in 60+ regions across AWS, Azure, GCP Global clusters for read/write anywhere deployments and multi-region fault tolerance Easy migrations with a consistent experience across cloud providers Enterprise-grade security & SLAs Network isolation, VPC peering, end-to-end encryption, and role-based access controls Encryption key management, LDAP integration, granular database auditing SOC 2 / ISO27001 / Privacy Shield / HIPAA / PCI Guaranteed reliability with SLAs Comprehensive monitoring Deep visibility into 100+ KPIs with proactive alerting Real-time performance tracking and Performance Advisor APIs to integrate with monitoring dashboards Managed backup Point-in-time data recovery Queryable backup snapshots Consistent snapshots of sharded deployments Cloud data mobility Sync and Serverless Simple, serverless functions for backend logic, service integrations, and APIs Database access from your frontend secured by straightforward, field-level access rules Database and authentication triggers to react to changes in real time
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  #MDBLocal Intelligently put datawhere you need it Locality Declare data locality rules for governance (e.g. data sovereignty), class of service & local low latency access Scalability Elastic horizontal scalability – add/remove capacity dynamically without downtime Workload Isolation Ability to run both operational & analytics workloads on same cluster, for timely insight and lower cost Highly Availability Built-in multi-region high availability, replication & automated failover
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  #MDBLocal MongoDB Atlas canput your entire database right next to users 10 ms 2 ms 10 ms 4 ms 4 ms 2 ms 10 ms 2 ms
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  #MDBLocal Put data whereyou need it: Locality Intelligent Distribution via Zoned Sharding • Policies to define data placement • Name a server by region, tag your documents by region and MongoDB does the rest • Documents automatically migrated as shard key ranges are modified • Global queries across all data in all zones Governance Class of Service Latency
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  #MDBLocal Jumpstart Objectives • Signup for Atlas • Setup an Organization • Configure a Project • Deploy a Cluster • Explore MongoDB Atlas Features • Connect to a production-ready environment Where are we headed?
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  #MDBLocal Production-Ready • Deploy acluster • Load Sample Data • Set Access • Analytics • Backups • Connection In 45 minutes?!?
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  DEMO MongoDB Atlas
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  #MDBLocal Connect with Python importpymongo from random import randint client = MongoClient(<<ATLAS CONNECTION STRING>>) num_docs = 5000 db = client.sflocal sites = ["Golden Gate Bridge", "Alcatraz", "Pier 39"] locations = ["AU", "US-CA", "US-OR", "CA", "DE", "GB", "IE", "IN", "MX"] for x in range(1, num_docs+1): attraction = { 'site' : sites[randint(0, (len(sites)-1))], 'location' : locations[randint(0, (len(locations)-1))], 'rating' : randint(1, 5) } result = db.reviews.insert_one(attraction) print('Saved {0} of {1} as {2}'.format(x, num_docs, result)) print('Rating creation completed')
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  https://alger.me/mongodb-university Next Steps TEST (withreckless abandon!) § Try the tutorials § Find us at the Leaf Lounge! § MongoDB University!
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  THANK YOU