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Quantifying Business Advantages: The Value of Database Selection
 

Quantifying Business Advantages: The Value of Database Selection

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In the digital economy, data is the raw currency. How an organization stores, manages, analyzes and uses data has a direct impact on its success. Consider the following examples: ...

In the digital economy, data is the raw currency. How an organization stores, manages, analyzes and uses data has a direct impact on its success. Consider the following examples:

- A leading insurance company delivering a new application in just 3 months, after struggling for 8 years with a legacy database.
- A global telecoms operators accelerating time to market by 4x and improving customer experience by 10x.
- A Tier 1 investment bank estimating savings of $40m.

View the slides to learn how database selection can drive quantified business advantage you can't afford to ignore.

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  • Database typically deeply embedded in a tech stack. Users never really get to see it – so does the choice really matter, and can you measure what it gives you <br /> Give you specific examples – will explore these and others in more detail later <br /> <br /> <br />
  • Examples: Deliver apps that weren’t previously possible – build in 3 months what couldn’t be built in previous 8 years
  • Drive 80% cost reduction
  • Why is the database so important
  • At the heart of the change is data, and the role in plays in modern apps <br /> • 90% of the world’s data was created in the last two years <br /> • 80% of enterprise data is unstructured <br /> • Unstructured data growing 2x faster than structured <br /> In the digital economy, data is the raw currency. How you stores, manages, analyzes and uses data has a direct impact on the your success.
  • RDBMS was only real database option up until relatively recently – great for structured data, but no good for multi-structured, polymorphic data generated by todays applications <br /> <br /> Even historically, the RDBMS only held 15-20% of an organisation’s information assets. We now have the tools and technologies that can harness the other 80%
  • To summarise the requirements of a modern database to meet needs of a modern apps, you need: <br /> Flexible data model to store multi-structured, rapidly changing data <br /> Run rich analytics <br /> Need to support agile dev methdologies to accommodate constantly changing requirements <br /> Scalable on commodity hardware to handle growth <br /> Cannot sacrifice enterprise quality – uptime or security <br />
  • Over 100k production deployments who have adopted MongoDB to support these requirements – across a wide range of use cases
  • Lets start to drill into some examples of where the choice of database has had a tangible business outcome – start with TTM
  • MetLife is one of the world’s largest insurance companies. <br /> Masses of data around 100m customers and 100+ products and policies stored in 70 different source systems <br /> Wanted to bring that together to create single view for better customer experience when they called into a CSR. Also identify risk of churn and cross-sell, upsell <br /> <br /> Started project back in 2005, most recent initiative working on a RDBMS had taken 2 years, cost $10ms, still not successful. Developing a single schema that could take data from 70 systems wasn’t possible, as soon as changes to source, then schema broke <br /> Realised needed to change assumptions <br /> Started with MongoDB – built a poc in 2 weeks, and went into production 3 months later <br /> Key was flex schema <br /> Used MongoDB subscription gaves access to expertise used in dev phase to get them on the right path, and stands with them in production. Also use MMS for proactive monitoring and alerts so can maintain uptime <br /> <br /> <br />
  • Launching an app in the worlds 2nd highest population – 1.3bn (17%), need to know it can scale <br /> Hike is India’s fastest growing messaging app – joint venture between Bharti and Softbank – 2 huge multinational companies <br /> Using MongoDB, scaled to over 15m users in 9 months. Key for them <br /> – MongoDB sharding and replica sets for scale and availability <br /> – MongoDB query framework for running complex analytics <br /> – MongoDB subscriptions for support, best practices and advanced security features <br />
  • Final example in this section <br /> One of world’s largest telecoms providers with operations across Europe and Latam <br /> Much like Metlife, needed to build single view of their subscriber <br /> Specifically focused on landline, mobile, IPTV, app store and location- based services. Like many telcos, these services all had their own databases, making it impossible to get real time view of the subscriber <br /> <br /> Started initial dev on Oracle database. <br /> To build single view, needed a schema with 20 separate tables, typical operations requiring 35 separate JOINs. Auth alone was an operation that joined 5 tables <br /> Didn’t scale. Also loads to EDW were taking too long. 3 versions of the prototype over 15 month period <br /> <br /> Knew needed a new approach – evaluated MongoDB <br /> More flex data model meant could represent data in 5 collections, rather than 20 tables, most operations hit 1 or 2 collections at most <br /> <br /> They engaged early, used MongoDB Dev Subscriptions and Training to get them on right path <br /> Result – compressed dev cycle by 4x – used 50% of the dev resource. Storage reduced by 4x, query latency by 10x
  • Looked at time to market – look at perf and availability – has direct impact on customer experience
  • one of the world’s leading relationship service providers, <br /> relies on compatibility matching system to introduce potential partners, <br /> relies on analyzing a user’s traits and preferences. 
 <br /> <br /> To run matching across their entire use base taking 15 days on RDBMS – too long. <br /> <br /> Looked for alternatives – found using flex data model and rich queries, along with ability to shard to scale out, they could reduce matching time to 12 hours – 95% improvement <br /> Use combination of consulting and subscriptions to put dev on right path and simplify their operations <br />
  • Maintain vehicle history database – enables potential buyers of used cars to verify provenance of a vehicle – service history, previous owners, damage reports <br /> <br /> Originally built on a K/V store, <br /> As database grew, hit scalability limits. Also very complex to run DR across multiple DCs, impact service availability <br /> <br /> So evaluated a range of solution – chose with MongoDB <br /> Couldn’t tolerate eventual consistency – added to much complexity to their apps <br /> MongoDB met their scalability requirements, got a cluster with around 50 nodes, distributed across 2 datacenters, serving 12.5bn docs – 10x faster than their existing K/V store <br />
  • final example in this section <br /> ADP one of the world’s leading HR and payroll outsourcing solutions provider, <br /> Wanted to extend access to their core HR apps to mobile devices <br /> Uptime and functionality were critical <br /> After extensive evaluation of multiple databases, ADP selected MongoDB <br /> MongoDB’s document model and dynamic schema made it fast for developers to build the application. <br /> Maintain functionality due to rich query model <br /> Replica sets and MMS application give them service continuity – through replication and proactive monitoring and alerting <br />
  • Final section – driving cost out <br />
  • Slide 25 – <br /> Tier1 bank migrated reference data mgt app from RDBMS to MongoDB <br /> <br /> App had a master copy of the data generated in New York, replicated to 12 global data centers consumed by local apps, and used for reconciliation and reporting. But took up to 36 hours for data replicate because of the complexity of the schema. Bank was operating on old data – hit with fines. Also high licensing and maintenance costs, high expensive h/w. <br />
  • Moved to MongoDB, using built in DC-aware replication, replicate across DCs in minutes <br /> Moved expense model from capex to opex <br /> <br /> In total, bank estimates saved $40m over 5 years <br /> <br />
  • Another RDBMS user, Shutterfly, built platform supporting digital photo products on Oracle. <br /> <br /> Need to release new products to market faster, run deeper analytics, reduce cost. <br /> Trying to represent complex objects in Oracle wasn’t scaling, so looked at alternatives, chose MongoDB <br /> <br /> Reduced dev sprints from months to weeks. They’ve simplified their code base by eliminating ORM – improved average query response time by 9x <br /> Reduced cost – 80% lower than Oracle <br /> <br /> In addition to the technology, use MMS for proactive monitoring. Speeds up issue resolution as all key metrics can be accessed by support team at MongoDB, means cut down going backwards and forwards with logs <br />
  • MongoDB subscription – bundle of services and features designed to make you successful faster <br /> <br /> Typically value starts before production, because consultative support can provide assistance in schema design, data migration H/W selection, sharding, testing. Then there with you in production – more than just break/fix – regularly check in to proactively address issues. Access to training and consulting <br /> <br /> Get access to advanced security features, including Kerberos auth, LDAP integration and auditing for compliance <br /> <br /> Also got access to Automation, DR and Monitoring <br />
  • MMS is the application to manage your MongoDB environment – ops teams love <br /> Automation – provision anything from single replica sets to large sharded clusters spanning regions in a single operation. Not just the database, provision underlying h/w on prem and in the cloud. Used to automate online upgrades – again, click of a button. Tech preview <br /> <br /> MMS Backup provides DR – only solution providing continuous incremental backup, point in time recovery and snapshots of sharded clusters <br /> <br /> MMS Monitoring – tracks over 100 variables including operations counters, queues, system utilisation. Can create alerts – sent to pagerduty, hip chat, email and text <br /> <br /> MMS is a free hosted service provided by MongoDB – so you connect your systems to it. Or you can deploy on-prem as part of a subscription <br /> <br />
  • How we hAlso dedicated consulting and training service delivered remotely or on site – brings MongoDB expertise to your project <br /> elp
  • Now have over 1k customers of these services – includes nearly 1/3 of Fortune 100, can see strong use across multiple industry sectors <br />   <br />
  • What I hope I’ve demonstrated is that a database can deliver quantifiable biz advantage <br /> <br />
  • What I hope I’ve demonstrated is that a database can deliver quantifiable biz advantage <br /> <br />
  • – final example is telecoms operator Orange who operate mainly in UK, Germany and France <br />   <br /> Running product catalog on a RDBMS, on-premise, but growth in users and content meant started to look to new app, DB and hosting capabilities <br /> Decided to move DB layer to MongoDB and rehost environment to AWS where they could scale on demand and take advantage of lower pricing. Architecture of AWS fits well with MongoDB. Saved $2m <br /> Key to success was working closely with MongoDB engineers to optimize app design and production playbooks <br />   <br />
  • Applications are getting much more sophisticated – Handle and aggregate mobile, social, sensor data and real-time analytical applications are essential for remaining relevant.
  • Semi-structured and unstructured data does not lend itself to be stored and processed in the rigid row and column format imposed by relational databases, and cannot be fully harnessed for analytics if stored in BLOBS or flat files. It is critical to select a database that can not just store complex data, but also enables rich query and analytics capabilities in order to increase business visibility across a variety of data assets.
  • Traditional s/w dev methodologies predicated on defining all the requirements at start of the project – any changes often meant changing the data model – so things get very slow. Reality is todays apps are developed much more iteratively where requirements change frequently, using agile methodologies. Need a database that can handle those changes, without having to change your schemas. Can do it in dev, and do it production, without downtime
  • Rise of commodity servers and cloud computing changes the cost model of infrastructure, companies keen to ride that economic curve. RDBMS designed for scale up, rely on ever larger hardware. Can scale them out, takes huge engineering efforts and lose lots of benefits of relational model – denormalise, lose JOINs, lose trx that cross nodes <br /> <br /> To take advantage of the economies, you need databases that can can scale out natively, with in built replication so they can take advangtage elasticity of the cloud and handle fact commodity servers do fail <br /> Need to ensure security of the data <br />

Quantifying Business Advantages: The Value of Database Selection Quantifying Business Advantages: The Value of Database Selection Presentation Transcript