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Cloudera Federal Forum 2014: EzBake, the DoDIIS App Engine
 

Cloudera Federal Forum 2014: EzBake, the DoDIIS App Engine

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General Manager of 42Six, a CSC Company, Matt Carroll, shares EzBake - a secure apps engine for DoDIIS.

General Manager of 42Six, a CSC Company, Matt Carroll, shares EzBake - a secure apps engine for DoDIIS.

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    Cloudera Federal Forum 2014: EzBake, the DoDIIS App Engine Cloudera Federal Forum 2014: EzBake, the DoDIIS App Engine Presentation Transcript

    • EzBake A Secure Apps Engine for DoDIIS Matthew Carroll, GM of 42six February 06, 2014
    • Outline Why Build an Apps Engine? The Architecture What’s Next 2!
    • The DoDIIS App Conundrum Budget Cuts •  •  •  •  •  There is not enough money to transition over 400+ apps within DIA (business and mission) Outsourcing IaaS to C2S and GovCloud needs to be monitored for cost reimbursable over time Application elasticity is critical to understanding true costs of ownership and maintenance Data is a much bigger cost than expected Need to consolidate systems engineering support Technology migration is not simple •  Most apps are CRUD based; write a report, find a report •  Security business logic is baked into each app •  Number one question: why can’t I choose the technology that best fits my app? Not a Big Data problem….yet •  On the order of TBs at best •  Highly connected but not big Security is the ultimate killer of time •  Most time is spent meeting PL3 needs and encrypting traffic 3!
    • Analytics are great but… THE COMMUNITY DRIVE TO ANALYTICS AND ENRICHMENT ENGINES HAS LEFT DIA PLAYING CATCH UP IN ITS MIGRATION OF APPS TO A COMMON PLATFORM. 1.  Make it as easy as possible for any legacy app to transition 2.  Will not dictate technologies 3.  Provide standards for security and access to datastores 4.  The platform must deploy across multiple brokers, i.e. EC2, OpenStack, VMware and be completely transparent to the app team 4!
    • Where to start? Starting was hard. But it became very clear that several epics were essential to migrate applications in an efficient manner: 1.  Streaming of data into applications must be done in a standard way. Velocity and size of data is not as much as a factor to DIA as is the method to which the data is consumed and distributed. To answer this a stream-based data interface must be built to support the nexus of data distribution within the environment, we call this Frack. 2.  Everyone likes the concept of migrating to NoSQL but it becomes unmanageable from a DevOps perspective if everyone picks their own database for their own use cases. Furthermore, the point is to be multi-tenant. So we created datasets, a means to expose indexing patterns instead of explicit databases, exposed through a common security layer. 3.  Too much time is spent on baking in non-application specific logic into each application vice supporting a common service tier. In order to build standards around common service-based functions we built Services. 5!
    • Integration vs. Engineering Of the major issues identified early on in the project the most hindering of issues was the deployment model. •  App teams are spending 80% of their time integrating to new database and new services vice building application functionality •  Applications would each follow their own System Installation Procedure (SIP) by which each would deploy their own software •  Scale was defined through provisioning of machines vice true automated elasticity •  Start developing within 1 hour and deploy capability within 30 days 6!
    • EzBake EzBake provides an integrated way to compose the different elements of your application: collecting, processing, storing, and querying data. •  Focus on application logic •  Simple API that leverages complex, distributed frameworks •  Easy to use local development kit •  Deploy in minutes •  Framework is accredited, applications inherit accreditation •  Subscription-based data-feed-model •  Automated elasticity •  Design for failure 7!
    • The Components The core of the platform is pure open-source solutions and is broken into the following primary components: •  Streaming Ingest (Frack): This is the interface for building data flow topologies which abstracts the physical stream processor •  Common Services (Procedures): Scaled and commonly used thrift services, typically utilized during streaming ingest •  Data Persistence (Dataset): These are our indexing patterns, called Datasets, exposed as Thrift services and abstracts the physical databases •  Query: Both direct access to Datasets and Aggregate Query across the various Datasets •  Security: Both at the data persistence and user access layers •  Batch Analytics: MapReduce abstractions that allow input from Datasets and output to Datasets and will leverage the GovCloud DataCloud •  Deployment: Currently use OpenShift for automated deployment but plan to migrate to Docker + YARN 8!
    • Technology Agnostic Each app has their own needs and it is not on the platform builder to force the team into a particular technology, rather offer a solution to meet the use case •  Instead of a jack-of-all-trades indexing for free text search, geospatial search, etc use mission specific indices for specific application logic needs •  Focus on storage patterns vice database specific operations thereby enforcing data access standards across the enterprise •  Allow for new cartridges for web frameworks including node.js, python, Ruby, etc. 9!
    • The Architecture 10!
    • Sharing Sharing is the key component of EzBake in order to achieve cost savings and provide agility for the application developer •  Sharing is exposed via the Common Services and the Aggregate Query •  The intent of the Common Services is to expose any functionality currently ingrained within stove-piped applications. By exposing that functionality as a service, other applications can leverage it, instead of application teams writing the same logic over and over again, such as entity extraction, date normalization, etc. •  The Common Services are wrapped in Thrift services, scaled out on the virtual infrastructure deployed through OpenShift •  The Aggregate Query is in development for delivery in EzBake v2.0, the current design will extend Impala to expose the EzBake Datasets as input for the distributed query engine •  App teams will expose “intents” within the Datasets for which they can respond, like a “person”, “place”, or “event” and the Impala engine will query plan and aggregate the results back to the requestor 11!
    • Security Built-in from the start, EzBake implements security across all features. •  Datasets are where the bulk of the security occurs, applying row level security to the data based on the user’s authorization string •  Row level security must be implemented in different ways, to support multiple types of datastores, for example, for the term dataset, which is ElasticSearch, we included a filter plugin that applies the boolean logic check at query time •  Embedding security across the platform allows the application teams to streamline their accreditation process 12!
    • Metering and Monitoring Data driven decisions •  Javascript API for web apps, Thrift API for services and REST for others •  Improve application usability/usefulness by examining analytics on usage patterns •  Diagnose issues with system, services and apps •  Determine cost allocation based on what agencies and organizations are using the system 13!
    • Timeline 14!
    • What’s Next EzBake provides an integrated way to compose the different elements of your application: collecting, processing, storing, and querying data. •  Distributed query via Impala (Intents are coming) •  Apache Spark integration (dynamic ranking) •  Graph support - Titan •  Change YARN to control Docker •  Upgrade to CDH5 •  Extend Apache Sentry 15!
    • Questions! Contact Us! Matthew Carroll GM, 42six matt@42six.com @mcarroll_