SlideShare a Scribd company logo
1 of 35
Brian Ritchie Chief Architect Payformance Corporation Email: brian.ritchie@gmail.com Blog: http://weblog.asp.net/britchie Web: http://www.dotnetpowered.com
[object Object],[object Object],[object Object]
[object Object],Source: IDC 2008
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],Application Application Application Application Mainframe Client-Server Database  as  Integration Point Service Service Service Oriented
 
[object Object],[object Object]
 
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],Utilized by CQRS (Command Query Responsibility Segregation)
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 
 
[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[ Blue ] [ Red ] [ Blue,1 ] [ Red,1 ] [ Orange ] [ Blue ] [ Blue ] [ Orange ] [ Orange,2 ] [ Blue,2 ] [ Red,1 ] [ Orange,2 ] [ Blue,3 ]
 
[object Object],[object Object],[object Object],[object Object],[object Object]
 
[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Given this document… And this index… Gives this table output http://ravendb.net/bundles/index-replication
[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

More Related Content

What's hot

Performance Analysis of Apache Spark and Presto in Cloud Environments
Performance Analysis of Apache Spark and Presto in Cloud EnvironmentsPerformance Analysis of Apache Spark and Presto in Cloud Environments
Performance Analysis of Apache Spark and Presto in Cloud EnvironmentsDatabricks
 
Apache Flink 101 - the rise of stream processing and beyond
Apache Flink 101 - the rise of stream processing and beyondApache Flink 101 - the rise of stream processing and beyond
Apache Flink 101 - the rise of stream processing and beyondBowen Li
 
Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...
Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...
Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...confluent
 
Spark streaming , Spark SQL
Spark streaming , Spark SQLSpark streaming , Spark SQL
Spark streaming , Spark SQLYousun Jeong
 
Introducing the Apache Flink Kubernetes Operator
Introducing the Apache Flink Kubernetes OperatorIntroducing the Apache Flink Kubernetes Operator
Introducing the Apache Flink Kubernetes OperatorFlink Forward
 
RxNetty vs Tomcat Performance Results
RxNetty vs Tomcat Performance ResultsRxNetty vs Tomcat Performance Results
RxNetty vs Tomcat Performance ResultsBrendan Gregg
 
Percona XtraDB Cluster
Percona XtraDB ClusterPercona XtraDB Cluster
Percona XtraDB ClusterKenny Gryp
 
MySQL Database Architectures - 2020-10
MySQL Database Architectures -  2020-10MySQL Database Architectures -  2020-10
MySQL Database Architectures - 2020-10Kenny Gryp
 
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...Databricks
 
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법Ji-Woong Choi
 
Understanding Data Partitioning and Replication in Apache Cassandra
Understanding Data Partitioning and Replication in Apache CassandraUnderstanding Data Partitioning and Replication in Apache Cassandra
Understanding Data Partitioning and Replication in Apache CassandraDataStax
 
Introduction to Spark Streaming
Introduction to Spark StreamingIntroduction to Spark Streaming
Introduction to Spark Streamingdatamantra
 
High Performance, Scalable MongoDB in a Bare Metal Cloud
High Performance, Scalable MongoDB in a Bare Metal CloudHigh Performance, Scalable MongoDB in a Bare Metal Cloud
High Performance, Scalable MongoDB in a Bare Metal CloudMongoDB
 
HandsOn ProxySQL Tutorial - PLSC18
HandsOn ProxySQL Tutorial - PLSC18HandsOn ProxySQL Tutorial - PLSC18
HandsOn ProxySQL Tutorial - PLSC18Derek Downey
 
MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...
MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...
MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...Jean-François Gagné
 
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
Deep Dive : Spark Data Frames, SQL and Catalyst OptimizerDeep Dive : Spark Data Frames, SQL and Catalyst Optimizer
Deep Dive : Spark Data Frames, SQL and Catalyst OptimizerSachin Aggarwal
 
Apache Spark vs Apache Flink
Apache Spark vs Apache FlinkApache Spark vs Apache Flink
Apache Spark vs Apache FlinkAKASH SIHAG
 
From my sql to postgresql using kafka+debezium
From my sql to postgresql using kafka+debeziumFrom my sql to postgresql using kafka+debezium
From my sql to postgresql using kafka+debeziumClement Demonchy
 

What's hot (20)

Performance Analysis of Apache Spark and Presto in Cloud Environments
Performance Analysis of Apache Spark and Presto in Cloud EnvironmentsPerformance Analysis of Apache Spark and Presto in Cloud Environments
Performance Analysis of Apache Spark and Presto in Cloud Environments
 
Apache Flink 101 - the rise of stream processing and beyond
Apache Flink 101 - the rise of stream processing and beyondApache Flink 101 - the rise of stream processing and beyond
Apache Flink 101 - the rise of stream processing and beyond
 
Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...
Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...
Show Me Kafka Tools That Will Increase My Productivity! (Stephane Maarek, Dat...
 
Spark streaming , Spark SQL
Spark streaming , Spark SQLSpark streaming , Spark SQL
Spark streaming , Spark SQL
 
Introducing the Apache Flink Kubernetes Operator
Introducing the Apache Flink Kubernetes OperatorIntroducing the Apache Flink Kubernetes Operator
Introducing the Apache Flink Kubernetes Operator
 
RxNetty vs Tomcat Performance Results
RxNetty vs Tomcat Performance ResultsRxNetty vs Tomcat Performance Results
RxNetty vs Tomcat Performance Results
 
Percona XtraDB Cluster
Percona XtraDB ClusterPercona XtraDB Cluster
Percona XtraDB Cluster
 
MySQL Database Architectures - 2020-10
MySQL Database Architectures -  2020-10MySQL Database Architectures -  2020-10
MySQL Database Architectures - 2020-10
 
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
Dr. Elephant for Monitoring and Tuning Apache Spark Jobs on Hadoop with Carl ...
 
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
[오픈소스컨설팅]Day #1 MySQL 엔진소개, 튜닝, 백업 및 복구, 업그레이드방법
 
Understanding Data Partitioning and Replication in Apache Cassandra
Understanding Data Partitioning and Replication in Apache CassandraUnderstanding Data Partitioning and Replication in Apache Cassandra
Understanding Data Partitioning and Replication in Apache Cassandra
 
MySQL Replication
MySQL ReplicationMySQL Replication
MySQL Replication
 
Introduction to Spark Streaming
Introduction to Spark StreamingIntroduction to Spark Streaming
Introduction to Spark Streaming
 
High Performance, Scalable MongoDB in a Bare Metal Cloud
High Performance, Scalable MongoDB in a Bare Metal CloudHigh Performance, Scalable MongoDB in a Bare Metal Cloud
High Performance, Scalable MongoDB in a Bare Metal Cloud
 
HandsOn ProxySQL Tutorial - PLSC18
HandsOn ProxySQL Tutorial - PLSC18HandsOn ProxySQL Tutorial - PLSC18
HandsOn ProxySQL Tutorial - PLSC18
 
Query logging with proxysql
Query logging with proxysqlQuery logging with proxysql
Query logging with proxysql
 
MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...
MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...
MySQL Parallel Replication (LOGICAL_CLOCK): all the 5.7 (and some of the 8.0)...
 
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
Deep Dive : Spark Data Frames, SQL and Catalyst OptimizerDeep Dive : Spark Data Frames, SQL and Catalyst Optimizer
Deep Dive : Spark Data Frames, SQL and Catalyst Optimizer
 
Apache Spark vs Apache Flink
Apache Spark vs Apache FlinkApache Spark vs Apache Flink
Apache Spark vs Apache Flink
 
From my sql to postgresql using kafka+debezium
From my sql to postgresql using kafka+debeziumFrom my sql to postgresql using kafka+debezium
From my sql to postgresql using kafka+debezium
 

Similar to Document Databases & RavenDB

REST vs WS-*: Myths Facts and Lies
REST vs WS-*: Myths Facts and LiesREST vs WS-*: Myths Facts and Lies
REST vs WS-*: Myths Facts and LiesPaul Fremantle
 
The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...
The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...
The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...Lucas Jellema
 
03 net saturday anton samarskyy ''document oriented databases for the .net pl...
03 net saturday anton samarskyy ''document oriented databases for the .net pl...03 net saturday anton samarskyy ''document oriented databases for the .net pl...
03 net saturday anton samarskyy ''document oriented databases for the .net pl...DneprCiklumEvents
 
The other Apache Technologies your Big Data solution needs
The other Apache Technologies your Big Data solution needsThe other Apache Technologies your Big Data solution needs
The other Apache Technologies your Big Data solution needsgagravarr
 
Above the cloud joarder kamal
Above the cloud   joarder kamalAbove the cloud   joarder kamal
Above the cloud joarder kamalJoarder Kamal
 
Couchbase - Yet Another Introduction
Couchbase - Yet Another IntroductionCouchbase - Yet Another Introduction
Couchbase - Yet Another IntroductionKelum Senanayake
 
Azure Platform
Azure Platform Azure Platform
Azure Platform Wes Yanaga
 
Technology Stack Discussion
Technology Stack DiscussionTechnology Stack Discussion
Technology Stack DiscussionZaiyang Li
 
Deep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDBDeep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDBArangoDB Database
 
The RESTful Soa Datagrid with Oracle
The RESTful Soa Datagrid with OracleThe RESTful Soa Datagrid with Oracle
The RESTful Soa Datagrid with OracleEmiliano Pecis
 
ArcReady - Architecting For The Cloud
ArcReady - Architecting For The CloudArcReady - Architecting For The Cloud
ArcReady - Architecting For The CloudMicrosoft ArcReady
 
Big Data, Ingeniería de datos, y Data Lakes en AWS
Big Data, Ingeniería de datos, y Data Lakes en AWSBig Data, Ingeniería de datos, y Data Lakes en AWS
Big Data, Ingeniería de datos, y Data Lakes en AWSjavier ramirez
 
Intro to-html-backbone
Intro to-html-backboneIntro to-html-backbone
Intro to-html-backbonezonathen
 
Ops Jumpstart: MongoDB Administration 101
Ops Jumpstart: MongoDB Administration 101Ops Jumpstart: MongoDB Administration 101
Ops Jumpstart: MongoDB Administration 101MongoDB
 

Similar to Document Databases & RavenDB (20)

CouchDB
CouchDBCouchDB
CouchDB
 
REST vs WS-*: Myths Facts and Lies
REST vs WS-*: Myths Facts and LiesREST vs WS-*: Myths Facts and Lies
REST vs WS-*: Myths Facts and Lies
 
Deep Dive on ArangoDB
Deep Dive on ArangoDBDeep Dive on ArangoDB
Deep Dive on ArangoDB
 
The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...
The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...
The Story of How an Oracle Classic Stronghold successfully embraced SOA (ODTU...
 
Switch to Backend 2023
Switch to Backend 2023Switch to Backend 2023
Switch to Backend 2023
 
Not only SQL
Not only SQL Not only SQL
Not only SQL
 
03 net saturday anton samarskyy ''document oriented databases for the .net pl...
03 net saturday anton samarskyy ''document oriented databases for the .net pl...03 net saturday anton samarskyy ''document oriented databases for the .net pl...
03 net saturday anton samarskyy ''document oriented databases for the .net pl...
 
The other Apache Technologies your Big Data solution needs
The other Apache Technologies your Big Data solution needsThe other Apache Technologies your Big Data solution needs
The other Apache Technologies your Big Data solution needs
 
Above the cloud joarder kamal
Above the cloud   joarder kamalAbove the cloud   joarder kamal
Above the cloud joarder kamal
 
Couchbase - Yet Another Introduction
Couchbase - Yet Another IntroductionCouchbase - Yet Another Introduction
Couchbase - Yet Another Introduction
 
Azure Platform
Azure Platform Azure Platform
Azure Platform
 
Technology Stack Discussion
Technology Stack DiscussionTechnology Stack Discussion
Technology Stack Discussion
 
No sql databases
No sql databasesNo sql databases
No sql databases
 
RavenDB overview
RavenDB overviewRavenDB overview
RavenDB overview
 
Deep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDBDeep dive into the native multi model database ArangoDB
Deep dive into the native multi model database ArangoDB
 
The RESTful Soa Datagrid with Oracle
The RESTful Soa Datagrid with OracleThe RESTful Soa Datagrid with Oracle
The RESTful Soa Datagrid with Oracle
 
ArcReady - Architecting For The Cloud
ArcReady - Architecting For The CloudArcReady - Architecting For The Cloud
ArcReady - Architecting For The Cloud
 
Big Data, Ingeniería de datos, y Data Lakes en AWS
Big Data, Ingeniería de datos, y Data Lakes en AWSBig Data, Ingeniería de datos, y Data Lakes en AWS
Big Data, Ingeniería de datos, y Data Lakes en AWS
 
Intro to-html-backbone
Intro to-html-backboneIntro to-html-backbone
Intro to-html-backbone
 
Ops Jumpstart: MongoDB Administration 101
Ops Jumpstart: MongoDB Administration 101Ops Jumpstart: MongoDB Administration 101
Ops Jumpstart: MongoDB Administration 101
 

More from Brian Ritchie

Transforming your application with Elasticsearch
Transforming your application with ElasticsearchTransforming your application with Elasticsearch
Transforming your application with ElasticsearchBrian Ritchie
 
Building Event-Driven Systems with Apache Kafka
Building Event-Driven Systems with Apache KafkaBuilding Event-Driven Systems with Apache Kafka
Building Event-Driven Systems with Apache KafkaBrian Ritchie
 
From Dev to Ops:Delivering an API to Production with Splunk
From Dev to Ops:Delivering an API to Production with SplunkFrom Dev to Ops:Delivering an API to Production with Splunk
From Dev to Ops:Delivering an API to Production with SplunkBrian Ritchie
 
Extending the Enterprise with MEF
Extending the Enterprise with MEFExtending the Enterprise with MEF
Extending the Enterprise with MEFBrian Ritchie
 
CQRS: Command/Query Responsibility Segregation
CQRS: Command/Query Responsibility SegregationCQRS: Command/Query Responsibility Segregation
CQRS: Command/Query Responsibility SegregationBrian Ritchie
 
IIS Always-On Services
IIS Always-On ServicesIIS Always-On Services
IIS Always-On ServicesBrian Ritchie
 

More from Brian Ritchie (7)

Transforming your application with Elasticsearch
Transforming your application with ElasticsearchTransforming your application with Elasticsearch
Transforming your application with Elasticsearch
 
Building Event-Driven Systems with Apache Kafka
Building Event-Driven Systems with Apache KafkaBuilding Event-Driven Systems with Apache Kafka
Building Event-Driven Systems with Apache Kafka
 
From Dev to Ops:Delivering an API to Production with Splunk
From Dev to Ops:Delivering an API to Production with SplunkFrom Dev to Ops:Delivering an API to Production with Splunk
From Dev to Ops:Delivering an API to Production with Splunk
 
Extending the Enterprise with MEF
Extending the Enterprise with MEFExtending the Enterprise with MEF
Extending the Enterprise with MEF
 
CQRS: Command/Query Responsibility Segregation
CQRS: Command/Query Responsibility SegregationCQRS: Command/Query Responsibility Segregation
CQRS: Command/Query Responsibility Segregation
 
IIS Always-On Services
IIS Always-On ServicesIIS Always-On Services
IIS Always-On Services
 
Scaling Out .NET
Scaling Out .NETScaling Out .NET
Scaling Out .NET
 

Document Databases & RavenDB

  • 1. Brian Ritchie Chief Architect Payformance Corporation Email: brian.ritchie@gmail.com Blog: http://weblog.asp.net/britchie Web: http://www.dotnetpowered.com
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.  
  • 7.
  • 8.  
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.  
  • 20.  
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26. [ Blue ] [ Red ] [ Blue,1 ] [ Red,1 ] [ Orange ] [ Blue ] [ Blue ] [ Orange ] [ Orange,2 ] [ Blue,2 ] [ Red,1 ] [ Orange,2 ] [ Blue,3 ]
  • 27.  
  • 28.
  • 29.  
  • 30.
  • 31.
  • 32. Given this document… And this index… Gives this table output http://ravendb.net/bundles/index-replication
  • 33.
  • 34.
  • 35.

Editor's Notes

  1. Relational database : Edgar Codd defined and coined the term at IBM's Almaden Research Center about 40 years ago.  Since that time, relational databases have become the foundation of nearly every enterprise system.
  2. Twitter generates 7TB/day (2PB+ year) – Hadoop for data analysis, Scribe for logging LinkedIn - Voldemort
  3. Scalability:  relational databases were not designed to handle and do not generally cope well with Internet-scale, “ big data ” applications.  Most of the big Internet companies (e.g., Google, Yahoo, Facebook) do not rely on RDBMS technology for this reason.
  4. Cassandra – Facebook Inbox Search Amazon Dynamo: not open source Voldemort: Open-Source implementation of Amazons Dynamo Key-Value Store.  Google Big Table: a sparse, distributed multi-dimensional sorted map
  5. A few of the top document databases are CouchDB , RavenDB , and MongoDB . CouchDB is an Apache project created by Damien Katz (built using Erlang ) and just reached a 1.0 status.  Damien has a background working on Lotus Notes & MySql.  RavenDB is built on using C# and has some interesting extension capabilities using .NET classes.  RavenDB was created by Ayende Rahien . Ayende Rahien is the creator of Rhino Mocks & much more. MongoDB is written in C++ and provides some unique querying capabilities.  MongoDB was originally developed by 10gen .
  6. Objects can be stored as documents :  The relational database impedance mismatch is gone.  Just serialize the object model to a document and go. Documents can be complex : Entire object models can be read & written at once.  No need to perform a series of insert statements or create complex stored procs. Documents are independent : Improves performance and decreases concurrency side effects. Low overhead – one read, one write. Open Formats : Documents are described using JSON or XML or derivatives.  Clean & self-describing. Schema free : Strict schemas are great, until they change.  Schema free gives flexibility for evolving system without forcing the existing data to be restructured.
  7. Web Related Data , such as user sessions, shopping cart, etc. - Due to its document based nature means that you can retrieve and store all the data required to process a request in a single remote call. Dynamic Entities , such as user-customizable entities, entities with a large number of optional fields, etc. - The schema free nature means that you don't have to fight a relational model to implement it. Persisted View Models - Instead of recreating the view model from scratch on every request, you can store it in its final form in a document database. That leads to reduced computation, reduced number of remote calls and improved overall performance. Large Data Sets - The underlying storage mechanism for Raven is known to scale in excess of 1 terabyte (on a single machine) and the non relational nature of the database makes it trivial to shard the database across multiple machines, something that Raven can do natively.
  8. In a multi-user environment, data on the screen is always stale. Due to this fact, we don't need a complicated ORM to pull "live" data out of our OLTP database.  The user interface needs to capture the user's intent, not just their input.  It can then build up commands that are submitted asynchronously to the services layer.  This is a more imperative way of doing things and provides the opportunity to inject business processes without changing the user interface. This allows our backend process to have as much time as it needs to perform the business logic & update the database.  Udi Dahan
  9. ESENT can handle up to 16 terrabytes on a single machine. Many teams at Microsoft—including The Active Directory, Windows Desktop Search, Windows Mail, Live Mesh, and Windows Update—currently rely on ESENT for data storage. And Microsoft Exchange stores all of its mailbox data (a large server typically has dozens of terrabytes of data) using a slightly modified version of the ESENT code. Part of Windows since Windows 2000.
  10. ESENT can handle up to 16 terrabytes on a single machine. Many teams at Microsoft—including The Active Directory, Windows Desktop Search, Windows Mail, Live Mesh, and Windows Update—currently rely on ESENT for data storage. And Microsoft Exchange stores all of its mailbox data (a large server typically has dozens of terrabytes of data) using a slightly modified version of the ESENT code. Part of Windows since Windows 2000.
  11. ASP.NET Music Store Sample http://www.asp.net/mvc/samples/mvc-music-store Ayende blog post on Porting sample to Raven http://ayende.com/Blog/archive/2010/05/18/porting-mvc-music-store-to-raven-the-data-model.aspx