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Data Liberty

Data Liberty. Alternatives to the shackles of limited scale in data solutions Andy Cross Windows Azure MVP Elastacloud. Social Media. Tell everyone I’m awesome #cloudburst I’m @ andybareweb. Data value at scale requires technology choices;.

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Data Liberty

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  1. Data Liberty Alternatives to the shackles of limited scale in data solutions Andy Cross Windows Azure MVP Elastacloud

  2. SocialMedia Tell everyone I’m awesome #cloudburst I’m @andybareweb

  3. Data value at scale requires technology choices; often prioritising data read traversal over operational characteristics of create/update/delete embracing hybrid data platforms with varied technology partners over homogenous estates establishing alternative skillsets, augmented with entrenched languages, trusting cloud over maintenance following robust engineering processes to provide rigour in a deterministic world

  4. Bravery leads to rewards; the winners will have data which shows them that they’ve won the commoditised query turns energy sucking data silos into profit centres new data traversal mechanisms lead to new connotative data expression everything you already know is relevant and valid; the constraints on how it is applied are not

  5. WHAT’S A DATA SCIENTISTS FAVOURITE LANGUAGE? Most developers have heard of Big Data. I’m going to show how Microsoft are increasingly relevant in this space. My talk is about architecture and approach. Note we’re talking Big Data and not strictly Data Science. But it’s always worth context so lets start with the history.

  6. Wikimedia commons IBM have been a leader in Big Data for years.

  7. We’re not as great as we’d hope; we’re often still bound by our ability to marshal our IO. Just as the speed of loading punchcards was historically a limiting factor, we are now limited by our capacity to ingest data on individual machines. This leads to ideas such as DFS and data locality.

  8. During the evolution of data we eventually moved to client/server and this was a big step up from dBase et al of the time. Fundamentally however, the tabular structured nature of data poses many changes; not least the long term effects of normalisation which trade off effective storage in the short term with long term offset compute which is required to reconstruct sets. This eventually leads to such ideas as NoSQL document and entity stores.

  9. Modelling of data provides a consistent challenge. Our world is highly connected and our brains are effective connectors of data. Real world data fits poorly into highly structured data sets. This leads to semi-structured and unstructured data formats and data queryability through relationship traversal

  10. The technologies shown today are primarily written in non-.net and non-Microsoft languages and frameworks. Every time we do this, I’ll show examples ONLY in the .net and Microsoft stacks. There are obviously challenges beyond language to running the alternative stacks; but remember in the Cloud you aren’t responsible for tuning a Linux cluster which has been running for 5 years. You should provision for a duration that is bounded by the likelihood of the cluster requiring routine maintenance.

  11. Hadoop – KEY FACTS Open Source; Apache Foundation. Java. Map Reduce framework for job distribution; Distributed File System for file access. In Windows Azure this is known as HDInsight.

  12. Hadoop is O(n) It exhibits linear performance; when the dataset doubles, the time taken to execute the algorithm doubles.

  13. Lets look at some scary Java Any children should look away now.

  14. Hadoop SDK C# integration Remote Data & Jobs Hive in C# Serialization

  15. Jobs publicclassSwedishSessionsJob : HadoopJob<SwedishSessionsMapper, SessionsReducer> { publicoverrideHadoopJobConfiguration Configure(ExecutorContext context) { var config = newHadoopJobConfiguration() { InputPath = "\"/AllSessions/*.gz\"", OutputFolder = "/SwedishSessions/" }; return config; } }

  16. Mapper publicclassSwedishSessionsMapper : MapperBase { publicoverridevoid Map(stringinputLine, MapperContext context) { if (inputLine.Contains("Country=Sweden") { context.IncrementCounter("SwedishSession"); context.EmitKeyValue(“SE", "1"); } } }

  17. Reducer publicclassSessionsReducer : ReducerCombinerBase { publicoverridevoid Reduce(string key, IEnumerable<string> values, ReducerContext context) { context.EmitKeyValue(key, values.Count()); } }

  18. Testing Hadoop Queries varinputData = "Country=Sweden&Name=Magnus"; var result = StreamingUnit.Execute<Jobs.SwedishJob>(new[]{inputData}); Assert.AreEqual("SE\t1", result.ReducerResult.First());

  19. Skill reuse Your existing development team can immediately realise value The frameworks facilitate deterministic testing for highly reliable queries Express elegant solutions in C# Familiar Unit Testing patterns Concise programmatic terseness Complex logic is best expressed in programmatic form

  20. Commoditised query Value of query Value De-provision Provision Cost Time Action Cost Execute

  21. HDInsight wins. * Tools are great but not friendly Automated provisioning and job execution services. Transient clusters limit exposure to poorly tooled* java estate. Persistence with Windows Azure Blob Storage as HDFS proxy known as Azure Storage Vault (ASV). Persistence in Windows Azure SQL Database for Hive Metastore. Javascript console.

  22. NoSQL Document and Entity Stores Examples in MongoDB Entity stores are similar; you can find a great example in Windows Azure Table Storage

  23. What is a document database? Relational Database Document Database { "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" } } { "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" } } { "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" } } { "_id" : ObjectId("51fccc57f82352d76653bdae"), "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" } } { "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" } } { "Name" : { "FirstName" : "Owen", "LastName" : "Grzegorek" }, "Company" : "Howard Miller Co", "Address" : { "Line1" : "15410 Minnetonka Industrial Rd", "Line2" : "Minnetonka", "Line3" : "Hennepin", "Line4" : "MN", "Line5" : "55345" }, "ContactDetails" : { "Phone" : "952-939-2973", "Fax" : "952-939-4663", "Email" : "owen@grzegorek.com", "Web" : "http://www.owengrzegorek.com" } } { "Name" : “Isaac Abraham", “Age" : “33“ “Football Team” : “Tottenham” “Icon” : } { "Name" : “Richard Conway", “Books Published” : “12”, “Specialises in” : “Data Science” } { "Name" : “Andy Cross", “Hometown" : “Blackpool“ }

  24. MongoDB Key Facts • General Purpose Operational Database • Real-time updates, ad-hoc queries and batch processing • Maps nicely with popular programming models e.g. .NET • Schema-free documents – lightweight and quick to get up and running • High Performance • Embedding documents – no expensive joins across tables • Indexes allow query optimization • High-speed saving of data (writes) • High Availability • Built in replication • Built in failover • Easy Scalability • “Sharding” allows easily spreading data across multiple databases • Replicated data can be spread throughout the cluster

  25. MongoDB is O(log n) It exhibits logarithmic performance; when the dataset doubles, the time taken to execute the algorithm increases by a fixed amount

  26. Strengths of MongoDB Good fit for .NET developers Low barrier to entry Uses well-known .NET technologies e.g. LINQ Good migration path from SQL-style development Works well as operational data store Batch processing capability for map reduce Flexible Massively scalable with well-defined replication model Self-managing – easily add new nodes High performance writes and eventually consistent reads Database is free to use (tooling is not!) Popular, so a relatively large community Designed for scalability Low cost

  27. Mongo SDK There are many different way to connect with MongoDB from a .net project. Official Wrapper Alternative Tool

  28. C# implementations publicclassBook { publicstring Author { get; set; } publicstring Title{ get; set; } } // "entities" is the name of the collection var books = database.GetCollection<Entity>("books"); Book book = new Book { Author = "Ernest Hemingway", Title = "For Whom the Bell Tolls" }; books.Insert(book); If your data is regularly structured, you can use domain classes:

  29. C# implementations BsonDocument person = newBsonDocument { { "name", "John Doe" }, { "address", newBsonDocument { { "street", "123 Main St." }, { "city", "Centerville" }, { "state", "PA" }, { "zip", 12345} }} }; var people = database.GetCollection<BsonDocument>("people"); people.Insert(person); If your data is irregularly structured or semi-structured, you can use a BSON object model:

  30. NoSQL Document Wins Semi-structured data first class citizen Built in MapReduce Operational and interactive Massively scalable

  31. Graph Databases, Neo4j KEY FACTS Open Source; Neotechnologies Java Runs equally well on Windows or Linux. In Windows Azure there are VMDepot images able to be deployed in a few simple steps. Additionally the Azure Linux VMs are a good fit for this database engine. There is an Open Source .net SDK available through Nuget and actively maintained primarily by an Australian company, Readify.

  32. Neo4j is O(1) It exhibits constant-time performance; that is, the algorithm takes the same time to execute irrespective of the size of the dataset.

  33. How O(1)? • Graphs don’t have tables. They don’t have collections. • They have nodes and relationships. • Rather than having to select out a whole table, we can identify a point on the graph • A start point • Follow the traversal of relationships from that point.

  34. http://www.apcjones.com/arrows/#

  35. Things we can do Find all the things formed in Sweden START sweden = node:countryIdx(“country=Sweden”) MATCH Sweden<-[:FORMED_IN]-something RETURN something; Find friends of friends START magnus = node:peopleIdx(“name=magnus”) MATCH magnus-[:FRIENDS]->friend-[:FRIENDS]->friendoffriend RETURN friendoffriend;

  36. NEO4J Client Open source Neo4j Client

  37. C# examples var query = neo4Jclient.Cypher .Start(new { sweden = Node.ByIndexLookup("countryIdx", "country", "sweden") }) .Match("sweden-[:FRIENDS]->friend-[:FRIENDS]->friendoffriend") .Return<Node<Friend>>("friendoffriend");

  38. Graph Database Wins • Modelled domains match cognitive processes • Optimised for traversal of relationships allow complex and “social” queries to emerge • LIKES of FRIENDS of COLLEAGUES • O(1) performance characteristics due to ability to START queries at arbitrary graph points.

  39. Summary • HDInsight brings Hadoop to Azure • Suited to Data Volume, Variety, Variability etc • MongoDB brings Document stores • Suited to Data Volume, Operational concerns • Neo4j brings Graph database • Suited to data relationship traversal

  40. Info@elastacloud.com Thanks

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