New Modeling Challenges: Big Data, Hadoop, Cloud

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1 New Modeling Challenges: Big Data, Hadoop, Cloud Karen Karen Lopez Love Your Data Senior Project Manager & Architect 1

2 Abstract We data architects have done a great job maturing the data management profession: we have tools with decades of usability, technical features and relational database support. Our methods have been tested and tailored across many industries. We have books, training materials, online communities and more.and yet the advent of new methods and tools is putting us at risk for becoming irrelevant on modern data-driven projects. What do we do when our teams use Hadoop and other NoSQL technologies Will our methods work? What about our tools? Do those teams even need a data architect? Are data management skills no longer needed? How can we leverage our existing enterprise data models? In this presentation Karen covers the basics of Big Data, Hadoop, and NoSQL then shows how modern data architects should respond to the ever-changing environment of data architectures. She'll help you separate the myths from the misunderstandings and show you how to continue to leverage your data modeling assets. 4 2

3 Disclosure I m a Data Architect / Modeler I am biased You have been warned NoSQLNow! - D'Antoni / Lopez POLL: Who Are You? 3

4 7 POLL: Architect Much? NoSQLNow! - D'Antoni / Lopez 8 POLL: Cloud Much? NoSQLNow! - D'Antoni / Lopez 4

5 NoSQLNow! - D'Antoni / Lopez POLL: SQL Much? NoSQL Much? Hadoop Much? What we will be doing Terminology, Foundations and Thoughts. Modern Architectural Components Architecting versus building Cost, benefit, risk Demos! 5

6 What would you like to do? Outcomes 12 Concepts Features Existing Tech New Tech Hybrid Evaluate Pilot Production Benefits Trade-offs What Where When Why Architects Devs DBAs Who Modeling Dev Test How Costs Trade-offs How much 6

7 Terminology and Thoughts Because we need a vocabulary to share ideas Hadoop Cloud Polyglot Schemaless Scale Up / Out Volume Read-Optimized Analytics Unstructured data Write-Optimized Data versus Processing BIG DATA Variable Data Data Reservoir Logical Data Model Data Lake 7

8 Polyschematic SQL Server HA / DR On Premises Volume External Data JBIC data ROI Relational Read-Optimized Persistence Velocity Business Intelligence Physical Data Model Data Swamp Architect Fit Business Technology Data Protection Cost, Benefit, Risk 8

9 BIG DATA [x] Vs Data so big it s awkward to work with Always capitalized Big Data A confusing term because it defines what it IS NOT. NoSQL Scale Not SQL? Not Relational? Not Only Relational? A confusing term because it defines what it IS NOT. 9

10 Terminology ACID BASE Atomic, Consistent, Isolated, Durable Basically available, Soft state, Eventual consistency Eventual consistency Schemaless Constraints / Have-to/ MUST / OBEY / Rigid / Inflexible Relational Tables with rows same columns with the same datatypes with the same constraints with the same domains On purpose With many benefits Write-optimized This is a FEATURE Transactionoptimized Data integrity Data quality Consistent 10

11 Data Modeling Now What Data Models? Reverse engineered data models (diagrams) Physical Data Models Faux Logical Data Models Logical Data Models Conceptual Data Models Data Models Traditional Process Conceptual (Data) Model Logical Data Model Physical Data Model(s) MART OLTP OLTP OLTP OLTP OLTP Aug 2014 OLTP OLTP MART OLTP 11

12 Relational Traditional Data Architect Involvement Project Initiation Architecture and Infrastructure Design SW Requirements Development Deployment 12

13 The Big Data Story Lots of data Coming at us fast Lots of variety in format & quality We want all the data Highly available It s web scale What do we really mean by scale? Bringing computing to the data Massively parallel processing Cheap, commodity hardware, but lots of it Optimized for Query/Reads/Questions/Telling stories 13

14 Can we fit another buzzword in? Cloud Enable on-demand scaling Pay as you go pricing Click to deploy Service licensing, not product licensing, if any Managed by others, not your data center But The Cloud is Different Fine Tuning nope Patching, maintaining Skills Professional development Optimized subsystems Putting off upgrades How you work Who you work for How you get new features How you stay up to date How you think about problems 14

15 We ve been down this road before Traditional transactional applications Reportingoptimized tables/struct ures Data Warehouse / Dimensional Modeling There was a lot of contention 15

16 NoSQL, Not Only SQL Relational Graph Columnar/Column Family Key Value Document Databases Others Graph Databases 16

17 Key Value Pair Columnar InfoAdvisors - infoadvisors.com Aug

18 Modern Architectural Components & Concepts Hadoop Ecosystem (a Zoo) Pipeline / Workflow (Oozie) Event Pipeline (Flume) NoSQL Database (HBase) Metadata (HCatalog) Scripting (Pig) Graph (Pegasus) Stats processing (RHadoop) Query (Hive) Distributed Processing (MapReduce) Distributed Storage (HDFS) Machine Learning (Mahout) Data Integration ( ODBC / SQOOP/ REST) Legend Red = Core Hadoop Blue = Data processing Purple = Microsoft integration points and value adds Yellow = Data Movement Green = Packages 18

19 Scripting (Pig) SQL-like Query (HiveQL) Hadoop Zoo data warehouse Distributed Processing (MapReduce) SQL-like Query (Impala) Distributed Storage (HDFS) More Hadoop STORM Real time Streaming Topologies vs. MapReduce Jobs 19

20 MapReduce Shuffle and sort data Get from large data to smaller data Parallel processing Hive SQL-like query language Abstraction on top of MapReduce Metastructure on top of HDFS 20

21 HBase Column Family NoSQL database Key Value Store Hundreds of Millions/Billions of rows Based on Google Big Table Another type of HDInsight Cluster Large, Schemaless (really, Schema on read) Optimized for retrieving specific rows from large datasets Strictly Consistent Pig Pig Latin GRUNT # Create the Pig job definition $0 = '$0'; $QueryString = "LOGS = LOAD 'wasb:///example/data/sample.log';" + "LEVELS = foreach LOGS generate REGEX_EXTRACT($0, '(TRACE DEBUG INFO WARN ERROR FATAL)', 1) as LOGLEVEL;" + "FILTEREDLEVELS = FILTER LEVELS by LOGLEVEL is not null;" + "GROUPEDLEVELS = GROUP FILTEREDLEVELS by LOGLEVEL;" + "FREQUENCIES = foreach GROUPEDLEVELS generate group as LOGLEVEL, COUNT(FILTEREDLEVELS.LOGLEVEL) as COUNT;" + "RESULT = order FREQUENCIES by COUNT desc;" + "DUMP RESULT;" $pigjobdefinition = New-AzureHDInsightPigJobDefinition -Query $QueryString - StatusFolder $statusfolder 21

22 YARN Hadoop operating system Support more use cases batch interactive stream processing. Management Framework Better scalability & cluster utilization capacity guarantees, fairness, and service-level agreements. Classic DW Architecture ETL Data Mart EDW Data Mart 22

23 Discussion - Who What technical skills are needed? What professional skills are needed? What personal characteristics are needed? Discussion - Tools What data tools are needed? What data architecture tools are needed? What project management tools are needed? 23

24 Discussion - Money How much does a traditional DW/BI project cost? How much does operations & maintenance cost? How much does the software cost? How much do servers cost? Modern DW Architecture Hadoop ETL Analytics Mart EDW Data Mart 24

25 DocumentDB In Azure, Database Service not a product Document based (JSON) SQL Queries { "glossary": { "title": "example glossary", "GlossDiv": { "title": "S", "GlossList": { "GlossEntry": { "ID": "SGML", Language", } } } } } "SortAs": "SGML", "GlossTerm": "Standard Generalized Markup "Acronym": "SGML", "Abbrev": "ISO 8879:1986", "GlossDef": { "para": "A meta-markup language, used to create markup languages such as DocBook.", "GlossSeeAlso": ["GML", "XML"] }, "GlossSee": "markup" What s new Computing Storage Icing 25

26 What s new Analytics Mart HDInsight & Blob Storage HDFS or Azure Blob Storage HDFS on compute nodes Compute have to run Blob storage is independent GeoRedundancy Reuse data Delete computing nodes Blob is cheaper Elastic blob MapReduce & HDFS 26

27 Scale and Nodes Scale up versus scale out Nodes, not beefier servers Data stays, clusters come and go Quick Create for playing Custom Create for architecting a solution Every design decision should include cost, benefit and risk - Karen Lopez 27

28 Common Themes In-memory capabilities Build Now, Analyze Later Read optimized workloads Optimized for bulk loads, not singleton updates Most products have HA/DR built in by design Framework Most products have scale out capabilities Text Cloud vs On-Premises Short Term Use Rapid Scale Test Use Cases Pay as you go Internet data source On-Premises Large long term implementations Well known workloads Shared clusters Large initial investment 28

29 Windows vs. Other OSs (Linux) Graph Databases 29

30 There s a book And it s FREE! Graphdatabases.com Document Databases JSON, BSON, XML, YAML 30

31 Key Value Pair Key Value Pair 31

32 Cassandra CQL SELECT balance FROM accounts WHERE account_id=3476 CREATE TABLE monkeyspecies ( species text PRIMARY KEY, common_name text, population varint, average_size int ) WITH comment='important biological records' AND read_repair_chance = 1.0; Columnar 64 32

33 => CREATE TABLE Retail.Product_Dimension ( Product_Key integer NOT NULL, Product_Description varchar(128), SKU_Number char(32) NOT NULL, Category_Description char(32), Department_Description char(32) NOT NULL, Package_Type_Description char(32), Package_Size char(32), Fat_Content integer, Diet_Type char(32), Weight integer, Weight_Units_of_Measure char(32), Shelf_Width integer, Shelf_Height integer, Shelf_Depth integer ); Vertica SQL SELECT balance FROM accounts WHERE account_id=3476 The CLOUD Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Database as a Service (DBaaS)66 33

34 Demo: Hadoop and Relational, Together HDInsight: Hadoop Clusters in the Cloud One of the enterprise-focused implementations of Hadoop Based on Windows Architecture Cloud service Demo-ed here as a representation of functionality, not a recommendation 34

35 HDInsight Demo Components Blob Storage Storage Container HDInsight Cluster PowerShell Azure SQL DB Demo Process Azure Administration Create Storage Account Create Container Create Optional SQL DB Create HDInsight Cluster Subscription Payment Naming Standard Location Metadata External Table Options Location 35

36 Let s talk billing. Per hour Per node head node security node data nodes Data egress, not ingress Geo-redundancy Blob versus HDFS HDFS HDInsight 36

37 Blob versus HDFS HDFS HDInsight Where Data Models Can Help Create a Physical Data Model of Key Data Sources One Entity per tab or Worksheet Someone Normalized (master data + combinations) Model the metadata definitions gotchas expected domains metadata Published in a shareable portal Cross model searchability Data Lineage Resuse 37

38 Demo: Sales and Rebates IRS Data 38

39 The Data Story * Demo: Columnar Graph Database 39

40 Exercise How Might a Modern hybrid data architecture changes YOUR ORGANIZATION? What questions might you be able to answer that you can t answer now? Discussion - Who What technical skills are needed? What professional skills are needed? What personal characteristics are needed? 40

41 Discussion - Tools What data tools are needed? What data architecture tools are needed? What project management tools are needed? Discussion - Money How much does a traditional DW/BI project cost? How much does operations & maintenance cost? How much does the software cost? How much do servers cost? 41

42 Modern Data Architect Involvement Project Initiation Architecture and Infrastructure Design SW + Data Requirements Development Deployment Anti-patterns for new DBs & Data It doesn t matter if data changes Schemaless means faster delivery We don t need a data architect We don t have to steward data We don t need ANY architects Its selfdocumented We don t have to understand data Cc Kazuhisa OTSUBO 42

43 External Data Licensing Propriety Datasets Open Datasets Crowsourced Formats XML CSV MS Word/PowerPoint/Excel PDF Dictionaries/Glossaries 43

44 10 Tips for Architects Understand the use cases for hybrid technologies. Evaluate / profile your data requirements for suitability for each database/datastore type. Understand the licensing / editions for commercial database features / products. Hadoop and other NoSQL technologies are optimized for read. Data Modeling and Analysis still happens, just later*. 44

45 10 Tips for Architects Hands on experience makes a real difference in your understanding. Evaluate which analytical features each product supports. Test your current development tools for support. Test your database design / data modeling tools. Leverage your existing metadata / models. Finally There business models of Open Source technologies means there are hundreds, maybe thousands of new things to learn. That s both a pro and a con for the architect. 45

46 Outcomes Concepts Features Existing Tech New Tech Hybrid Evaluate Pilot Production Benefits Trade-offs What Where When Why Architects Devs DBAs Who Modeling Dev Test How Costs Trade-offs How much Wrap up NoSQL = SQL + NoSQL External Data will become more important Modern Data Architecture = Hybrids, not either/or 46

47 Karen Lopez Love Your Data Senior Project Manager &

48 Thank You Thank You 48

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