Oracle OpenWorld 2016 Review - Focus on Data, BigData, Streaming Data, Machine Learning & Database Development

The AMIS Team Oracle OpenWorld 2016, Nieuwegein, 13th October 2016 Oracle OpenWorld 2016 Review Data - Database Development, BigData, BI

2 Data • ACID is expensive • OLTP is a niche • Moving data takes long – bring processing to data • SQL is omnipresent – expose all data in an SQL friendly way – Including NoSQL and data on Hadoop • Data from the past should be able to help us predict the future – Bring on machine learning (aka AI aka predictive analytics) – Aided by the citizen data scientist in (Big) Data Discovery • Fast Data (big data at high velocity) should be handled in real time – Enter: Streaming Analytics & Apache Kafka • Oh and ehm …. a next major release of Oracle Database is available – 12cR2 – only on the cloud for now – Highlights: Sharding, More PDB (“virtual database”), Approximate Query Processing, Leverage In-Memory even more, JSON document generation and faster JSON processing, Analytic Views

3 Learning • How do we learn? – Try something (else) => get feedback => learn • Eventually: – We get it (understanding) so we can predict the outcome of a certain action in a new situation – Or we have experienced enough situations to predict the outcome in most situations with high confidence • Through interpolation, extrapolation, etc. – We remain clueless

4 Machine Learning • Analyze Historical Data (input and result – training set) to discover Patterns & Models • Iteratively apply Models to [additional] Input (test set) and compare model outcome with known actual result to improve the model • Use Model to predict outcome for entirely new data

5 Data Discovery A B C D E F G 1104534 ZTR 0.1 anijs 2 36 T 631148 ESE 132 rivier 0 21 S -3 WGN 71 appel 0 1 - 1262300 ZTR 56 zes 2 41 T 315529 HVN 1290 hamer 0 11 - 788914 ASM 676 zwaluw 0 26 T 157762 HVN 9482 wie 0 6 - 946681 DHG 42 rond 1 31 T -31539 WGN 2423 bruin 0 0 - 47338 HVN 54 hamer 0 16 P

6 Scatter Plot Attribute F (Y-axis)vs Attribute A 0 5 10 15 20 25 30 35 40 45 -500000 0 500000 10000001500000 Y-Values Y-Values

7 Scatter Plot Attribute F (Y-axis)vs Attribute A 0 5 10 15 20 25 30 35 40 45 1960 1980 2000 2020 Age of Lucas Jellema vs Year Y-Values

8 Data Discovery Time City - - #Kids Age Level of Education 1104534 ZTR 0.1 anijs 2 36 T 631148 ESE 132 rivier 0 21 S -3 WGN 71 appel 0 1 - 1262300 ZTR 56 zes 2 41 T 315529 HVN 1290 hamer 0 11 - 788914 ASM 676 zwaluw 0 26 T 157762 HVN 9482 wie 0 6 - 946681 DHG 42 rond 1 31 T -31539 WGN 2423 bruin 0 0 - 47338 HVN 54 hamer 0 16 P

9 Machine Learning, Data Mining & Predictive Analytics

10 Recent developments • More compute capacity, smarter algorithms and better analytical tooling – Evolving Machine Learning – Smart text and speech analysis (NLP, ESA) – Real time predictions become a reality – Streaming (event) Analytics – Visualization – Citizen Data Scientist – SQL against Big Data • More data available & accessible (IoT, Social, Media, IT operations, business processes,…) • Better/larger/cheaper/faster data storage capabilities

11 Many cloud services around Big Data & Analytics

12 Big Data Integration Reference Architecture IngestPrepare Transform, Merge, Enrich Save GovernGovern Serve Analyze & Act Present, Leverage & ‘Action’ Extract Explore Purge

14 Mapping Oracle portfolio to Reference Architecture Big Data Discovery Data Visualization BI CS IT Analytics Security Analytics Log Analytics

15 Tip: OEMM - Oracle Enterprise Metadata Management

16 Citizen Data Scientist • Data Visualization CS • Big Data Preparation CS • Big Data Discovery CS • Machine Learning CS

17 Oracle Machine Learning Cloud Service

20 https://www.zeppelinhub.com/

22 Traditional approach • All enterprise data is in the Oracle [relational] Database – Except very unstructured documents - and sometimes even those

23 Center of the Data Universe is shifting

24 Variety in data – Data Tiering • How long relevant (hot vs cold vs dead)? • How fine grained and how accurate? • What is it used for? – By whom, where, in what way, using which tools • What format is it in/should it be in? • How to be processed? • What confidentiality & integrity is required? • How much of it?

25 Trends around data storage and data processing • Take processing to data [to reduce data movement] – Exadata SmartScan in Storage Cells (SQL & R processing) – Hadoop MapReduce/Spark – Coherence Processors – Streaming Analytics – Microservices, stand alone data domains • Distributed data partitions – for scalability and parallelization [and fault tolerance when also replicated]: – Shards (Oracle Database 12cR2) and Partitioned External Tables – TimesTen Velocity Scale – distributed In-Memory OLTP – Hadoop HDFS, Apache Kafka • New paradigms regarding transactional data – CQRS (for example Oracle Database In Memory (read) / In Flash/On disk (read/write), Write behind cache) – Event Sourcing, Transaction Log

26 Oracle Database • How much of your data – Arrives through (business) transactions that require true ACID? – Is involved in current business operations? – Will ever be updated [again]? – Plays a direct role in integrity [of other records]? – Is actively accessed [on a regular basis] ? – Really has to be in the OLTP engine? • How much of the data currently in your OLTP engine could be off-loaded – If that data remains accessible through SQL (even from within the OLTP engine, without altering existing queries) with reasonable response times • What if such off-loading – Improves performance of the OLTP engine for transactions – Shortens batch jobs [by engaging distributed, scale out processing options] – Opens up possibilities for advanced analytics – Potentially lowers the cost [licenses & specialized hardware] for the OLTP engine – Introduces some change and complexity

27 Oracle Big Data SQL • Big Data SQL: A ‘franchised query engine,’ enables scalable, integrated access in situ to the entire Big Data Management System (BDMS) – Meta data, Query execution, Workload Management, Data Optimization – Primary role for Oracle Database – foundation for BDMS See Statement of Direction: http://www.oracle.com/technetwork/database/bigdata-appliance/overview/sod-bdms-2015-04-final-2516729.pdf

Oracle Database Development 28

29 Oracle Forms • Release 12.2.1.1 is available – 12.2.2 (or 12.2.1.2) is planned for late 2016 – Support for Forms 12c: Premier Oct 2020, Extended 2023 – (and moving) • Forms usually runs in browser – using the Java JRE plugin for Applets – Modern browsers have stopped or will stop supporting the Java plugin – Forms will either have to run on outdated browsers (IE, old versions of Firefox or Chrome) or run outside the browser – The main changes around Forms are around running Forms outside the browser – as standalone Java Web Start (jnlp) application – Also: Forms Helper – script for customizing post-install environment (simplified WLST) • On Reports: – Reports 12c exists – it is the terminal release – From now on, reporting should be done using BI Publisher – BI Publisher has become part of the Developer Suite and will be included in the WebLogic Suite

31 APEX – 5.1 • Interactive Grid – A new rich, client-side region type that allows editing multiple rows of data in a dynamic, JSON-enabled grid, and supports multiple grids on a single page. • Master / Detail / Detail – Provide a wizard interface to define declarative master/detail/detail regions. • New Charting Engine – Include a new JavaScript (Oracle JET) based charting engine developed by Oracle which produces responsive and accessible HTML5 charts. – AnyChart is on the way out • Ability to have multiple tabs open to the same APEX application and isolate session state • Improved Wizards - fewer steps and more attributes set by default. • Declarative RTL Support –declarative methods to control user interface direction- support for both Left-to-Right and Right-To-Left languages. • Packaged Applications – Improved framework and enhancements to the packaged applications. • Status: EA 2 is available (hosted) as of September 2016 – APEX 5.1 Production – early 2017?

32 New in SQL in 12cR2 • Listagg improvements • Error handling for CAST function & new Validate_Conversion function • Materialized Views – Real Time Materialized Views (stale plus logs) – Statement Level Refresh • AL32UTF8 As the Default Database Character Set • New capabilities for generating JSON documents directly from SQL queries, improved JSON support in In Memory processing • Beyond 12cR2 • Approximate Query Processing (using HyperLogLog) • Analytical Views • Band Join- better performance for non-equijoins • Temporary, cached in memory tables for duration of cursor • Partitioned External Tables

33 New in PL/SQL in 12cR2 • Deprecated procedures and functions • Accessible by at procedure or function level • JSON support: generation of JSON documents using PL/SQL API and Oracle supplied Object Types (somewhat akin to XMLType) – JSON SQL functions available in PL/SQL expressions • Supplied package dbms_plsql_code_coverage to identify code units not touched in specific [test] scenarios • PL/Scope enhancements – more fine grained reporting • Edition Based Redefinition does ‘garbage collection’ – editioned objects no longer in use are cleaned up

34 Other Database Development News • JS Stored Procedures • SQL Developer GUI Debugger – One session can have another start debugging – At breakpoint: execute SQL to inspect run context – including PL/SQL state • SQLcl • ORDS – Oracle REST Data Services

36 Summary of Oracle OpenWorld 2016 • (5 days filled to brim with 1800+ sessions, 12 keynotes, 150+ demo booths, hundreds of vendors and quite a few rumors & hallway tales) • Infrastructure [as a Service] – Generation 2 Data Center – Network & IOPS (storage, NVMe, Flash) – Exadata SL • Abdication of the single, central, enterprise Oracle RDBMS – and the limelight for data – PDBs – Sharding – Hadoop & Spark (& SQL & R) – Machine Learning • Adoption of open source projects, industry trends & community darlings – Node.js, Docker, Microservices, Git(Hub), Python, Slack, …

37 Summary of Oracle OpenWorld 2016 (2) • Cloud [First] strategy – Migrate & Extend i/o [bidirectional] Lift & Shift – Cloud@Customer – Status and future of On Premises software (and yet Engineered Systems) – Ops in Oracle Data Centers – Subscription Models, Suites (i/o a la carte) – How fast can Oracle move [without spreading itself too thin]? • SaaS and [Unlimited] Applications – SaaS portfolio quite extensive – UX is important asset of the SaaS applications – Real cloud elements are improving (APIs, extensibility) – Traditional Apps are still evolving [as promised] – and seem to benefit from SaaS and technological advances across the board • Oracle Public Cloud consistency, architecture and the Dogfood Doctrine – Fabric and foundational components – Designated capabilities and mutual integration

38 Oracle OpenWorld 2016 Tag Cloud

Oracle OpenWorld 2016 Review - Focus on Data, BigData, Streaming Data, Machine Learning & Database Development

Oracle OpenWorld 2016 Review - Focus on Data, BigData, Streaming Data, Machine Learning & Database Development

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Oracle OpenWorld 2016 Review - Focus on Data, BigData, Streaming Data, Machine Learning & Database Development

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