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DOAG Big Data Days 2017 - Cloud Journey
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DOAG Big Data Days 2017 - Cloud Journey
1.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved. |Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | Harald Erb Oracle Business Analytics & Big Data 1 The New Data Lake Oracle’s elastisch skalierbare Big Data Cloud DOAG Big Data Days, 22. September 2017 Click-through version of Live-Demo
2.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved. | 2 Referent Harald Erb Sales Engineer, Information Architect Business Analytics & Big Data +49 (0)6103 397-403 harald.erb@oracle.com Meine bisherige Business Analytics Zeitreise
3.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved. | Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. 3
4.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. | 4 1 Einführung Data Lake & Data Labs Konzepte, Oracle Cloud
5.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved. 1876: Edison’s Invention Factory, Menlo Park, NJ
6.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 7 Line of Governance Data Lake Data Processing Data EnrichmentRaw Data Sets Curated & Transformed Data Sets Data Aggregation Data Lab Sandboxes Data Catalog Data Discovery Tools Transformations Prototyping Analytic Tools Enterprise Information Store Operational Data Store Data Federation & Virtualization Layer CommonSQLAccessto ALLData Orchestration, Scheduling & Monitoring Metadata Management Data Ingestion Batch Integration Real-Time Integration Data Streaming Data Wrangling Data Discovery / Business Intelligence Data Driven Applications Advanced Analytics Non-structured Sources Logs Social Media External Data Interactions Structured Data Master Data Applications Channels Data Stores Adhoc Files or Data Sets Data Management Logische Architektur
7.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. Data Lake 8 Data Lake Intake Tier Management Tier Consumption Tier Information Lifecycle Management Layer Metadata Layer Security & Governance Layer Data Discovery/ Business Intelligence Data Driven Applications Advanced Analytics Data Discovery Data Provisioning Source System Zone Transient Zone Raw Zone Connectivity Processing Interfaces for ODBC JDBC NFS File Shares Web Services REST API SFTP Polling Intake Processing Unstruktierte Daten Push/Realtime Semi-strukt. Daten Push-Pull Strukturierte Daten Pull File Validation Checks (Duplication, Integrity, Size, Periodicity) Data Integrity Checks (Column/Rec. Counts, Schema Validation) Lineage Tracking (Metadata Capture, Watermarks) Deep Integrity Checks (Bit Level Scans, Periodic Checksums) Data HubIntegration Data Profiling Data Cleansing Enrichment Metadata Collection Data Lineage Tracking Transformation Unstructured/ structured Profiling (Data completeness, Correctness, coherence) Deletion (Tuple, pairwise) Imputation (Mean/median predicted value) Structured Data (Table/Attr. level) Unstructured Data (Word/Document level: Stop words, stemming,...) Structured Data (Aggr., Decompos.) Unstructured Data (Extract.,Tagging, Entity Recognit. ) LoadDistribution Vertical:Parti-tioning (Range,Mod.,Key-Value,Random Horizontal:Pipelining Polystructured Data Sources Logs Social Media External Data Interactions Structured Data Master Data Applications Channels Data Stores * ) Vgl. P. Pasupuleti, B. S. Purra External Access Interfaces for SQL JDBC Web Services SFTP Push- /Pull- based Data Classification (Named entity class, Topic modelling, Text clustering) Relation Extraction (Column types, pattern, ref.. integrity, features, semantics) Indexing Data (Inverted Index, Faceted/Fuzzy Search, Semantic Analysis) Metadata publication (Catalog of Raw and Data Hub Zones) Data formatting (Standard/ custom) & Data selection (Row/column-, content-based) Konzept und denkbare Funktionsbereiche *)
8.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 10 Based on Raw Data Full Access to Data Sources (Select only) Complete Sandbox Environment Agile Experimentation “Fail Fast” Data Lab Key Requirements
9.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. Alex Sadovsky, Director of Data Science @ The Oracle Data Cloud describes how to embrace cloud computing, Hive, and Spark to create machine learning solutions at scale. YouTube URL Warum Cloud? Machine Learning at Scale Cluster zeitweise massiv aber nicht permanent benutzen
10.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. “Data Scientists should not be System Administrators • If hardware fails, throw it away • If someone messes up the OS, trash it • No support tickets, no time wasted” “Data scientists should not have to deal with system administrators • Science is about experimentation • Experimentation is about testing boundaries • No support tickets, no time wasted” “Don’t be afraid to throw money (more computer resources) at a problem • Engineers and their time are often more expensive than computer resources • “Burstable” solutions” Warum Cloud? Für experimentelles Arbeiten – einige Überlegungen von Alex Sadovsky
11.
Copyright © 2017,
Oracle and/or its affiliates. All rights reserved.Copyright © 2017, Oracle and/or its affiliates. All rights reserved. www.csm.ornl.gov/PR/clusters.jpg Selbstbau ist teuer! • Kosten für System Engineers • Kosten für zusätzliche Entwicklungzeiten • im Hinblick auf Personal (Gehalt) • im Hinblick auf verlagerte Arbeit • Kosten für besondere Developer Skills • Leute mit Python/SQL-Kenntnissen sind z.Z. noch leichter zu finden im Vergleich zu Spark-/Hive-Spezialisten • Be lazy. Warum nicht ~3..5€/Stunde für eine Single Cloud instance bezahlen – anstatt eine eigene Infrastruktur für Datenexperimente aufzubauen und zu warten? Warum Cloud? Case Study: Der 10-Node-Selbstbau Spark Cluster
12.
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