A Unified Approach to Data Processing and Analytics Big Data, Fast Data, All Data National Security Industry Focus
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Outline: Combining Brainstorming Deliverables Table of Contents 1. Introduction and Definition 2. Reference Architecture and Taxonomy 3. Requirements, Gap Analysis, and Suggested Best Practices 4. Future Directions and Roadmap 5. Security and Privacy - 10 Top Challenges 6. Conclusions and General Advice Appendix A. Terminology Glossary Appendix B. Solutions Glossary Appendix C. Use Case Examples Appendix D. Actors and Roles 1. Introduction and Definition The purpose of this outline is to illustrate how some initial brainstorming documents might be pulled together into an integrated deliverable. The outline will follow the diagram below. Section 1 introduces a definition of Big Data. An extended terminology Glossary is found in Appendix A. In section 2, a Reference Architecture diagram is presented followed by a taxonomy describing and extending the elements of the Reference Architecture. Section 3 maps requirements from use case building blocks to the Reference Architecture. A description of the requirement, a gap analysis, and suggested best practice is included with each mapping. In Section 4 future improvements in Big Data technology are mapped to the Reference Architecture. An initial Technology Roadmap is created on the requirements and gap analysis in Section 3 and the expected future improvements from Section 4. Section 5 is a placeholder for an extended discussion of Security and Privacy. Section 6 gives an example of some general advice. The Appendices provide Big Data terminology and solutions glossaries, Use Case Examples, and some possible Actors and Roles. Big Data Definition - “Big Data refers to the new technologies and applications introduced to handle increasing Volumes of data while enhancing data utilization capabilities such as Variety, Velocity, Variability, Veracity, and Value.” The key attribute is the large Volume of data available that forces horizontal scalability of storage and processing and has implications for all the other V-attributes. -
Oracle Metadata Management V12.2.1.3.0 New Features Overview
An Oracle White Paper October 12 th , 2018 Oracle Metadata Management v12.2.1.3.0 New Features Overview Oracle Metadata Management version 12.2.1.3.0 – October 12 th , 2018 New Features Overview Disclaimer This document is for informational purposes. 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 in this document remains at the sole discretion of Oracle. This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. This document and information contained herein may not be disclosed, copied, reproduced, or distributed to anyone outside Oracle without prior written consent of Oracle. This document is not part of your license agreement nor can it be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. 1 Oracle Metadata Management version 12.2.1.3.0 – October 12 th , 2018 New Features Overview Table of Contents Executive Overview ............................................................................ 3 Oracle Metadata Management 12.2.1.3.0 .......................................... 4 METADATA MANAGER VS METADATA EXPLORER UI .............. 4 METADATA HOME PAGES ........................................................... 5 METADATA QUICK ACCESS ........................................................ 6 METADATA REPORTING ............................................................. -
Finding Oracle® Table Metadata: When PROC CONTENTS Is Not Enough Jeremy W
Finding Oracle® Table Metadata: When PROC CONTENTS Is Not Enough Jeremy W. Poling, B&W Y-12 L.L.C., Oak Ridge, TN ABSTRACT Complex Oracle® databases often contain hundreds of linked tables. For SAS/ACCESS® Interface to Oracle software users who are unfamiliar with a database, finding the data they need and extracting it efficiently can be a daunting task. For this reason, a tool that extracts and summarizes database metadata can be invaluable. This paper describes how Oracle table metadata can be extracted from the Oracle data dictionary using the SAS/ACCESS LIBNAME statement in conjunction with traditional SAS® DATA step programming techniques. For a given Oracle table, we discuss how to identify the number of observations and variables in the table, variable names and attributes, constraints, indexes, and linked tables. A macro is presented which can be used to extract all the aforementioned metadata for an Oracle table and produce an HTML report. Once stored as an autocall macro, the macro can be used to quickly identify helpful information about an Oracle table that cannot be seen from the output of PROC CONTENTS. This paper assumes that the reader has a basic understanding of DATA step programming, the macro language, and the SAS/ACCESS LIBNAME statement. A basic understanding of relational database management system concepts is also helpful, but not required. INTRODUCTION Metadata is typically defined simply as “data about data.” For any SAS data set, a programmer can view useful metadata simply by using PROC CONTENTS. But, what if the table of interest resides in an Oracle database? Unfortunately, finding Oracle table metadata is not quite as straightforward as finding SAS data set metadata. -
Oracle Nosql Database
An Oracle White Paper November 2012 Oracle NoSQL Database Oracle NoSQL Database Table of Contents Introduction ........................................................................................ 2 Technical Overview ............................................................................ 4 Data Model ..................................................................................... 4 API ................................................................................................. 5 Create, Remove, Update, and Delete..................................................... 5 Iteration ................................................................................................... 6 Bulk Operation API ................................................................................. 7 Administration .................................................................................... 7 Architecture ........................................................................................ 8 Implementation ................................................................................... 9 Storage Nodes ............................................................................... 9 Client Driver ................................................................................. 10 Performance ..................................................................................... 11 Conclusion ....................................................................................... 12 1 Oracle NoSQL Database Introduction NoSQL databases -
Oracle Metadata Management (OMM) Solutions Oracle Metadata Management for Oracle Business Intelligence (OMM4OBI) Oracle Enterprise Metadata Management (OEMM)
Oracle Metadata Management (OMM) Solutions Oracle Metadata Management for Oracle Business Intelligence (OMM4OBI) Oracle Enterprise Metadata Management (OEMM) OMM Metadata Management 12.2.1.4.0 based on MIMM OEM 10.1.0 (01/31/2020) with Meta Integration® Metadata Management (MIMM) README for Release Changes 1. Overview The Oracle Metadata Management (OMM) solutions include two products: the Oracle Metadata Management for Oracle Business Intelligence (OMM4OBI) and the Oracle Enterprise Metadata Management (OEMM) Oracle Metadata Management for Oracle Business Intelligence is a software package for metadata management of Oracle environments. Oracle Metadata Management for Oracle Business Intelligence includes the following metadata management features: Metadata Harvesting from Oracle technologies Metadata Configuration and Stitching Metadata Browsing, Search and Reporting Metadata Collaboration (external URL, tagging, comments and review) Data Flow Lineage & Impact Analysis Metadata Explorer (simplified metadata user interface for business users) Oracle Enterprise Metadata Management is a software package for metadata management of multi-vendor environments and support for data governance. Oracle Enterprise Metadata Management includes all features of Oracle Metadata Management for Oracle Business Intelligence with the following extra metadata management features: Metadata Harvesting from multi-vendor technologies Metadata Version and Configuration Management (change management) Data Model Diagram Visualizer and Navigator Business Glossary -
Oracle® Nosql Database Changelog
Oracle® NoSQL Database Changelog Release 20.1 E91819-17 July 2020 Oracle NoSQL Database Changelog, Release 20.1 E91819-17 Copyright © 2011, 2020, Oracle and/or its affiliates. This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or “commercial computer software documentation” pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, reproduction, duplication, release, display, disclosure, modification, preparation of derivative works, and/or adaptation of i) Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs), ii) Oracle computer documentation and/or iii) other Oracle data, is subject to the rights and limitations specified in the license contained in the applicable contract. -
Data Governance with Oracle
Data Governance with Oracle Defining and Implementing a Pragmatic Data Governance Process with Oracle Metadata Management and Oracle Data Quality Solutions ORACLE WHITE P A P E R | SEPTEMBER 2015 Disclaimer 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. DATA GOVERNANCE WITH ORACLE Table of Contents Disclaimer 1 Introduction 1 First Define the Business Problem 2 Identify Executive Sponsor 3 Manage Glossary of Business Terms 4 Identify Critical Data Elements 4 Classify Data from an Information Security Perspective 5 Manage Business Rules 6 Manage Allowable Values for Business Terms 7 Support for Data Lineage and Impact Analysis 8 Manage Data Stewardship Workflows 10 Govern Big Data 11 Manage Data Quality Rules 12 Execute Data Quality Rules 12 View Data Quality Dashboard 16 Data Quality Remediation 16 Data Privacy and Security 17 Ingredients for Data Governance Success 17 Governance with Any Enterprise System 19 Align with Other Oracle Solutions 20 About the Author 22 DATA GOVERNANCE WITH ORACLE . DATA GOVERNANCE WITH ORACLE Introduction Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Data governance programs have traditionally been focused on people and process. In this whitepaper, we will discuss how key data governance capabilities are enabled by Oracle Enterprise Metadata Manager (OEMM) and Oracle Enterprise Data Quality (EDQ). -
Oracle Nosql Database and Cisco- Collaboration That Produces Results
Oracle NoSQL Database and Cisco- Collaboration that produces results 1 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. What is Big Data? SOCIAL BLOG SMART METER VOLUME VELOCITY VARIETY VALUE 2 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Why Is It Important? US HEALTH CARE US RETAIL MANUFACTURING GLOBAL PERSONAL EUROPE PUBLIC LOCATION DATA SECTOR ADMIN Increase industry Increase net Decrease dev., Increase service Increase industry value per year by margin by assembly costs by provider revenue by value per year by $300 B 60+% –50% $100 B €250 B “In a big data world, a competitor that fails to sufficiently develop its capabilities will be left behind.” 3 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Source: * McKinsey Global Institute: Big Data – The next frontier for innovation, competition and productivity (May 2011) Big Data in Action DECIDE ACQUIRE Make Better Decisions Using Big Data ANALYZE ORGANIZE 4 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Oracle Integrated Solution Stack DATA VARIETY HDFS HADOOP (MapReduce) In-DB Oracle Loader Mining for HADOOP Oracle NoSQL DB Oracle In-DB Exadata ‘R’ In-DB MapReduce OBIEE Oracle Data Analytics Advanced Oracle Database Integrator INFORMATION DENSITY ACQUIRE ORGANIZE ANALYZE DECIDE 5 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Big Data in Action DECIDE ACQUIRE Acquire all available, schema-based and non- relational data ANALYZE ORGANIZE 6 Copyright © 2011, Oracle and/or its affiliates. All rights reserved. Acquiring Big Data Challenge Process high volume, low- Application changes With sub-millisecond density information frequently Velocity from various data-sets 7 Copyright © 2011, Oracle and/or its affiliates. -
Oracle Big Data SQL Release 4.1
ORACLE DATA SHEET Oracle Big Data SQL Release 4.1 The unprecedented explosion in data that can be made useful to enterprises – from the Internet of Things, to the social streams of global customer bases – has created a tremendous opportunity for businesses. However, with the enormous possibilities of Big Data, there can also be enormous complexity. Integrating Big Data systems to leverage these vast new data resources with existing information estates can be challenging. Valuable data may be stored in a system separate from where the majority of business-critical operations take place. Moreover, accessing this data may require significant investment in re-developing code for analysis and reporting - delaying access to data as well as reducing the ultimate value of the data to the business. Oracle Big Data SQL enables organizations to immediately analyze data across Apache Hadoop, Apache Kafka, NoSQL, object stores and Oracle Database leveraging their existing SQL skills, security policies and applications with extreme performance. From simplifying data science efforts to unlocking data lakes, Big Data SQL makes the benefits of Big Data available to the largest group of end users possible. KEY FEATURES Rich SQL Processing on All Data • Seamlessly query data across Oracle Oracle Big Data SQL is a data virtualization innovation from Oracle. It is a new Database, Hadoop, object stores, architecture and solution for SQL and other data APIs (such as REST and Node.js) on Kafka and NoSQL sources disparate data sets, seamlessly integrating data in Apache Hadoop, Apache Kafka, • Runs all Oracle SQL queries without modification – preserving application object stores and a number of NoSQL databases with data stored in Oracle Database. -
Oracle Nosql Database EE Data Sheet
Oracle NoSQL Database 21.1 Enterprise Edition (EE) Oracle NoSQL Database is a multi-model, multi-region, multi-cloud, active-active KEY BUSINESS BENEFITS database, designed to provide a highly-available, scalable, performant, flexible, High throughput and reliable data management solution to meet today’s most demanding Bounded latency workloads. It can be deployed in on-premise data centers and cloud. It is well- Linear scalability suited for high volume and velocity workloads, like Internet of Things, 360- High availability degree customer view, online contextual advertising, fraud detection, mobile Fast and easy deployment application, user personalization, and online gaming. Developers can use a single Smart topology management application interface to quickly build applications that run in on-premise and Online elastic configuration cloud environments. Multi-region data replication Enterprise grade software Applications send network requests against an Oracle NoSQL data store to and support perform database operations. With multi-region tables, data can be globally distributed and automatically replicated in real-time across different regions. Data can be modeled as fixed-schema tables, documents, key-value pairs, and large objects. Different data models interoperate with each other through a single programming interface. Oracle NoSQL Database is a sharded, shared-nothing system which distributes data uniformly across multiple shards in a NoSQL database cluster, based on the hashed value of the primary keys. An Oracle NoSQL Database data store is a collection of storage nodes, each of which hosts one or more replication nodes. Data is automatically populated across these replication nodes by internal replication mechanisms to ensure high availability and rapid failover in the event of a storage node failure. -
An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data
S S symmetry Article An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data Shabana Ramzan 1, Imran Sarwar Bajwa 1,* and Rafaqut Kazmi 2 1 Department of Computer Science & IT, Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan; [email protected] 2 School of Computing, University of Technology Malaysia, Johor 81310, Malaysia; [email protected] * Correspondence: [email protected] Received: 26 October 2018; Accepted: 14 November 2018; Published: 3 December 2018 Abstract: Handling complexity in the data of information systems has emerged into a serious challenge in recent times. The typical relational databases have limited ability to manage the discrete and heterogenous nature of modern data. Additionally, the complexity of data in relational databases is so high that the efficient retrieval of information has become a bottleneck in traditional information systems. On the side, Big Data has emerged into a decent solution for heterogenous and complex data (structured, semi-structured and unstructured data) by providing architectural support to handle complex data and by providing a tool-kit for efficient analysis of complex data. For the organizations that are sticking to relational databases and are facing the challenge of handling complex data, they need to migrate their data to a Big Data solution to get benefits such as horizontal scalability, real-time interaction, handling high volume data, etc. However, such migration from relational databases to Big Data is in itself a challenge due to the complexity of data. In this paper, we introduce a novel approach that handles complexity of automatic transformation of existing relational database (MySQL) into a Big data solution (Oracle NoSQL). -
152-29: Creating an Intranet Toolbox of Selective Oracle Metadata
SUGI 29 Posters Paper 152-29 Creating an Intranet Toolbox of Selective Oracle Metadata John Charles Gober, Deborah Mullen, Jana Smith Bureau of the Census ABSTRACT The purpose of this paper is to demonstrate an alternative method of allowing users to view the attributes of their Oracle® Databases. This application is useful for programmers and developers who do not have access to Oracle Designer or other similar applications which allow visual representations of your databases. Even though this application is fairly simple in its appearance, it does give the user critical information about their database tables, variables, and indexes. By being able to cross reference tables with variables, variables with indexes, and indexes with tables this in turn will allow the rapid development of programs and applications which need to use these tables. INTRODUCTION The American Community Survey (ACS) is a new nationwide survey designed to provide communities a fresh look at how they are changing. It is intended to eliminate the need for the long form in the 2010 Census. The ACS will collect information from U.S. households similar to what was collected on the Census 2000 long form, such as income, commute time to work, home value, veteran status, and other important data. As with the official U.S. census, information about individuals will remain confidential. The introduction explains the purpose and scope of your paper and provides readers with any general information they need to understand your paper. The ACS will collect and produce population and housing information every year instead of every ten years. About three million households will be surveyed each year.