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Advanced Databases and Knowledge Management

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These days, there are other database technologies at play besides Hadoop. As more raw data is converted to action and knowledge, finding and understanding data requires other kinds of database technology. The days of the single-vendor database environment are over.

Join Kelle and John as they talk about new database management system (DBMS) technology, including some of the unique applications of graph databases, covering:

What is graph?
How is it used?
What are some other promising new database technologies?
Examples of Big Data, analytics and graphs at work

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Advanced Databases and Knowledge Management

  1. 1. The First Step in Information Management looker.com Produced by: MONTHLY SERIES In partnership with: Advanced Databases and Knowledge Management September 6, 2018
  2. 2. Welcome to Today’s Discussion  Overview of database management systems (DBMS) technologies  Scope of current DBMS technologies − Graph AND OTHER No SQL (non-Hadoop)  Analytics use cases  Knowledge management and future usage  Best practices and key takeaways  Q&A pg 2© 2018 First San Francisco Partners www.firstsanfranciscopartners.com
  3. 3. Overview of DBMS Technologies  A majority of organizations today have heterogeneous solutions for analytics.  In-house DBMS (vendor/architecture still important) vs. Cloud (vendor/architecture not as important).  No longer single model to multiple use case.  Evolving from DBMS as the global data store to technical requirement driven. pg 3© 2018 First San Francisco Partners www.firstsanfranciscopartners.com A database management system (DBMS) is system software for creating and managing databases. The DBMS provides users and programmers with a systematic way to create, retrieve, update and manage data. - TechTarget SearchSQLServer
  4. 4. “New Types” of DBMS pg 4© 2018 First San Francisco Partners www.firstsanfranciscopartners.com Old View •Hierarchical •Network •Relational •Object-oriented Expanded View •Columnar •Graph “New” View •No SQL (key- value, wide column, graph, or document) •Appear like original view PLUS specialized functionality Realistic View •Types and functions are blurred •e.g. some RDBMS have graph capability The emphasis is on the role the DBMS performs. The era of the dedicated single enterprise DBMS is over.
  5. 5. Progression of DBMS Emphasis and Scope pg 5© 2018 First San Francisco Partners www.firstsanfranciscopartners.com Mathematical Model Graph Inverted list B-tree Relational Storage Graph HDFS Columnar Time Value Architecture Hadoop Relational Hierarchical Columnar Graph Use case Transactional Content management Structured data analysis Advanced analytics Lineage and knowledge mapping Visualization
  6. 6. Mathematical Model Graph Inverted list B-tree Relational Storage Graph HDFS Columnar Time Value Architecture Hadoop Relational Hierarchical Columnar Graph Use case Transactional Content management Structured data analysis Advanced analytics Lineage and knowledge mapping Visualization Progression of DBMS Scope pg 6© 2018 First San Francisco Partners www.firstsanfranciscopartners.com
  7. 7. About Graph Databases  A Graph database is a database designed to treat the relationships between data as equally important to the data itself.  Less emphasis on a pre-defined model for structure.  Data is “stored” like we first draw it out – showing how each individual entity connects with or is related to others.  Think of complicated many-to- many-to-many relationships. pg 7© 2018 First San Francisco Partners www.firstsanfranciscopartners.com PERSON Name:John PERSON Name:Martha Married Year:1982 Node Label Property Relationship Name Property
  8. 8. 8 Interesting Or Boring? Senior Executives Mr. Smith Ms. Jones Mr. Subramanian … … … Copyright SingerLinks Consulting LLC 2017 www.singerlinks.com
  9. 9. 9 Interesting Or Boring? IPV4 20.5.100.10 20.5.100.11 20.5.100.12 20.5.101.10 20.5.101.11 … Senior Executives Mr. Smith Ms. Jones Mr. Subramanian … … … Copyright SingerLinks Consulting LLC 2017 www.singerlinks.com
  10. 10. 10 Interesting Or Boring? IPV4 20.5.100.10 20.5.100.11 20.5.100.12 20.5.101.10 20.5.101.11 … Senior Executives Mr. Smith Ms. Jones Mr. Subramanian … … … Copyright SingerLinks Consulting LLC 2017 www.singerlinks.com It’s the relationships that are interesting – what if this result is from a security audit?
  11. 11. 11 The Path from Boring to Interesting Graph Exec Business Capability Application System Computer System L2 Interface IPV4 JBOSS OWNS SUPPORTS EXECUTES ON RUNSON CONFIGURES BOUND EA TOOL APM Tool IT ASSET (CMDB Tool) NETWORK SCANNER Copyright SingerLinks Consulting LLC 2017 www.singerlinks.com Answering difficult questions typically requires many hops through multiple domains of data. This data probably exists, but is not linked together.
  12. 12. Multiple DBMS Technology Usage pg 12© 2018 First San Francisco Partners www.firstsanfranciscopartners.com Data Lake Graph Query Metadata and Lineage via Knowledge Map Update Metadata Data Uses Processes Data Changes Analytic Model Data Data
  13. 13. NoSQL DBMS pg 13© 2018 First San Francisco Partners www.firstsanfranciscopartners.com KEY-VALUE WIDE COLUMN DOCUMENT NON- HADOOP NO-SQL Project Voldemort Amazon Dynamo MongoDB* *MongoDB is also a key-value and wide column solution
  14. 14. DBMS Usage in Analytics  DBMS technology needs to cover: pg 14© 2018 First San Francisco Partners www.firstsanfranciscopartners.com Functionality of Data Warehouse Virtualization or Logical Data Warehouse Real time analytics Context Independent Multiple formats Lineage, metadata, and mapping of massive amounts of diverse content Foundation for AI Structured data analysis Advanced analytics Visualization
  15. 15. Varied DBMS Usage in Analytics — Sample Architecture  Graph for knowledge mapping and metadata  Document data base for document storage and use  Hadoop or other NoSQL for merging and analyzing varied content  Columnar for handling Vintage area BI and Reporting pg 15© 2018 First San Francisco Partners www.firstsanfranciscopartners.com Contemporary Area 1 Data Life Cycles Data Management Data Usage Vintage Area Legacy BI and Reporting Data Warehouse, ODS, Mart ETL, EAI, Msg, Copy Data Lake Hadoop Advanced Analytics RDBMS, SQL, Columnar, Transactional Metadata Logical DW Data Sources Knowledge Graph BIVisualization Document
  16. 16. Knowledge Management and Future Usage pg 16© 2018 First San Francisco Partners www.firstsanfranciscopartners.com “ Knowledge Management turns the potential capacity of raw “connected and collaborative intelligence”, i.e. all those brains at the end of the computer, into a “collective know-how” that will improve operations, competitiveness and value. ….. It is a SUM of information assets, …and most importantly, the un- captured, tacit expertise and experience resident in the minds of people.” “ Knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. ... The one real lacuna of this definition is that it, too, is specifically limited to an organization's own information and knowledge assets. “  The context, metadata and the relationships are as important as the values of the records. John Ladley Wikipedia
  17. 17. Knowledge Management and Future Usage  Blurs with AI and machine learning  Still retains old challenges that AI needs to take to heart (data quality/data movement/context)  When you present a sophisticated model, whether derived from exploration or a recognized pattern, you still need to apply what people ALREADY KNOW pg 17© 2018 First San Francisco Partners www.firstsanfranciscopartners.com FutureAnalytics Knowledge Management Machine Learning Artificial Intelligence Well Managed Data Supply Chain
  18. 18. DBMS Will Support Organizational Learning pg 18© 2018 First San Francisco Partners www.firstsanfranciscopartners.com SQL No SQL Hierarchical No SQL No SQL AI / Analytics Models Conclusion LEARNING AI “closed loop” rule Knowledge Graph SQL LEARNING CAPTURED LEARNING ACTION
  19. 19. Best Practices  Understand your data and usage  Determine what the need is for specialized DBMS  Test your current DBMS stack through a POC to see if the merchant vendor can really handle the task.  Understand data quality or business needs pg 19© 2018 First San Francisco Partners www.firstsanfranciscopartners.com
  20. 20. Key Takeaways pg 20© 2018 First San Francisco Partners www.firstsanfranciscopartners.com KEEP IN MIND…  Do you know what capabilities you are trying to enable?  Do you know business latencies ?  Are you looking at native or conceptual aspects for database use cases?  Are you keeping track of the vendors?  Can you manage and afford many DBMS or do you need to work hard with one or two large players?  You can’t buy one of everything
  21. 21. Please Share Your Questions and Comments MONTHLY SERIES
  22. 22. Thank you for joining us today! Our Thursday, October 4 #DIAnaltyics webinar is: Lessons Learned From Building a Data Supply Chain . John Ladley @jladley john@firstsanfranciscopartners.com Kelle O’Neal @kellezoneal kelle@firstsanfranciscopartners.com

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