NoSQL, Growing up at Oracle

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A couple of major players in the internet space, in particular Amazon, LinkedIn and Google, opened the eyes of the corporate world to the coming onslaught of a NoSQL workload. As with every new market opportunity, some young guns quickly jumped in to capitalize on the need and confusion, but things are starting to settle and NoSQL is maturing as Enterprise ready solutions break away with long sought after features. In this webcast, learn about NoSQL convergence from Oracle, the leader in data management and hear why some flavors of NoSQL are here to stay.

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.1

NoSQL, growing up at Oracle
Robert Greene, product management

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.3
Webcast Agenda
 Modern data management workload
 Lessons learned and applied
 System architecture is key
 Feature convergence on essentials
 A lasting NoSQL technology

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.4
modern data management workload
1. Write Intensive
2. Concurrent
3. Available
NoSQL, growing up at Oracle

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.5
Modern Workloads
Amazon constructs each custom user web page by sending requests to 150 different
services and data stores. Over 500 individual key-value lookups per page server.
Write & Read intensive

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.6
Modern workloads
Concurrent

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.7
Modern Workloads
 Built-in, always on
Available

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.8
system architecture is key
1. Linear scaling
2. Replicated
3. Asynchronous
4. Data local
NoSQL, growing up at Oracle

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.9
Architecture
 Elastic Auto Sharding
(split, add, contract)
Store
Shard
M
Shard
M
R
R R
Application
NoSQL Driver
R
 Writes to elected
node on ACID
 Reads from any
node in system
Shard
M
R
R
Expand and Rebalance
Shard
M
R
R
Linear scaling & replicated

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.10
Architecture
Asynchronous

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Architecture
Data local
Pkey A Value A
F1 F2 F3 V1 V2 V3
Pkey B Value B
F1 F2 F3 V1 V2 V3
Skey A Pkey A
V2 V3 F1 F2 F3
Skey B Pkey B
V2 V3 F1 F2 F3
Skey A Pkey A
V1 F1 F2 F3
Skey B Pkey B
V1 F1 F2 F3
Primary Records
Secondary Indexes
Shard 1
Writes
Reads
Shard 2
Shard N
Index on (V1)
Index on (V2, V3)
Value V1
V1a V2b V3c

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.13
lessons learned and applied
1. Non-web retail
2. Integrated
3. Use cases unknown
NoSQL, growing up at Oracle

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.14
Lessons learned
Non-Web Retail

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.15
Lessons learned
Authorization
DW Rules
Publisher
Rule
Manager
Customer
Rules
Fraud
Alerts
Rules Storage/Retrieval
RiskService
Ext Service Ext Streams
Integrated

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.16
Lessons learned
 New application: Retrieve all data for a TV
series (seasons, episodes, actors, and so on)
 Perfect for a NoSQL database
– One simple retrieval returns all information
– Much simpler than relational database; no joins
Future use cases unknown
 And then, a new feature request: Retrieve all data
for a given actor
 Incompatible with original NoSQL document model
 ‘Actors’ cannot be retrieved separately
 Simple feature to add with relational database
 Just another SQL query on the same schema

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.17
feature convergence on essentials
1. Transactions
2. Standards : SQL, Indexes
3. Secure : Auth, Roles, Audit
4. HA : Data Centers
NoSQL, growing up at Oracle

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.18
Feature Convergence
 Ease of use
– The 5% case
– Development simplification
– Process improvement
– Prevent index divergence
– Data consistency
Transactions

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.19
Feature Convergence
 Ease of use
– Data Modeling
– Integration
– SQL Query
 Late Binding (soft-schema)
 Indexes
 Easy JSON representation
Tables

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.20
Feature Convergence
Secure & Highly Available

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.21
A lasting NoSQL technology
1. Simple key : value access
2. Concurrent capable
3. Integrated
4. Manageable
NoSQL, growing up at Oracle

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.22
advanced key-value database
1. Don’t care what the value is
Tables: Primary key – Shard key
Data locality and indexing
BASE & Transactions
1. Don’t care what the value is
2. JSON API
1. Don’t care what the value is
2. JSON API
3. Table API
Oracle NoSQL Database

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.23
Join NoSQL Database Community
Twitter
https://twitter.com/#!/OracleNoSQL
LinkedIn
http://www.linkedin.com/groups?gid=4147754
Oracle’s NoSQL DB blog
https://blogs.oracle.com/nosql
Oracle Technology Network
http://bit.ly/1f0d8wU
Developer Webcast Series
http://bit.ly/1doV2jl
Oracle.com/BigData

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.24

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.25

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Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Showing ROI for Your Analytic Project

DATAVERSITY

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

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DATAVERSITY

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

Is Enterprise Data Literacy Possible?

DATAVERSITY

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

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Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

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DATAVERSITY

Data Governance Trends - A Look Backwards and Forwards

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As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends and Best Practices To Implement Today

DATAVERSITY

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

2023 Trends in Enterprise Analytics

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

Data Strategy Best Practices

DATAVERSITY

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Who Should Own Data Governance – IT or Business?

DATAVERSITY

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Data Management Best Practices

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

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NoSQL, Growing up at Oracle

2. NoSQL, growing up at Oracle Robert Greene, product management

3. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.3 Webcast Agenda  Modern data management workload  Lessons learned and applied  System architecture is key  Feature convergence on essentials  A lasting NoSQL technology

5. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.5 Modern Workloads Amazon constructs each custom user web page by sending requests to 150 different services and data stores. Over 500 individual key-value lookups per page server. Write & Read intensive

9. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.9 Architecture  Elastic Auto Sharding (split, add, contract) Store Shard M Shard M R R R Application NoSQL Driver R  Writes to elected node on ACID  Reads from any node in system Shard M R R Expand and Rebalance Shard M R R Linear scaling & replicated

11. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.12 Architecture Data local Pkey A Value A F1 F2 F3 V1 V2 V3 Pkey B Value B F1 F2 F3 V1 V2 V3 Skey A Pkey A V2 V3 F1 F2 F3 Skey B Pkey B V2 V3 F1 F2 F3 Skey A Pkey A V1 F1 F2 F3 Skey B Pkey B V1 F1 F2 F3 Primary Records Secondary Indexes Shard 1 Writes Reads Shard 2 Shard N Index on (V1) Index on (V2, V3) Value V1 V1a V2b V3c

14. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.15 Lessons learned Authorization DW Rules Publisher Rule Manager Customer Rules Fraud Alerts Rules Storage/Retrieval RiskService Ext Service Ext Streams Integrated

15. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.16 Lessons learned  New application: Retrieve all data for a TV series (seasons, episodes, actors, and so on)  Perfect for a NoSQL database – One simple retrieval returns all information – Much simpler than relational database; no joins Future use cases unknown  And then, a new feature request: Retrieve all data for a given actor  Incompatible with original NoSQL document model  ‘Actors’ cannot be retrieved separately  Simple feature to add with relational database  Just another SQL query on the same schema

16. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.17 feature convergence on essentials 1. Transactions 2. Standards : SQL, Indexes 3. Secure : Auth, Roles, Audit 4. HA : Data Centers NoSQL, growing up at Oracle

17. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.18 Feature Convergence  Ease of use – The 5% case – Development simplification – Process improvement – Prevent index divergence – Data consistency Transactions

18. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.19 Feature Convergence  Ease of use – Data Modeling – Integration – SQL Query  Late Binding (soft-schema)  Indexes  Easy JSON representation Tables

20. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.21 A lasting NoSQL technology 1. Simple key : value access 2. Concurrent capable 3. Integrated 4. Manageable NoSQL, growing up at Oracle

21. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.22 advanced key-value database 1. Don’t care what the value is Tables: Primary key – Shard key Data locality and indexing BASE & Transactions 1. Don’t care what the value is 2. JSON API 1. Don’t care what the value is 2. JSON API 3. Table API Oracle NoSQL Database

22. Copyright © 2014, Oracle and/or its affiliates. All rights reserved.23 Join NoSQL Database Community Twitter https://twitter.com/#!/OracleNoSQL LinkedIn http://www.linkedin.com/groups?gid=4147754 Oracle’s NoSQL DB blog https://blogs.oracle.com/nosql Oracle Technology Network http://bit.ly/1f0d8wU Developer Webcast Series http://bit.ly/1doV2jl Oracle.com/BigData

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