AMIS Oracle OpenWorld & CodeOne Review - Pillar 1 - Data (5 november 2018)

Oracle
OpenWorld &
CodeOne
Review
Pillar 1: Data
Data on OCI
Review of Oracle OpenWorld & CodeOne 2018 - #oowamis 1
Lucas Jellema, CTO of AMIS
Fall 2018
#oowamis

OLTP

The Oracle Database
• Managed Cloud offerings based on Open Source Datastores
Review of Oracle OpenWorld & CodeOne 2018 - #oowamis 350 Shades of Data 3

Quick Overview
• Oracle Database 18c (18.3) available in the cloud and on premises
• Now also as RPM install on Linux
• Oracle Database 19c is in beta
• Expected release “near end of calenda year (2018)”
• Big feature: Automatic Index Creation
• Autonomous Database Cloud services – live today
• Autonomous Data Warehouse
• Autonomous Transaction Processing

Cloud and Software
Engineered to Work Together

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |
What is the Autonomous Database Cloud?
Autonomous
Database
=
Database-Optimized
Infrastructure as a Service
Expanded Database
Automation
+ +
Policy-driven
Workload Optimization
and Machine Learning

Oracle Autonomous Database Key Attributes
Review of Oracle OpenWorld & CodeOne 2018 - #oowamis
Goal - Eliminate human labor/opportunity for error
Self-Driving
Automates all database and
infrastructure
management, monitoring,
tuning
Self-Securing
Protects from both
external attacks and
malicious internal users
Self-Repairing
Protects from all
downtime including
planned maintenance
11

Autonomous
only for Database
and Data Warehouse

SQL on Kafka
• Oracle Database works with
Kafka Topics as “external table”
• Publishing from Oracle
Database to a Kafka Topic is
on the roadmap for v2

Self-Securing
Autonomous
Database
Automated
Patching &
Upgrades
Separation of
Duties &
Auditing
Encrypted by
Default
At Rest & In Motion
Robust Baseline Security Posture

• Very large Oracle
customer with more than
15,000 databases
– 4 quarterly patches per year
– Average of 45 minutes per
database to apply the
patches (0.75 hours)
– 4*.75*15,000 = 45,000
hours per year spent in
patching*
– 22 skilled DBAs required
JUST to apply patches!
* Does not include OS patches
• SMB Oracle customer
with 20 databases
• 4 quarterly patches per
year
• Average of 45 minutes
per database to apply the
patches (0.75 hours)
• 4*.75*20 = 60 hours per
year spent on patching
• More than a week just to
keep up with patches!
The high cost of patching

• of [database]
cyberattacks are on databases that
administrators have failed to patch
for more than 9 months
– Mark Hurd, CEO Oracle
•
cost to Experian of last year’s data
breach, blamed on an unpatched
vulnerability in Apache Struts
The exorbitant cost of NOT patching

• Security patches applied every
quarter
– Patching can also occur off-cycle if a
zero-day exploit is discovered
• Patches are applied without
application downtime – further
reducing the cost of patching
Autonomous Database Security – Patching is automatic
Annual Patching costs: $0.00
Manpower required for patching: 0

• Encryption for Data at Rest
– Automatically configured – all
application data is encrypted within
the database
– Database Backups are also encrypted
• Encryption for Data in Motion
– Automatically configured – all network
access is encrypted to and from the
database
– Choice of two methods
• Oracle Native Network Encryption
• Transport Layer Security (TLS) v1.2 (default)
Autonomous Database Security - Encryption at rest & in motion

• No access to the Database Node or
File system
• Service Administration (by Oracle)
isolated from Database use and
administration (service consumer)
– Multi-Tenant
– Database Vault
• Database Vault Operations Control later this
year
Autonomous Database Separation of Duties

• Automation
– Most Database Administration tasks
fully automated
– Zero human interaction reduces
opportunities for human error
• Service-appropriate Restrictions
– Service Consumers not allowed to run
many Oracle Database administrative
commands and parameters
– Remove the ability to inadvertently
damage or impact performance
Controlling Human Error

Database Multilingual Engine

Time for another Smile!

Oracle Database XE – eXpress Edition
• Current version until very recently: XE 11gR2
• Available since October 2018: XE 18c, with yearly releases (19c, 20c, …)
• All functionality of single instance Oracle Database Enterprise Edition plus Options
• (including R, Databse Vault, Machine Learning, Spatial, Compression,
Multi Tenant – for 3 PDBs, Partitioning)
• Code and Data Compatible with other editions – including plug/unplug
• Resource Limitations for 18c:
• 2 CPUs
• 2 GB of memory
• 12 GB of disk space (using Compression effectively 40 GB of data)
• No patches or support

From Data to Actionable Intelligence

Database 12, 18, 19
• Who is using Sharding?
• China Telecom, Dyn DNS
• Who is using Multitenant?
• EBS not yet – but will take on 19c
• ATP is based on 19c
• Lacks some capabilities – enterprise R, file system or O/S access, stored
java
• ADWCS is 12.2 plus 18c plus 19c bits
• Autonomous (cloud and on premises; evolution of features, no revolution):
• Index Generation
• NetSuite (and others)
• Other features?
• XE 18c

Reference Architecture for Data Driven organizations
OLTP
IoT
Social
Analytics
Cloud

OLTP
IoT
Social
Analytics
Cloud

Connect to More Data
70
Oracle Analytics Cloud
• Analytics & Reporting
• Data Visualization
• Essbase

Oracle Analytics Cloud

Scale your Analytics!
73
Oracle
Analytics Cloud
Autonomous
Data Warehouse Cloud
Data Integration
Platform Cloud
Big Data Cloud

Data Prepare Recommendations
Transformation Examples Enrichment Examples

Data Flows for Extract-Prepare-Load (into Data Visualization Store)
You can branch a data flow into multiple connections to downstream nodes, which creates
multiple outputs from a data flow. For example, you can create a data flow from a sales
transactions data set, then branch and save the data into multiple data sets based on the region
of the sales transaction, such as west and east coast regions.

Essbase
EPM-PBCS
EPM-EPBCS
EPM-FCCS
EPM-EPRCS
EPM-FCCS-2
EPM-PCMCS
EPM-TRCS
EPM-EDM
FA-ERP
FA-CRM
FA-Projects
FA-Quota
FA-Cost
OAC
Essbase
OAC-DV
Data Flow
OAC
Data Lake
Exalytics On-Premise
11g
On-Premise
12c
OAC-DV
write back, ML
OAC-DV
CXO Dashboard
OAC-Essbase
Partners Cubus,
Dodeca
Who Uses Essbase?

DV Integration: Visualization and Write-back
• Create an Essbase Cube as a target of DV Data Flow
• Load data into an Essbase Cube and quickly analyze it
• Visualize Essbase cubes in DV
• Perform “Write-back” to an Essbase Cube from the DV Interface

Copyright © 2018, Oracle and/or its affiliates. All rights reserved. |Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 82
Data Science and Essbase and Oracle Data Studio
What is a notebook in data science?
The notebook interface addresses the inherent communication
challenges in data science by leveraging a style of programming
called Literate Programming. Literate Programming is a
paradigm in which modular blocks of code are interwoven with
blocks of supplementary context and explanation for that code
• Reference: https://blog.modeanalytics.com/data-science-notebook-
love-story/
Popular Notebooks
• Jupyter and Apache Zeppelin
Notebooks are browser plugins. Allows you
to mix formatted text with Python or Java
or SQL code
Today, Data Scientists use Notebooks for
their analysis using browser
Data Studio from Oracle Labs
• Oracle Data studio supports notebook and is now part of Essbase in OAC
• We created Python packages that allows you to invoke Essbase API from Data Studio Notebook
• Data Scientists -- from Essbase UI -- can now use Essbase cubes and their data in their notebooks for their
analysis and share those notebooks with their colleagues
• Oracle Data studio provides rich visualization capabilities which you can apply on top of Essbase data

OLTP
IoT
Social
Analytics
Cloud

But wait – there is more…
Autonomous Data Warehouse [Cloud]

OLTP
IoT
Social
Analytics
Cloud

Spark SQL wins
over Hive

DataScience.com – acquisition Spring 2018

Collborative Notebooks & Curated Runtime Environments

Forrester Wave
• On notebook-based
predictive analytics and
machine learning
solutions
95/278

Enterprise Data
consolidated in Cloud
Data lakes
• HDFS, Object storage
SQL on Hadoop is not
enough for B.I and
business users
Need fast visualization
directly on datalakes
Need open data
management platforms (
Spark )
Fast Analytics on Data Lakes – Key Drivers

Define business
dimensions, metrics,
hierarchies and semantics
Analytics,
Visualization
Data science/ML
99
What is SNAP?
High Performance
Indexer and
Query Optimizer
Legacy DW
Financial ERP
Sales CRM
Social Data
IoT Data
Data Sources: Users:ManageModel Analyze & Visualize
LOB
Analysts
Data Scientists
Developers
Confidential – Oracle Internal
Spark
SQL
SNAP Engine
Data
Lake
InMemorySNAPSegments Review of Oracle OpenWorld & CodeOne 2018 - #oowamis

AMIS Oracle OpenWorld & CodeOne Review - Pillar 1 - Data (5 november 2018)

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AMIS Oracle OpenWorld & CodeOne Review - Pillar 1 - Data (5 november 2018)