Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive Analytics

info@rittmanmead.com www.rittmanmead.com @rittmanmead
Big Data for Oracle Developers & DBAs -  
Towards Spark, Real-Time and Predictive Analytics
Mark Rittman, CTO, Rittman Mead
Riga Dev Day 2016, Riga, March 2016

info@rittmanmead.com www.rittmanmead.com @rittmanmead 2
•Mark Rittman, Co-Founder of Rittman Mead

‣Oracle ACE Director, specialising in Oracle BI&DW

‣14 Years Experience with Oracle Technology

‣Regular columnist for Oracle Magazine

•Author of two Oracle Press Oracle BI books

‣Oracle Business Intelligence Developers Guide

‣Oracle Exalytics Revealed

‣Writer for Rittman Mead Blog : 
http://www.rittmanmead.com/blog

•Email : mark.rittman@rittmanmead.com

•Twitter : @markrittman
About the Speaker

•Everyone’s talking about Hadoop and “Big Data”
Hadoop is the Big Hot Topic In IT / Analytics

•Gives us an ability to store more data, at more detail, for longer

•Provides a cost-effective way to analyse vast amounts of data

•Hadoop & NoSQL technologies can give us “schema-on-read” capabilities

•There’s vast amounts of innovation in this area we can harness

•And it’s very complementary to Oracle BI & DW
Why is Hadoop of Interest to Us?

Flexible Cheap Storage for Logs, Feeds + Social Data
$50k
Hadoop
Node
Voice + Chat
Transcripts
Call Center LogsChat Logs iBeacon Logs Website LogsCRM Data Transactions Social FeedsDemographics
Raw Data
Customer 360 Apps
Predictive  
Models
SQL-on-Hadoop
Business analytics
Real-time Feeds, 
batch and API

•Extend the DW with new data sources, datatypes, detail-level data

•Offload archive data into Hadoop but federate it with DW data in user queries

•Use Hadoop, Hive and MapReduce for low-cost ETL staging

Deploy Alongside Traditional DW as “Data Reservoir”
Data Transfer Data Access
Data Factory
Data Reservoir
Business
Intelligence Tools
Hadoop Platform
File Based
Integration
Stream
Based
Integration
Data streams
Discovery & Development Labs
Safe & secure Discovery and Development
environment
Data sets and
samples
Models and
programs
Marketing /
Sales Applications
Models
Machine
Learning
Segments
Operational Data
Transactions
Customer
Master ata
Unstructured Data
Voice + Chat
Transcripts
ETL Based
Integration
Raw
Customer Data
Data stored in
the original
format (usually
files) such as
SS7, ASN.1,
JSON etc.
Mapped
Customer Data
Data sets
produced by
mapping and
transforming
raw data

Incorporate Hadoop Data Reservoirs into DW Design
Virtualization& 
QueryFederation
Enterprise
Performance
Management
Pre-built &  
Ad-hoc  
BI Assets
Information 
Services
Data Ingestion
Information Interpretation
Access & Performance Layer
Foundation Data Layer
Raw Data Reservoir
Data  
Science
Data Engines &  
Poly-structured  
sources
Content
Docs Web & Social Media
SMS
Structured
Data 
Sources
•Operational Data
•COTS Data
•Master & Ref. Data
•Streaming & BAM
Immutable raw data reservoir
Raw data at rest is not interpreted
Immutable modelled data. Business
Process Neutral form. Abstracted from
business process changes
Past, current and future interpretation of
enterprise data. Structured to support agile
access & navigation
Discovery Lab Sandboxes Rapid Development Sandboxes
Project based data stores to
support specific discovery
objectives
Project based data stored to
facilitate rapid content /
presentation delivery
Data Sources

•Oracle Engineered system for big data processing and analysis

•Start with Oracle Big Data Appliance Starter Rack - expand up to 18 nodes per rack

•Cluster racks together for horizontal scale-out using enterprise-quality infrastructure
Oracle Big Data Appliance
Starter Rack + Expansion
• Cloudera CDH + Oracle software
• 18 High-spec Hadoop Nodes with
InfiniBand switches for internal
Hadoop traffic, optimised for network
throughput
• 1 Cisco Management Switch
• Single place for support for H/W + S/
W 
Deployed on Oracle Big Data Appliance
Oracle Big Data Appliance
Starter Rack + Expansion
• Cloudera CDH + Oracle software
• 18 High-spec Hadoop Nodes with
InfiniBand switches for internal
Hadoop traffic, optimised for network
throughput
• 1 Cisco Management Switch
• Single place for support for H/W + S/
W 
Enriched  
Customer Profile
Modeling
Scoring
Infiniband

•Hadoop, through MapReduce, breaks processing down into simple stages

‣Map : select the columns and values you’re interested in, pass through as key/value pairs

‣Reduce : aggregate the results

•Most ETL jobs can be broken down into filtering,  
projecting and aggregating

•Hadoop then automatically runs job on cluster

‣Share-nothing small chunks of work

‣Run the job on the node where the data is

‣Handle faults etc

‣Gather the results back in
Hadoop Tenets : Simplified Distributed Processing
Mapper
Filter, Project
Mapper
Filter, Project
Mapper
Filter, Project
Reducer
Aggregate
Reducer
Aggregate
Output 
One HDFS file per reducer, 
in a directory

•MapReduce jobs are typically written in Java, but Hive can make this simpler

•Hive is a query environment over Hadoop/MapReduce to support SQL-like queries

•Hive server accepts HiveQL queries via HiveODBC or HiveJDBC, automatically 
creates MapReduce jobs against data previously loaded into the Hive HDFS tables

•Approach used by ODI and OBIEE 
to gain access to Hadoop data

•Allows Hadoop data to be accessed just like  
any other data source (sort of...)
Hive as the Hadoop SQL Access Layer

•Data integration tools such as Oracle Data Integrator can load and process Hadoop data

•BI tools such as Oracle Business Intelligence 12c can report on Hadoop data

•Generally use MapReduce and Hive to access data

‣ODBC and JDBC access to Hive tabular data

‣Allows Hadoop unstructured/semi-structured 
data on HDFS to be accessed like RDBMS
Hive Provides a SQL Interface for BI + ETL Tools
Access direct Hive or extract using ODI12c
for structured OBIEE dashboard analysis
What pages are people visiting?
Who is referring to us on Twitter?
What content has the most reach?

•Most Oracle DBAs and developers know about Hadoop, but assume…
Common Developer Understanding of Hadoop Today
‣Hadoop is just for batch (because of the MapReduce JVN spin-up issue)
‣Hadoop is just for large datasets, not ad-hoc work or micro batches
‣Hadoop will always be slow because it stages everything to disk
‣All Hadoop can do is Map (select, filter) and Reduce (aggregate)
‣Hadoop == MapReduce

Hadoop is slow
and only for batch jobs
…isn’t it?
but …

T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or  
+61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India)
E : info@rittmanmead.com
W : www.rittmanmead.com
Hadoop is Now Real-Time, In-Memory and Analytics-Optimised

•MapReduce’s great innovation was to break processing down into distributed jobs

•Jobs that have no functional dependency on each other, only upstream tasks

•Provides a framework that is inﬁnitely scalable and very fault tolerant

•Hadoop handled job scheduling and resource management

‣All MapReduce code had to do was provide the “map” and “reduce” functions

‣Automatic distributed processing

‣Slow but extremely powerful
Hadoop 1.0 and MapReduce

•A typical Hive or Pig script compiles down into multiple MapReduce jobs

•Each job stages its intermediate results to disk

•Safe, but slow - write to disk, spin-up separate JVMs for each job
Compiling Hive/Pig Scripts into MapReduce
register /opt/cloudera/parcels/CDH/lib/pig/piggybank.jar
raw_logs = LOAD '/user/mrittman/rm_logs' USING TextLoader AS (line:chararray);
logs_base = FOREACH raw_logs
GENERATE FLATTEN
(REGEX_EXTRACT_ALL(line,'^(S+) (S+) (S+) [([w:/]+s[+-]d{4})]  
"(.+?)" (S+) (S+) "([^"]*)" "([^"]*)"')
)AS
(remoteAddr: chararray, remoteLogname: chararray, user: chararray,time: chararray);
logs_base_nobots = FILTER logs_base BY NOT (browser matches '.*(spider|robot|bot|slurp|bot.*');
logs_base_page = FOREACH logs_base_nobots GENERATE SUBSTRING(time,0,2) as day)  
AS (method:chararray, request_page:chararray, protocol:chararray), remoteAddr, status;
logs_base_page_cleaned = FILTER logs_base_page BY NOT (SUBSTRING(request_page,0,3) ==  
'/wp' or request_page == '/' or SUBSTRING(request_page,0,7) == '/files/'  
or SUBSTRING(request_page,0,12) == '/favicon.ico');
logs_base_page_cleaned_by_page = GROUP logs_base_page_cleaned BY request_page;
page_count = FOREACH logs_base_page_cleaned_by_page GENERATE FLATTEN(group)  
as request_page, COUNT(logs_base_page_cleaned) as hits;
…
store pages_and_post_top_10 into 'top_10s/pages'; JobId Maps Reduces Alias Feature Outputs
job_1417127396023_0145 12 2 logs_base,logs_base_nobots,logs_base_page,logs_base_page_cleaned, 
logs_base_page_cleaned_by_page,page_count,raw_logs GROUP_BY,COMBINER
job_1417127396023_0146 2 1 pages_and_post_details,pages_and_posts_trim,posts,posts_cleaned HASH_JOIN
job_1417127396023_0147 1 1 pages_and_posts_sorted SAMPLER
job_1417127396023_0148 1 1 pages_and_posts_sorted ORDER_BY,COMBINER
job_1417127396023_0149 1 1 pages_and_posts_sorted

•MapReduce 2 (MR2) splits the functionality of the JobTracker 
by separating resource management and job scheduling/monitoring

•Introduces YARN (Yet Another Resource Manager)

•Permits other processing frameworks to MR

‣For example, Apache Spark

•Maintains backwards compatibility with MR1

•Introduced with CDH5+
MapReduce 2 and YARN
Node 
Manager
Node 
Manager
Node 
Manager
Resource 
Manager
Client
Client

•Runs on top of YARN, provides a faster execution engine than MapReduce for Hive, Pig etc

•Models processing as an entire data ﬂow graph (DAG), rather than separate job steps

‣DAG (Directed Acyclic Graph) is a new programming style for distributed systems

‣Dataﬂow steps pass data between them as streams, rather than writing/reading from disk

•Supports in-memory computation, enables Hive on Tez (Stinger) and Pig on Tez

•Favoured In-memory / Hive v2  
route by Hortonworks
Apache Tez
InputData
TEZ DAG
Map()
Map()
Map()
Reduce()
OutputData
Reduce()
Reduce()
Reduce()
InputData
Map()
Map()
Reduce()
Reduce()

Tez Advantage - Drop-In Replacement for MR with Hive, Pig
set hive.execution.engine=mr
set hive.execution.engine=tez
4m 17s
2m 25s

•Another DAG execution engine running on YARN

•More mature than TEZ, with richer API and more vendor support

•Uses concept of an RDD (Resilient Distributed Dataset)

‣RDDs like tables or Pig relations, but can be cached in-memory

‣Great for in-memory transformations, or iterative/cyclic processes

•Spark jobs comprise of a DAG of tasks operating on RDDs

•Access through Scala, Python or Java APIs

•Related projects include

‣Spark SQL

‣Spark Streaming
Apache Spark

•Native support for multiple languages  
with identical APIs

‣Python - prototyping, data wrangling

‣Scala - functional programming features

‣Java - lower-level, application integration

•Use of closures, iterations, and other  
common language constructs to minimize code

•Integrated support for distributed + 
functional programming

•Uniﬁed API for batch and streaming
Rich Developer Support + Wide Developer Ecosystem
scala> val logfile = sc.textFile("logs/access_log")
14/05/12 21:18:59 INFO MemoryStore: ensureFreeSpace(77353)  
called with curMem=234759, maxMem=309225062
14/05/12 21:18:59 INFO MemoryStore: Block broadcast_2  
stored as values to memory (estimated size 75.5 KB, free 294.6 MB)
logfile: org.apache.spark.rdd.RDD[String] =  
MappedRDD[31] at textFile at <console>:15
scala> logfile.count()
14/05/12 21:19:06 INFO FileInputFormat: Total input paths to process : 1
14/05/12 21:19:06 INFO SparkContext: Starting job: count at <console>:1
...
14/05/12 21:19:06 INFO SparkContext: Job finished:  
count at <console>:18, took 0.192536694 s
res7: Long = 154563
scala> val logfile = sc.textFile("logs/access_log").cache
scala> val biapps11g = logfile.filter(line => line.contains("/biapps11g/"))
biapps11g: org.apache.spark.rdd.RDD[String] = FilteredRDD[34] at filter at <console>:17
scala> biapps11g.count()
...
14/05/12 21:28:28 INFO SparkContext: Job finished: count at <console>:20, took 0.387960876 s
res9: Long = 403

Accompanied by Innovations in Underlying Platform
Cluster Resource Management to 
support multi-tenant distributed services
In-Memory Distributed Storage, 
to accompany In-Memory Distributed Processing

•Most Oracle DWs process data in batches (or at best, micro-batches)

•Tools like ODI typically work in this way,  
often linking up with database CDC

•Hadoop systems are usually real-time, from the start

‣In the past, via Hadoop streaming, Flume etc

‣Batch loading then added for initial data load into system
Combining Real-Time Processing with Real-Time Loading
Hadoop
Node
Voice + Chat
Transcripts
Call Center LogsChat Logs iBeacon Logs Website Logs
Real-time Feeds
Raw Data

•Apache Flume is the standard way to transport log files from source through to target

‣Initial use-case was webserver log files, but can transport any file from A>B

‣Does not do data transformation, but can send to multiple targets / target types

‣Mechanisms and checks to ensure successful transport of entries

•Has a concept of “agents”, “sinks” and “channels”

‣Agents collect and forward log data

‣Sinks store it in final destination

‣Channels store log data en-route

•Simple configuration through INI files

‣Handled outside of ODI12c
Apache Flume : Distributed Transport for Log Activity

•Oracle GoldenGate is also an option, for streaming RDBMS transactions to Hadoop

•Leverages GoldenGate & HDFS / Hive Java APIs

•Sample Implementations on MOS Doc.ID 1586210.1 (HDFS) and 1586188.1 (Hive)

•Likely to be formal part of GoldenGate in future release - but usable now

•Can also integrate with Flume for delivery to HDFS - see MOS Doc.ID 1926867.1
GoldenGate for Continuous Streaming to Hadoop

•Developed by LinkedIn, designed to address Flume issues around reliability, throughput

‣(though many of those issues have been addressed since)

•Designed for persistent messages as the common use case

‣Website messages, events etc vs. log ﬁle entries

•Consumer (pull) rather than Producer (push) model

•Supports multiple consumers per message queue

•More complex to set up than Flume, and can use 
Flume as a consumer of messages

‣But gaining popularity, especially  
alongside Spark Streaming
Apache Kafka : Reliable, Message-Based

•Add mid-stream processing to ingestion process

•Sessionization, classiﬁcation, more complex transformation and ref data lookup

•Access to machine learning algorithms using MLib

‣Example implementation at: 
http://blog.cloudera.com/blog/2014/11/how-to-do-near- 
real-time-sessionization-with-spark-streaming-and- 
apache-hadoop/
Adding Real-Time Processing to Loading : Spark Streaming

Hadoop development
is only for Java programmers
…isn’t it?
but …

T : +44 (0) 1273 911 268 (UK) or (888) 631-1410 (USA) or  
+61 3 9596 7186 (Australia & New Zealand) or +91 997 256 7970 (India)
E : info@rittmanmead.com
W : www.rittmanmead.com
SQL Increasingly Used in Hadoop for Data Access

•Cloudera’s answer to Hive query response time issues

•MPP SQL query engine running on Hadoop, bypasses MapReduce
for direct data access

•Mostly in-memory, but spills to disk if required

•Uses Hive metastore to access Hive table metadata

•Similar SQL dialect to Hive - not as rich though and no support for
Hive SerDes, storage handlers etc
Cloudera Impala - Fast, MPP-style Access to Hadoop Data

•A replacement for Hive, but uses Hive concepts and 
data dictionary (metastore)

•MPP (Massively Parallel Processing) query engine 
that runs within Hadoop

‣Uses same ﬁle formats, security, 
resource management as Hadoop

•Processes queries in-memory

•Accesses standard HDFS ﬁle data

•Option to use Apache AVRO, RCFile, 
LZO or Parquet (column-store)

•Designed for interactive, real-time 
SQL-like access to Hadoop
How Impala Works
Impala
Hadoop
HDFS etc
BI Server
Presentation Svr
Cloudera Impala 
ODBC Driver
Impala
Hadoop
HDFS etc
Impala
Hadoop
HDFS etc
Impala
Hadoop
HDFS etc
Impala
Hadoop
HDFS etc
Multi-Node 
Hadoop Cluster

•Log into Impala Shell, run INVALIDATE METADATA command to refresh Impala table list

•Run SHOW TABLES Impala SQL command to view tables available

•Run COUNT(*) on main ACCESS_PER_POST table to see typical response time
Enabling Hive Tables for Impala
[oracle@bigdatalite ~]$ impala-shell
Starting Impala Shell without Kerberos authentication
[bigdatalite.localdomain:21000] > invalidate metadata;
Query: invalidate metadata
Fetched 0 row(s) in 2.18s
[bigdatalite.localdomain:21000] > show tables;
Query: show tables
+-----------------------------------+
| name |
+-----------------------------------+
| access_per_post |
| access_per_post_cat_author |
| … |
| posts |
|——————————————————————————————————-+
[bigdatalite.localdomain:21000] > select count(*)  
from access_per_post;
Query: select count(*) from access_per_post
+----------+
| count(*) |
+----------+
| 343 |
+----------+

•Signiﬁcant improvement over Hive response time

•Now makes Hadoop suitable for ad-hoc querying
Significantly-Improved Ad-Hoc Query Response Time vs Hive
|
Logical Query Summary Stats: Elapsed time 2, Response time 1, Compilation time 0 (seconds)
Logical Query Summary Stats: Elapsed time 50, Response time 49, Compilation time 0 (seconds)
Simple Two-Table Join against Hive Data Only
Simple Two-Table Join against Impala Data Only
vs

•Part of Oracle Big Data 4.0 (BDA-only)

‣Also requires Oracle Database 12c, Oracle Exadata Database Machine

•Extends Oracle Data Dictionary to cover Hive

•Extends Oracle SQL and SmartScan to Hadoop

•Extends Oracle Security Model over Hadoop

‣Fine-grained access control

‣Data redaction, data masking

‣Uses fast c-based readers where possible 
(vs. Hive MapReduce generation)

‣Map Hadoop parallelism to Oracle PQ

‣Big Data SQL engine works on top of YARN

‣Like Spark, Tez, MR2
Oracle Big Data SQL
Exadata 
Storage Servers
Hadoop 
Cluster
Exadata Database 
Server
Oracle Big 
Data SQL
SQL Queries
SmartScan SmartScan

•Oracle Database 12c 12.1.0.2.0 with Big Data SQL option can view Hive table metadata

‣Linked by Exadata conﬁguration steps to one or more BDA clusters

•DBA_HIVE_TABLES and USER_HIVE_TABLES exposes Hive metadata

•Oracle SQL*Developer 4.0.3, with Cloudera Hive drivers, can connect to Hive metastore
View Hive Table Metadata in the Oracle Data Dictionary
SQL> col database_name for a30
SQL> col table_name for a30
SQL> select database_name, table_name
2 from dba_hive_tables;
DATABASE_NAME TABLE_NAME
------------------------------ ------------------------------
default access_per_post
default access_per_post_categories
default access_per_post_full
default apachelog
default categories
default countries
default cust
default hive_raw_apache_access_log

•Big Data SQL accesses Hive tables through external table mechanism

‣ORACLE_HIVE external table type imports Hive metastore metadata

‣ORACLE_HDFS requires metadata to be speciﬁed

•Access parameters cluster and tablename specify Hive table source and BDA cluster
Hive Access through Oracle External Tables + Hive Driver
CREATE TABLE access_per_post_categories(
hostname varchar2(100),
request_date varchar2(100),
post_id varchar2(10),
title varchar2(200),
author varchar2(100),
category varchar2(100),
ip_integer number)
organization external
(type oracle_hive
default directory default_dir
access parameters(com.oracle.bigdata.tablename=default.access_per_post_categories));

•Brings query-oﬄoading features of Exadata 
to Oracle Big Data Appliance

•Query across both Oracle and Hadoop sources

•Intelligent query optimisation applies SmartScan 
close to ALL data

•Use same SQL dialect across both sources

•Apply same security rules, policies,  
user access rights across both sources
Extending SmartScan, and Oracle SQL, Across All Data

•SQL query engine that doesn’t require a formal (HCatalog) schema

•Infers the schema from the semi-structured dataset (JSON etc)

‣Allows users to analyze data without any ETL or up-front schema definitions.

‣Data can be in any file format such as text, JSON, or Parquet

‣Improved agility and flexibility 
vs formal modelling in Hive etc
Apache Drill
0: jdbc:drill:zk=local> select state, city, count(*) totalreviews
from dfs.`/<path-to-yelp-dataset>/yelp/yelp_academic_dataset_business.json`
group by state, city order by count(*) desc limit 10;
+------------+------------+--------------+
| state | city | totalreviews |
+------------+------------+--------------+
| NV | Las Vegas | 12021 |
| AZ | Phoenix | 7499 |
| AZ | Scottsdale | 3605 |
| EDH | Edinburgh | 2804 |
| AZ | Mesa | 2041 |
| AZ | Tempe | 2025 |
| NV | Henderson | 1914 |
| AZ | Chandler | 1637 |
| WI | Madison | 1630 |
| AZ | Glendale | 1196 |
+------------+------------+--------------+

•Addition of Spark as a back-end execution engine for Hive (and Pig)

•Has the advantage of making use of all existing Hive scripts, infrastructure

•But … probably is even more of a dead-end than Tez

‣Is still faster than Hive on MR

‣But Hive with column/in-memory optimized 
storage is now typically CPU bound

‣Spark consumes more CPU, Disk  
& Network IO than Tez

‣Additional translation overhead from  
RDDs to Hive’s “Row Containers”
Hive-on-Spark (and Pig-on-Spark)

•Spark SQL, and Data Frames, allow RDDs in Spark to be processed using SQL queries

•Bring in and federate additional data from JDBC sources

•Load, read and save data in Hive, Parquet and other structured tabular formats
Spark SQL - Adding SQL Processing to Apache Spark
val accessLogsFilteredDF = accessLogs
.filter( r => ! r.agent.matches(".*(spider|robot|bot|slurp).*"))
.filter( r => ! r.endpoint.matches(".*(wp-content|wp-admin).*")).toDF()
.registerTempTable("accessLogsFiltered")
val topTenPostsLast24Hour = sqlContext.sql("SELECT p.POST_TITLE, p.POST_AUTHOR, COUNT(*)  
as total  
FROM accessLogsFiltered a  
JOIN posts p ON a.endpoint = p.POST_SLUG  
GROUP BY p.POST_TITLE, p.POST_AUTHOR  
ORDER BY total DESC LIMIT 10 ")
// Persist top ten table for this window to HDFS as parquet file
topTenPostsLast24Hour.save("/user/oracle/rm_logs_batch_output/topTenPostsLast24Hour.parquet" 
, "parquet", SaveMode.Overwrite)

Choosing the Appropriate SQL Engine to Add to Hadoop

•Beginners usually store data in HDFS using text ﬁle formats (CSV) but these have limitations

•Apache AVRO often used for general-purpose processing

‣Splitability, schema evolution, in-built metadata, support for block compression

•Parquet now commonly used with Impala due to column-orientated storage

‣Mirrors work in RDBMS world around column-store

‣Only return (project) the columns you require across a wide table
Apache Parquet - Column-Orientated Storage for Analytics

•But Parquet (and HDFS) have signiﬁcant limitation for real-time analytics applications

‣Append-only orientation, focus on column-store  
makes streaming ingestion harder

•Cloudera Kudu aims to combine best of HDFS + HBase

‣Real-time analytics-optimised

‣Supports updates to data

‣Fast ingestion of data

‣Accessed using SQL-style tables 
and get/put/update/delete API
Cloudera Kudu - Combining Best of HBase and Column-Store

Hadoop is insecure
and has fragmented security
…doesn’t it?
but …

Consistent Security and Audit Now Emerging on Platform

•Clusters by default are unsecured (vunerable to account spooﬁng) & need Kerberos enabled

•Data access controlled by POSIX-style permissions on HDFS ﬁles

•Hive and Impala can Apache Sentry RBAC

‣Result is data duplication and complexity

‣No consistent API or abstracted security model
Hadoop Security Initially Was a Mess
/user/mrittman/scratchpad
/user/ryeardley/scratchpad
/user/mpatel/scratchpad
/data/rm_website_analysis/logfiles/incoming
/data/rm_website_analysis/logfiles/archive
/data/rm_website_analysis/tweets/incoming
/data/rm_website_analysis/tweets/archive

•Use standard Oracle Security over Hadoop & NoSQL

‣Grant & Revoke Privileges

‣Redact Data

‣Apply Virtual Private Database

‣Provides Fine-grain Access Control

•Great solution to extend existing Oracle 
security model over Hadoop datasets
Oracle Big Data SQL : Extend Oracle Security to Hadoop
Redacted
data
subset
SQL
JSON
Customer data
in Oracle DB
DBMS_REDACT.ADD_POLICY(
object_schema => 'txadp_hive_01',
object_name => 'customer_address_ext',
column_name => 'ca_street_name',
policy_name => 'customer_address_redaction',
function_type => DBMS_REDACT.RANDOM,
expression => 'SYS_CONTEXT(''SYS_SESSION_ROLES'',  
''REDACTION_TESTER'')=''TRUE'''
);

•Provides a higher level, logical abstraction for data (ie Tables or Views)

‣Can be used with Spark & Spark SQL, with Predicate pushdown, projection

•Returns schemed objects (instead of paths and bytes) in similar way to HCatalog

•Uniﬁed data access path allows platform-wide performance improvements

•Secure service that does not execute arbitrary user code

‣Central location for all authorization checks using Sentry metadata.
Cloudera RecordService

any predictive modelling
has to be done outside Hadoop, in R
…doesn’t it?
but …

•Part of Spark, extends Scala, Java & Python API

•Integrated workflow including ML pipelines

•Currently supports following algorithms:

‣Binary classification

‣Regression

‣Clustering

‣Collaborative filtering

‣Dimensionality Reduction
Spark MLLib : Adding Machine Learning Capabilities to Spark
// Compute raw scores on the test set.
val scoreAndLabels = test.map { point =>
val score = model.predict(point.features)
(score, point.label)
}
// Get evaluation metrics.
val metrics = new BinaryClassificationMetrics(scoreAndLabels)
val auROC = metrics.areaUnderROC()
println("Area under ROC = " + auROC)
// Save and load model
model.save(sc, "myModelPath")
val sameModel = SVMModel.load(sc, "myModelPath")

•Data enrichment tool aimed at domain experts, not programmers

•Uses machine-learning to automate  
data classification + profiling steps

•Automatically highlight sensitive data, 
and offer to redact or obfuscate

•Dramatically reduce the time required 
to onboard new data sources

•Hosted in Oracle Cloud for zero-install

‣File upload and download from browser

‣Automate for production data loads
Raw Data
Data stored in the
original format (usually
files) such as SS7, ASN.
1, JSON etc.
Mapped Data
Data sets produced by
mapping and
transforming raw data
Voice + Chat
Transcripts
Example Usage : Oracle Big Data Preparation Cloud Service

Identifying Schemas in Semi-/Unstructured Data

Use of Machine Learning to Identify Data Patterns
•Automatically profile, parse and classify incoming datasets using Spark MLLib Word2Vec
•Spot and obfuscate sensitive data automatically, automatically suggest column names

•Hadoop is evolving

‣Hadoop 2.0 breaks the dependency on MapReduce

‣Spark, Tez etc allow us to create execution plans that  
run in-memory, faster than before

‣New streaming models allow us to process data  
via sockets, micro batches or continuously

•And Oracle developers can make use of these new capabilities

‣Oracle Big Data SQL can access Hadoop data loaded in real-time

‣OBIEE, particularly in 11.1.1.9, can access Impala

‣ODI is likely to support Hive on Tez and Hive on Spark shortly,  
and will have support for Spark in the future
Summary

Big Data for Oracle Devs -  
Towards Spark, Real-Time and Predictive Analytics
Mark Rittman, CTO, Rittman Mead
Riga Dev Day 2016, Riga, March 2016

Big Data for Oracle Devs - Towards Spark, Real-Time and Predictive Analytics

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