BIWA2015 - Bringing Oracle Big Data SQL to OBIEE and ODI

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Bringing Oracle Big Data SQL 
to OBIEE 11g and ODI12c 
Mark Rittman, CTO, Rittman Mead
BIWA Summit 2015, January 2015, San Francisco

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About the Speaker
•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

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About Rittman Mead
•Oracle BI and DW Gold partner
•Winner of five UKOUG Partner of the Year awards in 2013 - including BI
•World leading specialist partner for technical excellence,  
solutions delivery and innovation in Oracle BI
•Approximately 80 consultants worldwide
•All expert in Oracle BI and DW
•Offices in US (Atlanta), Europe, Australia and India
•Skills in broad range of supporting Oracle tools:
‣OBIEE, OBIA
‣ODIEE
‣Essbase, Oracle OLAP
‣GoldenGate
‣Endeca

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Introducing Hadoop
•A new approach to data processing and data storage
•Rather than a small number of large, powerful servers, it spreads processing over 
large numbers of small, cheap, redundant servers
•Spreads the data you’re processing over  
lots of distributed nodes
•Has scheduling/workload process that sends  
parts of a job to each of the nodes 
- a bit like Oracle Parallel Execution
•And does the processing where the data sits  
- a bit like Exadata storage servers
•Shared-nothing architecture
•Low-cost and highly horizontal scalable
Job Tracker
Task Tracker Task Tracker Task Tracker Task Tracker
Data Node Data Node Task Tracker Task Tracker

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Apache Hive : SQL Access + Table Metadata Over HDFS
•Apache Hive provides a SQL layer over Hadoop, once we understand the structure (schema)
of the data we’re working with
•Exposes HDFS and other Hadoop data as tables and columns
•Provides a simple SQL dialect for queries called HiveQL
•SQL queries are turned into MapReduce jobs under-the-covers
•JDBC and ODBC drivers provide 
access to BI and ETL tools
•Hive metastore (data dictionary) 
leveraged by many other Hadoop tools
‣Apache Pig
‣Cloudera Impala
‣etc
SELECT a, sum(b) 
FROM myTable 
WHERE a<100 
GROUP BY a
Map 
Task
Map 
Task
Map 
Task
Reduce 
Task
Reduce 
Task
Result

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Hive SerDes & Storage Handlers
•Plug-in technologies that extend Hive to handle new data formats and semi-structured sources
•Typically distributed as JAR files, hosted on sites such as GitHub
•Can be used to parse log files, access data in NoSQL databases, Amazon S3 etc
CREATE EXTERNAL TABLE apachelog (
host STRING,
identity STRING,
user STRING,
time STRING,
request STRING,
status STRING,
size STRING,
referer STRING,
agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
"input.regex" = "([^ ]*) ([^ ]*) ([^ ]*) (-|[[^]]*])  
([^ "]*|"[^"]*") (-|[0-9]*) (-|[0-9]*)(?: ([^ "]*|"[^"]*")  
([^ "]*|"[^"]*"))?",
"output.format.string" = "%1$s %2$s %3$s %4$s %5$s %6$s %7$s %8$s %9$s"
)
STORED AS TEXTFILE
LOCATION '/user/root/logs';
CREATE TABLE tweet_data(
interactionId string,
username string,
content string,
author_followers int)
ROW FORMAT SERDE
'com.mongodb.hadoop.hive.BSONSerDe'
STORED BY
'com.mongodb.hadoop.hive.MongoStorageHandler'
WITH SERDEPROPERTIES (
'mongo.columns.mapping'='{"interactionId":"interactionId",
"username":"interaction.interaction.author.username",
"content":"interaction.interaction.content",
"author_followers_count":"interaction.twitter.user.followers_count"}'
)
TBLPROPERTIES (
'mongo.uri'='mongodb://cdh51-node1:27017/datasiftmongodb.rm_tweets'
)

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Oracle’s Big Data Products
•Oracle Big Data Appliance
‣Optimized hardware for Hadoop processing
‣Cloudera Distribution incl. Hadoop
‣Oracle Big Data Connectors, ODI etc
•Oracle Big Data Connectors
•Oracle NoSQL Database
•Oracle Data Integrator
•Oracle R Distribution
•OBIEE, BI Publisher and  
Endeca Info Discovery

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Oracle Big Data Connectors
•Oracle-licensed utilities to connect Hadoop to Oracle RBDMS
‣Bulk-extract data from Hadoop to Oracle, or expose HDFS / Hive data as external tables
‣Run R analysis and processing on Hadoop
‣Leverage Hadoop compute resources to offload ETL and other work from Oracle RBDMS
‣Enable Oracle SQL to access and load Hadoop data

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Oracle Business Analytics and Big Data Sources
•OBIEE 11g can also make use of big data sources
‣OBIEE 11.1.1.7+ supports Hive/Hadoop as a data source, via specific Hive ODBC drivers 
and Apache Hive Physical Layer database type
‣But practically, it comes with limitations
‣Current 11.1.1.7 version of OBIEE only ships with HiveServer1 ODBC drivers
‣HiveQL is a limited subset of ISO/Oracle SQL
‣… and Hive access is really slow

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Oracle Big Data SQL
•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
Exadata 
Storage Servers
Hadoop 
Cluster
Exadata Database 
Server
Oracle Big 
Data SQL
SQL Queries
SmartScan SmartScan

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View Hive Table Metadata in the Oracle Data Dictionary
•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
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

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Big Data SQL Server Dataflow
•Read data from HDFS Data Node
‣Direct-path reads
‣C-based readers when possible
‣Use native Hadoop classes otherwise 
•Translate bytes to Oracle 
•Apply SmartScan to Oracle bytes
‣Apply filters
‣Project columns
‣Parse JSON/XML
‣Score models Disks%
Data$Node$
Big$Data$SQL$Server$
External$Table$Services$
Smart$Scan$
RecordReader%
SerDe%
10110010%10110010%10110010%
1%
2%
3%
1
2
3

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Hive Access through Oracle External Tables + Hive Driver
•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
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));

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Use Rich Oracle SQL Dialect over Hadoop (Hive) Data
•Ranking Functions
‣rank, dense_rank, cume_dist,
percent_rank, ntile
•Window Aggregate Functions
‣Avg, sum, min, max, count, variance,
first_value, last_value
•LAG/LEAD Functions
•Reporting Aggregate Functions
‣Sum, Avg, ratio_to_report
•Statistical Aggregates
‣Correlation, linear regression family,
covariance
•Linear Regression
‣Fitting of ordinary-least-squares
regression line to set of number pairs
•Descriptive Statistics
•Correlations
‣Pearson’s correlation coefficients
•Crosstabs
‣Chi squared, phi coefficinet
•Hypothesis Testing
‣Student t-test, Bionomal test
•Distribution
‣Anderson-Darling test - etc.

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Leverages Hive Metastore for Hadoop Java Access Classes
•As with other next-gen SQL access layers, uses common Hive metastore table metadata
•Provides route to underlying Hadoop data for Oracle Big Data SQL c-based SmartScan

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Extending SmartScan, and Oracle SQL, Across All Data
•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

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Example : Using Big Data SQL to Add Dimensions to Hive Data
•We want to add country and post details to a Hive table containing page accesses
•Post and Country details are stored in Oracle RBDMS reference tables
Hive Weblog Activity table
Oracle Dimension lookup tables
Combined output 
in report form

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Create ORACLE_HIVE External Table over Hive Table
•Use the ORACLE_HIVE access driver type to create Oracle external table over Hive table
•ACCESS_PER_POST_EXTTAB now appears in Oracle data dictionary

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Import Oracle Tables, Create RPD joining Tables Together
•No need to use Hive ODBC drivers - Oracle OCI connection instead
•No issue around HiveServer1 vs HiveServer2; also Big Data SQL handles authentication 
with Hadoop cluster in background, Kerberos etc
•Transparent to OBIEE - all appear as Oracle tables
•Join across schemas if required

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Create Physical Data Model from Imported Table Metadata
•Join ORACLE_HIVE external table containing log data, to reference tables from Oracle DB

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Create Business Model and Presentation Layers
•Map incoming physical tables into a star schema
•Add aggregation method for fact measures
•Add logical keys for logical dimension tables
•Remove columns from fact table that aren’t measures

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Create Initial Analyses Against Combined Dataset
•Create analyses using 
full SQL features
•Access to Oracle RDBMS 
Advanced Analytics functions 
through EVALUATE, 
EVALUATE_AGGR etc
•Big Data SQL SmartScan feature 
provides fast, ad-hoc access 
to Hive data, avoiding MapReduce

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Oracle / Hive Query Federation at the RDBMS Level
•Oracle Big Data SQL feature (not BI Server) takes care of query federation
•SQL required for fact table (web log activity) access sent to Big Data SQL agent on BDA
•Only columns (projection) and rows (filtering) required to answer query sent back to Exadata
•Storage Indexes used on both Exadata Storage Servers and BDA nodes to skip block reads
for irrelevant data
•HDFS caching used to speed-up 
access to commonly-used 
HDFS data

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Access to Full Set of Oracle Join Types
•No longer restricted to HiveQL equi-joins - Big Data SQL supports all Oracle join operators
•Use to join Hive data (using View over external table) to a IP range country lookup table 
using BETWEEN join operator

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Add In Time Dimension Table
•Enables time-series reporting; pre-req for forecasting (linear regression-type queries)
•Map to Date field in view over ORACLE_HIVE table
‣Convert incoming Hive STRING field to Oracle DATE for better time-series manipulation

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Now Enables Time-Series Reporting Incl. Country Lookups

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Oracle Data Integrator 12c
•Oracle’s data integration tool for loading, transforming and integrating enterprise data
•Successor to Oracle Warehouse Builder, part of wider Oracle DI platform
•Connectivity to most RBDMS, file and application sources

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Part of the Wider Oracle Data Integration Platform
•Oracle Data Integrator for large-scale data integration across heterogenous sources and
targets
•Oracle GoldenGate for heterogeneous data replication and changed data capture
•Oracle Enterprise Data Quality for data profiling and cleansing
•Oracle Data Services Integrator 
for SOA message-based 
data federation

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Oracle Data Integrator on Hadoop
•ODI provides an excellent framework for running Hadoop ETL jobs
‣ELT approach pushes transformations down to Hadoop - leveraging power of cluster
•Hive, HBase, Sqoop and OLH/ODCH KMs provide native Hadoop loading / transformation
‣Whilst still preserving RDBMS push-down
‣Extensible to cover Pig, Spark etc
•Process orchestration
•Data quality / error handling
•Metadata and model-driven

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Combining Oracle and Hadoop (Hive) Data in Mappings
•Example scenario : log data in Hadoop needs to be enriched with customer data in Oracle
‣Hadoop (Hive) contains log activity and customer etc IDs
‣Reference / customer data held in Oracle RBDMS
•How do we create a mapping that joins both datasets?
movieapp_log_odistage.custid = CUSTOMER.CUSTID

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Options for Importing Oracle / RDBMS Data into Hadoop
•Could export RBDMS data to file, and load using
IKM File to Hive
•Oracle Big Data Connectors only export to Oracle,
not import to Hadoop
•One option is to use Apache Sqoop, and new  
IKM SQL to Hive-HBase-File knowledge module
•Hadoop-native, automatically runs in parallel
•Uses native JDBC drivers, or OraOop (for
example)
•Bi-directional in-and-out of Hadoop to RDBMS
•Join performed in Hive, using HiveQL
‣With HiveQL limitations (only equi-joins)
movieapp_log_odistage.custid =  
customer.custid
Sqoop extract

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New Option - Using Oracle Big Data SQL
•Oracle Big Data SQL provides ability for Exadata to reference Hive tables
•Use feature to create join in Oracle, bringing across Hive data through ORACLE_HIVE table

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Oracle Big Data SQL and Data Integration
•Gives us the ability to easily bring in Hadoop (Hive) data into Oracle-based mappings
•Allows us to create Hive-based mappings that use Oracle SQL for transforms, joins
•Faster access to Hive data for real-time ETL scenarios
•Through Hive, bring NoSQL and semi-structured data access to Oracle ETL projects
•For our scenario - join weblog + customer data in Oracle RDBMS, no need to stage in Hive

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Using Big Data SQL in an ODI12c Mapping
•By default, Hive table has to be exposed as an ORACLE_HIVE external table in Oracle first
•Then register that Oracle external table in ODI repository + model
External table creation in Oracle
Logical Mapping using just Oracle tables
1
2
Register in ODI Model
3

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Custom KM : LKM Hive to Oracle (Big Data SQL)
•ODI12c Big Data SQL example on BigDataLite VM uses a custom KM for Big Data SQL
‣LKM Hive to Oracle (Big Data SQL) - KM code downloadable from java.net
‣Allows Hive+Oracle joins by auto-creating ORACLE_HIVE extttab  
definition to enable Big Data SQL Hive table access

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ODI12c Mapping Creates Temp Exttab, Joins to Oracle
1
2
Register in ODI Model
3
4
Hive table AP uses LKM Hive to Oracle (Big Data SQL)
IKM Oracle Insert
Big Data SQL Hive External Table created as temp object
Main integration SQL routines uses regular Oracle SQL join

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Summary
•Oracle Big Data SQL extends Exadata capabilities over Hadoop and Hive
•Makes Hive Metastore visible through Oracle Data Dictionary
•Register Hive tables as ORACLE_HIVE external tables and include in Oracle SQL queries
•Used with OBIEE, allows RPDs to be created across both Oracle + Hive data, with 
query federation handled by Oracle RBDMS rather than BI Server
•Enables use of Oracle Advanced Analytics functions over Hadoop data
•Useful for ODI as way of using full set of join operators on Hive data, and simplifying 
the addition of Hive data to Oracle mappings
•For developers working with Exadata + BDA, useful addition to the data access toolkit

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Thank You for Attending!
•Thank you for attending this presentation, and more information can be found at http://
www.rittmanmead.com
•Contact us at info@rittmanmead.com or mark.rittman@rittmanmead.com
•Look out for our book, “Oracle Business Intelligence Developers Guide” out now!
•Follow-us on Twitter (@rittmanmead) or Facebook (facebook.com/rittmanmead)

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