From lots of reports (with some data Analysis)  to Massive Data Analysis (With some Reporting)

EVENT SPEAKER
DANISH BI MEETUP, SEP’ 2016
FROM LOTS OF REPORTS (WITH SOME DATA ANALYSIS)  
TO MASSIVE DATA ANALYSIS (WITH SOME REPORTING)
MARK RITTMAN, ORACLE ACE DIRECTOR

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

•Started back in 1996 on a bank Oracle DW project

•Our tools were Oracle 7.3.4, SQL*Plus, PL/SQL  
and shell scripts

•Went on to use Oracle Developer/2000 and Designer/2000

•Our initial users queried the DW using SQL*Plus

•And later on, we rolled-out Discoverer/2000 to everyone else

•And life was fun…
20 Years in Oracle BI and Data Warehousing

•Data warehouses provided a uniﬁed view of the business

‣Single place to store key data and metrics

‣Joined-up view of the business

‣Aggregates and conformed dimensions

‣ETL routines to load, cleanse and conform data

•BI tools for simple, guided access to information

‣Tabular data access using SQL-generating tools

‣Drill paths, hierarchies, facts, attributes

‣Fast access to pre-computed aggregates

‣Packaged BI for fast-start ERP analytics
Data Warehouses and Enterprise BI Tools
Oracle
MongoDB
Oracle
Sybase
IBM DB/2
MS SQL
MS SQL Server
Core ERP Platform
Retail
Banking
Call Center
E-Commerce
CRM
 
Business
Intelligence
Tools
 
Data Warehouse
Access & 
Performance 
Layer
ODS / 
Foundation 
Layer
4

•Examples were Crystal Reports, Oracle Reports, Cognos Impromptu, Business Objects

•Report written against carefully-curated BI dataset, or directly connecting to ERP/CRM

•Adding data from external sources, or other RDBMSs, 
was diﬃcult and involved IT resources

•Report-writing was a skilled job

•High ongoing cost for maintenance and changes

•Little scope for analysis, predictive modeling

•Often user frustration and pace of delivery
Reporting Back Then…
5

•For example Oracle OBIEE, SAP Business Objects, IBM Cognos

•Full-featured, IT-orientated enterprise BI platforms

•Metadata layers, integrated security, web delivery

•Pre-build ERP metadata layers, dashboards + reports

•Federated queries across multiple sources

•Single version of the truth across the enterprise

•Mobile, web dashboards, alerts, published reports

•Integration with SOA and web services
Then Came Enterprise BI Tools
6

info@rittmanmead.com www.rittmanmead.com @rittmanmead
Traditional Three-Layer Relational Data Warehouses
Staging Foundation / 
ODS
Performance / 
Dimensional
ETL ETL
BI Tool (OBIEE) 
with metadata 
layer
OLAP / In-Memory 
Tool with data load 
into own database
Direct 
Read
Data 
Load
Traditional structured 
data sources
Data 
Load
Data 
Load
Data 
Load
Traditional Relational Data Warehouse
•Three-layer architecture - staging, foundation and access/performance
•All three layers stored in a relational database (Oracle)
•ETL used to move data from layer-to-layer

From lots of reports (with some data Analysis)  to Massive Data Analysis (With some Reporting)

Lots of reports  
(with some data analysis)

Meanwhile…
The world got digitised

Reporting and Dashboards…
became self-service  
data discovery

Advanced analytics for everyone

The Gartner BI & Analytics Magic Quadrant 2016

Analytic Workflow
Component
Traditional BI Platform Modern BI Platform
Data source
Upfront dimensional modeling required (IT-built
star schemas)
Upfront modeling not required (flat files/
flat tables)
Data ingestion and
preparation
IT-produced IT-enabled
Content authoring Primarily IT staff, but also some power users Business users
Analysis
Predefined, ad hoc reporting, based on
predefined model
Free-form exploration
Insight delivery
Distribution and notifications via scheduled
reports or portal
Sharing and collaboration, storytelling,
open APIs
Gartner’s View of A “Modern BI Platform” in 2016

2007 - 2015
Died of ingratitude by business users

Just when we got the infrastructure right

Doesn’t anyone appreciate a single version of the truth?

Don’t say we didn’t warn you

No you can’t just export it to Excel

Watch out OLAP you’re next

•Data now landed in Hadoop clusters, NoSQL databases and Cloud Storage

•Flexible data storage platform with cheap storage, ﬂexible schema support + compute

•Data lands in the data lake or reservoir in raw form, then minimally processed

•Data then accessed directly by “data scientists”, or processed further into DW
Meet the New Data Warehouse : The “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

Hadoop is the new  
Data Warehouse

Hadoop : The Default Platform Today for Analytics
•Enterprise High-End RDBMSs such as Oracle can scale into the petabytes, using clustering

‣Sharded databases (e.g. Netezza) can scale further but with complexity / single workload trade-oﬀs

•Hadoop was designed from outside for massive horizontal scalability - using cheap hardware

•Anticipates hardware failure and makes multiple copies of data as protection

•More nodes you add, more stable it becomes

•And at a fraction of the cost of traditional 
RDBMS platforms

•Data from new-world applications is not like historic data

•Typically comes in non-tabular form

•JSON, log files, key/value pairs

•Users often want it speculatively

•Haven’t thought it through

•Schema can evolve

•Or maybe there isn’t one

•But the end-users want it now

•Not when you’re ready
35
But Why Hadoop? Reason #1 - Flexible Storage
Big Data Management Platform
Discovery & Development Labs 
Safe & secure Discovery and Development environment
Data sets and
samples
Models and
programs
Single Customer View
Enriched  
Customer Profile
Correlating
Modeling
Machine 
Learning
Scoring
Schema-on 
Read Analysis

But Why Hadoop? Reason #2 - Massive Scalability
•Enterprise High-End RDBMSs such as Oracle can scale

‣Clustering for single-instance DBs can scale to >PB

‣Exadata scales further by offloading queries to storage

‣Sharded databases (e.g. Netezza) can scale further

‣But cost (and complexity) become limiting factors

‣Typically $1m/node is not uncommon

•Hadoop’s main design goal was to enable virtually-limitless horizontal scalability

•Rather than a small number of large, powerful servers, it spreads processing over 
large numbers of small, cheap, redundant servers

•Processes the data where it’s stored, avoiding I/O bottlenecks

•The more nodes you add, the more stable it becomes!

•At an affordable cost - this is key

•$50k/node vs. $1m/node
•And … the Hadoop platform is a better fit for 
new types of processing and analysis

Big Data Platform - All Running Natively Under Hadoop
YARN (Cluster Resource Management)
Batch 
(MapReduce)
HDFS (Cluster Filesystem holding raw data)
Interactive 
(Impala, Drill, 
Tez, Presto)
Streaming + 
In-Memory 
(Spark, Storm)
Graph + Search 
(Solr, Giraph)
Enriched  
Customer Profile
Modeling
Scoring
But Why Hadoop? Reason #3 - Processing Frameworks
•Hadoop started by being synonymous with MapReduce, and Java coding

•But YARN (Yet another Resource Negotiator) broke this dependency

•Modern Hadoop platforms provide overall cluster resource management, 
but support multiple processing frameworks

•General-purpose (e.g. MapReduce)

•Graph processing

•Machine Learning

•Real-Time Processing (Spark Streaming, Storm)

•Even the Hadoop resource management framework 
can be swapped out

•Apache Mesos

Combine With DW for Old-World/New-World Solution

•Most high-end RDBMS vendors provide connectors to load data in/out of Hadoop platforms

‣Bulk extract

‣External tables

‣Query federation

•Use high-end RDBMSs 
as specialist engines

•a.k.a. "Data Marts"
But … Analytic RDBMSs Are The New Data Mart
Discovery & Development Labs 
Safe & secure Discovery and Development environment
 
Data 
Warehouse
Curated data :
Historical view
and business
aligned access
 
Business
Intelligence
Tools
Big Data Management Platform
Data sets and
samples
Models and
programs
Big Data Platform - All Running Natively Under Hadoop
YARN (Cluster Resource Management)
Batch 
(MapReduce)
HDFS (Cluster Filesystem holding raw data)
Interactive 
(Impala, Drill, 
Tez, Presto)
Streaming + 
In-Memory 
(Spark, Storm)
Graph + Search 
(Solr, Giraph)
Enriched  
Customer Profile
Modeling
Scoring

BI Innovation is happening 
around Hadoop

too slow 
for ad-hoc querying?

Hadoop 2.0 Processing Frameworks + Tools

•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

•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

•Kudu storage used with Impala - create tables using Kudu storage handler

•Can now UPDATE, DELETE and INSERT into Hadoop tables, not just SELECT and LOAD DATA
Example Impala DDL + DML Commands with Kudu
CREATE TABLE `my_first_table` (
`id` BIGINT,
`name` STRING
)
TBLPROPERTIES(
'storage_handler' = 'com.cloudera.kudu.hive.KuduStorageHandler',
'kudu.table_name' = 'my_first_table',
'kudu.master_addresses' = 'kudu-master.example.com:7051',
'kudu.key_columns' = 'id'
);
INSERT INTO my_first_table VALUES (99, "sarah");
INSERT IGNORE INTO my_first_table VALUES (99, "sarah");
UPDATE my_first_table SET name="bob" where id = 3;
DELETE FROM my_first_table WHERE id < 3;
DELETE c FROM my_second_table c, stock_symbols s WHERE c.name = s.symbol;

•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

•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)

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

Dataflow Pipelines  
are the new ETL

Copyright © 2015, Oracle and/or its affiliates. All rights reserved. |
Proprietary ETL
engines die circa
2015 – folded into
big data
Oracle Open World 2015 21
Proprietary ETL is Dead. Apache-based ETL is What’s Next
Scripted
SQL
Stored
Procs
ODI for
Columnar
ODI for
In-Mem
ODI for
Exadata
ODI for
Hive
ODI for
Pig & Oozie
1990’s
Eon of Scripts and PL-SQL Era of SQL E-LT/Pushdown Big Data ETL in Batch Streaming ETL
Period of Proprietary Batch ETL Engines
Informatica
Ascential/IBM
Ab Initio
Acta/SAP
SyncSort
1994
Oracle Data Integrator
ODI for
Spark
ODI for
Spark Streaming
Warehouse
Builder

Machine Learning & Search for  
“Automagic” Schema Discovery

•By definition there's lots of data in a big data system ... so how do you find the data you want?

•Google's own internal solution - GOODS ("Google Dataset Search")

•Uses crawler to discover new datasets

•ML classification routines to infer domain

•Data provenance and lineage

•Indexes and catalogs 26bn datasets

•Other users, vendors also have solutions

•Oracle Big Data Discovery

•Datameer

•Platfora

•Cloudera Navigator
Google GOODS - Catalog + Search At Google-Scale

•Came out if the data science movement, as a way to
"show workings"

•A set of reproducible steps that tell a story about the data

•as well as being a better command-line environment for
data analysis

•One example is Jupyter, evolution of iPython notebook

•supports pySpark, Pandas etc

•See also Apache Zepplin
Web-Based Data Analysis Notebooks

Meanwhile
in the real world …
https://www.youtube.com/watch?v=h1UmdvJDEYY

And Emerging Open-Source 
BI Tools and Platforms

And Emerging Open-Source 
BI Tools and Platforms
http://larrr.com/wp-content/uploads/2016/05/paper.pdf

See an example in action:
https://speakerdeck.com/markrittman/oracle-big-data-discovery-extending-into-machine-learning-a-quantified-self-case-study

http://www.rittmanmead.com

From lots of reports (with some data Analysis)  to Massive Data Analysis (With some Reporting)

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