The document discusses the future of analytics, data integration, and business intelligence (BI) on big data platforms like Hadoop. It covers how BI has evolved from old-school data warehousing to enterprise BI tools to utilizing big data platforms. New technologies like Impala, Kudu, and dataflow pipelines have made Hadoop fast and suitable for analytics. Machine learning can be used for automatic schema discovery. Emerging open-source BI tools and platforms, along with notebooks, bring new approaches to BI. Hadoop has become the default platform and future for analytics.

1. Mark Rittman, Oracle ACE Director
THE FUTURE OF ANALYTICS, DATA INTEGRATION
AND BI ON BIG DATA PLATFORMS
HADOOP USER GROUP IRELAND (HUG IRL)
Dublin, September 2016

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
2

3. OR AS I SAY AT PARTIES…
3

4. 4

5. BUT SERIOUSLY…
5

6. •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 Old-school BI & Data Warehousing
6

7. •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
7
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
7

8. •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…
8 8

9. •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
10 10

10. THEN CAME … BIG DATA
11

11. AND HADOOP
13

12. BIG, FAST AND FAULT-TOLERANT
14

13. •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
But Why Hadoop? Reason #1 - Flexible Storage
16
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

14. •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
But Why Hadoop? Reason #2 - Massive Scalability
17

15. •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
Reason #3 - Processing Frameworks
18
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

16. •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 Lake”
19
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

17. NEW STARTUPS ENABLING A HYBRID
“OLD WORLD/NEW WORLD” APPROACH
20

18. AND PERFECT FOR ANALYTICS
22

19. •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
Hadoop : The Default Platform Today for Analytics
23

20. BI INNOVATION IS HAPPENING
AROUND HADOOP
24

21. “WE’RE WINNING!”
27

23. BUT…
29

24. isn’t Hadoop Slow?

25. too slow
for ad-hoc querying?

26. WELCOME TO 2016
32

29. (HADOOP 2.0)
35

31. HADOOP IS NOW FAST
37

32. Hadoop 2.0 Processing Frameworks + Tools
38

33. •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
39

34. •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
Parquet - Column-Orientated Storage for Analytics
40

35. •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 - Best of HBase and Column-Store
41

36. •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
42
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;

37. AND IT’S NOW IN-MEMORY
43

39. Accompanied by Innovations in Underlying Platform
45
Cluster Resource Management to
support mulJ-tenant distributed services
In-Memory Distributed Storage,
to accompany In-Memory Distributed Processing

40. DATAFLOW PIPELINES
ARE THE NEW ETL
46

41. New ways to do BI

42. New ways to do BI

43. HADOOP IS THE NEW ETL ENGINE
49

44. 50Copyright © 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

45. MACHINE LEARNING & SEARCH FOR
“AUTOMAGIC” SCHEMA DISCOVERY
51

46. New ways to do BI

47. •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
53

48. A NEW TAKE ON BI
54

49. •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
55

50. AND EMERGING OPEN-SOURCE
BI TOOLS AND PLATFORMS
57

51. And Emerging Open-Source
BI Tools and Platforms
wp-content/uploads/2016/05/paper.pdf

53. And Emerging Open-Source
BI Tools and Platforms

54. WELCOME TO THE FUTURE
62

55. Mark Rittman, Oracle ACE Director
THE FUTURE OF ANALYTICS, DATA INTEGRATION
AND BI ON BIG DATA PLATFORMS
HADOOP USER GROUP IRELAND (HUG IRL)
Dublin, September 2016