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Mark Rittman, Oracle ACE Director
NEW WORLD HADOOP ARCHITECTURES (& WHAT
PROBLEMS THEY REALLY SOLVE) FOR DBAS
UKOUG DATABASE SIG MEETING
London, February 2017
•Oracle ACE Director, Independent Analyst
•Past ODTUG Exec Board Member + Oracle Scene Editor
•Author of two books on Oracle BI
•Co-founder & CTO of Rittman Mead
•15+ Years in Oracle BI, DW, ETL + now Big Data
•Host of the Drill to Detail Podcast (www.drilltodetail.com)
•Based in Brighton & work in London, UK
About The Presenter
2
BACK IN FEBRUARY
3
“Hi Mark, In things I have seen and read quite o6en people
start with a high-level overview of a product (e.g. Hadoop,
Ka@a), then describe the technical concepts (using all the
appropriate terminology) …”
“but I am usually le6 missing something. I think it's around
the area of what problems these technologies are solving
and how they are doing it? Without that context I'm finding
it all very academic”
“Many people say tradiKonal systems will sKll be
needed. Are these new technologies solving completely
different problems to those handled by tradi=onal IT?
Is there an overlap?”
•Started back in 1996 on a bank Oracle DW project
•Our tools were Oracle 7.3.4, SQL*Plus, PL/SQL and shell scripts
•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
20 Years in Old-school BI & Data Warehousing
5
Data Warehousing and BI at “Peak Oracle”
7
Oracle Data Management Platform as of Today
8
What Happened?
10
Let’s Go Back to 2003…
•Google needed to store and query their vast amount of server log files
•And wanted to do so using cheap, commodity hardware
•Google File System and MapReduce designed together for this use
Google File System and MapReduce
12
•GFS optimised for particular task at hand -
computing PageRank for sites
•Streaming reads for PageRank calcs, block writes for
crawler whole-site dumps
•Master node only holds metadata
•Stops client/master I/O being bottleneck, also acts as
traffic controller for clients
•Simple design, optimised for specific Google Need
•MapReduce focused on simple computations on
abstraction framework
•Select & filter (MAP) and reduce (aggregate) functions,
easily to distribute on cluster
•MapReduce abstracted cluster compute, HDFS
abstracted cluster storage
•Projects that inspired Apache Hadoop + HDFS
Google File System + MapReduce Key Innovations
13
How Traditional RDBMS Data Warehousing Scaled-Up
14
Shared-Everything	Architectures	(i.e.	
Oracle	RAC,	Exadata)
Shared-Nothing	Architectures

(e.g.	Teradata,	Netezza)
Problem #1 That Hadoop / NoSQL Solved :
Scaling Affordably
“Oracle scales infinitely and is free. Period”
•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
Cost and Complexity around Scaling DW Clusters
17
•A way of storing (non-relational) data cheaply and easily expandable
•Gave us a way of scaling beyond TB-size without paying $$$
•First use-cases were offline storage, active archive of data
Hadoop’s Original Appeal to Data Warehouse Owners
18
(c) 2013
Hadoop Ecosystem Expanded Beyond MapReduce
19
•Core Hadoop, MapReduce and HDFS
•HBase and other NoSQL Databases
•Apache Hive and SQL-on-Hadoop
•Storm, Spark and Stream Processing
•Apache YARN and Hadoop 2.0
•Solution to the problem of storing semi-structured data at-scale
•Built on Google File System
•Scale for capacity e.g., webtable
•100,000,000,000 pages,
•10 versions per page,
•20 KB / version = 20 PB of data
•Scale for throughput
•Hundreds of millions of users
•Tens of thousands to millions of queries/sec
•At low-latency with high-reliability
Google BigTable, HBase and NoSQL Databases
20
•Optimised for a particular task - fast
lookups of ts-versioned web data
•Data stored in multidimensional map keyed
on row, column + timestamp
•Master + data tablets stored on GFS cluster
nodes
•Simple key/value lookup with client doing
interpretation
•Innovation - focus on single job with
different needs to OLTP
•Formed inspiration for Apache HBase
How BigTable Scaled Beyond Traditional RDBMSs
21
•Original developed at Facebook, now foundational within Hadoop
•SQL-like language that compiles to MapReduce, Spark, HBase
•Solved the problem of enabling non-programmers to access big data
•And made Hadoop data transformation and aggregation code more productive
•JDBC and ODBC drivers for tool integration
Hive - Hadoop Discovers Set-Based Processing
22
•Hive is extensible to help with accessing and integrating new data sets
•SerDes : Serializer-Deserializers that interpret semi-structured sources
•UDFs + Hive Streaming : User-defined functions and streaming input
•File Formats : make use of compressed and/or optimised file storage
•Storage Handlers : use storage other than HDFS (e.g. MongoDB)
Apache Hive as SQL Access Engine For Everything
23
•Hadoop as low-cost ETL pre-processing engine - “ETL-offload”
•NoSQL database for landing real-time data at high speed/low latency
•Incoming data then aggregated and stored in RBDMS DW
Common Hadoop/NoSQL Use-Case (c) 2014
24
MartsData Warehouse
Σ Σ
Business
Intelligence
• Online
• Scalable
• Flexible
• Cost
Effective
Hadoop
25
Jump Ahead to 2012…
•Driven by pace of business, and user demands for more agility and control
•Traditional IT-governed data loading not always appropriate
•Not all data needed to be modelled right-away
•Not all data suited storing in tabular form
•New ways of analyzing data beyond SQL
•Graph analysis
•Machine learning
Data Warehousing and ETL Needed Some Agility
29
Problem #2 That Hadoop / NoSQL Solved :
Making Data Warehousing Agile
•Storing data in format it arrived in, and then applying schema at query time
•Suits data that may be analysed in different ways by different tools
•In addition, some datatypes may have schema embedded in file format
•Key benefit - fast arriving data of unknown value can get to users earlier
•Made possible by tools such as Apache Hive + SerDes,

Apache Drill and self-describing file formats, HDFS storage
Advent of Schema-on-Read, and Data Lakes
31
•Data now landed in Hadoop clusters, NoSQL databases and Cloud Storage
•Flexible data storage platform with cheap storage, flexible schema support + compute
•Solves the problem of how to store new types of data + choose best time/way to process it
•Hadoop/NoSQL increasingly used for all store/transform/query tasks
Meet the New Data Warehouse : The “Data Lake”
32
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 2.0 and YARN

(“Yet Another Resource Negotiator”)
Key Innovation : Separating how data is stored,

from how it is processed
•Hadoop started by being synonymous with MapReduce, and Java coding
•But YARN (Yet another Resource Negotiator) broke this dependency
•Hadoop now just handles resource management
•Multiple different query engines can run against data in-place
•General-purpose (e.g. MapReduce)
•Graph processing
•Machine Learning
•Real-Time Processing
Hadoop 2.0 - Enabling Multiple Query Engines
35
Technologies Emerged to Bridge Old/New World
36
FAST FORWARD TO NOW…
37
•New generation of big data platform services from Google, Amazon, Oracle
•Combines three key innovations from earlier technologies:
•Organising of data into tables and columns (from RDBMS DWs)
•Massively-scalable and distributed storage and query (from Big Data)
•Elastically-scalable Platform-as-a-Service (from Cloud)
Elastically-Scalable Data Warehouse-as-a-Service
38
… Which Is What I’m Working On Right Now
39
Example Architecture : Google BigQuery
40
41
•On-premise Hadoop, even with simple resilient clustering, will hit limits
•Clusters can reach 5000+ nodes, need to scale-up for demand peaks etc
•Scale limits are encountered way beyond those for DWs…
•… but future is elastically-scaled, query and compute-as-a-service
What Problem Did Analytics-as-a-Service Solve?
42
Oracle	Big	Data	Cloud	Compute	Edition	
Free	$300	developer	credit	at:

https://cloud.oracle.com/en_US/tryit
•And things come full-circle … analytics
typically requires tabular data
•Google BigQuery based-on DremelX
massively-parallel query engine
•But stores data columnar and provides SQL
interface
•Solves the problem of providing DW-like
functionality at scale, as-a-service
•This is the future … ;-)
BigQuery : Big Data Meets Data Warehousing
43
Mark Rittman, Oracle ACE Director
NEW WORLD HADOOP ARCHITECTURES (& WHAT
PROBLEMS THEY REALLY SOLVE) FOR DBAS
UKOUG DATABASE SIG MEETING
London, February 2017

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New World Hadoop Architectures (& What Problems They Really Solve) for Oracle DBAs

  • 1. Mark Rittman, Oracle ACE Director NEW WORLD HADOOP ARCHITECTURES (& WHAT PROBLEMS THEY REALLY SOLVE) FOR DBAS UKOUG DATABASE SIG MEETING London, February 2017
  • 2. •Oracle ACE Director, Independent Analyst •Past ODTUG Exec Board Member + Oracle Scene Editor •Author of two books on Oracle BI •Co-founder & CTO of Rittman Mead •15+ Years in Oracle BI, DW, ETL + now Big Data •Host of the Drill to Detail Podcast (www.drilltodetail.com) •Based in Brighton & work in London, UK About The Presenter 2
  • 4. “Hi Mark, In things I have seen and read quite o6en people start with a high-level overview of a product (e.g. Hadoop, Ka@a), then describe the technical concepts (using all the appropriate terminology) …” “but I am usually le6 missing something. I think it's around the area of what problems these technologies are solving and how they are doing it? Without that context I'm finding it all very academic” “Many people say tradiKonal systems will sKll be needed. Are these new technologies solving completely different problems to those handled by tradi=onal IT? Is there an overlap?”
  • 5. •Started back in 1996 on a bank Oracle DW project •Our tools were Oracle 7.3.4, SQL*Plus, PL/SQL and shell scripts •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 20 Years in Old-school BI & Data Warehousing 5
  • 6.
  • 7. Data Warehousing and BI at “Peak Oracle” 7
  • 8. Oracle Data Management Platform as of Today 8
  • 10. 10 Let’s Go Back to 2003…
  • 11.
  • 12. •Google needed to store and query their vast amount of server log files •And wanted to do so using cheap, commodity hardware •Google File System and MapReduce designed together for this use Google File System and MapReduce 12
  • 13. •GFS optimised for particular task at hand - computing PageRank for sites •Streaming reads for PageRank calcs, block writes for crawler whole-site dumps •Master node only holds metadata •Stops client/master I/O being bottleneck, also acts as traffic controller for clients •Simple design, optimised for specific Google Need •MapReduce focused on simple computations on abstraction framework •Select & filter (MAP) and reduce (aggregate) functions, easily to distribute on cluster •MapReduce abstracted cluster compute, HDFS abstracted cluster storage •Projects that inspired Apache Hadoop + HDFS Google File System + MapReduce Key Innovations 13
  • 14. How Traditional RDBMS Data Warehousing Scaled-Up 14 Shared-Everything Architectures (i.e. Oracle RAC, Exadata) Shared-Nothing Architectures
 (e.g. Teradata, Netezza)
  • 15. Problem #1 That Hadoop / NoSQL Solved : Scaling Affordably
  • 16. “Oracle scales infinitely and is free. Period”
  • 17. •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 Cost and Complexity around Scaling DW Clusters 17
  • 18. •A way of storing (non-relational) data cheaply and easily expandable •Gave us a way of scaling beyond TB-size without paying $$$ •First use-cases were offline storage, active archive of data Hadoop’s Original Appeal to Data Warehouse Owners 18 (c) 2013
  • 19. Hadoop Ecosystem Expanded Beyond MapReduce 19 •Core Hadoop, MapReduce and HDFS •HBase and other NoSQL Databases •Apache Hive and SQL-on-Hadoop •Storm, Spark and Stream Processing •Apache YARN and Hadoop 2.0
  • 20. •Solution to the problem of storing semi-structured data at-scale •Built on Google File System •Scale for capacity e.g., webtable •100,000,000,000 pages, •10 versions per page, •20 KB / version = 20 PB of data •Scale for throughput •Hundreds of millions of users •Tens of thousands to millions of queries/sec •At low-latency with high-reliability Google BigTable, HBase and NoSQL Databases 20
  • 21. •Optimised for a particular task - fast lookups of ts-versioned web data •Data stored in multidimensional map keyed on row, column + timestamp •Master + data tablets stored on GFS cluster nodes •Simple key/value lookup with client doing interpretation •Innovation - focus on single job with different needs to OLTP •Formed inspiration for Apache HBase How BigTable Scaled Beyond Traditional RDBMSs 21
  • 22. •Original developed at Facebook, now foundational within Hadoop •SQL-like language that compiles to MapReduce, Spark, HBase •Solved the problem of enabling non-programmers to access big data •And made Hadoop data transformation and aggregation code more productive •JDBC and ODBC drivers for tool integration Hive - Hadoop Discovers Set-Based Processing 22
  • 23. •Hive is extensible to help with accessing and integrating new data sets •SerDes : Serializer-Deserializers that interpret semi-structured sources •UDFs + Hive Streaming : User-defined functions and streaming input •File Formats : make use of compressed and/or optimised file storage •Storage Handlers : use storage other than HDFS (e.g. MongoDB) Apache Hive as SQL Access Engine For Everything 23
  • 24. •Hadoop as low-cost ETL pre-processing engine - “ETL-offload” •NoSQL database for landing real-time data at high speed/low latency •Incoming data then aggregated and stored in RBDMS DW Common Hadoop/NoSQL Use-Case (c) 2014 24 MartsData Warehouse Σ Σ Business Intelligence • Online • Scalable • Flexible • Cost Effective Hadoop
  • 25. 25 Jump Ahead to 2012…
  • 26.
  • 27.
  • 28.
  • 29. •Driven by pace of business, and user demands for more agility and control •Traditional IT-governed data loading not always appropriate •Not all data needed to be modelled right-away •Not all data suited storing in tabular form •New ways of analyzing data beyond SQL •Graph analysis •Machine learning Data Warehousing and ETL Needed Some Agility 29
  • 30. Problem #2 That Hadoop / NoSQL Solved : Making Data Warehousing Agile
  • 31. •Storing data in format it arrived in, and then applying schema at query time •Suits data that may be analysed in different ways by different tools •In addition, some datatypes may have schema embedded in file format •Key benefit - fast arriving data of unknown value can get to users earlier •Made possible by tools such as Apache Hive + SerDes,
 Apache Drill and self-describing file formats, HDFS storage Advent of Schema-on-Read, and Data Lakes 31
  • 32. •Data now landed in Hadoop clusters, NoSQL databases and Cloud Storage •Flexible data storage platform with cheap storage, flexible schema support + compute •Solves the problem of how to store new types of data + choose best time/way to process it •Hadoop/NoSQL increasingly used for all store/transform/query tasks Meet the New Data Warehouse : The “Data Lake” 32 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
  • 33. Hadoop 2.0 and YARN
 (“Yet Another Resource Negotiator”) Key Innovation : Separating how data is stored,
 from how it is processed
  • 34.
  • 35. •Hadoop started by being synonymous with MapReduce, and Java coding •But YARN (Yet another Resource Negotiator) broke this dependency •Hadoop now just handles resource management •Multiple different query engines can run against data in-place •General-purpose (e.g. MapReduce) •Graph processing •Machine Learning •Real-Time Processing Hadoop 2.0 - Enabling Multiple Query Engines 35
  • 36. Technologies Emerged to Bridge Old/New World 36
  • 37. FAST FORWARD TO NOW… 37
  • 38. •New generation of big data platform services from Google, Amazon, Oracle •Combines three key innovations from earlier technologies: •Organising of data into tables and columns (from RDBMS DWs) •Massively-scalable and distributed storage and query (from Big Data) •Elastically-scalable Platform-as-a-Service (from Cloud) Elastically-Scalable Data Warehouse-as-a-Service 38
  • 39. … Which Is What I’m Working On Right Now 39
  • 40. Example Architecture : Google BigQuery 40
  • 41. 41
  • 42. •On-premise Hadoop, even with simple resilient clustering, will hit limits •Clusters can reach 5000+ nodes, need to scale-up for demand peaks etc •Scale limits are encountered way beyond those for DWs… •… but future is elastically-scaled, query and compute-as-a-service What Problem Did Analytics-as-a-Service Solve? 42 Oracle Big Data Cloud Compute Edition Free $300 developer credit at:
 https://cloud.oracle.com/en_US/tryit
  • 43. •And things come full-circle … analytics typically requires tabular data •Google BigQuery based-on DremelX massively-parallel query engine •But stores data columnar and provides SQL interface •Solves the problem of providing DW-like functionality at scale, as-a-service •This is the future … ;-) BigQuery : Big Data Meets Data Warehousing 43
  • 44. Mark Rittman, Oracle ACE Director NEW WORLD HADOOP ARCHITECTURES (& WHAT PROBLEMS THEY REALLY SOLVE) FOR DBAS UKOUG DATABASE SIG MEETING London, February 2017