Mark Rittman, Independent Analyst, MJR Analytics

DATA INTEGRATION AND DATA WAREHOUSING FOR CLOUD, BIG DATA AND IOT: WHAT’S NEW, WHAT’S COMING … AND WHAT’S MISSING RIGHT NOW

BIG DATA WORLD, LONDON

London, March 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

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A BIT OF HISTORY…

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•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

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Oracle

MongoDB

Oracle

Sybase

IBMDB/2

MSSQL

MSSQLServer

CoreERPPlatformRetail

Banking

CallCenterE-Commerce

CRM

Business

IntelligenceTools

DataWarehouse

Access&Performance

Layer

ODS/Foundation

Layer

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Data Warehousing Back in Mid-2000’s

How Traditional RDBMS Data Warehousing Scaled-Up

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Shared-EverythingArchitectures(i.e.OracleRAC,Exadata)

Shared-NothingArchitectures(e.g.Teradata,Netezza)

•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

Around the Same Time…

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•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

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•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

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(c) 2013

•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

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•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

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•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

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•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

Data Warehousing Circa 2010 : The “Data Lake”

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DataTransfer DataAccess

DataFactory DataReservoir

BusinessIntelligenceTools

HadoopPlatform

FileBasedIntegration

StreamBased

Integration

Datastreams

Discovery&DevelopmentLabsSafe&secureDiscoveryandDevelopment

environment

Datasetsandsamples

Models andprograms

Marketing/SalesApplications

Models

MachineLearning

Segments

OperationalData

Transactions

CustomerMasterata

UnstructuredData

Voice+ChatTranscripts

ETLBasedIntegration

RawCustomerData

Datastoredintheoriginal

format(usuallyfiles)suchasSS7,ASN.1,JSONetc.

MappedCustomerData

Datasetsproducedbymappingandtransformingrawdata

DATA WAREHOUSING & BIG DATA TODAY…

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•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

On-Premise Big Data Analytics Hits Its Limits

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OracleBigDataCloudComputeEditionFree$300developercreditat:https://cloud.oracle.com/en_US/tryit

•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

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Example Architecture : Google BigQuery

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•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

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DATAFLOW PIPELINES ARE THE NEW ETL…

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New ways to do BI

New ways to do BI

MACHINE LEARNING & SEARCH FOR “AUTOMAGIC” SCHEMA DISCOVERY

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New ways to do BI

•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

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A NEW TAKE ON BI…

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•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

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AND EMERGING OPEN-SOURCE BI TOOLS AND PLATFORMS

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And Emerging Open-SourceBI Tools and Platforms

http://larrr.com/wp-content/uploads/2016/05/paper.pdf

And Emerging Open-SourceBI Tools and Platforms

… Which Is What I’m Working On Right Now

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THANK YOU

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Mark Rittman, Independent Analyst, MJR Analytics

DATA INTEGRATION AND DATA WAREHOUSING FOR CLOUD, BIG DATA AND IOT: WHAT’S NEW, WHAT’S COMING … AND WHAT’S MISSING RIGHT NOW

BIG DATA WORLD, LONDON

London, March 2017