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

EVENT SPEAKERDANISH BI MEETUP, SEP’ 2016

FROM LOTS OF REPORTS (WITH SOME DATA ANALYSIS) TO MASSIVE DATA ANALYSIS (WITH SOME REPORTING)

MARK RITTMAN, ORACLE ACE DIRECTOR

[email protected] 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 : [email protected]•Twitter : @markrittman

About the Speaker

mailto:[email protected]


•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

IBMDB/2

MSSQL

MSSQLServer

CoreERPPlatformRetail

Banking

CallCenterE-Commerce

CRM

Business

IntelligenceTools

DataWarehouse

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

[email protected] www.rittmanmead.com @rittmanmead

Traditional Three-Layer Relational Data Warehouses

Staging Foundation / ODS

Performance /Dimensional

ETL ETL

BI Tool (OBIEE)with metadatalayer

OLAP / In-Memory Tool with data load into own database

DirectRead

DataLoad

Traditional structureddata sources

DataLoad

DataLoad

DataLoad

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

And All Was Good…

(a big BI project)

Lots of reports (with some data analysis)

Meanwhile…The world got digitised

and connected.

and users got impatient…

Reporting and Dashboards…became self-service

data discovery

Advanced analytics for everyone

Cloud and SaaS have won

BI has changed


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 usersJust 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 ExcelWatch out OLAP you’re next

Analytic data platforms


•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”

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

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 traditionalRDBMS 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

BigDataManagementPlatform

Discovery&DevelopmentLabs Safe&secureDiscoveryandDevelopmentenvironment

Datasetsandsamples

Modelsandprograms

SingleCustomerViewEnriched

CustomerProfile

Correlating

Modeling

Machine Learning

Scoring

Schema-on ReadAnalysis


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 overlarge 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 fornew types of processing and analysis


BigDataPlatform-AllRunningNativelyUnderHadoop

YARN(ClusterResourceManagement)

Batch(MapReduce)

HDFS(ClusterFilesystemholdingrawdata)

Interactive (Impala,Drill, Tez,Presto)

Streaming+ In-Memory

(Spark,Storm)

Graph+Search(Solr,Giraph)

EnrichedCustomerProfile

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 RDBMSsas specialist engines•a.k.a. "Data Marts"

But … Analytic RDBMSs Are The New Data Mart

Discovery&DevelopmentLabs Safe&secureDiscoveryandDevelopmentenvironment

Data

Warehouse

Curateddata:Historicalviewandbusinessalignedaccess

Business

IntelligenceTools

BigDataManagementPlatform

Datasetsandsamples

Modelsandprograms

BigDataPlatform-AllRunningNativelyUnderHadoop

YARN(ClusterResourceManagement)

Batch(MapReduce)

HDFS(ClusterFilesystemholdingrawdata)

Interactive (Impala,Drill, Tez,Presto)

Streaming+ In-Memory

(Spark,Storm)

Graph+Search(Solr,Giraph)

EnrichedCustomerProfile

Modeling

Scoring

BI Innovation is happeningaround Hadoop

BI Innovation is happeningaround Hadoop

hold on though…

isn’t Hadoop Slow?

too slowfor ad-hoc querying?

welcome to 2016

(Hadoop 2.0)

Hadoop is now fast


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 tablesand 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;

and it’s now in-memory


•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 tosupport multi-tenant distributed services

In-Memory Distributed Storage,to accompany In-Memory Distributed Processing

Dataflow Pipelines are the new ETL

New ways to do BI

New ways to do BI

Hadoop is the new ETL Engine

[email protected] www.rittmanmead.com @rittmanmead 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

ScriptedSQL

StoredProcs

ODI forColumnar

ODI forIn-Mem

ODI forExadata

ODI forHive

ODI forPig & 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 InitioActa/SAPSyncSort

1994

Oracle Data Integrator

ODI forSpark

ODI forSpark Streaming

WarehouseBuilder

Machine Learning & Search for “Automagic” Schema Discovery

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

A New Take on BI


•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

Meanwhilein the real world …

https://www.youtube.com/watch?v=h1UmdvJDEYY

And Emerging Open-SourceBI Tools and Platforms


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


To see an example:

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

EVENT SPEAKERDANISH BI MEETUP, SEP’ 2016

FROM LOTS OF REPORTS (WITH SOME DATA ANALYSIS) TO MASSIVE DATA ANALYSIS (WITH SOME REPORTING)

MARK RITTMAN, ORACLE ACE DIRECTOR

Data & Analytics

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