IoT and Big Data - Iot Asia 2014

© 2014 MapR Technologies 1© 2014 MapR Technologies

The Internet of Things and Big Data: IntroJohn Berns, Solutions Architect, APAC - MapR TechnologiesApril 22nd, 2014

© 2014 MapR Technologies 2

What This Is; What This Is Not• It’s not specific to IoT

– It’s not about any specific type of data or protocol– It’s not specific to any particular industry

• It’s about processing big data– IoT data can be big data– IoT might be the biggest data of the coming decade– But it’s just big data– Same strategies & technologies apply


When Does Data Become “Big?”• When the size of the data, itself, becomes a problem• When the “old way” of processing data just doesn’t work

effectively• It’s “big” when we have to rethink:

– How we store that much data– How we move that much data– How we extract, load & transform that much data– How we explore and analyze that much data– How we process and get meaningful insights from that much data


C’mon! What does that mean in size?• Not gigabytes• Most likely not a few terabytes• Possibly not 10’s of terabytes• Probably 100’s of terabytes• Definitely petabytes


So How Do We Handle Big Data?• Distribute & parallelize!


MPP Analytic Databases or Hadoop


Big Data AnalyticsBridging classic & big data worlds

“Capture only what’s needed”

SQL performance and structure

Hadoop scale and flexibility

IT delivers a platform for storing, refining, and analyzing all data

sourcesBusiness explores data for questions worth answering

Big Data MethodMulti-structured & iterative analysis

IT structures the data to answer those questions

Business determines what questions to ask

Classic MethodStructured & Repeatable Analysis

“Capture in case it’s needed”


Philosophical DifferencesTraditional Methods• More power • Summarize data• Transform and store• Pre-defined schema• Move data -> compute• Less data / more complex

algorithms

Big Data• More machines• Keep all data • Transform on demand• Flexible / no schema• Move compute -> data• Mode data / simple

algorithms


answer = f(all data)• Save all raw data• Data immutability• Transform as needed• Result is based on the raw data


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Iot and Big Data: Hadoop as a Data PlatformJohn Berns, Solutions Architect, APAC - MapR TechnologiesApril 22nd, 2014


Hadoop: The Disruptive Technology at the Core of Big Data


Forces of AdoptionHadoop TAM comes from disrupting enterprise data warehouse and storage spending

Data IT Budgets

• Gartner, "Forecast Analysis: Enterprise IT Spending by Vertical Industry Market, Worldwide, 2010-2016, 3Q12 Update.“• Wall Street Journal, “Financial Services Companies Firms See Results from Big Data Push”, Jan. 27, 2014

$9,000

$40,000

<$1,000

2013 ENTERPRISE STORAGE

IT BUDGETS GROWING AT 2.5%

2014 2015 2016 2017 DATABASE WAREHOUSE

DATA GROWING AT 40% $ PER TERABYTE

HADOOP


Hadoop 101 (External Presentation)

http://www.slideshare.net/jfxberns/hadoop-101-v2


Hadoop Hardware


Typical Compute Node

• Two CPUs, each with 4-8 cores per CPU• 32-128 GB Memory• 6-24 hard disks• 2-4 10GB Network cards


Hadoop Ecosystem


Ecosystem of Projects Built of Hadoop


SQL On Hadoop


SQL on Hadoop• Generally data has no inherent “schema”• Schema is defined by user / interpreted from structure• Schema is applied during processing• One file can have many schemas applied• Works for many kinds of data—but not all

– Temperature sensor data? Sure– Video feeds? Not really


Key Use Cases

• Exploratory analysis on large scale raw data

• Unknown value• No defined schema• Variety of data types

• Large-scale SQL queries on long history

• Well defined schema• Known value, but high cost in

existing systems

2Big Data Analysis Big Data Exploration


What is Driving the Need for SQL-on-Hadoop?Organizations are looking for• Reuse existing tools and skills to unlock Hadoop data to broader

audience

• Analysis on new types of data

• More complete data analysis

• More up-to-date and real-time data analysis (not just “after the fact”)


Drill 1.0 Hive 0.13 with Tez Impala 1.x Presto 0.56 Shark 0.8 Vertica

Latency Low Medium Low Low Medium Low

Files Yes (all Hive file formats)

Yes (all Hive file formats)

Yes (Parquet, Sequence, …)

Yes (RC, Sequence, Text)



HBase/M7 Yes Yes Various issues No Yes No

Schema Hive or schema-less

Hive Hive Hive Hive Proprietary or Hive

SQL support ANSI SQL HiveQL HiveQL (subset) ANSI SQL HiveQL ANSI SQL + advanced analytics

Client support ODBC/JDBC ODBC/JDBC ODBC/JDBC ODBC/JDBC ODBC/JDBC ODBC/JDBC, ADO.NET, …

Large joins Yes Yes No No No Yes

Nested data Yes Limited No Limited Limited Limited

Hive UDFs Yes Yes Limited No Yes No

Transactions No No No No No Yes

Optimizer Limited Limited Limited Limited Limited Yes

Concurrency Limited Limited Limited Limited Limited Yes

SQL on Hadoop: Many OptionsFlexibility to choose when to use which based on use case


ENTERPRISE DATA HUB

MARKETINGANALYTICS

RISKANALYTICS

OPERATIONS INTELLIGENCE

• Multi-structured data staging & archive

• ETL / DW optimization• Mainframe optimization

• Data exploration

• Recommendation engines & targeting

• Ad optimization• Pricing analysis• Lead scoring

• Network security monitoring

• Security information & event management

• Fraudulent behavioral analysis

• Supply chain & logistics• System log analysis• Manufacturing quality assurance

• Preventative maintenance

• Sensor analysis

Proven Hadoop Production Success


Other Tools & Frameworks of Note


Pig

• Procedural Language• Loops, if-then statements


• Map Reduce Framwork• Lingual: SQL-like operations• Pattern: Machine Learning Applications• Scalding: Cascading for Scala• Cascalog: Cascading for Clojure


• Python, Scala and Java• Spark powers a stack of high-level tools including

– Shark for SQL, – MLlib for machine learning, – GraphX, and – Spark Streaming.

• You can combine these frameworks seamlessly in the same application.


• Machine Learning / Predictive Analytics– Collaborative Filtering– Linear / Logistic Regression– Naïve Bayes– Random Forests– K-Mean Clustering– Canopy Clustering– Principal Component Analysis


• Database on Hadoop• Highly scalable• Columnar – Flexible schema• Data source for Map Reduce and Spark jobs


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Iot and Big Data: Architectures & Use CasesJohn Berns, Solutions Architect, APAC - MapR TechnologiesApril 22nd, 2014


NoSQL


NoSQL Databases• No-SQL or “Not only” SQL• Give up some of the functionality of traditional relational

databases for speed and scalability• Types

– Key-Value – Columnar– Document– Graph

• NoSQL databases favor flexible schemas


HBase


Queues


Queues• Just like a queue at an amusement park • First-in-first out• Queues messages or events


Message Queue


Stream Processing


Stream Processing• Handles data at high velocity• If Hadoop is the ocean, streams are the firehose• Processing in near real-time


Storm


Batch Processing


Combination Architectures


Lambda Architecture


Complex Architectures Using Many Big Data Technologies


Wanna Play?

• http://www.mapr.com/products/mapr-sandbox-hadoop


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IoT and Big Data - Iot Asia 2014

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