big data: hype or necessity?
DESCRIPTION
Presentation given at an internal Televic R&D meeting. Audience consisted of about about 20 R&D engineers.TRANSCRIPT
Big Data
Big Data: hype or necessity?
Dr. ir. ing. Bart Vandewoestyne
Sizing Servers Lab, Howest, Kortrijk
Televic R&D meeting - April 25, 2014
1 / 69
Big Data
Outline
1 IntroductionBig Data?
2 Big Data TechnologyHadoopPig, HiveNoSQL
3 Big Data in my company?
4 Conclusions
2 / 69
Big Data
Introduction
Outline
1 IntroductionBig Data?
2 Big Data TechnologyHadoopPig, HiveNoSQL
3 Big Data in my company?
4 Conclusions
3 / 69
Big Data
Introduction
Big Data?
Exponential growth of data
© 2013 International Business Machines Corporation 4
Big Data: This is just the beginning
2010
Volu
me in
Exabyte
s
9000
8000
7000
6000
5000
4000
3000
2015
Percentage of uncertain data Pe
rce
nt o
f unce
rtain
data
100
80
60
40
20
0
You are here
Sensors & Devices
VoIP
Enterprise Data
Social Media
4 / 69
Big Data
Introduction
Big Data?
Examples
Facebook hosts ≈ 10 billion photos ≈ 1 petabyte
Large Hadron Collider: will produce ≈ 15 petabytes per year
5 / 69
Big Data
Introduction
Big Data?
Examples
RFID readers vehicle GPS traces
Smart energy meters
6 / 69
Big Data
Introduction
Big Data?
Examples relevant to Televic
Seattle’s Children Hospital Google Now
Union Pacific
Automatic reschedulingSensors in rails, GPS, RFID in terminals,. . .Weather forecast,. . .
7 / 69
Big Data
Introduction
Big Data?
Big Data definition
Definition of Big Data depends on who you ask:
Big Data
“Multiple terabytes or petabytes.”(according to some professionals)
“I don’t know.”(today’s big may be tomorrow’s normal)
“Relative to its context.”
8 / 69
Big Data
Introduction
Big Data?
Quotes on Big Data
“Big data” is a subjective label attached to situations inwhich human and technical infrastructures are unable tokeep pace with a company’s data needs.
It’s about recognizing that for some problems otherstorage solutions are better suited.
9 / 69
Big Data
Introduction
Big Data?
The Three V’s
Volume The amount of data is big.
Variety Different kinds of data:
structuredsemi-structuredunstructured
Velocity Speed-issues to consider:
How fast is the data available for analysis?How fast can we do something with it?
Other V’s: Veracity, Variability, Validity, Value,. . .
10 / 69
Big Data
Introduction
Big Data?
Structured data
Structured data
Pre-defined schema imposed on the data
Highly structured
Usually stored in a relational database system
Example
numbers: 20, 3.1415,. . .
dates: 21/03/1978
strings: ”Hello World”
. . .
Roughly 20% of all data out there is structured.
11 / 69
Big Data
Introduction
Big Data?
Semi-structured data
Semi-structured data
Inconsistent structure.
Cannot be stored in rows and tables in a typical database.
Information is often self-describing (label/value pairs).
Example
XML, SGML,. . .
BibTeX files
logs
tweets
sensor feeds
. . .
12 / 69
Big Data
Introduction
Big Data?
Semi-structured data: examples
Example
<?xml version="1.0"?>
<catalog>
<book id="bk101">
<author>Gambardella, Matthew</author>
<title>XML Developer’s Guide</title>
<genre>Computer</genre>
<price>44.95</price>
</book>
</catalog>
13 / 69
Big Data
Introduction
Big Data?
Unstructured data
Definition (Unstructured data)
Lacks structure or parts of it lack structure.
Example
multimedia: videos, photos,audio files,. . .
email messages
free-form text
word processing documents
presentations
reports
. . .
Experts estimate that 80 to 90 % of the data in anyorganization is unstructured.
14 / 69
Big Data
Introduction
Big Data?
Data Storage and Analysis
Storage capacity of hard drives has increased massively overthe years.
Access speeds have not kept up.
Example (Reading a whole disk)
Year Storage Capacity Transfer Speed Time
1990 1370 MB 4.4 MB/s ≈ 5 minutes2010 1 TB 100 MB/s > 2.5 hours
Solution: work in parallel!
Using 100 drives (each holding 1/100th of the data),reading 1 TB takes less than 2 minutes.
15 / 69
Big Data
Introduction
Big Data?
Working in parallel
Problems
1 Hardware failure?
2 Combining data from different disks for analysis?
Solutions
1 HDFS: Hadoop Distributed Filesystem
2 MapReduce: programming model
16 / 69
Big Data
Big Data Technology
Outline
1 IntroductionBig Data?
2 Big Data TechnologyHadoopPig, HiveNoSQL
3 Big Data in my company?
4 Conclusions
17 / 69
Big Data
Big Data Technology
Big Data Landscape
18 / 69
Big Data
Big Data Technology
Hadoop
Hadoop
Hadoop is VMware, but the other way around.
19 / 69
Big Data
Big Data Technology
Hadoop
Hadoop as the opposite of a virtual machine
VMware
1 take one physical server
2 split it up
3 get many small virtualservers
Hadoop
1 take many physical servers
2 merge them all together
3 get one big, massive, virtualserver
20 / 69
Big Data
Big Data Technology
Hadoop
Hadoop: core functionality
HDFS Self-healing, high-bandwidth, clustered storage.
MapReduce Distributed, fault-tolerant resource management,coupled with scalable data processing.
21 / 69
Big Data
Big Data Technology
Hadoop
HDFS architecture
22 / 69
Big Data
Big Data Technology
Hadoop
MapReduce
23 / 69
Big Data
Big Data Technology
Hadoop
MapReduce
24 / 69
Big Data
Big Data Technology
Hadoop
Hadoop: applications
Example Hadoop stack:
→ Hadoop distributions25 / 69
Big Data
Big Data Technology
Hadoop
Example Hadoop distributions
26 / 69
Big Data
Big Data Technology
Hadoop
Hadoop vs RDBMS
Relational Database Management Systems (RDBMS):
Very fast to max speed!
some queries → msecs
other queries → hours, days
use when
latency is importantACID transactions(banking,. . . )100% SQL compliance
Unstructured data → BLOB:-(
27 / 69
Big Data
Big Data Technology
Hadoop
Hadoop vs RDBMS
Hadoop:
Slower to (higher) maxspeed. . .
some queries → seconds,minutes
other queries → seconds!!!
Use when:
throughput importantscalability of storage/compute(un|semi)structured datacomplex data processing(NoSQL, Java, C, Python,. . . )
28 / 69
Big Data
Big Data Technology
Pig, Hive
Apache Hadoop essentials: technology stack
29 / 69
Big Data
Big Data Technology
Pig, Hive
Pig
MapReduce requires programmers
think in terms of map and reducefunctions,more than likely use the Java language.
Pig provides a high-level language (PigLatin) that can be used by
AnalystsData ScientistsStatisticiansEtc. . .
30 / 69
Big Data
Big Data Technology
Pig, Hive
Pig Latin
Pig Latin
Originally from Yahoo! to allow analysts to access data.
Dataflow language.
Makes it simpler to write MapReduce programs.
Abstracts you from specific details→ focus on data processing.
Has User Defined Functions (UDFs).
Compiles script into a set of MapReduce jobs.
31 / 69
Big Data
Big Data Technology
Pig, Hive
Pig example
Load users Load pages
Filterby age
Join onname
Groupon URL
Countclicks
Orderby clicks
Take top 5
Input data
file with user data
file with website data
Your task
Find the top 5 most visitedpages by users aged 18-25.
32 / 69
Big Data
Big Data Technology
Pig, Hive
In MapReduce
. . . 170 lines of Java MapReduce code . . .
33 / 69
Big Data
Big Data Technology
Pig, Hive
In Pig Latin
Example
Users = load ’users’ as (name, age);
Fltrd = filter Users by age >= 18 and age <= 25;
Pages = load ’pages’ as (user, url);
Jnd = join Fltrd by name, Pages by user;
Grpd = group Jnd by url;
Smmd = foreach Grpd generate group, COUNT(Jnd) as clicks;
Srtd = order Smmd by clicks desc;
Top5 = limit Srtd 5;
store Top5 into ’top5sites’;
Only 9 lines of Pig Latin.
34 / 69
Big Data
Big Data Technology
Pig, Hive
Hive
Originated at Facebook to analyze log data.
HiveQL: Hive Query Language, similar to standard SQL.
Queries are compiled into MapReduce jobs.
Has command-line shell, similar to e.g. MySQL shell.
35 / 69
Big Data
Big Data Technology
Pig, Hive
Hive: example
Example (Create table to hold weather data)
CREATE TABLE records (year STRING,
temperature INT,
quality INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ’\t’;
Example (Populate Hive with the data)
LOAD DATA LOCAL INPATH ’input/sample.txt’
OVERWRITE INTO TABLE records;
36 / 69
Big Data
Big Data Technology
Pig, Hive
Hive: example
Example (Run query)
hive> SELECT year, MAX(temperature)
> FROM records
> WHERE temperature != 9999
> AND (quality = 0 OR quality = 1)
> GROUP BY year;
1949 111
1950 22
37 / 69
Big Data
Big Data Technology
NoSQL
NoSQL
38 / 69
Big Data
Big Data Technology
NoSQL
RDBMS: Codd’s 12 rules
Codd’s 12 rules
A set of rules designed to define what is required from a databasemanagement system in order for it to be considered relational.
Rule 0 The Foundation rule
Rule 1 The Information rule
Rule 2 The guaranteed access rule
Rule 3 Systematic treatment of null values
Rule 4 Active online catalog based on the relational model
. . . . . .
39 / 69
Big Data
Big Data Technology
NoSQL
ACID
ACID
A set of properties that guarantee that database transactions areprocessed reliably.
Atomicity A transaction is all or nothing.
Consistency Only transactions with valid data.
Isolation Simultaneous transactions will not interfere.
Durability Written transaction data stays there “forever”(even in case of power loss, crashes, errors,. . . ).
40 / 69
Big Data
Big Data Technology
NoSQL
Scaling up
What if you need to scale up your RDBMS in terms of
dataset size,
read/write concurrency?
This usually involves
breaking Codds rules,
loosening ACID restrictions,
forgetting conventional DBA wisdom,
loose most of the desirable properties that made RDBMS soconvenient in the first place.
NoSQL to the rescue!
41 / 69
Big Data
Big Data Technology
NoSQL
NoSQL
NoSQL
‘Invented’ by Carl Strozzi in 1998 (for his file-based database)
“Not only SQL”
It’s NOT about
saying that SQL should never be used,
saying that SQL is dead.
42 / 69
Big Data
Big Data Technology
NoSQL
NoSQL databases
Four emerging NoSQL categories:
43 / 69
Big Data
Big Data Technology
NoSQL
Key-Value stores or ‘the big hash table’
Keys Values
13a1
13a2
13a3
Nexus 32 GB
Nexus 16 GB
Nexus 08 GB
Most basic type of NoSQLdatabases.
Aggregation of key-valuepairs.
Typically only 4 operations:
create(key, value)read(key)update(key, value)delete(key)
Fast, scalable, less complex.
Mainly used for systems with simple queries (caches etc. . . . )
44 / 69
Big Data
Big Data Technology
NoSQL
Key-Value stores or ’the big hash table’
45 / 69
Big Data
Big Data Technology
NoSQL
Column-oriented DBMS
Example
Id LastName FirstName Salary
10 Smith Joe 4000012 Jones Mary 5000011 Johnson Cathy 4400022 Jones Bob 55000
Row-based:10,Smith,Joe,40000;12,Jones,Mary,50000;11,Johnson,Cathy,44000;22,Jones,Bob,55000
Column-based:10,12,11,22;Smith,Jones,Johnson,Jones;Joe,Mary,Cathy,Bob;40000,50000,44000,55000
46 / 69
Big Data
Big Data Technology
NoSQL
Column family based databases
Like column-oriented DBMS, but with a twist
Columns and supercolumns ≈ RDBMS table columns
Family of columns ≈ RDBMS table
Keyspace ≈ RDBMS database
47 / 69
Big Data
Big Data Technology
NoSQL
Column family based databases
Most complex NoSQL database type.
Based on Google’s BigTable paper.
More flexibility than traditional RDBMS:adding (super)columns is always possible.
Excellent for analysis and mass treatment of data(via Map-Reduce type operations)
48 / 69
Big Data
Big Data Technology
NoSQL
Document databases
Data is stored as a collection ofdocuments(JSON, XML,. . . but also PDF,Excel,. . . )
Documents → collection ofkey-value pairs
Values can be
simple valuesarraysanother document (collection ofkey-values)
Schemaless
Quite well queryable
49 / 69
Big Data
Big Data Technology
NoSQL
Document databases
Example (Document 1)
{
FirstName: "Bob",
Address: "5 Oak St.",
Hobby: "sailing"
}
Example (Document 2)
{
FirstName: "Jonathan",
Address: "15 Wanamassa Road",
Children: [
{Name: "Michael", Age: 10},
{Name: "Jennifer", Age: 8},
{Name: "Samantha", Age: 5},
{Name: "Elena", Age: 2}
]
}
Best suited for custom queries like the ones in RDBMS.
Quite popular for Content Management Systems.
50 / 69
Big Data
Big Data Technology
NoSQL
Document databases: examples
51 / 69
Big Data
Big Data Technology
NoSQL
Graph databases
Julie Steve
RockMusic
Bob BMW
Fido Jim IBM
Sister in-Law To
Listen
s To
Listens To
Married
To Brother O
f
Drives
Works For
Collea
gue
Of
Works ForHas Pet
Based on graph theory.
Employ nodes (objects) and edges (relations between objects).
52 / 69
Big Data
Big Data Technology
NoSQL
Graph databases: examples
Well-suited for problems with network-structure:
mine data from social media“customers who bought this also looked at. . . ”relations between personshealthcare ontologies ???. . .
53 / 69
Big Data
Big Data Technology
NoSQL
Us the right tool for the right job!
http://db-engines.com/
54 / 69
Big Data
Big Data in my company?
Outline
1 IntroductionBig Data?
2 Big Data TechnologyHadoopPig, HiveNoSQL
3 Big Data in my company?
4 Conclusions
55 / 69
Big Data
Big Data in my company?
Typical RDBMS scaling story
1. Initial Public Launch
From local workstation → remotely hosted MySQL instance.
2. Service popularity ↑, too many reads hitting the database
Add memcached to cache common queries. Reads are now nolonger strictly ACID; cached data must expire.
3. Popularity ↑↑, too many writes hitting the database
Scale MySQL vertically by buying a beefed-up server:
16 cores
128 GB of RAM
banks of 15 k RPM hard drives
Costly
56 / 69
Big Data
Big Data in my company?
Typical RDBMS scaling story
4. New features → query complexity ↑, now too many joins
Denormalize your data to reduce joins.(Thats not what they taught me in DBA school!)
5. Rising popularity swamps the server; things are too slow
Stop doing any server-side computations.
57 / 69
Big Data
Big Data in my company?
Typical RDBMS scaling story
6. Some queries are still too slow
Periodically prematerialize the most complex queries, and try tostop joining in most cases.
7. Reads are OK, writes are getting slower and slower. . .
Drop secondary indexes and triggers (no indexes?).
If you stay up at nightworrying about your database(uptime, scale, or speed), you
should seriously considermaking a jump from theRDBMS world to HBase.
58 / 69
Big Data
Big Data in my company?
Two types of companies (personal view)
‘Core Big Data’ company
Core business = big data processing, crunching, analyzing,. . .
Example
Google, Facebook,. . .
Smart metering companies
Video/Image processing companies
Biotech companies with sequencing data
Lots of healthcare data???
. . .
59 / 69
Big Data
Big Data in my company?
Two types of companies (personal view)
‘General Big Data’ company
Some other core business.
Lots of useful data is available.
Desirable: business analytics, process optimization,. . .
Example
Supermarkets → customer cards
Transport firms → GPS-traces
Hospitals → patient and medical info???
. . .
60 / 69
Big Data
Big Data in my company?
Use-cases of Big Data
‘Core Big Data’ company
Big Data
crunching,
hacking,
processing,
analyzing,
. . .
‘General Big Data’ company
Business Analytics
improve decision-making,
gain operational insights,
increase overallperformance,
track and analyzeshopping patterns,
. . .
Both
Explore! Discover hidden gems!
61 / 69
Big Data
Big Data in my company?
Some examples
IBM: predict heart diseaselong before it strikes.
Predict and stop the spreadof infectious disease
62 / 69
Big Data
Big Data in my company?
Some examples
How to predict wine quality?
Skip tasting! Use science!
Weather seems the keyvariable.
Correlate historical weather& wine data.
Reduce fuel cost andimprove driver safety byanalyzing geolocation data
63 / 69
Big Data
Big Data in my company?
Big Data in your company
Big data is typically a division of the IT-department.
Requires skilled people:
sysadmins
software developers
data-scientists
visualization experts
. . .
Advice, trend (Andrew McAfee)
Give geeks a seat at the decision-making table.
64 / 69
Big Data
Big Data in my company?
Big Data in your company
65 / 69
Big Data
Big Data in my company?
IWT TETRA project
Data mining: van relationele database naar Big Data.
Dates
Submitted: 12/03/2014
Notification of acceptance: July, 2014
Runs from 01/10/2014 – 01/10/2016
People involved
Wannes De Smet (researcher)
Bart Vandewoestyne (researcher)
Johan De Gelas (project coordinator)
Thanks for being interested project partner :-)
66 / 69
Big Data
Conclusions
Outline
1 IntroductionBig Data?
2 Big Data TechnologyHadoopPig, HiveNoSQL
3 Big Data in my company?
4 Conclusions
67 / 69
Big Data
Conclusions
Conclusions
“Big” can be small too.
The Big Data landscape is huge.
RDBMS and SQL are not dead.
The right tool for the right job!
Your company can benefit from Big Data technology.
We can help.
Be brave in your quest. . .
68 / 69
