Big Data: hype or necessity?
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Big Data: hype or necessity?

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Presentation given at an internal Televic R&D meeting. Audience consisted of about about 20 R&D engineers.

Presentation given at an internal Televic R&D meeting. Audience consisted of about about 20 R&D engineers.

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Big Data: hype or necessity? Big Data: hype or necessity? Presentation 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 Introduction Big Data? 2 Big Data Technology Hadoop Pig, Hive NoSQL 3 Big Data in my company? 4 Conclusions 2 / 69
  • Big Data Introduction Outline 1 Introduction Big Data? 2 Big Data Technology Hadoop Pig, Hive NoSQL 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 VolumeinExabytes 9000 8000 7000 6000 5000 4000 3000 2015 Percentage of uncertain data Percentofuncertaindata 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 rescheduling Sensors 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 in which human and technical infrastructures are unable to keep pace with a company’s data needs. It’s about recognizing that for some problems other storage 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: structured semi-structured unstructured 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 any organization is unstructured. 14 / 69
  • Big Data Introduction Big Data? Data Storage and Analysis Storage capacity of hard drives has increased massively over the 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 minutes 2010 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 Introduction Big Data? 2 Big Data Technology Hadoop Pig, Hive NoSQL 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 virtual servers Hadoop 1 take many physical servers 2 merge them all together 3 get one big, massive, virtual server 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 distributions 25 / 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 important ACID transactions (banking,. . . ) 100% SQL compliance Unstructured data → BLOB :-( 27 / 69
  • Big Data Big Data Technology Hadoop Hadoop vs RDBMS Hadoop: Slower to (higher) max speed. . . some queries → seconds, minutes other queries → seconds!!! Use when: throughput important scalability of storage/compute (un|semi)structured data complex 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 reduce functions, more than likely use the Java language. Pig provides a high-level language (Pig Latin) that can be used by Analysts Data Scientists Statisticians Etc. . . 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 Filter by age Join on name Group on URL Count clicks Order by clicks Take top 5 Input data file with user data file with website data Your task Find the top 5 most visited pages 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 database management 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 are processed 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 so convenient 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 NoSQL databases. Aggregation of key-value pairs. 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 40000 12 Jones Mary 50000 11 Johnson Cathy 44000 22 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 of documents (JSON, XML,. . . but also PDF, Excel,. . . ) Documents → collection of key-value pairs Values can be simple values arrays another document (collection of key-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 Rock Music Bob BMW Fido Jim IBM Sister in-Law To Listens To Listens To M arried To Brother Of Drives W orks For Colleague 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 persons healthcare 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 Introduction Big Data? 2 Big Data Technology Hadoop Pig, Hive NoSQL 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 no longer 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 to stop 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 night worrying about your database (uptime, scale, or speed), you should seriously consider making a jump from the RDBMS 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 overall performance, track and analyze shopping patterns, . . . Both Explore! Discover hidden gems! 61 / 69
  • Big Data Big Data in my company? Some examples IBM: predict heart disease long before it strikes. Predict and stop the spread of 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 key variable. Correlate historical weather & wine data. Reduce fuel cost and improve driver safety by analyzing 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 Introduction Big Data? 2 Big Data Technology Hadoop Pig, Hive NoSQL 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
  • Big Data Conclusions Questions? Questions? bart@sizingservers.be 69 / 69