Hadoop is an open source distributed processing platform for large data sets across clusters of commodity hardware. It allows for the distributed processing of large data sets across clusters of computers using simple programming models. Hadoop features include a distributed file system (HDFS), a MapReduce programming model for large scale data processing, and an ecosystem of projects including HBase, Pig, Hive, and ZooKeeper. Hadoop is well suited for batch processing large amounts of structured and unstructured data, providing scalability and fault tolerance. However, it is not as suitable for low latency queries or updating existing data.

1. Hadoop and the Rise of Big Data
February 21, 2013
Donald Miner
@donaldpminer
Donald.Miner@emc.com

2. About Don

3. Hadoop
• Distributed platform up to thousands of nodes
• Data storage and application framework
• Started at Yahoo!
• Open source
• Based on a few Google papers (2003, 2004)
• Runs on commodity hardware
I’M HERE TO TELL YOU WHY HADOOP IS AWESOME

4. Hadoop users
• Yahoo! • Riot Games
• Facebook • ComScore
• eBay • Twitter
• AOL • LinkedIn
Hadoop Companies
• Cloudera, Hortonworks, EMC/Greenplum, IBM
• Numerous startups

5. Buzzword glossary
• Unstructured & Structured Data
• NoSQL
• Big Data (volume, velocity, variety)
• Data Science
• Cloud computing

6. Hadoop component overview
• Core components:
– HDFS (Hadoop Distributed File System)
– MapReduce (Data analysis framework)
• Ecosystem
– HBase (key-value store)
– Pig (high-level data analysis language)
– Hive (SQL-like data analysis language)
– ZooKeeper (stores metadata)
– Other stuff

7. Use cases
• Text processing
– Indexing, counting, processing
• Large-scale reports
• Data science
• Mixing data sources (data lakes)
• Ad targeting
• Image/Video/Audio processing
• Cybersecurity

8. HDFS
• Stores files in folders (that’s it)
– Nobody cares what’s in your files
• Chunks large files into blocks (~64MB-1GB)
• Blocks are scattered all over the place
• 3 replicates of each block (better safe than sorry)
• One NameNode (might be sorry)
– Knows which computers blocks live on
– Knows which blocks belong to which files
• One DataNode per computer (slaves!)
– Hosts files

9. HDFS Demonstration

10. MapReduce
• Analyzes data in HDFS where the data is
• Jobs are split into Mappers and Reducers
• JobTracker – keeps track of running jobs
• TaskTracker – one per computer, executes tasks
• Mappers (you code this)
– Loads data from HDFS
– Filter, transform, parse
– Outputs (key, value) pairs
• Reducers (you code this, too)
– Groups by the mapper’s output key
– Aggregate, count, statistics
– Outputs to HDFS

11. MapReduce Demonstration

12. Hadoop ecosystem
• HDFS and MapReduce don’t do everything
• Pig – high-level language
grpd = GROUP logs BY userAgent;
counts = FOREACH grpd GENERATE group,
AVG(logs.timeMicroSec)/1.0E+06 AS loadTimeSec;
byCount = ORDER counts BY loadTimeSec DESC;
top = limit byCount 15;
• Hive – high-level SQL language
SELECT grp, SUM(col2), COUNT(*) FROM table1 GROUP BY grp;
• HBase – key/value store

13. Cool thing #1: Linear Scalability
• HDFS and MapReduce scale linearly
• If you have twice as many computers, things run
twice as fast
• If you have twice as much data, things run twice
as slow
• If you have twice as many computers, you can
store twice as much data
• This stays true (some minor caveats)
• DATA LOCALITY!!

14. Cool thing #2: Schema on Read
Before:
ETL, schema design, tossing out original data
NOW:
LOAD DATA  ????  PROFIT!!
Data is parsed/interpreted as it is loaded out of HDFS
What implications does this have?
Keep original data around!
Have multiple views of the same data!
Store first, figure out what to do with it later!

15. Cool thing #3: Transparent Parallelism
RPC?
Code deployment?
Network programming?
Data center fires? Distributed stuff?
Inter-process communication?
Fault tolerance? Message passing?
Threading?
Locking?
With MapReduce, I DON’T CARE
… I just have to fit my solution into this tiny box
Solution MapReduce

16. Cool thing #4: Cheap
• Commodity hardware (meh)
• Open source (people cost more though)
• Add more hardware later

17. How to get started
• Install Hadoop in a Linux VM
– Wait how is this helpful?? Hadoop is distributed!
• Use Google (seriously)
• Some prerequisites: Java, Linux, Data, Time

18. Stuff Hadoop is good at
• Batch processing
• Processing lots of data
• Outputting lots of data
• Storing lots of historical data
• Flexible analysis of data
• Dealing with unstructured or structured data

19. Stuff Hadoop is not good at
• Hadoop is a freight truck, not a sports car
• Updating data (think “append-only”)
• Being easy to use
– Java
– Administration
• Hadoop is not good storage (don’t throw away
your EMC stuff!)

20. QUESTIONS?
Hadoop and the Rise of Big Data
February 21, 2013
Donald Miner
@donaldpminer
Donald.Miner@emc.com