SlideShare a Scribd company logo
Apache Hadoop HBASE
Sheetal Sharma
Intern At IBM Innovation Centre
HBase is ..
● A distributed data store that can scale horizontally to
1,000s of commodity servers and petabytes of indexed
storage.
● Designed to operate on top of the Hadoop distributed file
system (HDFS) or Kosmos File System (KFS, aka
Cloudstore) for scalability, fault tolerance, and high
availability.
Benefits
● Distributed storage
● Table-like in data structure
multi-dimensional map
● High scalability
● High availability
● High performance
HBase Is Not …
● Tables have one primary index, the row key.
● No join operators.
● Scans and queries can select a subset of available
columns, perhaps by using a wildcard.
● There are three types of lookups:
Fast lookup using row key and optional timestamp.
Full table scan
Range scan from region start to end.
HBase Is Not …(2)
● Limited atomicity and transaction support.
- HBase supports multiple batched mutations of single
rows only.
- Data is unstructured and untyped.
● No accessed or manipulated via SQL.
- Programmatic access via Java, REST, or Thrift APIs.
- Scripting via JRuby.
Why HBase ?
● HBase is a Bigtable clone.
● It is open source
● It has a good community and promise for the
future
● It is developed on top of and has good integration
for the Hadoop platform, if you are using Hadoop
already.
● It has a Cascading connector.
When to use HBase
HBase benefits than RDBMS
● No real indexes
● Automatic partitioning
● Scale linearly and automatically with new nodes
● Commodity hardware
● Fault tolerance
● Batch processing
HBase: Part of Hadoop’s
Ecosystem
HBase is built on top of HDFS
HBase files are
internally stored
in HDFS
HBase vs. HDFS
● Both are distributed systems that scale to hundreds or thousands
of nodes
● HDFS is good for batch processing (scans over big files)
Not good for record lookup
Not good for incremental addition of small batches
Not good for updates
HBase vs. HDFS (Cont’d)
● HBase is designed to efficiently address the above points
Fast record lookup
Support for record-level insertion
Support for updates (not in place)
● HBase updates are done by creating new versions of values
HBase vs. HDFS (Cont’d)
If application has neither random reads or writes  Stick to HDFS
HBase vs. RDBMS
HBase Data Model
● Data is divided into various tables
● Table is composed of columns, columns are grouped into column-
families
HBase Storage Model
● Partitioning
- A table is horizontally partitioned into regions, each region is
composed of sequential range of keys
- Each region is managed by a RegionServer, a single
RegionServer may hold multiple regions
●
Persistence and data availability
- HBase stores its data in HDFS, it doesn't replicate
RegionServers and relies on HDFS replication for data
availability.
- Region data is cached in-memory
* Updates and reads are served from in-memory
cache (MemStore)
* MemStore is flushed periodically to HDFS
* Write Ahead Log (stored in HDFS) is used for
durability of updates
HBase: Keys and Column
Families
Each record is divided into Column Families
Each row has a Key
Each column family consists of one or more Columns
Row key
Time
Stamp
Column
“ content
s:”
Column “ anchor:”
“ com.apac
he.ww
w”
t12
“ <html>
…”
t11
“ <html>
…”
t10
“ anchor:apache
.com”
“ APACH
E”
“ com.cnn.w
ww”
t15
“ anchor:cnnsi.co
m”
“ CNN”
t13
“ anchor:my.look.
ca”
“ CNN.co
m”
t6
“ <html>
…”
t5
“ <html>
…”
t3
“ <html>
…”
•
Key
• Byte array
• Serves as the primary
key for the table
• Indexed far fast lookup
•
Column Family
• Has a name (string)
• Contains one or more
related columns
•
Column
• Belongs to one column
family
• Included inside the row
•
familyName:columnNa
me
Column family named “Contents”
Column family named “anchor”
Column named “apache.com”
Row key
Time
Stamp
Column
“ content
s:”
Column “ anchor:”
“ com.apac
he.ww
w”
t12
“ <html>
…”
t11
“ <html>
…”
t10
“ anchor:apache
.com”
“ APACH
E”
“ com.cnn.w
ww”
t15
“ anchor:cnnsi.co
m”
“ CNN”
t13
“ anchor:my.look.
ca”
“ CNN.co
m”
t6
“ <html>
…”
t5
“ <html>
…”
t3
“ <html>
…”
•
Version Number
• Unique within each
key
• By default System’s
timestamp
• Data type is Long
•
Value (Cell)
• Byte array
Version number for each row
value
HBase Architecture
Three Major Components
•
The HBaseMaster
• One master
•
The HRegionServer
• Many region servers
•
The HBase client
HBase Components
•
Region
• A subset of a table’s rows, like horizontal range partitioning
• Automatically done
•
RegionServer (many slaves)
• Manages data regions
• Serves data for reads and writes (using a log)
•
Master
• Responsible for coordinating the slaves
• Assigns regions, detects failures
• Admin functions
Big Picture
ZooKeeper
•
HBase depends on
ZooKeeper
•
By default HBase manages
the ZooKeeper instance
• E.g., starts and stops
ZooKeeper
•
HMaster and HRegionServers
register themselves with
ZooKeeper
Creating a Table
HBaseAdmin admin= new HBaseAdmin(config);
HColumnDescriptor []column;
column= new HColumnDescriptor[2];
column[0]=new HColumnDescriptor("columnFamily1:");
column[1]=new HColumnDescriptor("columnFamily2:");
HTableDescriptor desc= new
HTableDescriptor(Bytes.toBytes("MyTable"));
desc.addFamily(column[0]);
desc.addFamily(column[1]);
admin.createTable(desc);
Operations On Regions: Get()
•
Given a key  return corresponding record
•
For each value return the highest version
● Can control the number of versions you want
Get() Select value from table where
key=‘com.apache.www’ AND
label=‘anchor:apache.com’
Row key
Time
Stamp
Column “anchor:”
“com.apache.www”
t12
t11
t10 “anchor:apache.com” “APACHE”
“com.cnn.www”
t9 “anchor:cnnsi.com” “CNN”
t8 “anchor:my.look.ca” “CNN.com”
t6
t5
t3
Operations On Regions: Scan()
Scan()
Select value from table
where anchor=‘cnnsi.com’
Row key
Time
Stamp
Column “anchor:”
“com.apache.www”
t12
t11
t10 “anchor:apache.com” “APACHE”
“com.cnn.www”
t9 “anchor:cnnsi.com” “CNN”
t8 “anchor:my.look.ca” “CNN.com”
t6
t5
t3
Operations On Regions: Put()
● Insert a new record (with a new key), Or
● Insert a record for an existing key
Implicit version number
(timestamp)
Explicit version number
Operations On Regions: Delete()
•
Marking table cells as deleted
•
Multiple levels
• Can mark an entire column family as deleted
• Can make all column families of a given row as deleted
•
All operations are logged by the RegionServers
•
The log is flushed periodically
Altering a Table
Disable the table before changing the schema
Logging Operations
HBase Deployment
Master
node
Slave
nodes
References
● Introduction to Hbase
trac.nchc.org.tw/cloud/raw-
attachment/wiki/.../hbase_intro.ppt
● web.cs.wpi.edu/~cs525/s13-MYE/lectures/5/HBase.pptx
● www-users.cselabs.umn.edu/classes/Spring.../Hadoop-HBase-
Tutorial.ppt
● www.cs.kent.edu/~jin/Cloud12Spring/HbaseHivePig.pptx
Apache hadoop hbase

More Related Content

What's hot (20)

PPTX
Relational databases vs Non-relational databases
James Serra
 
PPTX
Apache hive introduction
Mahmood Reza Esmaili Zand
 
PPTX
Apache Spark Architecture
Alexey Grishchenko
 
PPTX
Apache HBase™
Prashant Gupta
 
PDF
Hadoop Ecosystem | Big Data Analytics Tools | Hadoop Tutorial | Edureka
Edureka!
 
PDF
HBaseCon 2012 | Lessons learned from OpenTSDB - Benoit Sigoure, StumbleUpon
Cloudera, Inc.
 
PPTX
Mongodb basics and architecture
Bishal Khanal
 
PPTX
Apache Tez - A New Chapter in Hadoop Data Processing
DataWorks Summit
 
PPTX
Hive
Manas Nayak
 
PPTX
Introduction to Hadoop
Dr. C.V. Suresh Babu
 
PDF
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
Databricks
 
PPTX
Apache spark
TEJPAL GAUTAM
 
PDF
Introduction to column oriented databases
ArangoDB Database
 
KEY
Introduction to memcached
Jurriaan Persyn
 
PDF
Building Robust ETL Pipelines with Apache Spark
Databricks
 
PDF
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Databricks
 
PPTX
Introduction to Pig
Prashanth Babu
 
PPTX
Caching
Nascenia IT
 
PPT
7. Key-Value Databases: In Depth
Fabio Fumarola
 
PPT
Hadoop Security Architecture
Owen O'Malley
 
Relational databases vs Non-relational databases
James Serra
 
Apache hive introduction
Mahmood Reza Esmaili Zand
 
Apache Spark Architecture
Alexey Grishchenko
 
Apache HBase™
Prashant Gupta
 
Hadoop Ecosystem | Big Data Analytics Tools | Hadoop Tutorial | Edureka
Edureka!
 
HBaseCon 2012 | Lessons learned from OpenTSDB - Benoit Sigoure, StumbleUpon
Cloudera, Inc.
 
Mongodb basics and architecture
Bishal Khanal
 
Apache Tez - A New Chapter in Hadoop Data Processing
DataWorks Summit
 
Introduction to Hadoop
Dr. C.V. Suresh Babu
 
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
Databricks
 
Apache spark
TEJPAL GAUTAM
 
Introduction to column oriented databases
ArangoDB Database
 
Introduction to memcached
Jurriaan Persyn
 
Building Robust ETL Pipelines with Apache Spark
Databricks
 
Deep Dive into Spark SQL with Advanced Performance Tuning with Xiao Li & Wenc...
Databricks
 
Introduction to Pig
Prashanth Babu
 
Caching
Nascenia IT
 
7. Key-Value Databases: In Depth
Fabio Fumarola
 
Hadoop Security Architecture
Owen O'Malley
 

Similar to Apache hadoop hbase (20)

PPTX
HBASE, HIVE , ARCHITECTURE AND WORKING EXAMPLES
harikumar288574
 
PPTX
CCS334 BIG DATA ANALYTICS UNIT 5 PPT ELECTIVE PAPER
KrishnaVeni451953
 
PDF
Hbase 20141003
Jean-Baptiste Poullet
 
PPTX
Hbase
AllsoftSolutions
 
PPTX
HBase.pptx
vijayapraba1
 
PPT
Chicago Data Summit: Apache HBase: An Introduction
Cloudera, Inc.
 
PPTX
01 hbase
Subhas Kumar Ghosh
 
PDF
Hbase: an introduction
Jean-Baptiste Poullet
 
PPTX
Introduction To HBase
Anil Gupta
 
PPTX
HBase in Practice
DataWorks Summit/Hadoop Summit
 
PPTX
Hadoop - Apache Hbase
Vibrant Technologies & Computers
 
PPTX
HBase in Practice
larsgeorge
 
PPTX
HBase.pptx
Sadhik7
 
PPTX
Unit II Hadoop Ecosystem_Updated.pptx
BhavanaHotchandani
 
PPTX
Introduction to HBase
Byeongweon Moon
 
PPT
HBASE Overview
Sampath Rachakonda
 
PDF
Facebook keynote-nicolas-qcon
Yiwei Ma
 
PDF
支撑Facebook消息处理的h base存储系统
yongboy
 
PDF
Facebook Messages & HBase
强 王
 
PPTX
BDA: Introduction to HIVE, PIG and HBASE
tripathineeharika
 
HBASE, HIVE , ARCHITECTURE AND WORKING EXAMPLES
harikumar288574
 
CCS334 BIG DATA ANALYTICS UNIT 5 PPT ELECTIVE PAPER
KrishnaVeni451953
 
Hbase 20141003
Jean-Baptiste Poullet
 
HBase.pptx
vijayapraba1
 
Chicago Data Summit: Apache HBase: An Introduction
Cloudera, Inc.
 
Hbase: an introduction
Jean-Baptiste Poullet
 
Introduction To HBase
Anil Gupta
 
HBase in Practice
DataWorks Summit/Hadoop Summit
 
Hadoop - Apache Hbase
Vibrant Technologies & Computers
 
HBase in Practice
larsgeorge
 
HBase.pptx
Sadhik7
 
Unit II Hadoop Ecosystem_Updated.pptx
BhavanaHotchandani
 
Introduction to HBase
Byeongweon Moon
 
HBASE Overview
Sampath Rachakonda
 
Facebook keynote-nicolas-qcon
Yiwei Ma
 
支撑Facebook消息处理的h base存储系统
yongboy
 
Facebook Messages & HBase
强 王
 
BDA: Introduction to HIVE, PIG and HBASE
tripathineeharika
 
Ad

More from sheetal sharma (9)

PDF
Db import&amp;export
sheetal sharma
 
PDF
Db import&amp;export
sheetal sharma
 
ODP
Hadoop Introduction
sheetal sharma
 
ODP
Apache hadoop
sheetal sharma
 
ODP
Apache hive1
sheetal sharma
 
PDF
Telecommunication Analysis (3 use-cases) with IBM watson analytics
sheetal sharma
 
PDF
Telecommunication Analysis(3 use-cases) with IBM cognos insight
sheetal sharma
 
PPTX
Sentiment Analysis App with DevOps Services
sheetal sharma
 
PPTX
Watson analytics
sheetal sharma
 
Db import&amp;export
sheetal sharma
 
Db import&amp;export
sheetal sharma
 
Hadoop Introduction
sheetal sharma
 
Apache hadoop
sheetal sharma
 
Apache hive1
sheetal sharma
 
Telecommunication Analysis (3 use-cases) with IBM watson analytics
sheetal sharma
 
Telecommunication Analysis(3 use-cases) with IBM cognos insight
sheetal sharma
 
Sentiment Analysis App with DevOps Services
sheetal sharma
 
Watson analytics
sheetal sharma
 
Ad

Recently uploaded (20)

PDF
“Voice Interfaces on a Budget: Building Real-time Speech Recognition on Low-c...
Edge AI and Vision Alliance
 
PDF
Transcript: Book industry state of the nation 2025 - Tech Forum 2025
BookNet Canada
 
PPTX
Digital Circuits, important subject in CS
contactparinay1
 
PDF
Agentic AI lifecycle for Enterprise Hyper-Automation
Debmalya Biswas
 
PDF
Go Concurrency Real-World Patterns, Pitfalls, and Playground Battles.pdf
Emily Achieng
 
PDF
“NPU IP Hardware Shaped Through Software and Use-case Analysis,” a Presentati...
Edge AI and Vision Alliance
 
PPTX
The Project Compass - GDG on Campus MSIT
dscmsitkol
 
PDF
Future-Proof or Fall Behind? 10 Tech Trends You Can’t Afford to Ignore in 2025
DIGITALCONFEX
 
PDF
Newgen 2022-Forrester Newgen TEI_13 05 2022-The-Total-Economic-Impact-Newgen-...
darshakparmar
 
PPTX
Agentforce World Tour Toronto '25 - MCP with MuleSoft
Alexandra N. Martinez
 
PPTX
Future Tech Innovations 2025 – A TechLists Insight
TechLists
 
PDF
NLJUG Speaker academy 2025 - first session
Bert Jan Schrijver
 
PPTX
Agentforce World Tour Toronto '25 - Supercharge MuleSoft Development with Mod...
Alexandra N. Martinez
 
PDF
Newgen Beyond Frankenstein_Build vs Buy_Digital_version.pdf
darshakparmar
 
PDF
How do you fast track Agentic automation use cases discovery?
DianaGray10
 
PPTX
MuleSoft MCP Support (Model Context Protocol) and Use Case Demo
shyamraj55
 
PDF
Book industry state of the nation 2025 - Tech Forum 2025
BookNet Canada
 
PDF
Staying Human in a Machine- Accelerated World
Catalin Jora
 
PDF
AI Agents in the Cloud: The Rise of Agentic Cloud Architecture
Lilly Gracia
 
PDF
CIFDAQ Market Wrap for the week of 4th July 2025
CIFDAQ
 
“Voice Interfaces on a Budget: Building Real-time Speech Recognition on Low-c...
Edge AI and Vision Alliance
 
Transcript: Book industry state of the nation 2025 - Tech Forum 2025
BookNet Canada
 
Digital Circuits, important subject in CS
contactparinay1
 
Agentic AI lifecycle for Enterprise Hyper-Automation
Debmalya Biswas
 
Go Concurrency Real-World Patterns, Pitfalls, and Playground Battles.pdf
Emily Achieng
 
“NPU IP Hardware Shaped Through Software and Use-case Analysis,” a Presentati...
Edge AI and Vision Alliance
 
The Project Compass - GDG on Campus MSIT
dscmsitkol
 
Future-Proof or Fall Behind? 10 Tech Trends You Can’t Afford to Ignore in 2025
DIGITALCONFEX
 
Newgen 2022-Forrester Newgen TEI_13 05 2022-The-Total-Economic-Impact-Newgen-...
darshakparmar
 
Agentforce World Tour Toronto '25 - MCP with MuleSoft
Alexandra N. Martinez
 
Future Tech Innovations 2025 – A TechLists Insight
TechLists
 
NLJUG Speaker academy 2025 - first session
Bert Jan Schrijver
 
Agentforce World Tour Toronto '25 - Supercharge MuleSoft Development with Mod...
Alexandra N. Martinez
 
Newgen Beyond Frankenstein_Build vs Buy_Digital_version.pdf
darshakparmar
 
How do you fast track Agentic automation use cases discovery?
DianaGray10
 
MuleSoft MCP Support (Model Context Protocol) and Use Case Demo
shyamraj55
 
Book industry state of the nation 2025 - Tech Forum 2025
BookNet Canada
 
Staying Human in a Machine- Accelerated World
Catalin Jora
 
AI Agents in the Cloud: The Rise of Agentic Cloud Architecture
Lilly Gracia
 
CIFDAQ Market Wrap for the week of 4th July 2025
CIFDAQ
 

Apache hadoop hbase

  • 1. Apache Hadoop HBASE Sheetal Sharma Intern At IBM Innovation Centre
  • 2. HBase is .. ● A distributed data store that can scale horizontally to 1,000s of commodity servers and petabytes of indexed storage. ● Designed to operate on top of the Hadoop distributed file system (HDFS) or Kosmos File System (KFS, aka Cloudstore) for scalability, fault tolerance, and high availability.
  • 3. Benefits ● Distributed storage ● Table-like in data structure multi-dimensional map ● High scalability ● High availability ● High performance
  • 4. HBase Is Not … ● Tables have one primary index, the row key. ● No join operators. ● Scans and queries can select a subset of available columns, perhaps by using a wildcard. ● There are three types of lookups: Fast lookup using row key and optional timestamp. Full table scan Range scan from region start to end.
  • 5. HBase Is Not …(2) ● Limited atomicity and transaction support. - HBase supports multiple batched mutations of single rows only. - Data is unstructured and untyped. ● No accessed or manipulated via SQL. - Programmatic access via Java, REST, or Thrift APIs. - Scripting via JRuby.
  • 6. Why HBase ? ● HBase is a Bigtable clone. ● It is open source ● It has a good community and promise for the future ● It is developed on top of and has good integration for the Hadoop platform, if you are using Hadoop already. ● It has a Cascading connector.
  • 7. When to use HBase
  • 8. HBase benefits than RDBMS ● No real indexes ● Automatic partitioning ● Scale linearly and automatically with new nodes ● Commodity hardware ● Fault tolerance ● Batch processing
  • 9. HBase: Part of Hadoop’s Ecosystem HBase is built on top of HDFS HBase files are internally stored in HDFS
  • 10. HBase vs. HDFS ● Both are distributed systems that scale to hundreds or thousands of nodes ● HDFS is good for batch processing (scans over big files) Not good for record lookup Not good for incremental addition of small batches Not good for updates
  • 11. HBase vs. HDFS (Cont’d) ● HBase is designed to efficiently address the above points Fast record lookup Support for record-level insertion Support for updates (not in place) ● HBase updates are done by creating new versions of values
  • 12. HBase vs. HDFS (Cont’d) If application has neither random reads or writes  Stick to HDFS
  • 14. HBase Data Model ● Data is divided into various tables ● Table is composed of columns, columns are grouped into column- families
  • 15. HBase Storage Model ● Partitioning - A table is horizontally partitioned into regions, each region is composed of sequential range of keys - Each region is managed by a RegionServer, a single RegionServer may hold multiple regions ● Persistence and data availability - HBase stores its data in HDFS, it doesn't replicate RegionServers and relies on HDFS replication for data availability. - Region data is cached in-memory * Updates and reads are served from in-memory cache (MemStore) * MemStore is flushed periodically to HDFS * Write Ahead Log (stored in HDFS) is used for durability of updates
  • 16. HBase: Keys and Column Families Each record is divided into Column Families Each row has a Key Each column family consists of one or more Columns
  • 17. Row key Time Stamp Column “ content s:” Column “ anchor:” “ com.apac he.ww w” t12 “ <html> …” t11 “ <html> …” t10 “ anchor:apache .com” “ APACH E” “ com.cnn.w ww” t15 “ anchor:cnnsi.co m” “ CNN” t13 “ anchor:my.look. ca” “ CNN.co m” t6 “ <html> …” t5 “ <html> …” t3 “ <html> …” • Key • Byte array • Serves as the primary key for the table • Indexed far fast lookup • Column Family • Has a name (string) • Contains one or more related columns • Column • Belongs to one column family • Included inside the row • familyName:columnNa me Column family named “Contents” Column family named “anchor” Column named “apache.com”
  • 18. Row key Time Stamp Column “ content s:” Column “ anchor:” “ com.apac he.ww w” t12 “ <html> …” t11 “ <html> …” t10 “ anchor:apache .com” “ APACH E” “ com.cnn.w ww” t15 “ anchor:cnnsi.co m” “ CNN” t13 “ anchor:my.look. ca” “ CNN.co m” t6 “ <html> …” t5 “ <html> …” t3 “ <html> …” • Version Number • Unique within each key • By default System’s timestamp • Data type is Long • Value (Cell) • Byte array Version number for each row value
  • 19. HBase Architecture Three Major Components • The HBaseMaster • One master • The HRegionServer • Many region servers • The HBase client
  • 20. HBase Components • Region • A subset of a table’s rows, like horizontal range partitioning • Automatically done • RegionServer (many slaves) • Manages data regions • Serves data for reads and writes (using a log) • Master • Responsible for coordinating the slaves • Assigns regions, detects failures • Admin functions
  • 22. ZooKeeper • HBase depends on ZooKeeper • By default HBase manages the ZooKeeper instance • E.g., starts and stops ZooKeeper • HMaster and HRegionServers register themselves with ZooKeeper
  • 23. Creating a Table HBaseAdmin admin= new HBaseAdmin(config); HColumnDescriptor []column; column= new HColumnDescriptor[2]; column[0]=new HColumnDescriptor("columnFamily1:"); column[1]=new HColumnDescriptor("columnFamily2:"); HTableDescriptor desc= new HTableDescriptor(Bytes.toBytes("MyTable")); desc.addFamily(column[0]); desc.addFamily(column[1]); admin.createTable(desc);
  • 24. Operations On Regions: Get() • Given a key  return corresponding record • For each value return the highest version ● Can control the number of versions you want
  • 25. Get() Select value from table where key=‘com.apache.www’ AND label=‘anchor:apache.com’ Row key Time Stamp Column “anchor:” “com.apache.www” t12 t11 t10 “anchor:apache.com” “APACHE” “com.cnn.www” t9 “anchor:cnnsi.com” “CNN” t8 “anchor:my.look.ca” “CNN.com” t6 t5 t3
  • 27. Scan() Select value from table where anchor=‘cnnsi.com’ Row key Time Stamp Column “anchor:” “com.apache.www” t12 t11 t10 “anchor:apache.com” “APACHE” “com.cnn.www” t9 “anchor:cnnsi.com” “CNN” t8 “anchor:my.look.ca” “CNN.com” t6 t5 t3
  • 28. Operations On Regions: Put() ● Insert a new record (with a new key), Or ● Insert a record for an existing key Implicit version number (timestamp) Explicit version number
  • 29. Operations On Regions: Delete() • Marking table cells as deleted • Multiple levels • Can mark an entire column family as deleted • Can make all column families of a given row as deleted • All operations are logged by the RegionServers • The log is flushed periodically
  • 30. Altering a Table Disable the table before changing the schema
  • 33. References ● Introduction to Hbase trac.nchc.org.tw/cloud/raw- attachment/wiki/.../hbase_intro.ppt ● web.cs.wpi.edu/~cs525/s13-MYE/lectures/5/HBase.pptx ● www-users.cselabs.umn.edu/classes/Spring.../Hadoop-HBase- Tutorial.ppt ● www.cs.kent.edu/~jin/Cloud12Spring/HbaseHivePig.pptx