SlideShare a Scribd company logo
1 of 16
Download to read offline
Introducing BigSheets
Spreadsheet-Style Tool
for IBM InfoSphere BigInsights

Cynthia M. Saracco
Senior Solution Architect
IBM Silicon Valley Lab
What is BigSheets?

Browser-based analytics tool for business users.

Why BigSheets?

How can BigSheets help?

Business users need a non-technical approach
for analyzing Big Data.

Translating untapped data into actionable
business insights is a common requirement.

Built-in “readers” can work with data in
several common formats (JSON, CSV, TSV, …)

Visualizing and drilling down into enterprise
and Web data promotes new business
intelligence.

2

Spreadsheet-like interface enables business
users to gather and analyze data easily.

Users can combine and explore various types
of data to identify “hidden” insights.

© 2013 IBM Corporation
What you can do with BigSheets
Model “big data”
collected from various
sources in spreadsheetlike structures
Filter and enrich content
with built-in functions
Combine data in different
workbooks
Visualize results through
spreadsheets, charts
Export data into common
formats (if desired)
No programming knowledge needed!
3

© 2013 IBM Corporation
Sample Scenario
Data gathering

Data storage

• WebCrawler app
• DBMS import app
• BoardReader app
• Accelerators
• Flume
• Hadoop commands
• -...

• Distributed file system
• Web-based file browser
and administration

Data exploration,
manipulation, and
analysis
• BigSheets

InfoSphere BigInsights

Blue italics = IBM technology
4

© 2013 IBM Corporation
Technology

5

© 2013 IBM Corporation
Working with BigSheets
Create workbook (spreadsheet-style structure) to model target data
Customize workbook through graphical editor and built-in functions
– Filter data
– Manipulate data (e.g., concatenate fields)
– Combine data from multiple workbooks

“Run” workbook: apply work to full data set
Explore results in spreadsheet format and/or create charts
Optionally, export your data

6

© 2013 IBM Corporation
What are Workbooks?
Spreadsheet-like structures defined by user
Based on data accessible in BigInsights

7

© 2013 IBM Corporation
Creating a Workbook (one approach)
From BigSheets tab of
Web console, click New
Workbook button
Supply input
– Workbook name
– Source file (select from file
system directory tree)
– Appropriate “reader” (data
format translator)
• Built-in readers for Web
data, JSON, CSV, TSV,
Hive, etc.
• User-written plug-ins
supported

Save the workbook

8

© 2013 IBM Corporation
Customizing a workbook
Work with built-in editor
Add / delete columns
Filter data
Specify formulas to compute
new values using
spreadsheet-style syntax
Apply built-in or custom macro
functions
– Supplied text analytic functions
for popular business entities:
person, location, phone number,
etc.

...
9

© 2013 IBM Corporation
Visualizing results
Built-in charting facility aids analysis
Pie charts, bar charts, tag clouds, maps, etc.
Hover over sections to reveal details

10

© 2013 IBM Corporation
Exporting data
Useful for sharing with downstream applications
Several common formats supported
Save to distributed file system or display in browser (Save As -> local file)

11

© 2013 IBM Corporation
On-demand videos
Available on YouTube’s IBM Big
Data Channel at
http://www.youtube.com/user/ibm
bigdata
“Analyzing Social Media for IBM
Watson”
“Big Data Patent Analysis with
BigSheets”
“Big Data for Business Users”
“BigSheets in Action”
See the full list of videos at
http://tinyurl.com/biginsights
12

© 2013 IBM Corporation
Supplemental

13

© 2013 IBM Corporation
Inspecting runtime statistics

14

© 2013 IBM Corporation
Displaying the workflow diagram

15

© 2013 IBM Corporation
Built-in text analysis functions
Included with BigInsights
Version 2.1
BigSheets functions for
extracting common business
entities from text-based
columns
– Address, EmailAddress, Country,
Person, etc.
– Based on pre-built text extractor
library provided with BigInsights

Add Sheet -> Function ->
Categories -> entities

16

© 2013 IBM Corporation

More Related Content

What's hot

What's hot (20)

Fog Computing and the Internet of Things
Fog Computing and the Internet of ThingsFog Computing and the Internet of Things
Fog Computing and the Internet of Things
 
Big Data Evolution
Big Data EvolutionBig Data Evolution
Big Data Evolution
 
Big data Analytics
Big data AnalyticsBig data Analytics
Big data Analytics
 
Data Analytics for IoT
Data Analytics for IoT Data Analytics for IoT
Data Analytics for IoT
 
Module1 Mobile Computing Architecture
Module1 Mobile Computing ArchitectureModule1 Mobile Computing Architecture
Module1 Mobile Computing Architecture
 
Big data lecture notes
Big data lecture notesBig data lecture notes
Big data lecture notes
 
Aneka platform
Aneka platformAneka platform
Aneka platform
 
Chapter 1 big data
Chapter 1 big dataChapter 1 big data
Chapter 1 big data
 
Comet Cloud
Comet CloudComet Cloud
Comet Cloud
 
MOBILE BI
MOBILE BIMOBILE BI
MOBILE BI
 
HADOOP TECHNOLOGY ppt
HADOOP  TECHNOLOGY pptHADOOP  TECHNOLOGY ppt
HADOOP TECHNOLOGY ppt
 
Fog computing ( foggy cloud)
Fog computing  ( foggy cloud)Fog computing  ( foggy cloud)
Fog computing ( foggy cloud)
 
Ibm big data-platform
Ibm big data-platformIbm big data-platform
Ibm big data-platform
 
Big data ppt
Big  data pptBig  data ppt
Big data ppt
 
Pervasive Computing
Pervasive ComputingPervasive Computing
Pervasive Computing
 
Google App Engine
Google App EngineGoogle App Engine
Google App Engine
 
Artificial intelligence and IoT
Artificial intelligence and IoTArtificial intelligence and IoT
Artificial intelligence and IoT
 
Grid computing
Grid computing Grid computing
Grid computing
 
IOT DATA MANAGEMENT AND COMPUTE STACK.pptx
IOT DATA MANAGEMENT AND COMPUTE STACK.pptxIOT DATA MANAGEMENT AND COMPUTE STACK.pptx
IOT DATA MANAGEMENT AND COMPUTE STACK.pptx
 
Virtual Private Networks (VPN) ppt
Virtual Private Networks (VPN) pptVirtual Private Networks (VPN) ppt
Virtual Private Networks (VPN) ppt
 

Viewers also liked

InfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUX
InfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUXInfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUX
InfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUX
IBMInfoSphereUGFR
 
Using BigSheets for Spreadsheet-like Analytics
Using BigSheets for Spreadsheet-like AnalyticsUsing BigSheets for Spreadsheet-like Analytics
Using BigSheets for Spreadsheet-like Analytics
Dinesh Kumar.V
 

Viewers also liked (20)

Big Data: Introducing BigInsights, IBM's Hadoop- and Spark-based analytical p...
Big Data: Introducing BigInsights, IBM's Hadoop- and Spark-based analytical p...Big Data: Introducing BigInsights, IBM's Hadoop- and Spark-based analytical p...
Big Data: Introducing BigInsights, IBM's Hadoop- and Spark-based analytical p...
 
Overview - IBM Big Data Platform
Overview - IBM Big Data PlatformOverview - IBM Big Data Platform
Overview - IBM Big Data Platform
 
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...
Big Data: Getting off to a fast start with Big SQL (World of Watson 2016 sess...
 
Big Data: Getting started with Big SQL self-study guide
Big Data:  Getting started with Big SQL self-study guideBig Data:  Getting started with Big SQL self-study guide
Big Data: Getting started with Big SQL self-study guide
 
Big Data: SQL on Hadoop from IBM
Big Data:  SQL on Hadoop from IBM Big Data:  SQL on Hadoop from IBM
Big Data: SQL on Hadoop from IBM
 
Hadoop Summit Japan 2011 Fall - LT by IBM
Hadoop Summit Japan 2011 Fall - LT by IBMHadoop Summit Japan 2011 Fall - LT by IBM
Hadoop Summit Japan 2011 Fall - LT by IBM
 
InfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUX
InfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUXInfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUX
InfoSphere Streams Technical Overview - Use Cases Big Data - Jerome CHAILLOUX
 
Big data presentation (2014)
Big data presentation (2014)Big data presentation (2014)
Big data presentation (2014)
 
Using BigSheets for Spreadsheet-like Analytics
Using BigSheets for Spreadsheet-like AnalyticsUsing BigSheets for Spreadsheet-like Analytics
Using BigSheets for Spreadsheet-like Analytics
 
Big Data: Explore Hadoop and BigInsights self-study lab
Big Data:  Explore Hadoop and BigInsights self-study labBig Data:  Explore Hadoop and BigInsights self-study lab
Big Data: Explore Hadoop and BigInsights self-study lab
 
Big Data: Querying complex JSON data with BigInsights and Hadoop
Big Data:  Querying complex JSON data with BigInsights and HadoopBig Data:  Querying complex JSON data with BigInsights and Hadoop
Big Data: Querying complex JSON data with BigInsights and Hadoop
 
Big Data: InterConnect 2016 Session on Getting Started with Big Data Analytics
Big Data:  InterConnect 2016 Session on Getting Started with Big Data AnalyticsBig Data:  InterConnect 2016 Session on Getting Started with Big Data Analytics
Big Data: InterConnect 2016 Session on Getting Started with Big Data Analytics
 
Big Data: Working with Big SQL data from Spark
Big Data:  Working with Big SQL data from Spark Big Data:  Working with Big SQL data from Spark
Big Data: Working with Big SQL data from Spark
 
Big Data: SQL query federation for Hadoop and RDBMS data
Big Data:  SQL query federation for Hadoop and RDBMS dataBig Data:  SQL query federation for Hadoop and RDBMS data
Big Data: SQL query federation for Hadoop and RDBMS data
 
Big Data, Big Thinking: Simplified Architecture Webinar Fact Sheet
Big Data, Big Thinking: Simplified Architecture Webinar Fact SheetBig Data, Big Thinking: Simplified Architecture Webinar Fact Sheet
Big Data, Big Thinking: Simplified Architecture Webinar Fact Sheet
 
Big Data: Big SQL and HBase
Big Data:  Big SQL and HBase Big Data:  Big SQL and HBase
Big Data: Big SQL and HBase
 
IBM Hadoop-DS Benchmark Report - 30TB
IBM Hadoop-DS Benchmark Report - 30TBIBM Hadoop-DS Benchmark Report - 30TB
IBM Hadoop-DS Benchmark Report - 30TB
 
Big Data & Analytics Architecture
Big Data & Analytics ArchitectureBig Data & Analytics Architecture
Big Data & Analytics Architecture
 
Help Desk Presentation 09202009
Help Desk Presentation 09202009Help Desk Presentation 09202009
Help Desk Presentation 09202009
 
Machine Data Analytics
Machine Data AnalyticsMachine Data Analytics
Machine Data Analytics
 

Similar to Big Data: Technical Introduction to BigSheets for InfoSphere BigInsights

ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...
ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...
ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...
Christoph Adler
 
Extreme SSAS- SQL 2011
Extreme SSAS- SQL 2011Extreme SSAS- SQL 2011
Extreme SSAS- SQL 2011
Itay Braun
 
Data Architecture Process in a BI environment
Data Architecture Process in a BI environmentData Architecture Process in a BI environment
Data Architecture Process in a BI environment
Sasha Citino
 

Similar to Big Data: Technical Introduction to BigSheets for InfoSphere BigInsights (20)

ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...
ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...
ICS UserGroup - 2015 - Infrastructure Assessment - Analyze, Visualize and Opt...
 
NLS Banking Solutions - NQuest BI
NLS Banking Solutions - NQuest BINLS Banking Solutions - NQuest BI
NLS Banking Solutions - NQuest BI
 
engage 2015 - - 2015 - Infrastructure Assessment - Analyze, Visualize and Op...
engage 2015 -  - 2015 - Infrastructure Assessment - Analyze, Visualize and Op...engage 2015 -  - 2015 - Infrastructure Assessment - Analyze, Visualize and Op...
engage 2015 - - 2015 - Infrastructure Assessment - Analyze, Visualize and Op...
 
Extreme SSAS- SQL 2011
Extreme SSAS- SQL 2011Extreme SSAS- SQL 2011
Extreme SSAS- SQL 2011
 
Business intelligent
Business intelligentBusiness intelligent
Business intelligent
 
Power BI for Big Data and the New Look of Big Data Solutions
Power BI for Big Data and the New Look of Big Data SolutionsPower BI for Big Data and the New Look of Big Data Solutions
Power BI for Big Data and the New Look of Big Data Solutions
 
SharePoint - You've got it, now what?
SharePoint - You've got it, now what?SharePoint - You've got it, now what?
SharePoint - You've got it, now what?
 
New dimensions for_reporting
New dimensions for_reportingNew dimensions for_reporting
New dimensions for_reporting
 
Data Architecture Process in a BI environment
Data Architecture Process in a BI environmentData Architecture Process in a BI environment
Data Architecture Process in a BI environment
 
Create Your First SQL Server Cubes
Create Your First SQL Server CubesCreate Your First SQL Server Cubes
Create Your First SQL Server Cubes
 
Microsoft Fabric Introduction
Microsoft Fabric IntroductionMicrosoft Fabric Introduction
Microsoft Fabric Introduction
 
Sap BusinessObjects 4
Sap BusinessObjects 4Sap BusinessObjects 4
Sap BusinessObjects 4
 
Mihai_Nuta
Mihai_NutaMihai_Nuta
Mihai_Nuta
 
Azure Synapse Analytics Overview (r1)
Azure Synapse Analytics Overview (r1)Azure Synapse Analytics Overview (r1)
Azure Synapse Analytics Overview (r1)
 
SPS Vancouver 2018 - What is CDM and CDS
SPS Vancouver 2018 - What is CDM and CDSSPS Vancouver 2018 - What is CDM and CDS
SPS Vancouver 2018 - What is CDM and CDS
 
Libera la potenza del Machine Learning
Libera la potenza del Machine LearningLibera la potenza del Machine Learning
Libera la potenza del Machine Learning
 
IBM Operations Analytics For z Systems V2.2 - Client Short Pres
IBM Operations Analytics For z Systems V2.2 - Client Short PresIBM Operations Analytics For z Systems V2.2 - Client Short Pres
IBM Operations Analytics For z Systems V2.2 - Client Short Pres
 
OC Big Data Monthly Meetup #6 - Session 1 - IBM
OC Big Data Monthly Meetup #6 - Session 1 - IBMOC Big Data Monthly Meetup #6 - Session 1 - IBM
OC Big Data Monthly Meetup #6 - Session 1 - IBM
 
SD Big Data Monthly Meetup #4 - Session 1 - IBM
SD Big Data Monthly Meetup #4 - Session 1 - IBMSD Big Data Monthly Meetup #4 - Session 1 - IBM
SD Big Data Monthly Meetup #4 - Session 1 - IBM
 
How does Microsoft solve Big Data?
How does Microsoft solve Big Data?How does Microsoft solve Big Data?
How does Microsoft solve Big Data?
 

Recently uploaded

Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 

Recently uploaded (20)

[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdfCyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, AdobeApidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 

Big Data: Technical Introduction to BigSheets for InfoSphere BigInsights

  • 1. Introducing BigSheets Spreadsheet-Style Tool for IBM InfoSphere BigInsights Cynthia M. Saracco Senior Solution Architect IBM Silicon Valley Lab
  • 2. What is BigSheets? Browser-based analytics tool for business users. Why BigSheets? How can BigSheets help? Business users need a non-technical approach for analyzing Big Data. Translating untapped data into actionable business insights is a common requirement. Built-in “readers” can work with data in several common formats (JSON, CSV, TSV, …) Visualizing and drilling down into enterprise and Web data promotes new business intelligence. 2 Spreadsheet-like interface enables business users to gather and analyze data easily. Users can combine and explore various types of data to identify “hidden” insights. © 2013 IBM Corporation
  • 3. What you can do with BigSheets Model “big data” collected from various sources in spreadsheetlike structures Filter and enrich content with built-in functions Combine data in different workbooks Visualize results through spreadsheets, charts Export data into common formats (if desired) No programming knowledge needed! 3 © 2013 IBM Corporation
  • 4. Sample Scenario Data gathering Data storage • WebCrawler app • DBMS import app • BoardReader app • Accelerators • Flume • Hadoop commands • -... • Distributed file system • Web-based file browser and administration Data exploration, manipulation, and analysis • BigSheets InfoSphere BigInsights Blue italics = IBM technology 4 © 2013 IBM Corporation
  • 6. Working with BigSheets Create workbook (spreadsheet-style structure) to model target data Customize workbook through graphical editor and built-in functions – Filter data – Manipulate data (e.g., concatenate fields) – Combine data from multiple workbooks “Run” workbook: apply work to full data set Explore results in spreadsheet format and/or create charts Optionally, export your data 6 © 2013 IBM Corporation
  • 7. What are Workbooks? Spreadsheet-like structures defined by user Based on data accessible in BigInsights 7 © 2013 IBM Corporation
  • 8. Creating a Workbook (one approach) From BigSheets tab of Web console, click New Workbook button Supply input – Workbook name – Source file (select from file system directory tree) – Appropriate “reader” (data format translator) • Built-in readers for Web data, JSON, CSV, TSV, Hive, etc. • User-written plug-ins supported Save the workbook 8 © 2013 IBM Corporation
  • 9. Customizing a workbook Work with built-in editor Add / delete columns Filter data Specify formulas to compute new values using spreadsheet-style syntax Apply built-in or custom macro functions – Supplied text analytic functions for popular business entities: person, location, phone number, etc. ... 9 © 2013 IBM Corporation
  • 10. Visualizing results Built-in charting facility aids analysis Pie charts, bar charts, tag clouds, maps, etc. Hover over sections to reveal details 10 © 2013 IBM Corporation
  • 11. Exporting data Useful for sharing with downstream applications Several common formats supported Save to distributed file system or display in browser (Save As -> local file) 11 © 2013 IBM Corporation
  • 12. On-demand videos Available on YouTube’s IBM Big Data Channel at http://www.youtube.com/user/ibm bigdata “Analyzing Social Media for IBM Watson” “Big Data Patent Analysis with BigSheets” “Big Data for Business Users” “BigSheets in Action” See the full list of videos at http://tinyurl.com/biginsights 12 © 2013 IBM Corporation
  • 14. Inspecting runtime statistics 14 © 2013 IBM Corporation
  • 15. Displaying the workflow diagram 15 © 2013 IBM Corporation
  • 16. Built-in text analysis functions Included with BigInsights Version 2.1 BigSheets functions for extracting common business entities from text-based columns – Address, EmailAddress, Country, Person, etc. – Based on pre-built text extractor library provided with BigInsights Add Sheet -> Function -> Categories -> entities 16 © 2013 IBM Corporation