This document discusses big data testing. It describes what big data testing involves, such as checking characteristics like data accuracy, consistency, and completeness. It outlines testing parameters, stages of big data testing, benefits like improved business decisions and market targeting, challenges like handling large datasets and tools used. It emphasizes the importance of big data testing for enterprises to ensure data quality and reduce downtimes and data threats. It invites the reader to check out offerings for big data testing services.

1. BIG DATATESTING
VERIFY STRUCTURED AND
UNSTRUCTURED DATA SETS
BUGRAPTOR
S

2. Big Data Testing
It involves checking various
characteristics like conformity,
accuracy, duplication,
consistency, validity, data
completeness, etc.

3. Testing Parameters
• DATA STORAGE
• COMMIT LOGS
• CONCURRENCY
• CACHING
• TIMEOUTS
• JVM PARAMETERS
• MESSAGE QUEUE

4. Why Big Data Testing
Big data deals with the data that are large and
complex, difficult to process and does not fit
well into tables. If Big Data systems not tested
appropriately, then it can affect business.

5. Big Data Testing Stages
DATA STAGING
VALIDATION
MAP REDUCE
VALIDATION
OUTPUT VALIDATION
PHASE

6. Overall
Benefits
DATA ACCURACY
QUALITY COST
IMPROVED BUSINESS DECISIONS
MINIMIZES LOSSES AND INCREASES
REVENUES
IMPROVED MARKET TARGETING AND
STRATEGIZING

7. Tools For Bigdata Testing
• Hadoop
• HPCC
• Storm
• Qubole
• Cassandra
• CouchDB

8. Challenges in Big Data Testing
AUTOMATION VIRTUALIZATION LARGE DATASET
Automated tools are not
equipped to handle
unexpected problems that
arise during
testing
Virtual machine latency
creates timing problems
in real time big data
testing.
Need to verify more data
and need to do it faster

9. Best Practices
TESTING BASED ON REQUIREMENTS
SAVE TIME
PRIORITIZE THE FIXING OF BUGS
CLEAR TEST OBJECTIVE
COMMUNICATION

10. HOW BIG DATA TESTING
WILL BENEFIT YOUR
ENTERPRISE?

11. Real-time Data
Validation
There are need of some filtering, sorting and analysis to ensure that the captured
data is valid and useful.

12. Reduced
Downtime
Deployment of Big Data applications revolving around predictive analytics. It helps to
improve data quality and related processes of the application which further reduces
the overall downtime.

13. Less Threat to Data Quality
Rigorous testing of data can save data from becoming
degradable and redundant.

14. Does Your Big
Data Require
Testing?Checkout our offerings for
Bigdata Testing Services

15. Let's work
together
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USA
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