Testing Big Data application is more a verification of its data processing rather than testing the individual features. It demands a high level of testing skills as the processing is very fast.
2. AGENDA
➔ Why Big Data ??
➔ Areas Of Big Data and Some Use Case
➔ What is Big Data Testing
➔ Challenges we face in Big Data Testing
➔ Approach or strategies to be follow while Big Data testing
7. 360° View of the Customer
It include demographic data, like customers’ names, addresses, household income and family
members, as well as sales information about which types of policies the customers hold.
It could also pull information from the company’s customer relationship management (CRM)
solution about the customers’ past interactions with the firm and even provide links to transcripts
of recent calls, email messages or chat sessions.
8. Price Optimization
● For any company, the goal is to set prices so that they maximize their
income.
● If the price is too high, they will sell fewer products, decreasing their net
returns. But if the price is too low, they may leave money on the table.
11. Testing Big Data application is more a verification of its data processing rather than testing the
individual features.
It demands a high level of testing skills as the processing is very fast. Processing may be of three
types
Batch Processing : Batch processing is where the processing happens of blocks of data that have
already been stored over a period of time.
Real Time Processing : Processing is done on real Data.
Interactive Processing: In interactive processing Data is already stored and analysed.
12.
13. What We Can Test In Big Data Application
Testing in big data projects is typically related to :
Functionality
Performance
Database
14. DataBase Testing can be divided into three steps:
Step 1: Data Staging Validation
Step 2: Process Validation
Step 3: Output Validation Phase
15. Data Staging Validation
● Correct data is pulled in hadoop system.
● Correct Data is extracted and loaded at correct HDFS
location.
● Compare source data with loaded data on Hadoop.
16. Process Validation
In this step the tester validates that the data obtained after
processing through the big data application is accurate. This
also involves testing the accuracy of the data generated from
Map Reduce or similar processes.
17. Output Validation Phase
In this step the tester validate that the output from the big data
application is correctly stored in the data warehouse.
They also verify that the data is accurately being represented in the
business intelligence system or any other target UI.
18. Performance Testing
Performance testing includes testing of job completion time,
memory utilization, data throughput.
Performance testing of the big data application focuses on the
following areas.
● Data Loading And Throughput
● Data Processing Speed
● Sub-System Performance
20. Functional Testing of Big Data Applications
Functional testing of the applications is quite similar in nature to testing of
normal software applications.
Functional testing of big data applications is performed by testing the front end
application based on user requirements.
The front end can be a web based application which interfaces with Hadoop
(or a similar framework on the back end).
21. Challenges we face in Big Data Testing
● Automation
Automated tools are not equipped to handle unexpected problems that arise during testing
● Large Dataset
○ Need to verify more data and need to do it faster
○ Need to automate the testing effort
○ Need to be able to test across different platform
24. Identify Infrastructural Changes:
● As we know infrastructure of data play a major role in big Data Testing
that's why We should always need to have an eye on Company’s
database, As analysis on old Data will not help in any growth of the
organization.
● If the old company data was stored in traditional formats it might not
facilitate the running of complex algorithms and analysis.
25. Establish Talent Pool:
● Human Resources is one of the most critical aspects of Big Data
Testing.
● Your Big Data team must have statisticians to make sense out of
data, business analysts to communicate insights to the decision
makers
26. Obsess Over Customer Satisfaction:
● The key use of Big Data is to generate insights that can help companies
serve their customers in a better way.
● Customer oriented marketing is the new way of approaching the market
and making revenues.
● At the end of the day, you need to communicate to your customer that
you are there to solve a problem and not just to make money.
27. Be Agile:
It is an universal truth that we can not ensure that our planned objective will execute
in planned way, There may exist many obstacles which were initially unknown while
implementing disruptive Technologies.
Always Ready to face challenges during the development phase
We might need to adjust our budget, people based on the circumstances and
insights you gather.
It is best to start with a high-level plan and make changes as the need be
Using Big Data
Cost Savings : Some tools of Big Data like Hadoop and Cloud-Based Analytics can bring cost advantages to business when large amounts of data are to be stored and these tools also help in identifying more efficient ways of doing business.
Time Reductions :The high speed of tools like Hadoop and in-memory analytics can easily identify new sources of data which helps businesses analyzing data immediately and make quick decisions based on the learnings.
New Product Development : By knowing the trends of customer needs and satisfaction through analytics you can create products according to the wants of customers.
Understand the market conditions : By analyzing big data you can get a better understanding of current market conditions. For example, by analyzing customers’ purchasing behaviors, a company can find out the products that are sold the most and produce products according to this trend. By this, it can get ahead of its competitors.
Control online reputation: Big data tools can do sentiment analysis. Therefore, you can get feedback about who is saying what about your company. If you want to monitor and improve the online presence of your business, then, big data tools can help in all this.
This might sound far-fetched and futuristic, but many companies today already have systems like this one in place, and they are using them to improve customer satisfaction and increase revenues and margins.
. Other than these Social Media Analysis and Response,Recommendation Engines,Security Intelligence are the use cases
Now some Real life examples:
Two step verification
Customer Support System
Recommended Ads on facebook
Discount/Missing you messages on your registered mobile
How a big data application works:
In first step Data is collected from different source Data and stored in Hadoop.
Then on this Stored Data ETL(Extract, Transform and Load)
After ET result is stored in DWH
From DWH we fetch informative Data for better decisions, greater efficiencies and higher profits.
Through BI(Business Intelligence).
batch processing is execution of series of operation on data without manual intervention.
Once data is loaded and analyzed, users will begin querying the data. Big data repositories present two common problems with interactive analysis: how to craft queries and how to keep response times low.
As we all know Testing An Big Data Application is quite Difficult All we can do is test if all the processes are woking fine or not and we can check or validate application's working at two points
Data Loading And Throughput: In this test the tester observes the rate at which data is consumed from different sources like sensor, logs etc, into the system
Data Processing Speed: In this test we measure the speed with the data is processed using MapReduce jobs.
Sub-System Performance: In this test we measure the performance of various individual components which are part of the overall application.
Automation testing for Big data requires someone with a technical expertise