Tools and languages covered
- Big Data
- Apache Pig
- Database Testing
- Architecture Testing
Overview of Big Data Testing Training in Chennai
Big data is a collection of data sets that can be in any format, including files, photographs, and many others. Various approaches are used to examine this organized and unstructured databases. The main objective of big data testing is to validate your big data application with respect to each of its specific characteristics, ensuring that the data is flawless, of the highest possible quality, and guarantee the data’s correctness throughout data processing. All the necessary ideas are covered in our Big Data testing course material, preparing you to become a skilled Big data tester.
- Big Data Testing is a process that comprises assessing and evaluating the functionality of the Big Data Applications. Big Data is a gathering of enormous amounts of data that typical storage technologies are unable to handle.
- Most consumers may wonder, “Why exactly we require Big Data Testing?” Take a look at a familiar scenario when a bank experienced a severe failure. The Customer Bank Location Pin Code field is designated as CL by the bank database’s designers, along with the Customer ID and Customer Phone columns.
- The customer ID and customer phone number key-value pairs are what the bank is aiming to create. In this case, a typing mistake causes the MapReduce Algorithm to malfunction between the letters P and L.
- In the key-value pairs CL, the CP (Customer Phone Number) is then substituted (Customer bank location Pin). Customers will no longer have access to OTP or phone banking services.
- Testing an application that manages terabytes of data would require whole new levels of expertise and original thought. Three scenarios serve as the foundation for the basic and crucial tests on which the Quality Assurance Team focuses. Similarly,
- • Batch Data Processing Demonstration
- • Processing of Real-Time Data
- • Interactive data processing
- Batch Data Processing Demonstration
- The batch data processing test consists of test processes that process the data when the applications are processed using batch processing storage units like HDFS. The majority of the Batch Process Testing comprises
- Executing the program while using invalid inputs and changing the data volume
- Processing of Real-Time Data
- In the Real-Time Data Processing Test, the data is handled while the application is in Real-Time Data Processing mode. Tools for Real-Time Processing, such as Spark, are used to operate the application.
- In real-time testing, the application is evaluated in a live setting while being examined for stability.
- Interactive data processing
- The Interactive Data Processing Test incorporates real-world testing methods that communicate with the application from the perspective of a real-world user. HiveSQL and other interactive processing technologies are used in interactive data processing mode.
- The following phases define the general approach to testing a Big Data Application.
- • Ingestion of Data
- • Validation of the Output
- • Data Processing
- Ingestion of Data
- Using extraction tools, data is first imported from the source into the Big Data System. HDFS, MongoDB, or any comparable storage system might be used. The loaded data is then double-checked for mistakes and missing values.
- Data Processing
- The key-value pairs for the data are produced at this step. Later, the MapReduce logic is applied to all nodes to see if the method is working properly. A data validation process is carried out here to ensure that the result is as intended.
- Validation of the Output
- At this point, the created output is ready to be moved to the data warehouse. The transformation logic is confirmed here, as is the data integrity, and the key-value pairs at the location are validated for correctness.
- There are several categories in which to test a Big Data Application. A few of the key categories are shown below.
- • Unit Testing
- • Functional Testing
- • Non-Functional Testing
- • Performance Testing
- • Architecture
- Unit Testing
- Unit testing in Big Data is comparable to unit testing in other types of applications. The entire Big Data Application is separated into segments, and each section is extensively tested with numerous possible outcomes. If the segment fails, it is returned to development and improvements.
- Functional Testing
- Functional testing may also refer to the many stages of testing a big data application. The Big Data Application is intended to handle large amounts of data. With such a large amount and diversity of data, it is common to encounter data difficulties such as faulty data, duplicate values, metadata, missing values, and so on.
- This is why the pioneers in big data testing created the technique for big data functional testing. The following are the stages in which big data is tested.
- • Data Validation Phase
- • Data Integrity Phase
- • Data Ingestion Phase
- • Data Processing Phase
- • Data Storage Phase
- • Report Generation Phase
- Non-Functional Testing
- The Non-Functional Testing phase addresses Big Data’s three key aspects and features. The Big Data Volume, Velocity, and ultimately Variety Non-Functional Testing consists of five steps.
- • Data Quality Control
- • Infrastructure
- • Data Safety
- • Data Efficiency
- • Mechanism for Fail-over Testing
- Performance Testing
- Performance testing focuses on the performance provided by all components of the big data system. The following categories are included in performance testing.
- • Data Collecting Phase
- • Data Ingesting Phase
- • Data Processing
- • Component Peripheral testing
- The goal of architecture testing is to develop a reliable Hadoop architecture. Big Data Processing Application design is critical to achieving smooth operations. Poorly built building results in chaos such as,
- • Performance Decline
- • Failure of a Node
- • Data Latency is Excessive
- • High maintenance may be required.
- Validation of Data Staging
- • The initial phase in big data testing is the pre-Hadoop stage. It entails process validation.
- • Data from numerous sources should be checked to determine whether or not the data pulled is correct.
- • To ensure that the data in Hadoop and the source data match, they should be compared.
- • The location of the data in HDFS should also be confirmed.
- Validation for “MapReduce”
- MapReduce validation occurs after stage validation. During this step, the tester checks the business logic verification on each node and then validates them after running against several nodes, verifying that the business logic is correct.
- • The Map Reduce procedure works well.
- • On the data, criteria for data accumulation or segregation are applied.
- • Key-value pairs are produced.
- • Validate the data after the Map-Reduce operation.
- Phase of Output Validation
- The output validation procedure is the last stage. The output data files are created and prepared for transmission to an Enterprise Data Warehouse or any other system as needed.
- The actions to take in the third stage are as follows.
- • To ensure that the modification rules are appropriately applied
- • To ensure data integrity and successful data loading into the specified system
- • By comparing the target data to the HDFS file system data to ensure that no data corruption has occurred.
- Companies use this powerful Business Analytics solution to improve their overall performance. In this online Big data testing course, you will discover the abilities required to work with spreadsheets, substantial databases, and process debugging. If you have a certification in big data testing programming, you may apply for work in high-growth companies that are looking for big data experts to assist them accelerate their decision-making.
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The Learners Journey
We will prepare you on how to face Big Data Testing interviews along with this you will also have the process like students enquire, counseling, live demo, admission process, evaluation, certification, interview, and placement support.
Curriculum for Big Data Testing Course
- Introduction to Big Data
- What is Big data Testing?
- Advantages and Disadvantages of Big Data Testing
- Understanding Hadoop and Hadoop ecosystems
- Data formats in Big data
- Data flow concepts
- Hadoop Architecture
- Introduction to Hive
- HiveQL Syntax
- Use cases
- Map Reduce
- Creating Map Reduce Application
- Test cases
- PIG Architecture
- PIG use cases
- Data Analysis
Big data tools and use cases
- Introduction to Oozie
- What is Sqoop and Sqoop testing
- Testing with Pig and Sqoop
- Hadoop Components Use cases and installation
Big data testing
- Understanding Big data Testing
- Why do we need Big data Testing?
- Types of Big data Testing
- Big Data Testing Strategy
- Test cases
- Introduction to Database Testing
- Data flow and work flow
- Data staging validation
- Input and output validation
- Why we need Architecture Testing
- Testing approach
- Performance Testing
- Functional Testing
Testing of Big data application
- Test Environment
- Writing Test cases
- Executing test strategy
- Applying Testing Steps
- Big data test with real time data
- Roles and Responsibilities of a Tester in Big data application
- Testing challenges
- Advanced Testing trends
- Big data Testing project and interview question discussion
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Big Data Testing Trainers Profile
- Our trainer provides you with the of theoretical and practical Big data testing training.
- Our big data trainer is real-time professional with 5+ years of industry expertise in the big data domain.
- Our trainers are industry- experts and subject specialist who have running the application providing big data testing training to the students.
- Our trainers help students to establish their professional profiles and give rigorous training on interview preparation and handling techniques to boost their confidence.
Key Features of Big Data Testing Training
Real-Time Experts as Trainers
You will get the convenience to Learn from the Experts from the current industry, to share their Knowledge with Learners. Grab your slot with us.
We provide the Real-time Projects execution platform with the best-learning Experience for the students with Project and chance to get hire.
We have protected tie-up with more than 1200+ leading Small & Medium Companies to Support the students. once they complete the course.
Globally recoganized certification on course completion, and get best exposure in handling live tools & management in your projects.
We serve the best for the students to implement their passion for learning with an affordable fee. You also have instalment to pay your fees.
We intend to provide a great learning atmosphere for the students with flexible modes like Classroom or Online Training with fastrack mode
Bonus Takeaways at BTree
- Students benefits from outstanding big data test real-world experience.
- EMI options for both Debit and Credit cards.
- Career camp for fresher and working professional (IT or Non-IT).
- We provide with free demo session.
- We offer E-book for all courses.
- 10+ live project and supportive placement.
- Both online and offline are recorded using a secure method.
- We assist the students to prepare for mock interviews and resume building.
Big Data Testing Certification
- Our Big Data Testing Certification validates your technical knowledge earned during the training program.
- After completing the Big Data Testing Course, you will have a thorough understanding and practical skill in analyzing structured and unstructured data and performing Big Data Operations.
- With the aid of this certification, you may boost the value of your CV and obtain prominent career positions in major MNCs.
Our Team will help you with the registration process completely along with free demo sessions.
Every course training is built in a way that learners become job ready for the skill learned.
Along with our expert trainers our placement team brings in many job opportunities with preparation.
Get placed within 50 days of course completion with an exciting salary package at top MNCs globally.
Career Path after Big Data Testing Course
Big Data Testing Training Options
Our ultimate aim is to bring the best in establishing the career growth of the students in each batch individually. To enhance and achieve this, we have highly experienced and certified trainers to extract the best knowledge on Big Data Testing Certification. Eventually, we offer three modes of training options for the students to impart their best innovations using the Big Data tools & course skills. For more references and to choose a training mode, Contact our admission cell at +91-7397396665
- 40+ hours of e-Learning
- Work on live Big Data Testing tools
- 3 mock tests (50 Questions Each)
- Work on real-time industrial projects
- Equipped online classes with flexible timings
- 24×7 Trainers support & guidance
- 40+ hours of Big Data Testing classes
- Access live tools and projects
- 3 Mock exams with 50 Questions
- Live project experience
- Lifetime access to use labs
- 24×7 Trainers & placement support
- 45 hours of immense corporate training
- Support through our expert team
- 3 Mock exams (60 questions each)
- Work on real-time Big Data Testing projects
- Life-time support from our corporate trainers
- 24×7 learner aid and provision
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Advantages of Big data testing
- Big Data testing is useful in a variety of different ways in addition to the ones discussed above. Here are 5 such beneficial Big Data testing statistics that can impact your business:
- Minimized downtime
- It is a truth that many apps require data to function actively. Bad data has a tendency to reduce the application’s efficacy and performance. There are instances where businesses are unable to monitor data health, which causes downtime, during the gathering and dissemination of data on apps. Big Data testing, which may assist in altering data quality and related application processes that ultimately lower the total downtime, is an intelligent response to this problem.
- Better Marketing Techniques>
- Businesses are focusing on gaining advantages from big data as they build their digital marketing strategies right now. As web technology develops, it is now practical for organizations to compile vast volumes of data based on user behavior and history. For each visitor to the website, this data may be transformed into a convincing, unique experience. To put it briefly, big data testing may help organizations build up optimization targets so they can make better selections.
- Superior Data Security
- To preserve the degree of confidence built up by their clients, businesses engaging in client applications must ensure data confidentiality. The brand name is in the danger zone in the event of a breach in data security. To reduce the likelihood of a data security failure, it is advised to do big data testing at all levels.
- ROI Enhancement
- Enterprises must become more competitive while developing their big data and predictive analytics strategy. Prior to any analysis or processing, testing should be added as a required activity to guarantee that the organization is working with the proper data and can anticipate better results. Such a performance is a great illustration of increasing ROI and gaining a sizable advantage over rivals.
- controlled consistency
- Enterprises frequently employ a variety of programs to manage various data sets, which might result in inconsistent data. When the results of Big Data and Predictive Analytics are not consistent, it is unquestionably a great shame for enterprises. Big Data testing makes it possible to identify data variability in advance and take appropriate measures to eliminate the uncertainty.
Future of Big Data Testing
- • According to Hitesh, the necessity and demand for big data testing is only going to grow exponentially in the upcoming years due to the rising use of mobile devices, social media, and IoT.
- • Big data testing will be more in demand across a range of industries, which will lead to an increase in demand for qualified big data testers. Big data testing will then become a major area of attention for all the quality-focused enterprises since general testing and troubleshooting during the development of any product—including big data—follows a similar pattern.
- • It requires businesses to adopt this new strategy since data is now the company’s most valuable asset; otherwise, it may be very challenging to exist without data and the appropriate data analysis tools. Because of this, every business is ready to use the appropriate methods to gather, store, analyze, and test big data. They must be able to sift through massive volumes of data, see trends, and develop sound conclusions in order to make better judgments, enhance society, and propel our economy ahead.
- • When a company is prepared to gather and preserve the right data, it is often helpful for deconstructing a wide variety of threats. When such information is taken into account during the decision-making process, it becomes a fantastic resource for making wise decisions. However, it is evident that all important business decisions depend on the accuracy and quality of the data that was acquired.
- • As a result, great data accuracy may help companies arrive at the ideal moment. In order to extract knowledge and make it available at the appropriate moment, the information must be as precise as possible. Applications that go through load testing with different data types and volumes can handle a lot of data quickly and make it available when needed.
Big Data Testing Tools
- HDFS (Hadoop Distribution File System):
- • Apache Hadoop testing tool HDFS is a distributed file system that manages massive amounts of data.
- • Scalable from one to thousands of servers, each giving local processing and storage.
- • Is one of the key components of Apache Hadoop, along with MapReduce and YARN.
- High-Performance Computing Cluster and is a collection of several servers (computers) known as nodes. It is a data-intensive computing platform that is open source.
- By offering parallel architecture for system, data, and pipeline, the design delivers great performance in testing.
Cloudera, often known as CDH (Cloudera Distribution for Hadoop), is designed primarily to integrate Hadoop with over a dozen other essential open source projects.
- • Meets enterprise-level technology deployment requirements
- • Provides free platform distribution of Apache Hadoop, Apache Impala, and Apache Spark.
- • Increases security and governance.
- • Allows businesses to collect, manage, regulate, and disseminate massive amounts of data.
- A free and open-source NoSQL distributed database that can manage enormous volumes of data over several commodity computers. With no single point of failure, it provides high scalability and availability.
- This open source, free testing tool provides real-time unstructured data processing and is programmable in any language. Storm ensures the processing of any amount of data and is dependable at scale and fault-proof. Numerous use cases are available for this cross-platform technology, including log processing, real-time analytics, machine learning, and continuous computation.
Big data testing vs ETL testing
- ETL testing
- ETL testing includes the following methods:
- • Validation of data flow from source to target system.
- • Data count verification in the source and destination systems.
- • Validating data extraction and transformation in accordance with requirements and expectations.
- • examining the preservation of table relations, like as joins and keys, during the transition.
- Tools used often for ETL testing include QuerySurge, Informatica, etc.
- Data base testing
- Database testing places a greater emphasis on data quality, data consistency, and valid values. It entails the following operations:
- • examining the upkeep of the primary and foreign keys.
- • verifying the validity of the data values in a table’s columns.
- • Testing the correctness of data in columns. The number of month column, for example, should not be larger than 12.
- • Verifying for missing data in columns. Check to see whether there are any null columns that should have a valid value.
- Tools for testing databases frequently used include Selenium, QTP, etc.
Big data testing challenges
- Testing unstructured data will inevitably provide difficulties, particularly for those who are just starting to employ big data techniques. To ensure that you constantly adhere to data testing best practices, this big data testing course reveals both large data testing difficulties and solutions.
- Heterogeneity and Incompleteness of Data
- Problem: Many firms now store Exabyte of data in order to execute everyday operations. To ensure that this vast amount of data is accurate and pertinent to the company, testers must audit it. Even with hundreds of QA testers, it is difficult to manually test this amount of data.
- Solution: Big data automation is essential to your big data testing plan. In actuality, data automation systems are created to examine the accuracy of this quantity of data. Assign QA engineers who are experienced in developing and running automated tests for big data applications.
- High Scalability
- Problem: The networking, computing, and database accessibility for the big data application can all be severely impacted by a major rise in workload volume. Big data applications may not be able to meet high workload demands even if they are meant to handle massive volumes of data.
- Solution: The following data testing techniques should be used in your methods
- Clustering Techniques: Distribute vast volumes of data evenly throughout all cluster nodes. These big data files may then be simply divided into parts and stored on multiple cluster nodes. Machine reliance is reduced by duplicating file pieces and storing them on other nodes.
- Data Partitioning: This method to big data automation is less difficult and easier to implement. Through data partitioning, your QA testers may do parallelism at the CPU level.
- Management of Test Data
- Problem: It’s difficult to handle test data when your QA testers don’t understand it. When it comes to transferring, processing, and storing test data, tools used in big data situations will only take your team so far if your QA team does not understand the components inside the big data system.
- Solution: To begin, your QA team should collaborate with both your marketing and development teams to understand data extraction from various resources, data filtering, and pre and post-processing algorithms. Provide sufficient training to your QA engineers who are responsible for running test cases through your big data automation solutions so that test data is always maintained properly.
Salary packages for Big data tester
- An Entry Level Big Data Tester with less than three years of experience receives an annual pay of 3.7 Lakhs.
- The average starting salary for a Big Data Tester at various firms is around 6.0 Lakhs per year. A Big Data Tester must have at least 4 years of experience. A mid-career Big Data Tester with 4-9 years of experience receives an annual pay of 7.5 lakhs.
Advanced benefits at BTree
Our placement team supports in interview preparation process and will also help you with technical readiness with access to questions material.
BTree has created and re-write more than 300+ job-winning resumes and job cover letters for our learners at no additional cost driven by the course fees.
Recently Placed Candidates
It was a great experience at btree systems, the best thing I would say, is the way of teaching and the experienced professional who instruct the live projects and he does not hesitate to answer all my queries. The processes of attending mock interviews along with the technical training will boost my confidence level to complete my job interview, now I am hired by Tesco as a Big data tester.
After several research with several training institutes, I ended up with btree systems. Working on real-time projects & case studies will help us build hands-on experience. My trainer was so helpful in replaying, and solving issues, and clear and easy-to-understand concepts and I completed my certification and got a job in a reputed company.
I was a slow learner I couldn’t get any job. Then I choose btree for big data testing as my friend convey that they are amazing in providing the best training with job assistance. After joining, I started to pick up on each and every concept. Today I have been placed as a Software tester on Wipro, thanks to my trainer being patient enough to solve all my queries and guild me throughout. Always grateful.
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FAQ on Big Data Testing
If I miss the session?
- No worries. Even I miss the classes, I go to see the recording session of classes even big data testing course, we promise that the material will be made up as soon as humanly possible. Each student will receive a Flexi pass, which they may use whenever it suits them and which expires after 90 days.
How to enroll in the Big data testing training at BTree Systems?
- You can sign up for the big data testing Training in Chennai at BTree Systems by calling our Support Number +91-7397396665. You can also enrol in person at our office.
Will this session be only theory oriented?
- Absolutely not. We at BTree Systems are more concerned with giving proper practical training rather than just theoretical. We ensure that a student is able to deal with any form of real-world circumstance.
What are the modes of training offered for Big data testing course?
- BTree Systems offers a wide range of effective training methods, including:
- Classroom instruction
- One-on-one instruction
- Fast-track instruction
- Online instruction with a live teacher
- Customized instruction
Can I access the course material in online?
- Yes, students have lifetime access to the study materials, videos, and top MNC interview questions on the student site.
Does BTree Systems provide job assistance after the course completion?
- We assign students to placement sites that are suited to their specific needs. To assist students, feel at ease throughout the difficult interview process, we provide development programs that include mock interviews and presenting skills.
Do you provide any career guidance?
- Yes, we provide a career guidance camp for both new and experienced workers (IT or non-IT).
What are available payment options?
- You can pay in any of the ways listed below, and both offline and online training will send you an email receipt right away. We accept all popular payment methods. EMI replacements were just included to all of our courses.
- Option for EMI with a debit or credit card.
- Online Banking, Google Pay, PhonePe, PayPal, and Paytm
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