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 A research problem is like a puzzle that scientists or researchers want to solve. To pick a good problem, think about what you like, look at what others have done, and find a question that nobody has fully answered. Consider if the question is important and if you have the time and tools to figure it out. Talk to people who know about the topic and get their advice. Make sure the problem matches your goals and what you want to achieve with your research.

 1. **Set Goals:** Clearly say what you want to find out with your exploration. 2. **Read Up:** Look at what others have already studied about your topic. 3. **Ask Questions:** Figure out specific things you want to learn. 4. **Pick How to Study:** Choose ways like talking to people or surveys to get your answers. 5. **Get Info:** Actually go and collect the information using the methods you picked. 6. **Look for Patterns:** Check the info for any common things or interesting points. 7. **Understand:** Think about what the patterns might mean. 8. **Tell Others:** Share what you found, knowing it's just a first look. 9. **Adjust Goals:** Use your discoveries to tweak what you want to find out next. 10. **Finish Up:** Sum up what you learned and suggest what could be studied more later on.

 **Research Design:** Research design is like a plan for a research project. It's how scientists decide to collect and analyze information to answer their questions. A good plan makes sure the study is organized and can give useful answers. **Survey Design Method:** Imagine you want to ask a bunch of people about something. Survey design is the way you plan how to do it: 1. **Say What You Want:** First, be clear about what you want to find out. What questions do you have? 2. **Choose How to Ask:** Decide if you'll use written questions (like a form) or talk to people (interviews). 3. **Pick People to Ask:** Choose a group of people that represents the larger group you're interested in. 4. **Ask Good Questions:** Create questions that are easy to understand and not tricky. Avoid questions that might make people answer in a certain way. 5. **Order Your Questions:** Put your questions in a logical order. Start with simple ones before asking more personal stuff. 6. **Test Your ...

 A hypothesis is like a smart guess that scientists make before doing an experiment. It's not just any guess; it comes from what we already know. Scientists then check if their guess is right by doing tests or watching things carefully. **Nature of Hypothesis:** 1. **Smart Guess:** It's not a random idea; it comes from what we already know. 2. **Testable:** You can check if the guess is right by doing tests or watching things. 3. **Specific:** It talks about one thing and gives a clear idea for the research. 4. **Maybe:** It's not a fact; we might change it based on what we find in the research. **Characteristics of Hypothesis:** 1. **Clear Words:** It's said in a way that everyone understands for the research to follow. 2. **Checkable:** You can test and see if the guess is right or wrong. 3. **Can Be Proven Wrong:** There's a way to show if the guess is not right, an important part of science. 4. **About the Research:** It talks directly about what the research is...

 A researcher is someone who explores things to find out more about them. Good researchers are curious and always want to learn new things. They think carefully about what they find, are patient when things take a long time, and are fair and honest. They pay close attention to details, can change their plans if needed, and don't give up easily. Researchers also need to explain what they find in a way that others can understand.

 Research in research methodology can be broadly categorized into quantitative and qualitative methods. Quantitative research involves numerical data and statistical analysis, while qualitative research focuses on non-numerical data, exploring attitudes, behaviors, and experiences. Within these categories, various approaches like experimental, survey, case study, ethnographic, and more exist, each suited to different research questions and goals.

 Q1: Long Answer Questions 1. Explain in detail how schema changes in NoSQL and RDBMS with an example. 2. What is Incremental Migration? Explain it in detail. 3. Write a short note on Transition Phase.

 Q1: Short Answer Questions 1. What is NoSQL? 2. Write any four points why NoSQL Database is used. 3. What is key value pair database? 4. Enlist the names of databases. 5. What is version stamp? 6. Define consistency. 7. What is sharding in NoSQL? 8. Write any four advantages of NoSQL Database. 9. What is Column Family Model? 10.What are Aggregate Data Models in NoSQL? 11.What is NoSQL data modeling? 12.Define Strong Consistency. 13.What is Eventual Consistency Q2: Long Answer Questions 1. What is the difference between JSON and XML? 2. Explain features of NoSQL in detail. 3. Explain any two Aggregate Data Models with suitable example 4. What is Graph Based Model and Key Value Model of Aggregate Data Models? Explain it in detail. 5. Explain advantages and disadvantages of NoSQL Database. 6. Explain distribution Models with an example. 7. Explain Map Reduce function in detail.

Neo4j is like a smart friend for data. You can talk to it in a special language (Cypher) to ask questions. It's flexible, letting you change things easily, and it comes with tools to solve problems in connected data, like finding the best routes or important people in a network. 

Cassandra works like a team where every computer (node) is equally important. The data is organized into categories (keyspaces), and each category has its own tables (column families). These tables are split into smaller parts (partitions) that each node takes care of. To be safe, copies of data are made and stored on different nodes (replication). This way, if one computer acts up, there are backup copies ready. It's like a teamwork strategy to manage and protect data.

 Imagine you have lots of information about people and their relationships. In a regular database, you might use tables to store data about individuals and another table to show connections between them. However, this can get messy when relationships become complex. Now, enter the graph database. It organizes data like a social network, where each person is a node, and the relationships between them are edges. This structure makes it super efficient to represent and navigate complex relationships. For example, if you want to find friends of friends or discover the shortest path between two individuals, a graph database excels at these types of queries. It's like having a roadmap of relationships, making it ideal for scenarios like social networks, fraud detection, or even logistics where understanding connections is crucial.

 Write 4 features of document database in very simple words Flexible Schema: Document databases can store data without a fixed structure, allowing you to add or change fields easily. Nested Data: They support nested or hierarchical data structures, enabling you to represent complex relationships within a single document. Querying: Document databases provide powerful querying capabilities, making it easy to retrieve specific information from large amounts of data. Scalability: These databases are designed to scale horizontally, meaning you can handle increased loads by adding more servers, ensuring performance as your data grows.