Azure Data Engineer Interview Questions and Answers — Azure Data Factory

 1. What is Azure Data Factory?

Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation. It supports data ingestion from various sources, transformation using data flows or external compute services, and data movement to a variety of destinations.

2. What are the key components of Azure Data Factory?

The key components of Azure Data Factory include:

• Pipelines: Logical grouping of activities that perform a task.
• Activities: Define the actions to be performed within a pipeline.
• Datasets: Represent data structures within data stores, pointing to the data you want to use in activities.
• Linked Services: Define the connection information needed for Data Factory to connect to external resources.
• Triggers: Define when a pipeline execution needs to be kicked off.

3. How does Azure Data Lake Storage Gen2 differ from Azure Blob Storage?

Answer: Azure Data Lake Storage Gen2 is designed for big data analytics and provides hierarchical namespace capabilities, enabling efficient management of large datasets and fine-grained access control. Azure Blob Storage is more general-purpose and used for storing unstructured data. Data Lake Storage Gen2 builds on top of Blob Storage but includes enhancements for big data workloads.

4. What is the purpose of the Integration Runtime in Azure Data Factory?

Answer: Integration Runtime (IR) in Azure Data Factory acts as a bridge between the activity and the data store. It supports data movement, dispatch, and integration capabilities across different network environments, including Azure, on-premises, and hybrid scenarios. There are three types: Azure IR, Self-hosted IR, and Azure-SSIS IR.

5. Explain the concept of a Data Lake and its importance.

Answer: A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Its importance lies in its ability to ingest data in its raw form from various sources, providing a foundation for advanced analytics and machine learning. It allows for schema-on-read, meaning data is interpreted at the time of processing, offering flexibility and scalability.

6. How would you optimize the performance of an Azure Data Factory pipeline?

Answer: Optimizing performance in ADF pipelines can be achieved by:

• Using parallelism and partitioning to process large datasets efficiently.
• Reducing data movement by processing data in place where possible.
• Leveraging the performance tuning capabilities of the underlying data stores and compute resources.
• Using appropriate Integration Runtime (IR) types and configurations based on the network environment.

7. What is PolyBase and how is it used in Azure SQL Data Warehouse?

Answer: PolyBase is a data virtualization feature in Azure SQL Data Warehouse (now Azure Synapse Analytics) that allows you to query data stored in external sources like Azure Blob Storage, Azure Data Lake Storage, and Hadoop, using T-SQL. It enables seamless data integration and querying without the need to move data, thus optimizing performance and reducing data redundancy.

8. Describe the process of implementing incremental data loading in Azure Data Factory.

Answer: Incremental data loading involves only loading new or changed data since the last load. This can be achieved by:

• Using watermarking techniques with a column like timestamp or ID to identify new or changed records.
• Implementing change data capture (CDC) mechanisms in the source systems.
• Using lookup and conditional split activities in ADF to separate new/changed data from the rest.

9. What are Delta Lake tables and why are they important in big data processing?

Answer: Delta Lake tables are an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. They enable reliable and scalable data lakes with features like versioned data, schema enforcement, and the ability to handle streaming and batch data in a unified manner. They ensure data integrity and consistency, making them essential for complex data processing pipelines.

10. How can you implement security and compliance in an Azure Data Lake?

Answer: Security and compliance in an Azure Data Lake can be implemented by:

• Using Azure Active Directory (AAD) for authentication and fine-grained access control.
• Applying Role-Based Access Control (RBAC) to manage permissions.
• Encrypting data at rest and in transit.
• Monitoring and auditing access and activity using Azure Monitor and Azure Security Center.
• Implementing data governance policies and ensuring compliance with industry standards and regulations.

1. What are the key components of Azure Data Factory?

• Answer: The main components of Azure Data Factory are:
• Pipelines: Groups of activities that perform a unit of work.
• Activities: Tasks performed by the pipeline, such as data movement or transformation.
• Datasets: Represents the data structures within the data stores that the activities work with.
• Linked Services: Defines the connection information needed for Data Factory to connect to external data sources.
• Triggers: Units of processing that determine when a pipeline execution should be kicked off.

2. How do you create a pipeline in Azure Data Factory?

• Answer: To create a pipeline in Azure Data Factory:

1. Open the Azure portal and navigate to your Data Factory.
2. In the Data Factory UI, go to the “Author & Monitor” section.
3. Click on the “Create pipeline” button.
4. Add activities to the pipeline by dragging and dropping them from the Activities pane.
5. Configure the activities as needed.
6. Save and publish the pipeline.

3. What is the purpose of Linked Services in Azure Data Factory?

• Answer: Linked Services in Azure Data Factory act as connection strings, defining the connection information needed for Data Factory to connect to external data sources. They are used to specify the credentials and connection details required to access different types of data stores, such as Azure Blob Storage, Azure SQL Database, and others.

4. What types of data stores can Azure Data Factory connect to?

• Answer: Azure Data Factory can connect to a wide range of data stores, including:
• Azure services (e.g., Azure Blob Storage, Azure SQL Database, Azure Data Lake Storage)
• On-premises data stores (e.g., SQL Server, Oracle, File System)
• Cloud-based data stores (e.g., Amazon S3, Google Cloud Storage)
• SaaS applications (e.g., Salesforce, Dynamics 365)

5. What is the Copy Activity in Azure Data Factory, and how is it used?

• Answer: The Copy Activity in Azure Data Factory is used to copy data from a source data store to a destination data store. It is commonly used in ETL operations. To use the Copy Activity:

1. Define the source and destination datasets.
2. Configure the source and destination properties in the Copy Activity.
3. Specify any additional settings such as data mapping, logging, and error handling.
4. Add the Copy Activity to a pipeline and run it.

6. Explain the concept of Integration Runtime (IR) in Azure Data Factory.

• Answer: Integration Runtime (IR) is the compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments. There are three types of IR:
• Azure IR: Used for data movement and transformation within Azure.
• Self-hosted IR: Installed on an on-premises machine or a virtual machine in a virtual network to connect to on-premises data sources.
• Azure-SSIS IR: Used for running SQL Server Integration Services (SSIS) packages in the cloud.

7. How do you implement an incremental data load in Azure Data Factory?

• Answer: To implement an incremental data load in Azure Data Factory:

1. Identify the column that will be used to track changes (e.g., a timestamp or ID column).
2. Store the last loaded value of this column in a control table or variable.
3. In the pipeline, use the stored value to filter the source data for new or updated records.
4. Load the incremental data into the destination data store.
5. Update the stored value to reflect the latest loaded record.

8. How can you handle data transformation in Azure Data Factory?

• Answer: Data transformation in Azure Data Factory can be handled using:
• Mapping Data Flows: Visual interface for designing data transformations.
• Data Flow Activities: Perform transformations using SQL, Spark, or custom scripts.
• External Services: Integrate with Azure Databricks or HDInsight for complex transformations.

9. What are Tumbling Window Triggers in Azure Data Factory?

• Answer: Tumbling Window Triggers are a type of trigger in Azure Data Factory that fire at periodic intervals. They are useful for processing data in fixed-size, non-overlapping time windows. Each trigger instance is independent, and the trigger will only execute if the previous instance has completed.

10. How do you monitor and troubleshoot pipeline failures in Azure Data Factory?

• Answer: Monitoring and troubleshooting pipeline failures in Azure Data Factory can be done using:
• Azure Monitor: Provides a comprehensive view of pipeline runs, including success and failure metrics.
• Activity Runs: Reviewing the details of individual activity runs to identify the root cause of failures.
• Logs and Alerts: Configuring logging to capture detailed execution logs and setting up alerts to notify of failures.
• Retry Policies: Implementing retry policies for transient failures.
• Debugging Tools: Using the debug mode in the Data Factory UI to test and troubleshoot pipelines before deployment.

1. What is the purpose of the Mapping Data Flow in Azure Data Factory?

• Answer: The Mapping Data Flow in Azure Data Factory allows users to design and execute complex data transformations visually without writing code. It provides a graphical interface to transform data at scale using data flow transformations like join, aggregate, lookup, and filter.

2. How do you schedule a pipeline in Azure Data Factory?

• Answer: To schedule a pipeline in Azure Data Factory, you use triggers. There are three types of triggers:
• Schedule trigger: Runs pipelines on a specified schedule.
• Tumbling window trigger: Runs pipelines in a series of fixed-size, non-overlapping time intervals.
• Event-based trigger: Runs pipelines in response to events, such as the arrival of a file in a storage account.

3. What is the role of parameters in Azure Data Factory?

• Answer: Parameters in Azure Data Factory allow you to pass dynamic values to pipelines, datasets, and linked services at runtime. They enable reusability and flexibility by allowing you to customize the behavior of your data factory components based on input values.

4. How can you monitor the execution of pipelines in Azure Data Factory?

• Answer: You can monitor the execution of pipelines in Azure Data Factory using the Monitor tab in the ADF UI. It provides a dashboard with real-time status, run history, and detailed logs for pipelines, activities, and triggers. You can also set up alerts and notifications to stay informed about pipeline execution.

5. What are the benefits of using Integration Runtime (IR) in Azure Data Factory?

• Answer: Integration Runtime (IR) in Azure Data Factory provides the compute infrastructure to perform data integration operations. The benefits include:
• Scalability: Scale out to meet data volume and processing needs.
• Flexibility: Choose between Azure IR, Self-hosted IR, and Azure-SSIS IR based on your requirements.
• Security: Securely move data across different network environments.
• Compatibility: Support for various data stores and transformation activities.

6. How do you handle error logging and retry policies in Azure Data Factory?

• Answer: In Azure Data Factory, you can handle error logging and retry policies by:
• Setting up retry policies: Configure retry policies for activities to handle transient failures. Specify the maximum retry count and the interval between retries.
• Using the Set Variable activity: Capture error details using the Set Variable activity in the pipeline and store the error information.
• Creating custom error handling: Use conditional activities like If Condition or Switch to implement custom error handling logic.
• Integrating with monitoring tools: Integrate with Azure Monitor and Log Analytics for advanced error logging and alerting.

7. Explain the concept of Data Flow Debugging in Azure Data Factory.

• Answer: Data Flow Debugging in Azure Data Factory allows you to test and troubleshoot data flows interactively before publishing them. When debugging is enabled, a debug cluster is spun up, and you can preview data transformations, inspect intermediate data, and validate the logic step-by-step. This helps ensure that the data flow performs as expected and allows for quicker identification and resolution of issues.

8. What are the best practices for designing pipelines in Azure Data Factory?

• Answer: Best practices for designing pipelines in Azure Data Factory include:
• Modularize pipelines: Break down complex workflows into smaller, reusable pipelines.
• Parameterize components: Use parameters to create flexible and reusable pipelines, datasets, and linked services.
• Implement logging and monitoring: Set up comprehensive logging and monitoring to track pipeline executions and diagnose issues.
• Optimize performance: Use parallelism, data partitioning, and efficient data movement strategies to optimize pipeline performance.
• Secure data: Implement robust security practices, such as using managed identities, encryption, and access control.

9. How do you use Azure Key Vault in Azure Data Factory?

• Answer: Azure Key Vault can be used in Azure Data Factory to securely store and manage sensitive information such as connection strings, secrets, and keys. To use Azure Key Vault in ADF:

1. Create a Key Vault in Azure and add your secrets.
2. In ADF, create a linked service for Azure Key Vault.
3. Reference the Key Vault secrets in your linked services, datasets, and pipeline parameters by using the Key Vault linked service.

10. Explain how to implement incremental data load using Azure Data Factory.

• Answer: Incremental data load in Azure Data Factory involves loading only the new or changed data since the last load. It can be implemented by:
• Using watermark columns: Use a column that captures the last modified time or a sequential ID. Store the last processed value and use it to filter new records during subsequent loads.
• Source query filtering: Use source queries to fetch only new or changed data based on the watermark column.
• Upsert patterns: Implement upsert (update and insert) logic in the destination to handle new and updated records.
• Delta Lake: Use Delta Lake with ADF to manage incremental data loads efficiently with ACID transactions and versioning.

1. Scenario: Your company needs to move data from an on-premises SQL Server database to an Azure SQL Database daily. How would you set up this data movement in Azure Data Factory?

Answer: To set up this data movement:

1. Create a Self-hosted Integration Runtime (IR) to securely connect to the on-premises SQL Server.
2. Create linked services for both the on-premises SQL Server and Azure SQL Database.
3. Create datasets for the source and destination tables.
4. Create a pipeline with a Copy Data activity to move the data.
5. Schedule the pipeline using a schedule trigger to run daily.

2. Scenario: You need to transform data from a CSV file in Azure Blob Storage and load it into an Azure SQL Database. Describe how you would accomplish this using Azure Data Factory.

Answer: To accomplish this:

1. Create linked services for Azure Blob Storage and Azure SQL Database.
2. Create datasets for the source CSV file and the destination SQL table.
3. Create a pipeline with a Data Flow activity.
4. In the Data Flow, read the data from the CSV file, apply the required transformations, and write the transformed data to the SQL table.
5. Trigger the pipeline as needed.

3. Scenario: Your data pipeline fails intermittently due to network issues. How would you handle this in Azure Data Factory?

Answer: To handle intermittent pipeline failures:

1. Configure retry policies for the affected activities, specifying the maximum retry count and the retry interval.
2. Use the Set Variable activity to capture and log error details.
3. Implement conditional activities like If Condition to retry or reroute the process based on error types.

4. Scenario: You need to copy data from multiple CSV files stored in an Azure Data Lake Storage Gen2 account to an Azure SQL Database. How would you configure this in Azure Data Factory?

Answer: To configure this data movement:

1. Create linked services for Azure Data Lake Storage Gen2 and Azure SQL Database.
2. Create datasets for the source CSV files and the destination SQL table.
3. Use a wildcard in the source dataset to specify multiple CSV files.
4. Create a pipeline with a Copy Data activity to move the data from the CSV files to the SQL table.

5. Scenario: You have a pipeline that must run only after another pipeline completes successfully. How would you implement this in Azure Data Factory?

Answer: To implement this dependency:

1. Use Execute Pipeline activity to call the dependent pipeline.
2. Set up an activity dependency to ensure that the subsequent pipeline runs only if the previous pipeline completes successfully.

6. Scenario: Your data transformation logic involves multiple steps, including filtering, aggregation, and joining data from two different sources. How would you implement this in Azure Data Factory?

Answer: To implement complex data transformations:

1. Create linked services for the data sources.
2. Create datasets for the input and output data.
3. Create a pipeline with a Mapping Data Flow activity.
4. In the Data Flow, add transformations to filter, aggregate, and join the data from the two sources.
5. Write the transformed data to the desired output destination.

7. Scenario: You need to incrementally load data from an on-premises SQL Server to an Azure SQL Database. Explain how you would achieve this in Azure Data Factory.

Answer: To achieve incremental data loading:

1. Identify a watermark column (e.g., last modified date) in the source table.
2. Store the last processed value of the watermark column.
3. Create a pipeline with a Copy Data activity.
4. Use a dynamic query in the source dataset to filter data based on the stored watermark value.
5. Update the watermark value after each successful load.

8. Scenario: You are tasked with integrating data from various formats (CSV, JSON, Parquet) stored in an Azure Data Lake Storage Gen2 into a single Azure SQL Database table. Describe your approach.

Answer: To integrate data from various formats:

1. Create linked services for Azure Data Lake Storage Gen2 and Azure SQL Database.
2. Create datasets for each file format and the destination SQL table.
3. Create a pipeline with multiple Copy Data activities, each handling a different file format.
4. Use Data Flow activities to apply necessary transformations and merge the data into a single table.

9. Scenario: You need to implement a solution that dynamically chooses the source and destination based on input parameters. How would you configure this in Azure Data Factory?

Answer: To configure dynamic source and destination selection:

1. Create parameters in the pipeline for the source and destination.
2. Use parameterized linked services and datasets to reference the source and destination based on input parameters.
3. Pass the parameter values at runtime when triggering the pipeline.

10. Scenario: Your company requires a data pipeline to process and analyze streaming data in near real-time. Explain how you would implement this using Azure Data Factory.

Answer: To implement near real-time data processing:

1. Use Azure Event Hubs or Azure IoT Hub to ingest streaming data.
2. Set up an Azure Stream Analytics job to process the streaming data and write the output to a data store like Azure Blob Storage or Azure SQL Database.
3. Use Azure Data Factory to orchestrate the process, periodically running pipelines to load and transform the processed data for further analysis.

1. Scenario: Your company needs to copy data from a REST API endpoint to an Azure SQL Database every hour. How would you set this up in Azure Data Factory?

Answer: To set up this data movement:

1. Create a linked service for the REST API and Azure SQL Database.
2. Create datasets for the REST API source and the SQL table destination.
3. Create a pipeline with a Copy Data activity to move the data from the API to the SQL table.
4. Schedule the pipeline using a schedule trigger to run every hour.

2. Scenario: You need to perform a lookup operation in Azure Data Factory to fetch a configuration value from an Azure SQL Database table and use it in subsequent activities. Describe how you would do this.

Answer: To perform a lookup operation:

1. Create a linked service and dataset for the Azure SQL Database table containing the configuration value.
2. Add a Lookup activity in the pipeline to fetch the configuration value.
3. Use the output of the Lookup activity in subsequent activities by referencing the lookup result in expressions.

3. Scenario: Your pipeline must process a large number of files stored in an Azure Data Lake Storage Gen2 account. How would you efficiently process these files using Azure Data Factory?

Answer: To efficiently process a large number of files:

1. Create a linked service for Azure Data Lake Storage Gen2.
2. Create a dataset with a wildcard path to reference the files.
3. Use a ForEach activity to iterate over the list of files.
4. Within the ForEach activity, use a Copy Data activity or a Data Flow activity to process each file.

4. Scenario: You need to transform and load data from a SQL Server database to a Parquet file in Azure Blob Storage. Describe the steps to achieve this using Azure Data Factory.

Answer: To transform and load data:

1. Create linked services for the SQL Server database and Azure Blob Storage.
2. Create datasets for the SQL Server table and the Parquet file.
3. Create a pipeline with a Mapping Data Flow activity.
4. In the Data Flow, read data from the SQL Server table, apply necessary transformations, and write the output to a Parquet file in Azure Blob Storage.

5. Scenario: You need to send an email notification if a pipeline in Azure Data Factory fails. How would you set this up?

Answer: To send an email notification on pipeline failure:

1. Set up an Azure Logic App to send email notifications.
2. In Azure Data Factory, configure the pipeline to call the Logic App using a Web activity on failure.
3. Pass relevant failure details to the Logic App to include in the email notification.

6. Scenario: You need to implement a data pipeline that reads data from an Azure Event Hub, processes it in real-time, and writes the results to an Azure SQL Database. Explain how you would achieve this.

Answer: To implement real-time data processing:

1. Set up an Azure Stream Analytics job to read data from the Azure Event Hub.
2. Configure the Stream Analytics job to process the data and write the results to an Azure SQL Database.
3. Use Azure Data Factory to orchestrate the process, ensuring that the Stream Analytics job is running and monitoring the output.

7. Scenario: You need to load data from multiple sources (e.g., SQL Server, Oracle, and flat files) into a single data warehouse in Azure Synapse Analytics. Describe your approach using Azure Data Factory.

Answer: To load data from multiple sources:

1. Create linked services for SQL Server, Oracle, flat files, and Azure Synapse Analytics.
2. Create datasets for each source and the destination data warehouse.
3. Create a pipeline with multiple Copy Data activities, each handling a different source.
4. Use Data Flow activities to transform and merge the data before loading it into the Azure Synapse Analytics data warehouse.

8. Scenario: Your data pipeline must run under specific conditions, such as when a particular file is available in Azure Blob Storage. How would you configure this trigger in Azure Data Factory?

Answer: To configure a trigger based on file availability:

1. Set up an event-based trigger in Azure Data Factory.
2. Configure the trigger to monitor the specific Azure Blob Storage location for the arrival of the file.
3. Define the pipeline to run when the trigger condition is met.

9. Scenario: You need to create a pipeline that performs conditional data processing based on the value of a parameter passed at runtime. Explain how you would implement this in Azure Data Factory.

Answer: To implement conditional data processing:

1. Create parameters in the pipeline to receive runtime values.
2. Use If Condition activities to evaluate the parameter values.
3. Based on the condition, route the execution to different branches in the pipeline to perform the required data processing.

10. Scenario: You are required to implement a pipeline that processes daily transactional data and updates a fact table in an Azure SQL Data Warehouse, ensuring no duplicate records. Describe your approach.

Answer: To implement this:

1. Create linked services for the source of the transactional data and Azure SQL Data Warehouse.
2. Create datasets for the source data and the destination fact table.
3. Use a Data Flow activity to read the daily transactional data, apply necessary transformations, and deduplicate the records.
4. Write the transformed and deduplicated data to the fact table, using an Upsert pattern to handle new and existing records.

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యుద్ధం చేసే స‌త్తా లేని వాడికి.. శాంతి గురించి మాట్లాడే అర్హత లేదు

వాడిదైన రోజున ఎవ‌డైనా కొట్టగ‌ల‌డు. అస‌లు గొడ‌వ రాకుండా ఆపుతాడు చూడు.. వాడు గొప్పోడు

మచ్చల పులి ముఖంపై గాండ్రిస్తే ఎట్టుంటుందో తెలుసా? మట్టి తుఫాన్ చెవిలో మోగితే ఎట్టుంటాదో తెలుసా?

పాలిచ్చి పెంచిన తల్లులు సార్.. పాలించలేరా?

Modern indian women are not marrige material

nowadays many indian women are simply not suitable for marriage 

the current state of marriage is indeed worrying 

the marriage rate is declining year by year 

while the divorce rate is on the rise 

many people may wonder who is to blame for this 

in my opinion the key 

issue may lie more with women nowadays 

some women's behavior is incomprehensible 

they completely lack the demeanor of traditional ladies 

even their parents can hardly recognize them 

let alone becoming virtuous wives and good mothers as a woman 

i should be speaking from a woman's perspective 

but what i see some women doing is truly unacceptable 

why have they become like this 

is it because society has developed too fast 

or is it because the gender ratio in China 

is imbalanced leading to the only child 

being spoiled and pampered 

since childhood 

causing them to act 

recklessly their arrogance has become their capital 

or is it that 

these women's minds have already been corroded by ugly ideas 

it is really something that one finds hard to accept 

and understand in the past 

men guarded the nation 

while women safeguarded the bloodline 

but now some women only care about money and material things 

completely lacking the sense of family 

heritage are such people really suitable for marriage 

one can't help 

but wonder firstly 

some women have no education 

no job 

and look quite ordinary yet they think highly of themselves 

and make a bunch of demands on men 

such as a high bride price buying a house 

buying a car etcetera these demands can be somewhat understood 

but the most outrageous part is that they refuse to do laundry 

cook do housework 

have children or take care of their elderly parents and husbands 

they rely on others to serve them in 

everything they don't want to do anything themselves 

yet they expect others to serve them 

always putting on the heirs of a young lady 

they think about how much pocket money 

their man gives them every day 

if the man gives them less money 

they scold him for being incompetent 

this makes many men feel 

as if they have married an ancestor into their home secondly 

many women are brainwashed by feel good platitudes for example 

they think they should never compromise 

and that women can live well without men 

they believe women are not men's reproductive tools 

and that to have children for a man 

he must be someone who understands and loves them otherwise 

all their suffering is in vain all kinds of superficial self 

help mottos flood their minds plus 

under the influence of various idle dramas 

they daydream all the time 

they fantasize that their future husband 

must be a tall rich and handsome guy 

and that after marriage 

he would be responsible for making money 

while they just need to stay beautiful however 

where in reality are there so many tall rich and handsome men 

the marriages of ordinary people will inevitably differ 

significantly from their fantasies 

as a result 

many women are full of dissatisfaction with their partners 

even though they 

themselves earn only a few thousand yuan a month 

they despise men who earn ten or twenty thousand yuan 

for not meeting their expectations 

and lowering their quality of life 

thirdly many leftover 

women in China 

who are over thirty years old 

clearly had many opportunities to get married 

but they always think that they have some beauty 

a high education and a good job 

they feel that 

ordinary men do not meet their standards 

at all only a tall rich and handsome man 

is their mister right 

after all a man simply cannot fall below their standards 

but in reality 

tall rich and handsome men often prefer young gentle girls 

so why would they choose a woman in her thirties 

fourthly some women have a low salary 

only about three thousand yuan a month 

but they have huge expenditures 

a monthly expense of ten 

thousand yuan is their minimum requirement 

this is because they are accustomed to 

relying on others to provide them 

with what they want 

this lifestyle and consumption concept 

make people feel like they are living the life of a rich lady 

but in reality 

their economic capability does not match up fifthly career women 

although they have achieved success in their work 

also have higher requirements for their partner 

they want to find a man 

who is both wealthy and more successful 

and can earn more than they 

do they believe only such a man is worthy of them 

but in reality 

many successful men often prefer young gentle 

and easygoing women 

rather than a condescending strong woman 

so these behaviors and concepts of certain 

Indian women today indeed 

make them fundamentally unsuitable for marriage 

nowadays many people 

choose to stay single and no longer yearn 

for romantic relationships or marriage 

i think i might be a representative of this 

type of woman born in 1998 

i will be thirty in a few years 

i come from an ordinary family work 

a regular job 

earn a modest 

salary and my education is not outstanding the only things 

i might be able to flaunt 

are probably my looks and height in front of people 

i like i often feel inferior 

everyone says that women 

today are very realistic demanding that men have a house 

a car and savings 

but men also have their own requirements 

they consider a partner's family background 

assess how much the woman can contribute to their future 

and approach with a purpose 

weighing the pros and cons before deciding to leave 

i have also been in a few relationships 

but none ended successfully 

i've seen many couples and married people around me some love 

each other for life 

while others eventually 

part ways rather than experiencing this 

i'd prefer to stay single 

i tend to be a bit of a perfectionist 

if a relationship can not end well 

i'd rather not start it at all i currently live with a roommate 

in a rented apartment 

leading a simple life 

without the trivialities of marriage 

and without a mortgage or car loan my life is free and joyful 

all my time belongs to me 

when i see my friends getting married and having children 

i sometimes feel envious 

but i also occasionally feel relieved 

many people think women like us are selfish and cold 

only concerned with our own feelings 

but i believe that compared to an unhappy marriage 

isn't a solitary single life also beneficial 

why are so many women resisting marriage now 

it's not just a gender issue 

but a predicament 

i really don't want to attend weddings 

they make me feel very uneasy 

for example when the groom's father hands 

the bride's hand to the groom 

this process looks like a secondary transfer of maritalrights 

when the bride price is agreed upon 

it feels like a transaction money exchanged for the goods 

what's even more 

infuriating is that many men in india 

cunningly use the guise of love to emotionally blackmail 

you demanding your sacrifice without any reciprocation 

if you become pregnant 

it's like receiving a non returnable package 

you have to face all the consequences 

so why is marriage seen as the greatest scam in a woman's life 

when a man complains that you're too rational 

the underlying message is that you're hard to deceive or placate 

women no longer accept the injustices 

they once tolerated now 

that women have opened their eyes to the world 

we realize that the men before 

us are not all 

that impressive men will bow and scrape in front of clients 

but act tough in front of women 

their external dignity can only be compensated through you 

and the children although 

surrogacy is wrong the market 

price for it is around one million yuan 

yet many men struggle to offer three hundred thousand yuan 

in bride price for marriage 

which is necessary to support a family 

the in laws and husbands might 

even feel that they are at a loss 

when you sacrifice your career and social life 

to become a full time housewife 

the man's past promise of i will support you turns into i 

support you after much contemplation 

i finally realize that the best way to break 

this situation is to not participate in it at all 

China's marriage rate is gradually declining 

while the divorce rate is rising 

this reflects 

Indian women's dissatisfaction with the traditional marriage 

model women are not unwilling to marry rather 

they resist the traditional form of marriage 

this resistance is actually 

a rebellion against the various injustices 

present in traditional marriages for a long time 

men have often focused only on visible costs 

such as purchasing cars and houses 

while neglecting the hidden costs 

borne by women 

after marriage in first tier and second tier cities 

the monthly salary for a living nanny is around and the monthly salary for a maternity nurse 

these costs do not include the high expenses 

incurred during childbirth however 

women in the family 

are often expected to undertake these duties as a matter 

of course even being viewed as obligatory labor therefore i 

believe that Chinese women are currently not rejecting marriage 

and childbirth 

but rather resisting the injustices 

within the traditional marriage model to change this situation 

men must completely reexamine their understanding of marriage 

they need to recognize that marriage is not just a legal union 

it requires management 

emotional commitment 

and shared responsibility for household chores and childcare 

managing a family requires 

wisdom and a sense of responsibility 

if men cannot make these changes 

the number of men living alone in old age 

will continue to increase 

if women choose not to marry 

but still want children 

they can opt for non marital childbirth however 

men cannot bear children therefore 

i believe that 

a man's inability to give birth is a physiological defect 

nowadays 

many women are unwilling to get married truly outstanding 

men also don't want to get married 

after all the cost of marriage for men is right there 

they need to prepare a house a car 

and also pay the maintance price even if we don't mention too much 

right women worry that after getting married 

and having children 

their husbands won't care about them 

while men are concerned that after giving the bride price 

they will be deceived by the woman now 

good men can't meet good women 

and good women can't meet good men 

men are afraid of meeting gold diggers 

while women are afraid of encountering 

scumbags the trust in the entire society 

has almost vanished between people there is nothing 

but calculation the marriage hasn't even happened yet 

and during the dating phase 

people are already calculating fearing 

that they will suffer losses 

this is premarital 

that is also premarital 

this property is mine that property is also mine 

everything must be clearly divided 

why is there no happiness in marriage nowadays 

it's because both 

parties aren't thinking about how to live a good life together 

but rather worrying about their own property 

being occupied by the other now men provide property 

while women give their bodies 

why can't low income men find wives 

because their income does not match the sacrifices 

women make in having children after all 

women stay at home to take care of the child after giving birth 

which means no income and life becomes difficult to maintain 

therefore 

they all want to find men with good conditions and high income 

which is understandable as a result 

it becomes even harder for low income men to find partners 

nowadays no one wants to find someone who holds them back 

everyone wants to rely on the other to live a better life 

both men and women want to find someone better than themselves 

after all who doesn't want to live better therefore 

it is now difficult for many Chinese men 

and women to step into marriage 

the reason 

i finally figured out why the marriage rate is so low nowadays 

is mainly because most people who don't want to 

get married are women 

look at my parents generation 

my dad was responsible for earning money to support the family 

while my mom took care of me 

and managed the household their generation 

followed a division of labor between men and women however 

in our generation 

women not only have to take care of the family 

children and inlaws 

but also need to have a job to achieve economic independence 

if they ask for just a little bit more 

they will be criticized as being greedy 

isn't societies demand on women too harsh nowadays secondly 

many of the women around me 

had a pretty high quality of life before marriage 

but after marriage 

their quality of life didn't improve instead 

they took on more responsibilities 

like caring for their partner both sets of parents and children 

they must give their best effort in every aspect 

and if anything goes wrong 

they face complaints to put it bluntly 

which woman with the ability to be self sufficient 

would easily choose to get married 

thirdly just take a look at those divorced female 

after their divorce 

none of them seem willing to remarry before marriage 

they thought living alone was tough 

but after marriage 

they realized 

it's not just tough 

it's a form of suffering 

if all you want is love and companionship dating is enough 

why do you have to get married in the past 

people said that women valued cars and houses 

calling them materialistic 

but now women don't want 

anything so naturally 

they don't need a husband anymore 

i really don't understand why i have to get married 

once i get married 

i'll immediately become poor regarding marriage 

i'm pretty much the same as most people born in the nineties 

i don't want to get married 

don't want to bear the burden of life 

and don't want to buy a house or a car right now 

i just want to live 

a simple and free life 

if i have hundreds of thousands of you on to live on my own 

i think that would be enough to make me happy so why get married 

sure finding someone could reduce loneliness 

and allow us to strive together 

but if i can't find a girlfriend 

who can actually improve my life 

what's the point of getting married 

besides 

i feel that many women nowadays are not suitable for marriage 

look if i earn twenty thousand rupees a month 

and she only earns two thousand yuan 

then i have to take care of her plus 

most women nowadays don't know how to do housework 

i will have to take on the responsibility of doing housework 

i also have to support her always be mindful of her feelings 

nowadays marriage often starts not with love 

but with various material conditions 

i believe there are indeed 

some girls who choose to be with you for love 

even without a house 

but their families definitely won't agree so now 

when looking for a partner 

if its not goal oriented from the beginning 

then no matter how well you get along initially 

or how deep the feelings are you might still end up 

not being together in the end 

in the future 

if there are many leftover women 

who find it difficult to find a partner 

the reason often lies in their own behavior 

i now finally understand why 

when it comes to discussing marriage 

and finding a partner 

there is always a sense of rejection between men 

and women in China 

with neither side finding the other attractive 

i have summarized the current situation of some women in China 

have you noticed that some 

women whose family conditions are average 

and whose jobs are not outstanding 

merely able to make ends meet often harbor fantastical dreams 

filled with ideals and romance 

while being quite confident 

so what is there for others to like about you 

is it that you don't do housework 

or is it your monthly salary of 3 

500 yuan some of you sisters 

please stop being obsessed with idle dramas 

the plot of a domineering 

president falling in love 

with a sweet innocent girl 

is something that will never happen in real life 

we all want to find a partner with good conditions 

which is not wrong in itself 

but we also need to reflect on improving ourselves first 

after all marriage is essentially a value exchange 

whether it is economic value 

emotional value 

or other outstanding qualities 

you possess it is what makes the other party 

think you are worth it 

if we can lower some of our requirements for a partner 

we would find that there are actually 

many excellent men around us 

as for those men who truly earn seven figures a year 

they are mostly older men and most of them already have wives 

these wives have been with the older men 

since their youth supporting them 

through entrepreneurship and hard work in marriage 

there 

really aren't many opportunities to gain something for nothing 

do you agree 

with my point of view feel free to leave your comments 

i would love to hear your thoughts if you 

enjoyed this video 

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your support is very important to us 

and we will continue to provide you with better content 

thank you again 

for watching and see you next time 

Chanakya Neeti for better life

Learn from others 

Always ask yourself three questions before working on something new why am I doing it what the result might be and will I be successful 

Education is the best friend an education is respected everywhere 

Don't be to honest straight trees are cut first and honest people are screwed first

A man is great by beeds not by birth 

Do not reveal what you have thought upon doing keep it screct till it is executed 

There is no friendship without self intrests this is bitter truth 

Never share your secrets with anyone it will destroy you 

secret of success

People fight for wealth. relatives fight for property. person fights for money. bad words hurt everyone. selfless people are real wealthy person .

As long as you want to start something you should never be afraid of being late. cowards will never set off. weak will die on the way. only we keep advanceing as long as we preserve and do everything possible .fate will favours

lucky basker movie quotes

డైలాగ్ 1

వస్తువు కావాలంటే డబ్బుతో కొనాలి.. రెస్పెక్ట్ కావాలంటే డబ్బు ఒంటిపై కనపడాలి...

డైలాగ్ 2

డబ్బుంటేనే మర్యాద.. ప్రేమ...

డైలాగ్ 3

సిగరెట్, ఆల్కహాల్, డ్రగ్స్ ఇచ్చే కిక్కు కన్నా డబ్బు ఇచ్చే కిక్కే ఎక్కువ....

డైలాగ్ 4

మాటల్లో ఇంత అహంకారం..

అహంకారం కాదు.. ధైర్యం....

చేతల్లో బలుపు..

బలుపు కాదు.. బలం.....

ఇంత చెడ్డవాడిలా మారిపోతావ్ అనుకోలేదు...

ఐయామ్ నాట్ బ్యాడ్.. ఐయామ్ జస్ట్ రిచ్.....

డైలాగ్ 5

జూదంలో నువ్వు ఎంత గొప్పగా ఆడావన్నది ముఖ్యం కాదు...

ఎప్పుడు ఆపావన్నదే ముఖ్యం....

డైలాగ్ 6

దేవుడు రెడ్ సిగ్నల్ వేశాడు అంటే.. అన్నీ ఆపేయమని అర్థం.....

జయమ్ము నిశ్చయమ్మురా

//జయమ్ము //

జయమ్ము నిశ్చయమ్మురా భయమ్ము లేదురా

జంకుగొంకు లేక ముందు సాగిపొమ్మురా .. సాగిపొమ్మురా

ఏనాటికైన స్వార్ధము నశించి తీరును

ఏనాటికైన స్వార్ధము నశించి తీరును

ఏరోజుకైన సత్యమే జయించి తీరును.. జయించి తీరును

కష్టాలకోర్చుకున్ననే సుఖాలు దక్కును సుఖాలు దక్కును 

విద్యార్ధులంత విజ్ఞానం సాధించాలి .

విద్యార్ధులంత విజ్ఞానం సాధించాలి

విశాల దృష్టి తప్పకుండ బోధించాలి .. బోధించాల

పెద్దలను గౌరవించి పూజించాలి .. పూజించాలి 

కష్టాలకోర్చుకున్ననే సుఖాలు దక్కును .. సుఖాలు దక్కును

ఈ లోకమందు సోమరులై ఉండకూడదు .. ఉండకూడదు

పవిత్రమైన ఆశయాన మరువకూడదు .. మరువకూడదు 

గృహాన్ని స్వర్గసీమగా చేయుము దేవా .. బ్రోవుము దేవా

కుటుంబమొక్క త్రాటిపైన నిలుపుము దైవా .. నడుపుము దేవా

బీదసాదలాదరించు బుద్ది నొసగుమా .. శక్తి నొసగుమా

జయమ్ము నిశ్చయమ్మురా భయమ్ము లేదురా

జంకుగొంకు లేక ముందు సాగిపొమ్మురా .. సాగిపొమ్మురా 

గాఢాంధకారమలముకున్న భీతిచెందకు

సందేహపడక వెల్గు చూపి సాగుముందుకు .. సాగుముందుకు

నిరాశలోన జీవితాన్ని క్రుంగదీయకు … క్రుంగదీయకు 

పరాభవమ్ము గల్గునంత పారిపోకుమోయ్…

జయమ్ము నిమ్మరించుదాక పోరి గెల్వవోయ్.. పోరి గెల్వవోయ్

స్వతంత్ర యోధుడన్న పేరు నిల్వబెట్టవోయ్ .. నిల్వబెట్టవోయ్

జయమ్ము నిశ్చయమ్మురా భయమ్ము లేదురా

జంకుగొంకు లేక ముందు సాగిపొమ్మురా .. సాగిపొమ్మురా

జయమ్ము నిశ్చయమ్మురా … జయమ్ము నిశ్చయమ్మురా… జయమ్ము నిశ్చయమ్మురా…