Harshita Mathur

I am Harshita Mathur, and I have 3 years of experience in my current company. I was initially an intern at Syllable Technologies, where I worked on multiple migration projects. In these projects, I converted Oracle queries and SQL queries into PySpark and Spark SQL. I have experience with this type of migration project. I have completed some basic certifications in Azure and data breaks, including the DP 900, DP 203, and a Databricks engineering certification. In addition, I have completed the Databricks engineering professional certification. My background in project development involves using the Pentaho tool, which includes multiple transformations with JavaScript codes and SQL codes. The client's requirement was to reduce the time it took to execute all the bureau data, which was taking 2 to 3 days on the Pentaho side. I have worked on a BFSI project, which involved bureau data with customer names and basic details. I successfully migrated the JavaScript codes and SQL codes into PySpark and Spark SQL, which I implemented in Databricks to reduce execution time.

So in a.NET application analytics, we have to analyze our data whether it is consistent or not. And to ensure the data consistency between the.NET and the application developer, we have to follow some steps. Like, first of all, the transactional consistency in which we use a transaction to ensure that all the database operations that we need to execute together are wrapped into a transaction. Okay? And then the stored procedures and batch processing in which the ETL process we use to migrate the extract and transform. And this process is used to handle the data in batches, like, as your data factory can orchestrate these tools. And then the consistency control in which the optimization consistency is there, which we can control all the mechanisms. And then the data validation and integrity in which the data validation rules are there. And then event-driven architecture is there, and then we can say that asynchronous processing is there, in which message queues and background services are there. So these kinds of strategies we are using to ensure our data is consistent. Thank you.

Okay. So there are many ways to optimize our SQL query in which when we have bulk of data in which we can see that the large data set is generated. So we can use a clustered index. We can use the partitioning. So these are the techniques we can use. So to identify the queries like SQL Server and the standard events or the query store to find the query that is running slowly. And then the optimization of indexes is there in which the appropriate indexes have to be done to ensure that the index maintenance in which there are the regularly rebuilt and reorganized. And then the refinement of a query design in which to avoid the select, we have to specify only the columns as early as possible in the query. So join optimization using we can use the indexes and the temporary tables. We can use the partitioning and do the query hints and the options are there in which the query hints say that it will guide the SQL query to the optimizer in choosing the best execution plans. And then the leverage of your specific feature in which we can use the elastic pools and read scale out. We have to monitor and adjust our data in which performance monetization is there, which when we can continuously monitor the performance using the Azure SQL analytics query optimization insight and the other monitoring tools. And then we can review the database design by using the normalization process in which the data is properly normalized to avoid the redundancy of the data, and we can use the denormalization. In some cases, denormalization might be necessary to read heavy workloads to improve our performance. Proper data types have to be used. Caching and pre-aggregations have to be used. So these types of steps we can take to optimize our SQL query. Thank you.

Application performance for a high volume of data retrieval within a database. To optimize the.NET application performance for the high volume of data, we have to follow some steps, like efficient queries. We have to use parameterized queries to prevent SQL injection and enhance our query plan to reuse. And then, we can use connection management, using pooling to minimize the overload of opening and closing database connections, ensuring that connections are open and then the data access point patterns are there. We can use asynchronous programming to prevent blocking threads during database operations. And then, caching, we can implement caching strategies, like Azure Reddy is caching to reduce the load on the database for frequent access data. Batch processing is there, in which we can use single transformations to reduce the number of round trips to the data. Databricks configurations are there, like configurations mean that the Azure SQL database performance levels, that is DTUs and vCores, match the workload requirement using SQL database built-in features like auto tuning and performance recommendations are there. Monitoring and profiting are there. Scalability is there, in which you'll use it for designing this capability by partitioning the data using features like elastic pools if needed. So, in this way, we can optimize the.NET applications. Thank you.

How do you design the corporate? Okay. So, there are multiple points to design a.NET application that interacts with the Azure SQL. First of all, a project setup is required in which we can create a.NET project. Start by creating a new.NET project, and then add an entry to the framework that is used to install the necessary Entity Framework Core packages via NuGet, and then configure a database context in which we can create DB context classes. These classes will manage the database to connect with it and track the changes. And then configure the connection string in which we use to store the Azure SQL connections, using secure proper JSON. And then we can ensure the data integrity in which the data annotations and the fluent APIs are there, in which we can use the data annotation and the fluent API configurations to ensure that the data integrity constraints, like indexes, unique keys, fields, and the relationships are there. And then the migration process is there, then we can use to create migrations. We can use Entity Framework database and schemas. And then the transaction process is there. We can use a transaction to ensure that the data is consistent, by using a transaction for operations that affect multiple entities. And then we can use consistency control, in which to implement consistent tokens and then the validation, in which model validation and security, we use to create secure methods to store access and our connection string and use parameterized queries to prevent attacks. So these are the basic steps we have to use. Thank you.

So to refactor the crucial section of the Python code, it includes some basic steps like modularization, breaking the monolithic code into smaller, self-contained modules. We can then use design patterns to implement patterns such as the factory, singleton, and strategy to enhance code reusability. We can also adapt the cleanup or code principles, following principles such as naming, avoiding magic numbers, and writing clear comments to make our code easier to understand and maintain. We can then use the SDKs for Python to interact with Azure data services. These SDKs are designed to simplify the integration process and handle many low-level details. We can implement dependency injection to manage services' dependencies and allow for better testing. We can also use error handling and loading, and then use optimized data access patterns. We can utilize the SOR function and logic apps, and then refactor and unit testings are there. We can use the code base and integration points with Azure data services. This document will help us understand the code structure and facilitate future maintenance. So, refactoring this critical section of Python code can help reduce complexity while ensuring seamless integration.

K. So, okay. So, this issue in the code is that, like, with the escape sequence in the echo statement, in PHP, the correct way to include the new line character in a string is to use slash n, double slash n. So, like this way. And, these changes ensure that the new line character is properly recognized, and the error message will be displayed correctly. So we can use try, then get data from API, then catch exception, then echo caught exception and get message slash n. So, this is the correct way. And okay. So, in addition to fixing the new line character, this ensures that the expectation classes you are catching matches the type of expectation that get data from API might throw. And if the API calls throw a different type of exception, for example, a custom exception, then you need to catch the specific type as well. So thank you.

The issue in the T SQL snippet lies within the insert into orders statement. Like, especially the problem is with the customer ID value being inserted. The data type mismatch in the insert order statement is likely due to the customer ID being defined as an integer since it is used as a foreign key. And the referencing the customer ID in the customer table, which is an integer. So in the insert order statement, the value being inserted into the customer ID column is a string, not an integer. This mismatch caused an error just because you are trying to insert a string into a column that expects an integer. To fix the customer ID value in the order table, that should match the customer ID from the customer table, which is an integer. And then we can use the insert into customers, that is the table name, and then the customer ID, customer name, customer contact name, and the country values, 1, and then the string, Tom, comma, and then Ericsson, and then the Denmark. Begin tran begin transaction, insert into orders, order ID, customer ID, order date, values, that is, values, and then 10248, comma, 1, and then the commit transaction. So, insert into the customer table, customer ID as an integer, num customer name as a string, and the contact number, Tom, Ericsson, and then the country Denmark, which is correct from the second 21, assuming Denmark is in the intended country. So, this is the code we have to use. And the order ID 10248, and the customer ID that is matching the customer ID from the customer table. And then the order ID got that in, that inserts the correct date and time. So this is the data type issue. Thank you.

So if we talk about the dimension modeling update, that is to design basically a SQL database schema for efficient changes during dynamical modeling. So we have to use some technologies like star schema, in which we use a star schema with a central fact table surrounded by dimension tables. This specifies a query and allows for the effect update and then partitioning, in which the fact table can be partitioned to manage data growth efficiently and enable faster data loading also. And then, indexes in which we can implement appropriate indexes using clustered and non-clustered indexes to enhance query performance and facilitate updates. And then, column store indexes, we can consider column store indexes for large fact tables to improve analytical query performance. Okay. And then the temporal tables. Temporal tables can be leveraged to track historical changes in the dimensional table, and it enables easy rollback and auditing purposes also. So, data compression is also there in which we can utilize the data compression technique to optimize storage and improve query performance. And we can also use Polybase, in which we can integrate external data sources, then we can use Polybase to make our query efficient and load data into various sources. So these are the steps we have to use, and we can design the SQL schemas that will support efficient changes during dimensional modeling updates and Azure-based services. Thank you.

Okay. So, basically, to ensure the data analytics capabilities in a.NET application using Azure Community Services. So, we can utilize services like Azure text analytics for sentiment analysis or language detection. And the Azure computer vision for image analysis. We can use the Azure speech services for speech-to-text conversion and text-to-speech capabilities. And then the Azure translator for language translation. These services can integrate into your app through Azure SDKs or REST APIs, allowing you to extract insights from text, images, and speech data to ensure our data analytics process. Additionally, we can leverage machine learning processes for custom model training and deployment to further enhance data analysis and capabilities. So, my approach is to utilize Azure cognitive services. Thank you. That's where we can enhance the data analysis capability in.NET.

Python-based data programming workflow. To integrate machine learning services into the Python-based data processing workflow, we set up our Azure machine learning workspace, create an Azure machine learning workspace to manage our machine learning resources, and install the Azure ML SDKs by using the Python, with PIP install. Then, to authenticate, you authenticate your subscription credentials to access the workspaces, and we prepare the data using the Python libraries like Pandas, NumPy, SciPy, and these kinds of libraries we can use. So, we define an experiment in the ML to encapsulate your data flow and model training and evaluation, and then create a compute target in which we can set up a compute target in the Azure machine learning, such as Azure Databricks to run your experiment. And then, we train the model using the machine learning to train the machine learning model as the compute target, deploy the model, and then monitor and manage the data so that we can continuously monitor the data to deploy the model using the Azure machine learning, with the monitoring and management tool. Thank you.