To set up the AWS Cognito for the registration/login flow, follow these steps: First Flow: User Registration in Cognito1. Install the following NuGet packages in your .NET project:   <PackageReference Include="Amazon.AspNetCore.Identity.Cognito" Version="3.0.1" /> <PackageReference Include="Amazon.Extensions.Configuration.SystemsManager" Version="5.0.0" /> <PackageReference Include="AWSSDK.SecretsManager" Version="3.7.101.27" /> Declare AWS configuration values in appsettings: "Region": "me-south-1", "UserPoolClientId": "UserPoolClientId", "UserPoolClientSecret": "UserPoolClientSecret", "UserPoolId": "me-south-pool"   Additional Configuration Add authentication in program/startup files to enable sign-in with Cognito. 2. Create a CognitoUserPool with a unique ID in the controller: private readonly CognitoUserPool _pool; private readonly CognitoUserManager<CognitoUser> _userManager; var user = _pool.GetUser(registerUserRequest.LoginId); 3.Add user attributes (email, phone number, custom attributes) using user.Attributes.Add().   user.Attributes.Add(CognitoAttribute.Email.AttributeName, registerUserRequest.Email); user.Attributes.Add(CognitoAttribute.PhoneNumber.AttributeName, registerUserRequest.Mobile); user.Attributes.Add("custom:branch_code", registerUserRequest.BranchCode); user.Attributes.Add("custom:preferred_mode", preferedMode); 4. Create the user: cognitoResponse = await _userManager.CreateAsync(user, registerUserRequest.Password); Check cognitoResponse.Succeeded to determine if the user was created successfully.   Second Flow: User Login with Cognito 1.Search for the user in Cognito using the login ID: var cognitoUser = await _userManager.FindByIdAsync(loginUserRequest.LoginId);   2.Set a password for the Cognito model: var authRequest = new InitiateSrpAuthRequest {    Password = loginUserRequest.Password };   3.Use StartWithSrpAuthAsync to get the session ID: var authResponse = await cognitoUser.StartWithSrpAuthAsync(authRequest);   4.Add MFA method and validate using MFA auth if needed. For MFA validation, set the MFA settings in Cognito:v ar authRequest = new RespondToMfaRequest {        SessionID = validateLoginUserRequest.SessionId,        MfaCode = validateLoginUserRequest.Otp,        ChallengeNameType = ChallengeNameType.SMS_MFA }; authResponse = await cognitoUser.RespondToMfaAuthAsync(authRequest);   Extract tokens from Cognito: authResponse.AuthenticationResult.IdToken authResponse.AuthenticationResult.RefreshToken   Forgot Password Flow 1.Search for the user with LoginId in Cognito and call ForgotPasswordAsync: var user = await _userManager.FindByIdAsync(loginUserRequest.LoginId); await user.ForgotPasswordAsync();   2.Optionally, call ConfirmForgotPassword method in Cognito. _userManager.ConfirmForgotPassword(userID, token, newPassword, CancellationToken cancellationToken) Here, understanding AWS Cognito Authentication Methods and Utilizing Them as Needed.  

In the dynamic landscape of data processing, Apache Kafka stands out as a robust and scalable distributed event streaming platform. This blog post aims to demystify Kafka, guiding you through its installation process step by step, and unraveling the concepts of topics, producers, and consumers.  Understanding Kafka:  1. What is Kafka? Apache Kafka is an open-source distributed streaming platform that excels in handling real-time data feeds. Originally developed by LinkedIn, Kafka has evolved into a powerful solution for building scalable and fault-tolerant data pipelines.  Installing Kafka:  2. Step-by-Step Installation Guide: Let's dive into the installation process for Kafka:  Prerequisites: Before installing Kafka, ensure you have Java installed on your machine, as Kafka is built on Java.  Download Kafka: Visit the official Apache Kafka website (https://kafka.apache.org/) and download the latest stable release. Unzip the downloaded file to your preferred installation directory.  Start Zookeeper: Kafka relies on Zookeeper for distributed coordination. Navigate to the Kafka installation directory and start Zookeeper:  bin/zookeeper-server-start.sh config/zookeeper.properties    Start Kafka Broker: Open a new terminal window and start the Kafka broker:  bin/kafka-server-start.sh config/server.properties  Congratulations! You now have Kafka up and running on your machine.  Kafka Concepts:    3. Topics:  Definition: In Kafka, a topic is a category or feed name to which messages are published by producers and from which messages are consumed by consumers.  Creation: Create a topic using the following command:  kafka-topics.bat --create   --topic MyTopics --bootstrap-server localhost:9092 --partitions 3 --replication-factor 1  List out Topics : To see all the topics will use following command :  \bin\windows\kafka-topics.bat --list --bootstrap-server localhost:9092    4. Producers and Consumers:  Producers: Producers are responsible for publishing messages to Kafka topics. You can create a simple producer using the following command:  bin/kafka-console-producer.sh --topic myTopic --bootstrap-server localhost:9092  Consumers: Consumers subscribe to Kafka topics and process the messages. Create a consumer with:  bin/kafka-console-consumer.sh --topic myTopic --bootstrap-server localhost:9092 --from-beginning    Conclusion:  Apache Kafka is a game-changer in the world of real-time data processing. By following this step-by-step guide, you've successfully installed Kafka and gained insights into key concepts like topics, producers, and consumers. Stay tuned for more in-depth Kafka tutorials as you explore the vast possibilities of this powerful streaming platform. 

In the ever-evolving landscape of business intelligence (BI), the need for seamless interaction with data is paramount. Imagine a world where you could effortlessly pose natural language questions to your datasets and receive insightful answers in return. Welcome to the future of BI, where the power of conversational interfaces meets the robust capabilities of Domo. This blog post serves as your comprehensive guide to implementing a BI ChatBot within the Domo platform, a revolutionary step towards making data exploration and analysis more intuitive and accessible than ever before. Gone are the days of wrestling with complex queries or navigating through intricate dashboards. With the BI ChatBot in Domo, users can now simply articulate their questions in plain language and navigate through datasets with unprecedented ease. Join us on this journey as we break down the process into manageable steps, allowing you to harness the full potential of BI ChatBot integration within the Domo ecosystem. Whether you're a seasoned data analyst or a business professional seeking data-driven insights, this guide will empower you to unlock the true value of your data through natural language interactions. Get ready to elevate your BI experience and transform the way you interact with your datasets. Let's dive into the future of business intelligence with the implementation of a BI ChatBot in Domo.   Prerequisites: ChatGPT API Key: Prepare for the integration of natural language to SQL conversion by obtaining a ChatGPT API Key. This key will empower your system to seamlessly translate user queries in natural language into SQL commands. DOMO Access: Ensure that you have the necessary access rights to create a new application within the Domo platform. This step is crucial for configuring and deploying the BI ChatBot effectively within your Domo environment.   1: Integrate the HTML Easy Bricks App. Begin the process by incorporating the HTML Easy Bricks App into your project. Navigate to the AppStore and add the HTML Easy Bricks to your collection. Save it to your dashboard for easy access. Upon opening the App for the first time, it will have a default appearance. To enhance its visual appeal and functionality, customize it by incorporating the HTML and CSS code. This transformation will result in the refined look illustrated below.   Image 1: DOMO HTML Easy Brick UI   2: Map/Connect the Dataset to the Card. In this phase, establish a connection between the dataset and the card where users will pose their inquiries. Refer to the image below, where the "Key" dataset is linked to "dataset0." Extend this mapping to accommodate up to three datasets. If your project involves more datasets, consider using the DDX-TEN-DATASETS App instead of HTML Easy Bricks for a more scalable solution. This ensures seamless integration and accessibility for users interacting with various datasets within your Domo environment.   Image 2: Attach Dataset With Card   3: Execute the Query on the Dataset for Results. In this phase, you'll implement the code to execute a query on the dataset, fetching the desired results. Before this, initiate a call to the ChatGPT API to dynamically generate an SQL query based on the user's natural language question. It's essential to note that the below code is designed to only accept valid column names in the query, adhering strictly to MySQL syntax. To facilitate accurate query generation from ChatGPT, create a prompt that includes the dataset schema and provides clear guidance for obtaining precise SQL queries. Here is a call to the ChatGPT API to get SQL Query. VAR GPTKEY = 'key' VAR Prompt = 'Write effective prompt' $.ajax({             url: 'https://api.openai.com/v1/chat/completions',             headers: {               'Authorization': 'Bearer ' + GPTKEY,               'Content-Type': 'application/json'             },             method: 'POST',             data: JSON.stringify({               model: 'gpt-3.5-turbo',               messages: Prompt,               max_tokens: 100,               temperature: 0.5,               top_p: 1.0,               frequency_penalty: 0.0,               presence_penalty: 0.0             }),             success: function (response) {                   //Write code to store the Query into the variable            } });   Refer to the code snippet below for executing the query on Domo and retrieving the results. var domo = window.domo; var datasets = window.datasets; domo.post('/sql/v1/'+ 'dataset0', SQLQuery, {contentType: 'text/plain'}).then(function(data) {   //Write your Java or JQuery code to print data. });   The above code will accept the SQL queries generated by ChatGPT. It's important to highlight that, in the code, there is a hardcoded specification that every query will be applied to the dataset mapped as 'dataset0'. It's advisable to customize this part based on user selection. The code is designed to accept datasets with names such as 'dataset0', 'dataset1', and so forth. Ensure that any modifications align with the chosen dataset for optimal functionality, you can also use the domo.get method to get data for more information visit here. The outcome will be presented in JSON format, offering flexibility for further processing. You can seamlessly transfer this data to a table format and display or print it as needed.   Conclusion Incorporating a BI ChatBot in Domo revolutionizes data interaction, seamlessly translating natural language queries into actionable insights. The guide's step-by-step approach simplifies integration, offering both analysts and business professionals an intuitive and accessible data exploration experience. As datasets effortlessly respond to user inquiries, this transformative synergy between ChatGPT and Domo reshapes how we extract value from data, heralding a future of conversational and insightful business intelligence. Dive into this dynamic integration to propel your decision-making processes into a new era of efficiency and accessibility.

In this article, we will review how to create a data flow task package of SSIS in Console Application with an example. Requirements Microsoft Visual Studio 2017 SQL Server 2014 SSDT Article  Done with the above requirements? Let's start by launching Microsoft Visual Studio 2017. Create a new Console Project with .Net Core.  After creating a new project, provide a proper name for it. In Project Explorer import relevant references and ensure that you have declared namespaces as below: using Microsoft.SqlServer.Dts.Pipeline.Wrapper; using Microsoft.SqlServer.Dts.Runtime; using RuntimeWrapper = Microsoft.SqlServer.Dts.Runtime.Wrapper;   To import above namespaces we need to import below refrences.   We need to keep in mind that, above all references should have same version.   After importing namespaces, ask user for the source connection string, destination connection string and table that will be copied to destination. string sourceConnectionString, destinationConnectionString, tableName; Console.Write("Enter Source Database Connection String: "); sourceConnectionString = Console.ReadLine(); Console.Write("Enter Destination Database Connection String: "); destinationConnectionString = Console.ReadLine(); Console.Write("Enter Table Name: "); tableName = Console.ReadLine();   After Declaration, create instance of Application and Package. Application app = new Application(); Package Mipk = new Package(); Mipk.Name = "DatabaseToDatabase";   Create OLEDB Source Connection Manager to the package. ConnectionManager connSource; connSource = Mipk.Connections.Add("ADO.NET:SQL"); connSource.ConnectionString = sourceConnectionString; connSource.Name = "ADO NET DB Source Connection";   Create OLEDB Destination Connection Manager to the package. ConnectionManager connDestination; connDestination= Mipk.Connections.Add("ADO.NET:SQL"); connDestination.ConnectionString = destinationConnectionString; connDestination.Name = "ADO NET DB Destination Connection";   Insert a data flow task to the package. Executable e = Mipk.Executables.Add("STOCK:PipelineTask"); TaskHost thMainPipe = (TaskHost)e; thMainPipe.Name = "DFT Database To Database"; MainPipe df = thMainPipe.InnerObject as MainPipe;   Assign OLEDB Source Component to the Data Flow Task. IDTSComponentMetaData100 conexionAOrigen = df.ComponentMetaDataCollection.New(); conexionAOrigen.ComponentClassID = "Microsoft.SqlServer.Dts.Pipeline.DataReaderSourceAdapter, Microsoft.SqlServer.ADONETSrc, Version=14.0.0.0, Culture=neutral, PublicKeyToken=89845dcd8080cc91"; conexionAOrigen.Name = "ADO NET Source";   Get Design time instance of the component and initialize it. CManagedComponentWrapper instance = conexionAOrigen.Instantiate(); instance.ProvideComponentProperties();   Specify the Connection Manager. conexionAOrigen.RuntimeConnectionCollection[0].ConnectionManager = DtsConvert.GetExtendedInterface(connSource); conexionAOrigen.RuntimeConnectionCollection[0].ConnectionManagerID = connSource.ID;   Set the custom properties. instance.SetComponentProperty("AccessMode", 0); instance.SetComponentProperty("TableOrViewName", "\"dbo\".\"" + tableName + "\"");   Reinitialize the source metadata. instance.AcquireConnections(null); instance.ReinitializeMetaData(); instance.ReleaseConnections();   Now, Add Destination Component to the Data Flow Task. IDTSComponentMetaData100 conexionADestination = df.ComponentMetaDataCollection.New(); conexionADestination.ComponentClassID = "Microsoft.SqlServer.Dts.Pipeline.ADONETDestination, Microsoft.SqlServer.ADONETDest, Version=14.0.0.0, Culture=neutral, PublicKeyToken=89845dcd8080cc91"; conexionADestination.Name = "ADO NET Destination";   Get Design time instance of the component and initialize it. CManagedComponentWrapper instanceDest = conexionADestination.Instantiate(); instanceDest.ProvideComponentProperties();   Specify the Connection Manager. conexionADestination.RuntimeConnectionCollection[0].ConnectionManager = DtsConvert.GetExtendedInterface(connDestination); conexionADestination.RuntimeConnectionCollection[0].ConnectionManagerID = connDestination.ID;   Set the custom properties. instanceDest.SetComponentProperty("TableOrViewName", "\"dbo\".\"" + tableName + "\"");   Connect the source to destination component: IDTSPath100 union = df.PathCollection.New(); union.AttachPathAndPropagateNotifications(conexionAOrigen.OutputCollection[0], conexionADestination.InputCollection[0]);   Reinitialize the destination metadata. instanceDest.AcquireConnections(null); instanceDest.ReinitializeMetaData(); instanceDest.ReleaseConnections();   Map Source input Columns and Destination Columns foreach (IDTSOutputColumn100 col in conexionAOrigen.OutputCollection[0].OutputColumnCollection) {     for (int i = 0; i < conexionADestination.InputCollection[0].ExternalMetadataColumnCollection.Count; i++)     {         string c = conexionADestination.InputCollection[0].ExternalMetadataColumnCollection[i].Name;         if (c.ToUpper() == col.Name.ToUpper())         {             IDTSInputColumn100 column = conexionADestination.InputCollection[0].InputColumnCollection.New();             column.LineageID = col.ID;             column.ExternalMetadataColumnID = conexionADestination.InputCollection[0].ExternalMetadataColumnCollection[i].ID;         }     } }   Save Package into the file system. app.SaveToXml(@"D:\Workspace\SSIS\Test_DB_To_DB.dtsx", Mipk, null);   Execute package. Mipk.Execute(); Conclusion In this article, we have explained one of the alternatives for creating SSIS packages using .NET console application. In case you have any questions, please feel free to ask in the comment section below.   RELATED BLOGS: Basics of SSIS(SQL Server Integration Service)

Productivity is the grouping of smart preparation and dedicated hard work. Being continuously productive in your work is hard. End of each working day, there are chances are that you are not satisfied with what you have been gifted. Productivity could be constantly value-added, but here are 8 pointers that can have truly work for you. Fortunately, a computer, smartphone, and a little know-how are all you need. 1. Speed Reading Saves Your Time. In your daily working schedule, you might have to read huge passages of text quickly, think about figuring out how to speed read. Under normal circumstances, a person reads around 200 to 250 words per minute (WPM) and this is slow compared to speed readers because of sub-vocalization – you unconsciously read every word to yourself as you read. Eliminating sub-vocalization is one of the essential fundamentals of speed reading. You can use Speed Reader (by P Garrison) app for your android phone and tablet, for google chrome you’ll use Spreed extension and for iOS try Speed Reading HD. These apps are used the Rapid Serial Visual Presentation technique. In this technique, you’ll see one word at a time very quickly in the center of your screen. You’ll observe that you’re rapidly capable to read 300wpm and with a little more practice you’ll reach 500-600wpm. You’ll have the capacity for reading three times quicker with a couple of days of practice. 2. Take a Break Reminder Every day we are working hard sitting in front of the computer for quite a long time. But we never distinguish that it is so critical to step away from the desk and take a break. Break empowers us to work more proficiently and it will improve our physical and mental health as well. A familiar way to take a break without connecting anything is the use of an online timer atwww.onlineclock.net. Go to this website and set your comfortable and right time to remind you about the break. Your device must be attached to the speakers to hear sounds. Smartphone users are also connected with the help of applications. Android devises users use Countdown Time Widget, Blackberry users use Countdown Timer app and iPhone users use Simple Repeat Timer applications. 3. Accelerate Your Computer and Handy Devices Remember that time is money. If your computers, mobiles, and other devices are running slow, you’ll drop your extra valuable time for the small stuff. You can add those extra minutes to your productivity when your gadgets are working properly and promptly. Glary Utilities is a free, powerful, and all-in-one utility for cleaning your Windows PC. For Mac use OnyX to delete caches, remove a certain number of files and folders that may become cumbersome, and more. Clean Master is a free optimizer android app, ultimately it is all about increasing phone performance, deleting junk and spam files, and protecting against trojans, viruses, and malware. 4. Synchronize Your Phone and Computer to get Notifications on your screen We all suffer from a bad experience to want something from the computer to phone. Get all notifications from computer to phone and vice versa here is best all in one application called Pushbullet. Pushbullet is quick, simple, and works the way you need it to. Pushbullet is available for android, iPhone, Google Chrome, and Mozilla Firefox. When you send a document with Pushbullet, its’ naturally downloaded so you can open it right from your notifications. 5. Set Up an Automated Systematic Backup Process You are working on something and you didn’t get a backup of those files means your data is not secure. So, you must have set a backup system. There are two types of getting data backup, online data backup, and offline data backup. You can use the external hard drive to get offline data backup and for online backup, there are many data storage and synchronization services are available. With the help of SyncBack and Carbonitecloud backup service, you can set your automate data backup process. SyncBack is a really good software for backup and synchronizing the files to your targeted drive and also you’ll get an external backup to CD, DVD, and more devices. Carbonite is a cloud backup service that takes backup of your documents, music files, images, emails, and many more. You’ll also use Google Drive. Google Drive allows you to store up to 15GB data as complimentary. 6. Learn Common Keyboard Shortcuts Keyboard Shortcuts can help you to increase your work speed. Let’s take an example, I want to print my document. For the print I click on the file menu, then click on the print option, and then I show a pop-up box for print or simply press Ctrl + P, so using of keyboard shortcut will save time. Many keyboard shortcuts enable us to complete our work easier and save time. You’ll create a note of shortcut keys to your software and print on paper and stick to your desk, whenever you want some specific work with your software check the shortcut key and get your work done.