Simplifying API Responses with AutoWrapper.Core in .NET Core. Handling API responses effectively is a crucial aspect of building robust and user-friendly applications. In .NET Core applications, the AutoWrapper.Core library comes to the rescue, providing a streamlined way to structure and standardize API responses. In this blog post, we'll explore how to use AutoWrapper.Core to create fixed responses for different status codes in your API. Firstly, you'll need to install the AutoWrapper.Core NuGet package. Add the following line to your project's .csproj file: <PackageReference Include="AutoWrapper.Core" Version="4.5.1" /> This package simplifies the process of handling API responses and ensures a consistent format for success, error, and data messages.   Example: Login Method Let's consider a common scenario, the login method, where we want to ensure fixed responses for both successful and unsuccessful attempts. [HttpPost("Login")] public async Task<ApiResponse> Login([FromBody] Login model) { var user = await _userService.GetUserByName(model.UserName); if (user != null && await _userService.CheckUserPassword(user, model.Password)) { var userResponse = await _tokenService.GenerateToken(user); return new ApiResponse(message: "Login Successfully.", result: userResponse, statusCode: 200); } return new ApiResponse(message: "Invalid Credential.", result: null, statusCode: 401); } In this example, we're using AutoWrapper.Core's ApiResponse class to encapsulate our responses. For a successful login attempt (status code 200), we return a positive message along with the user response. In case of invalid credentials (status code 401), an appropriate error message is provided. ApiResponse Class Now, let's take a closer look at the ApiResponse class from AutoWrapper.Core: namespace AutoWrapper.Wrappers; public class ApiResponse { public string Version { get; set; } [JsonProperty(DefaultValueHandling = DefaultValueHandling.Ignore)] public int StatusCode { get; set; } public string Message { get; set; } [JsonProperty(DefaultValueHandling = DefaultValueHandling.Ignore)] public bool? IsError { get; set; } public object ResponseException { get; set; } public object Result { get; set; } [JsonConstructor] public ApiResponse(string message, object result = null, int statusCode = 200, string apiVersion = "1.0.0.0") { StatusCode = statusCode; Message = message; Result = result; Version = apiVersion; } public ApiResponse(object result, int statusCode = 200) { StatusCode = statusCode; Result = result; } public ApiResponse(int statusCode, object apiError) { StatusCode = statusCode; ResponseException = apiError; IsError = true; } public ApiResponse() { } } The ApiResponse class provides flexibility in constructing responses with different components such as the message, result, and status code. It helps maintain a standardized format for all API responses. Create a Custom Wrapper: AutoWrapper allows you to create a custom wrapper by implementing the IApiResponse interface. You can create a class that implements this interface to customize the fixed response. Here's an example: Create a Custom Wrapper: AutoWrapper allows you to create a custom wrapper by implementing the IApiResponse interface. You can create a class that implements this interface to customize the fixed response. Here's an example: using AutoWrapper.Wrappers; public class CustomApiResponse<T> : ApiResponse<T> { public string CustomProperty { get; set; } public CustomApiResponse(T result, string customProperty) : base(result) { CustomProperty = customProperty; } } Configure AutoWrapper: In your Startup.cs file, configure AutoWrapper to use your custom wrapper. You can do this in the ConfigureServices method: services.AddAutoWrapper(config => { config.UseCustomSchema<CustomApiResponse<object>>(); }); Replace CustomApiResponse<object> with the custom wrapper class you created. Use Custom Wrapper in Controller Actions: Now, you can use your custom wrapper in your controller actions. For example: [ApiController] [Route("api/[controller]")] public class MyController : ControllerBase { [HttpGet] public IActionResult Get() { // Your logic here var data = new { Message = "Hello, World!" }; // Use the custom wrapper var response = new CustomApiResponse<object>(data, "CustomProperty"); return Ok(response); } } Customize the CustomApiResponse according to your needs, and use it in your controller actions. This way, you can integrate AutoWrapper with other packages and customize the fixed response format in your .NET application.   In conclusion, by incorporating AutoWrapper.Core into your .NET Core applications, you can simplify the handling of API responses, making your code more readable, maintainable, and user-friendly. Consider adopting this approach to enhance the overall developer experience and ensure consistency in your API communication.

Are you grappling with performance issues in your project? Look no further—Application Insights is here to help! In this blog post, I'll guide you through the process of configuring and implementing Application Insights to supercharge your application's performance monitoring. Step 1: Installing the Application Insights Package The first crucial step is to integrate the Application Insights package into your project. Simply add the following PackageReference to your project file: <PackageReference Include="Microsoft.ApplicationInsights.AspNetCore" Version="2.22.0" /> And Register service in Program.cs or Startup.cs : builder.Services.AddApplicationInsightsTelemetry(); builder.Services.ConfigureTelemetryModule<DependencyTrackingTelemetryModule>((module, o) => { module.EnableSqlCommandTextInstrumentation = true; }); Add connection string in appsettings.json :  "ApplicationInsights": {   "InstrumentationKey": "" } This sets the stage for a seamless integration of Application Insights into your application. Step 2: Unleashing the Power of Application Insights Now that the package is part of your project, let's dive into the benefits it brings to the table:  1. Identify Performance Bottlenecks Application Insights allows you to track the execution time of individual stored procedures, queries, and API calls. This invaluable information helps you pinpoint areas that require optimization, paving the way for improved performance.  2. Monitor Database Interactions Efficiently analyze the database calls made by specific APIs within your application. With this visibility, you can optimize and fine-tune database interactions for enhanced performance.  3. Comprehensive Error and Exception Tracking Application Insights goes beyond performance monitoring by providing detailed information about errors, traces, and exceptions. This level of insight is instrumental in effective troubleshooting, allowing you to identify and resolve issues swiftly.  Step 3: Integration with Azure for Data Collection and Analysis To maximize the benefits of Application Insights, consider integrating it with Azure for comprehensive data collection and analysis. This step amplifies your ability to make informed decisions regarding performance optimization and problem resolution. In conclusion, Application Insights equips you with the tools needed to elevate your application's performance. By identifying bottlenecks, monitoring database interactions, and offering comprehensive error tracking, it becomes a cornerstone for effective troubleshooting and optimization. Stay tuned for more tips and insights on how to harness the full potential of Application Insights for a high-performing application!

In this blog, we will explore Azure Databricks, a cloud-based analytics platform, and how it can be used to parse a CSV file from Azure storage and then store the data in a database. Additionally, we will also learn how to process stream data and use Databricks notebook in Azure Data Pipeline.   Azure Databricks Overview Azure Databricks is an Apache Spark-based analytics platform that provides a collaborative workspace for data scientists, data engineers, and business analysts. It is a cloud-based service that is designed to handle big data and allows users to process data at scale. Databricks also provides tools for data analysis, machine learning, and visualization. With its integration with Azure Storage, Azure Data Factory, and other Azure services, Azure Databricks can be used to build end-to-end data processing pipelines.   Parsing CSV File from Azure BlobStorage to Database using Azure Databricks Azure Databricks can be used to parse CSV files from Azure Storage and then store the data in a database. Here are the steps to accomplish this:   Configure Various Azure Components 1. Create Azure Resource Group Image 1 2. Create Azure DataBricks Resource  Image 2 3. Create SQL Server Resource  Image 3 4. Create SQL Database Resource Image 4 5. Create Azure Storage Account  Image 5 6. Create Azure DataFactory Resource  Image 6 7. Launch Databricks Resource Workspace  Image 7 8. Create Computing Cluster  Image 8 9. Create New Notebook  Image 9   Parsing CSV File from Azure Storage to Database using Azure Databricks Azure Databricks can be used to parse CSV files from Azure Storage and then store the data in a database. Here are the steps to accomplish this: 1. Create a cluster: First, create a cluster in Azure Databricks as above. A cluster is a group of nodes that work together to process data. 2. Import all the necessary models in the databricks notebook  %python from datetime import datetime, timedelta from azure.storage.blob import BlobServiceClient, generate_blob_sas, BlobSasPermissions import pandas as pd import pymssql import pyspark.sql Code 1 3. Mount Azure Storage: Next, mount the Azure Storage account in Databricks as follows #Configure Blob Connection storage_account_name = "storage" storage_account_access_key="***********************************" blob_container = "blob-container" Code 2 4. Establish The DataBase Connection #DB connection conn = pymssql.connect(server='****************.database.windows.net', user='*****', password='*****', database='DataBricksDB') cursor = conn.cursor() Code 3 5. Parse CSV file: Once the storage account is mounted, you can parse the CSV file using the following code #get a list of all blob from the container blob_list = [] for blob_i in container_client.list_blobs(): blob_list.append(blob_i.name) # print(blob_list)      df_list = [] #Generate SAS key for each file and load to the dataframe  for blob_i in blob_list:     print(blob_i)     sas_i = generate_blob_sas(account_name = storage_account_name,                              container_name = blob_container,                              blob_name = blob_i,                              account_key = storage_account_access_key,                              permission = BlobSasPermissions(read=True),                              expiry = datetime.utcnow() + timedelta(hours=12))       sas_url = 'https://' + storage_account_name +'.blob.core.windows.net/' + blob_container + '/' +blob_i     print(sas_url)          df=pd.read_csv(sas_url)     df_list.append(df) Code 4 6. Transform and Store data in a database: Finally, you can store the data in a database using the following code #Truncate Table Sales Truncate_Query = "IF EXISTS (SELECT * FROM sysobjects WHERE name='sales' and xtype='U') truncate table sales" cursor.execute(Truncate_Query) conn.commit()   # SQL Query For Table Creation create_table_query = "IF NOT EXISTS (SELECT * FROM sysobjects WHERE name='sales' and xtype='U') CREATE TABLE sales (REGION  varchar(max),COUNTRY  varchar(max),ITEMTYPE  varchar(max),SALESCHANNEL  varchar(max),ORDERPRIORITY  varchar(max),ORDERDATE  varchar(max),ORDERID  varchar(max),SHIPDATE  varchar(max),UNITSSOLD  varchar(max),UNITPRICE  varchar(max),UNITCOST  varchar(max),TOTALREVENUE  varchar(max),TOTALCOST  varchar(max),TOTALPROFIT  varchar(max))IF NOT EXISTS (SELECT * FROM sysobjects WHERE name='sales' and xtype='U') CREATE TABLE sales (REGION  varchar(max),COUNTRY  varchar(max),ITEMTYPE  varchar(max),SALESCHANNEL  varchar(max),ORDERPRIORITY  varchar(max),ORDERDATE  varchar(max),ORDERID  varchar(max),SHIPDATE  varchar(max),UNITSSOLD  varchar(max),UNITPRICE  varchar(max),UNITCOST  varchar(max),TOTALREVENUE  varchar(max),TOTALCOST  varchar(max),TOTALPROFIT  varchar(max))" cursor.execute(create_table_query) conn.commit()   #Insert Data From Main DataFrame for rows in df_combined.itertuples(index=False,name=None):     row = str(list(rows))     row_data = row[1:-1]     row_data = row_data.replace("nan","''")     row_data = row_data.replace("None","''") insert_query = "insert into sales (REGION,COUNTRY,ITEMTYPE,SALESCHANNEL,ORDERPRIORITY,ORDERDATE,ORDERID,SHIPDATE,UNITSSOLD,UNITPRICE,UNITCOST,TOTALREVENUE,TOTALCOST,TOTALPROFIT) values ("+row_data+")"     print(insert_query)     cursor.execute(insert_query) conn.commit() Code 5 As, Shown here The data from all the files is loaded to the SQL server Table Image 10   Azure Databricks notebook can be used to process stream data in Azure Data Pipeline. Here are the steps to accomplish this: 1. Create a Databricks notebook: First, create a Databricks notebook in Azure Databricks. A notebook is a web-based interface for working with code and data. 2. Create a job: Next, create a job in Azure Data Factory to execute the notebook. A job is a collection of tasks that can be scheduled and run automatically. 3. Configure the job: In the job settings, specify the Azure Databricks cluster and notebook that you want to use. Also, specify the input and output datasets. 4. Write the code: In the Databricks notebook, write the code to process the stream data. Here is an example code: #from pyspark.sql.functions import window stream_data = spark.readStream \     .format("csv") \     .option("header", "true") \     .schema("<schema>") \     .load("/mnt/<mount-name>/<file-name>.csv")   stream_data = stream_data \     .withWatermark("timestamp", "10 minutes") \     .groupBy(window("timestamp", "10 Code 6   How To Use Azure Databrick notebook in Azure Data Factory pipeline and configure the DataFlow Pipeline Using it. Image 11 1. Create ADF Pipeline  Image 12 2. Configure Data Pipeline  Image 13 3. Add Trigger To the PipeLine  Image 14 4. Configure the trigger  Image 15   These capabilities make Azure Databricks an ideal platform for building real-time data processing solutions. Overall, Azure Databricks provides a scalable and flexible solution for data processing and analytics, and it's definitely worth exploring if you're working with big data on the Azure platform. With its powerful tools and easy-to-use interface, Azure Databricks is a valuable addition to any data analytics toolkit.

Apache Kafka is the numerous common buffer solution deployed together with the ELK Stack. Kafka is deployed within the logs delivery and the indexing units, acting as a segregation unit for the data being collected: In this blog, we’ll see how to deploy all the components required to set up a resilient logs pipeline with Apache Kafka and ELK Stack: Filebeat – collects logs and forwards them to a Kafka topic. Kafka – brokers the data flow and queues it. Logstash – aggregates the data from the Kafka topic, processes it and ships to Elasticsearch. Elasticsearch – indexes the data. Kibana – for analyzing the data.   My environment: To perform the steps below, I set up a single Ubuntu 18.04 VM machine on AWS EC2 using local storage. In real-life scenarios, you will probably have all these components running on separate machines. I started the instance in the public subnet of a VPC and then set up a security group to enable access from anywhere using SSH and TCP 5601 (for Kibana). Using Apache Access Logs for the pipeline, you can use VPC Flow Logs, ALB Access logs etc. We will start by installing the main component in the stack — Elasticsearch. Login to your Ubuntu system using sudo privileges. For the remote Ubuntu server using ssh to access it. Windows users can use putty or Powershell to log in to Ubuntu system. Elasticsearch requires Java to run on any system. Make sure your system has Java installed by running the following command. This command will show you the current Java version. sudo apt install openjdk-11-jdk-headless Check the installation is successful or not by the below command ~$ java — versionopenjdk 11.0.3 2019–04–16OpenJDK Runtime Environment (build 11.0.3+7-Ubuntu-1ubuntu218.04.1)OpenJDK 64-Bit Server VM (build 11.0.3+7-Ubuntu-1ubuntu218.04.1, mixed mode, sharing) Finally, I added a new elastic IP address and associated it with the running instance. The example logs used for the tutorial are Apache access logs.   Step 1: Installing Elasticsearch We will start by installing the main component in the stack — Elasticsearch. Since version 7.x, Elasticsearch is bundled with Java so we can jump right ahead with adding Elastic’s signing key: Download and install the public signing key: wget -qO - https://artifacts.elastic.co/GPG-KEY-elasticsearch | sudo apt-key add - Now you may need to install the apt-transport-https package on Debian before proceeding: sudo apt-get install apt-transport-https echo "deb https://artifacts.elastic.co/packages/7.x/apt stable main" | sudo tee -a /etc/apt/sources.list.d/elastic-7.x.list Our next step is to add the repository definition to our system: echo “deb https://artifacts.elastic.co/packages/7.x/apt stable main” | sudo tee -a /etc/apt/sources.list.d/elastic-7.x.list You can install the Elasticsearch Debian package with: sudo apt-get update && sudo apt-get install elasticsearch Before we bootstrap Elasticsearch, we need to apply some basic configurations using the Elasticsearch configuration file at: /etc/elasticsearch/elasticsearch.yml: sudo su nano /etc/elasticsearch/elasticsearch.yml Since we are installing Elasticsearch on AWS, we will bind Elasticsearch to the localhost. Also, we need to define the private IP of our EC2 instance as a master-eligible node: network.host: "localhost" http.port:9200 cluster.initial_master_nodes: ["<InstancePrivateIP"] Save the file and run Elasticsearch with: sudo service elasticsearch start To confirm that everything is working as expected, point curl to: http://localhost:9200, and you should see something like the following output (give Elasticsearch a minute or two before you start to worry about not seeing any response): {   "name" : "elasticsearch",   "cluster_name" : "elasticsearch",   "cluster_uuid" : "W_Ky1DL3QL2vgu3sdafyag",   "version" : {     "number" : "7.2.0",     "build_flavor" : "default",     "build_type" : "deb",     "build_hash" : "508c38a",     "build_date" : "2019-06-20T15:54:18.811730Z",     "build_snapshot" : false,     "lucene_version" : "8.0.0",     "minimum_wire_compatibility_version" : "6.8.0",     "minimum_index_compatibility_version" : "6.0.0-beta1"   },   "tagline" : "You Know, for Search" }   Step 2: Installing Logstash Next up, the “L” in ELK — Logstash. Logstash and installing it is easy. Just type the following command. sudo apt-get install logstash -y Next, we will configure a Logstash pipeline that pulls our logs from a Kafka topic, processes these logs and ships them on to Elasticsearch for indexing. Verify Java is installed: java -version openjdk version "1.8.0_191" OpenJDK Runtime Environment (build 1.8.0_191-8u191-b12-2ubuntu0.16.04.1-b12) OpenJDK 64-Bit Server VM (build 25.191-b12, mixed mode) Let’s create a new config file: Since we already defined the repository in the system, all we have to do to install Logstash is run: sudo nano /etc/logstash/conf.d/apache.conf Next, we will configure a Logstash pipeline that pulls our logs from a Kafka topic, processes these logs, and ships them on to Elasticsearch for indexing. Let’s create a new config file: input {   kafka {     bootstrap_servers => "localhost:9092"     topics => "apache"     } } filter {     grok {       match => { "message" => "%{COMBINEDAPACHELOG}" }     }     date {     match => [ "timestamp" , "dd/MMM/yyyy:HH:mm:ss Z" ]     }   geoip {       source => "clientip"     } } output {   elasticsearch {     hosts => ["localhost:9200"]   } } As you can see — we’re using the Logstash Kafka input plugin to define the Kafka host and the topic we want Logstash to pull from. We’re applying some filtering to the logs and we’re shipping the data to our local Elasticsearch instance.   Step 3: Installing Kibana Let’s move on to the next component in the ELK Stack — Kibana. As before, we will use a simple apt command to install Kibana: sudo apt-get install kibana We will then open up the Kibana configuration file at: /etc/kibana/kibana.yml, and make sure we have the correct configurations defined: server.port: 5601 server.host: "<INSTANCE_PRIVATE_IP>" elasticsearch.hosts: ["http://<INSTANCE_PRIVATE_IP>:9200"] Then enable and start the Kibana service: sudo systemctl enable kibana sudo systemctl start kibana We would need to install Firebeat. Use: sudo apt install filebeat   Open up Kibana in your browser with http://<PUBLIC_IP>:5601. You will be presented with the Kibana home page.

You may have heard infrastructure as code(IaC), But do you know what infrastructure is? Why do we need infrastructure as code? What are the benefits of infrastructure as code? Is it safe and secure?    What is Infrastructure as Code(IoC)? Infrastructure as code (IaC) means to manage and upgrade your environments as infrastructure using configuration files. Terraform provides infrastructure as code for provisioning, compliance, and management across any public cloud, private data center, and third-party service. Enables teams to write, share, manage, and automate any infrastructure using version control With automated policy enforcement for security, compliance, and operational best practices and Enable developers to provision their desired infrastructure from within their workflows. IOC has a high impact on the Business perspective by providing Increased Productivity, Reduced Risk, Reduced Cost   Why do we use Infrastructure as Code(IoC)? Terraform is a simple human-readable configuration language, to define the desired topology of infrastructure resources VCS Integration Write, version, review, and collaborate on Terraform code using your preferred version control system Workspaces Workspaces decompose monolithic infrastructure into smaller components, or "micro-infrastructures". These workspaces can be aligned to teams for role-based access control. Variables Granular variables allow easy reuse of code and enable dynamic changes to scale resources and deploy new versions. Runs Terraform uses two-phased provisioning a plan (dry run) & apply (execution). Plans can be inspected before execution to ensure expected behavior and safety. Infrastructure State The state file is a record of currently provisioned resources. State files enable a versioned history of the infrastructure and are encrypted at rest. Versions can be inspected to see incremental changes. Policy as Code Sentinel is a policy as a code framework to automate multi-cloud governance.   What are the benefits of Infrastructure as Code(IoC)? Infrastructure as Code enables Infrastructure teams to test the applications in staging environments or development environment early - likely in the development cycle Infrastructure as Code Saves You Time and Money We can have a version history like when the infrastructure is upgraded and who has done it from the code itself. Else we have to ask to check the Infrastructure admin to look into logs and which is very time-consuming. We can check it into version control and I get versioning. Now we can see an incremental history of who changed what Use Infrastructure as Code to build update and manage any cloud, infrastructure, or services Terraform makes it easy to re-use configurations for the environment for similar infrastructure, helping you avoid mistakes and save time. We can use the same configuration code for the different staging Production and development environments. Terraform supports many Providers to be built from just a simple and less line of code. Major providers are as follows AWS Azure GitHub GitLab Google Cloud Platform VMWare Docker  and  200+ more. A Simple example to create an Ec2 Instance with just a few lines of code. resource "aws_instance" "ec2_instance" {   ami = "ami-*******"   instance_type = "t2.micro"   vpc_security_group_ids = ["${aws_security_group.*****.id}"]   key_name = "${aws_key_pair.****.id}"   tags {     Name = "New-EC2-Instance"   } } But First, we have to write code for which provider we are writing our code. To do so  here is the simple basic code to assign a provider provider "aws" {   region = "us-west-2"   ## PROVIDE CREDENTIALS } Now to Create your Ec2 Instance in AWS. We have to run the commands. So terraform has Four commands to check and apply the infrastructure changes, Init Plan Apply Destroy.   1. Init $ terraform init We can understand from the name of the command that is used to initialize something. So here terraform will be initialized in our code which will create some basic backend and tfstate files in folders for internal use. 2. Plan $ terraform plan As we do compile in some code languages, it will check for the compilation errors and plan what is going to happen when we run the script to generate infrastructure code. It will show you what resources are going to be created and what will be the configuration. 3. Apply $ terraform apply It is time to run the script and check what is being generated from the scripts. So the command will execute the script and apply the changes in our infrastructure, which will generate some resources for what we have written in the code.  4. Destroy $ terraform destroy This command is used when we want to remove or destroy the resource. After some time we don't need that resource then we just run the command which will destroy the resource. And your money is saved.