Introduction:

Integrating GitHub Actions for Continuous Integration and Continuous Deployment (CI/CD) in AWS Lambda deployments is a modern approach to automating the software development lifecycle. GitHub Actions provides a platform for automating workflows directly from your GitHub repository, making it a powerful tool for managing AWS Lambda functions.

Understanding GitHub Actions CI/CD Using Lambda

Integrating GitHub Actions for CI/CD with AWS Lambda streamlines the deployment process, enhances code quality, and reduces the time from development to production. By automating the testing and deployment of Lambda functions, teams can focus on building features and improving the application rather than managing infrastructure and deployment logistics. This integration is essential to modern DevOps practices, promoting agility and efficiency in software development.

Prerequisites:

• GitHub Account and Repository:
• AWS Account:
• AWS IAM Credentials:

DEMO:

First, we will create a folder structure like below & open it in Visual Studio.

After this, open AWS Lambda and create a function using Python with the default settings. Once created, we will see the default Python script. Ensure that the file name in AWS Lambda matches the one we created under the src folder.

Now, we will create a GitHub repository with the same name as our folder, LearnLambdaCICD. Once created, it will prompt us to configure the repository. We will follow the steps mentioned in the GitHub Repository section to initialize and sync the repository.

Next, create a folder named .github/workflows under the main folder. Inside the workflows folder, create a file named deploy_cicd.yaml with the following script.

As per this YAML, we need to set up the AWS_DEFAULT_REGION according to the region we are using. In our case, we are using ap-south-1. We will also need the ARN number from the AWS Lambda page, and we will use that same value in our YAML file.

We then need to configure the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY. To do this, navigate to the AWS IAM role and create a new access key.

Once created, we will use the same access key and secret access key in our YAML file. Next, we will map these access keys in our GitHub repository by navigating to Settings > Secrets and variables > Actions and configuring the keys.

Updates:

We will update the default code in the lambda_function.py file in Visual Studio. This way, once the pipeline builds successfully, we can see the changes in AWS Lambda as well. This modified the file as shown below:

Our next step will be to push the code to the Git repository using the following commands:

• Git add .
• Git commit -m “Last commit”
• Git push

Once the push is successful, navigate to GitHub Actions from your repository. You will see the pipeline deploying and eventually completing, as shown below. We can further examine the deployment process by expanding the deploy section. This will allow us to observe the steps that occurred during the deployment.

Now, when we navigate to AWS Lambda to check the code, we can see that the changes we deployed have been applied.

We can also see the directory changes in the left pane of AWS Lambda.

Conclusion:

As we can see, integrating GitHub Actions for CI/CD with AWS Lambda automates and streamlines the deployment process, allowing developers to focus on building features rather than managing deployments. This integration enhances efficiency and reliability, ensuring rapid and consistent updates to serverless applications. By leveraging GitHub’s powerful workflows and AWS Lambda’s scalability, teams can effectively implement modern DevOps practices, resulting in faster and more agile software delivery.

Building GitLab CI/CD Pipelines with AWS Integration

GitLab CI/CD (Continuous Integration/Continuous Deployment) is a powerful, integrated toolset within GitLab that automates the software development lifecycle (SDLC). It simplifies the process of building, testing, and deploying code, enabling teams to deliver high-quality software faster and more efficiently.

Understanding GitLab CI/CD

Getting started with GitLab CI/CD is simple. Start by creating a GitLab account and setting up a project for your application if you don’t have then install and configure a GitLab Runner, a tool responsible for executing the tasks defined in your .gitlab-ci.yml file. The runner handles building, testing, and deploying your code, ensuring the pipeline works as intended. This setup streamlines your development process and helps automate workflows efficiently.

What is a GitLab Pipeline?

A pipeline automates the process of building, testing, and deploying applications. CI (Continuous Integration) means regularly merging code changes into a shared repository. CD (Continuous Deployment/Delivery) automates releasing the application to its target environment.

Related CODE: In this step, you push your local code changes to the remote repository and commit any updates or modifications.

CI Pipeline: Once your code changes are committed and merged, you can run the build and test jobs defined in your pipeline. After completing these jobs, the code is ready to be deployed to staging and production environments.

Important Terms in GitLab CI/CD

1. .gitlab-ci.yaml file

A .gitlab-ci.yml file in a GitLab repository is used to define the Continuous Integration/Continuous Deployment (CI/CD) pipeline configuration. This file contains instructions on building, testing, and deploying your project.

2. Gitlab-Runner

In GitLab CI/CD, a “runner” refers to the agent that executes the jobs defined in the .gitlab-ci.yml pipeline configuration. Runners can be either shared or specific to the project.

Here’s how runners work:

1. Shared Runners: GitLab provides shared runners available to all projects within a GitLab instance. These runners are managed by GitLab administrators and can be used by any project. Shared runners are convenient if we don’t want to set up and manage our own runners.
2. Specific Runners: We can also set up our own runners that are dedicated to our project. These runners can be deployed on our infrastructure (e.g., on-premises servers, cloud instances) or using a variety of methods like Docker, Kubernetes, shell, or Docker Machine. Specific runners offer more control over the execution environment and can be customized to meet the specific needs of our project.

3. Pipeline:

Pipelines are made up of jobs and stages:

• Jobs define what you want to do. For example, test code changes, or deploy to a dev environment.
• Jobs are grouped into stages. Each stage contains at least one job. Common stages include build, test, and deploy.
• You can run the pipeline either manually or from the pipeline schedule Job.

First is manually means directly commit, when you merged or commit any changes into code pipeline directly trigger.

And second is by using rules for that, you need to create a scheduled job.

 4. Schedule Job:

We use scheduled jobs to automate pipeline execution. To create a scheduled job, follow these steps:

1. Navigate to Schedule Settings: Go to Build, select Pipeline Schedules, and click Create New Schedule.
2. Configure Schedule Details:
   1. Description: Enter a name for the scheduled job.
   2. Cron Timezone: Set the timezone according to your requirements.
   3. Interval Pattern: Define the cron schedule to determine when the pipeline should run. If you   prefer to run it manually by clicking the play button when needed, uncheck the Activate button at the end.
   4. Target Branch: Specify the branch where the cron job will run.
3. Add Variables: Include any variables mentioned in the rules section of your .gitlab-ci.yml file to ensure the pipeline runs correctly.
   1. Input variable key = SCHEDULE_TASK_NAME
   2. Input variable value = prft-deployment

Demo

Prerequisites for GitLab CI/CD 

• GitLab Account and Project: You need an active GitLab account and a project repository to store your source code and set up CI/CD workflows.
• Server Environment: You should have access to a server environment, like a AWS Cloud, where your install gitlab-runner.
• Version Control: Using a version control system like Git is essential for managing your source code effectively. With Git and a GitLab repository, you can easily track changes, collaborate with your team, and revert to previous versions whenever necessary.

Configure Gitlab-Runner

• Launch an AWS EC2 instance with any operating system of your choice. Here, I used Ubuntu. Configure the instance with basic settings according to your requirements.
• SSH into the EC2 instance and follow the steps below to install GitLab Runner on Ubuntu.

1. sudo apt install -y curl
2. curl -L https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh | sudo bash
3. sudo apt install gitlab-runner

After installing GitLab Runner, proceed to register it. Navigate to GitLab, go to Settings, then CI/CD, and under Runners, click on the three dots to access the registration options.

And copy-paste the below cmd:

Run the following command on your EC2 instance and provide the necessary details for configuring the runner based on your requirements:

1. URL: Press enter to keep it as the default.
2. Token: Use the default token and press enter.
3. Description: Add a brief description for the runner.
4. Tags: This is critical; the tag names define your GitLab Runner and are referenced in your .gitlab-ci.yml file.
5. Notes: Add any additional notes if required.
6. Executor: Choose shell as the executor.

Check GitLab-runner status and active status using the below cmd:

• gitlab-runner verify
• gitlab-runner list

Check gitlab-runner is active in gitlab also:

Navigate to GitLab, then go to Settings and select GitLab Runners.

 Configure gitlab-ci.yaml file

• Stages: Stages that define the sequence in which jobs are executed.
  • build
  • deploy
• Build-job: This job is executed in the build stage, the first run stage.
  • Stage: build
  • Script:
    • Echo “Compiling the code…”
    • Echo “Compile complete.”‘
  • Rules:
    • if: ‘$CI_PIPELINE_SOURCE == “schedule” && $SCHEDULE_TASK_NAME == “prft-deployment”‘
  • Tags:
    • prft-test-runner
• Deploy-job: This job is executed in the deploy stage.
  • Stage: deploy   #It will only execute when both jobs in the build job & test job (if added) have been successfully completed.
  • script:
    • Echo “Deploying application…”
    • Echo “Application successfully deployed.”
  • Rules:
    • if: ‘$CI_PIPELINE_SOURCE == “schedule” && $SCHEDULE_TASK_NAME == “prft-deployment”‘
  • Tags:
    • prft-test-runner

Note: If needed, you can add a test job similar to the BUILD and DEPLOY jobs.

Run Pipeline

Since the Cron job is already configured in the schedule, simply click the Play button to automatically trigger your pipeline.

To check pipeline status, go to Build and then Pipeline. Once the Build Job is successfully completed, the Test Job will start, and once the Test Job is completed, the deploy job will start.

Output

We successfully completed BUILD & DEPLOY Jobs.

Build Job

Deploy Job

Conclusion

As we can see, the BUILD & DEPLOY jobs pipeline has successfully passed.

We’ve provided a brief overview of GitLab CI/CD pipelines and a practical demonstration of how its components work together. Hopefully, everything is running smoothly on your end!

Migrating to a cloud-delivered Contact Center as a Service (CCaaS) solution can revolutionize how your organization delivers customer service. However, this transition requires careful planning and execution to avoid disruptions. Assuming you have selected a CCaaS platform that aligns with your organizational needs, the following best practices outline key considerations for a seamless migration.

A successful migration to CCaaS not only enhances operational efficiency and scalability but also ensures a significant improvement in service delivery, directly impacting customer satisfaction and retention. Organizations should consider the risks of not embracing modern cloud-based customer care solutions, which can

include diminished customer service capabilities and potential costs due to outdated or inflexible systems. Moreover, organizations that delay this shift risk falling behind competitors who can adapt more quickly to market demands and customer needs. Thus, embarking on a well-planned migration journey is imperative for companies aiming to optimize their customer care operations and secure a competitive advantage in their respective markets.

1. Physical Infrastructure Migration

Understanding your current environment is critical for a successful transition. Start with a thorough site review to document the infrastructure and identify unique user requirements. Engage with call center managers, team leaders, and power users to uncover specific needs and configured features such as whisper settings, omnichannel components, call management, etc.

Factors such as bandwidth and latency are paramount for seamless operations. Evaluate your facility’s connectivity for both on-site and remote users, ensuring it aligns with the CCaaS product requirements. Fortunately, modern CCaaS solutions such as Amazon Connect, Twilio Flex and Five9 supply agent connectivity tools to verify that workers have sufficient resources to provide good customer service over various channels.

Additionally, document call treatments and station-specific configurations like call coverage paths. Legacy components requiring continued functionality should be cataloged to prepare for integration.

2. Change Management Planning

Change management is essential to mitigate risks and maximize adoption. A staged cutover strategy is recommended over a single-event migration, allowing for gradual testing and adjustments.

Develop a robust testing strategy to validate the platform’s performance under real-world conditions. Complement this with an organizational enablement strategy to train users and ensure they are comfortable with the new system. Adoption by your business units and users is one of the most critical factors which will determine the success of your CCaaS migration.

3. Operational Considerations

Operational continuity is vital during migration. Start by understanding the reporting requirements for business managers to ensure no loss of visibility into critical metrics. Additionally, review monitoring processes to maintain visibility into system performance post-migration.

4. Integration Planning

Integrating legacy infrastructure with the new CCaaS platform can present significant challenges. Document existing components, including FXO/FXS interfaces, Workforce Management solutions, FAX systems, wallboards, and specialty dialers. Verify that integrations comply with any regulatory requirements, such as HIPAA or FINRA.

Interactive Voice Response (IVR) systems often require specific integrations with local data sources or enterprise middleware. Assess these integrations to ensure call flows function as intended. For specialized applications, verify that they meet operational needs within the new environment.

5. Fault Tolerance and Disaster Recovery

Testing fault tolerance and disaster recovery capabilities are critical steps in any CCaaS migration. Develop and execute a failsafe testing plan to ensure resilience against both premise-level and carrier-level failures. It is important to align to your IT organization’s standards for recovery time objective (RTO) and business up-time expectations. Disaster recovery plans must reflect these measures and be tested to protect against potential downtime.

6. Scalability and Compliance

CCaaS solutions must scale with your business. Validate scalability by conducting load tests and documenting performance metrics. Compliance is equally important—ensure your migration adheres to industry standards like HIPAA, FedRAMP, or FINRA through thorough compliance testing and documentation.

Conclusion

A successful CCaaS migration hinges on meticulous planning, comprehensive testing, and strong change management. By following these best practices, you can minimize risks, ensure operational continuity, and set your organization up for long-term success with its new contact center platform. The result? An enhanced customer experience and a contact center infrastructure that grows with your business.

What are Legacy Systems? Why is Upgrading those Systems Required?

Upgrading means more than just making practical improvements to keep things running smoothly. It addresses immediate needs rather than chasing a perfect but impractical solution. The situation could spiral out of control if things don’t function properly in real-time.

One such incident happened on January 4, 2024, when South Africa’s Department of Home Affairs was taken offline nationwide due to a mainframe failure. In simple terms, Mainframe failures in such contexts are usually high-stakes issues because they impact the core infrastructure that supports vital public services. In South Africa, where the Department of Home Affairs handles a range of essential services such as passports, identity documents, and immigration control,  A mainframe failure can have widespread repercussions, leading to backlogs, delays, and potential administrative chaos. The South African Department of Home Affairs provides a clear example of a critical legacy system facing significant risks due to its outdated technology and operational challenges. 

Addressing these issues through modernization and digital transformation is crucial for improving service delivery and ensuring the system’s continued effectiveness and security. One cannot work on migrating the legacy system in one go, as the business and functional side of testing is a must. A planned and systematic approach is needed while upgrading the legacy system.

Question: What is the solution to avoid such a case?
Answer: Modernization of Legacy code.

Legacy code modernization is improving and updating outdated software systems to make them more maintainable, scalable, and compatible with modern technologies. Let’s understand this using Apigee (an API Management tool).

1. Scalability

Legacy system: Legacy systems were designed to provide solutions for the respected tasks but there was no scalability as records used to get limited within Infrastructure for improvements in business.
Apigee: Due to its easy scalability, centralized monitoring, and integration capabilities, Apigee helped the organization plan its approach to business improvements.

2. Security

Legacy system: One of the simplest methods for authenticating users in Legacy Systems was “Basic Authentication,” where the client sends a username and password in every HTTP request. This method was Vulnerable to man-in-the-middle (MITM) attacks if not combined with HTTPS. Credentials are exposed on each request.

Apigee: Using Apigee, the organization can quickly implement modern security features like OAuth, API key validation, rate limiting, and threat protection (e.g., bot detection) without changing the core logic of the APIs.

3. User and Developer Experience

Legacy system: The legacy API lacks good documentation, making it harder for external developers to integrate with it. Most systems tend to have a SOAP-based communication format.
Apigee: Apigee provides a built-in API portal, automatic API documentation, and testing tools, improving the overall developer experience and adoption of the APIs so that integration with other tools can be easy and seamless with modern standards.

There are now multiple ways to migrate data from legacy to modern systems, which are listed below.

1. Big Bang Migration
2. Phased Migration
3. Parallel Migration
4. Pilot Migration
5. Hybrid Migration
and more…

Although these things are known to the legacy system owners, they are very selective and picky when finalizing a migration plan. They are only aware of the short-term goal, i.e., to get the code up and running in production. Because when we are speaking of legacy systems, all there is left is code and a sigh of relief that it is still up and running.  For most systems, there is no documentation, code history, revisions, etc., and that’s why it could fail on a large scale if something goes wrong.

I have found some points that need to be ensured before finalizing the process of migrating from legacy systems to modern systems.

1. Research and Analysis

We need to understand the motives behind the development of the Legacy system since there is no or insufficient documentation.

In the study, we can plan to gather historical data to understand the system’s behavior. We need to dig deeper to find something that could help us understand the system better.

2. Team Management

After studying the system, we can estimate the team size and resource management. Such systems are way older when it comes to the tech on which they are running. So, it is hard to gather resources with such outdated skills. In that case, management can cross-skill existing resources into such technologies.

I believe adding the respective numbers of junior engineers would be best, as they would get exposure to challenges, which can help them improve their skills.

3. Tool to Capture Raw Logs

Analyzing the raw logs can talk more about the system, as this is the way communication is happening to complete each task requested by the system. By breaking down the data into layman’s language, understanding at what time requests are high by timestamps,

What parameter data consists of, and by such information, we can tell system behavior and plan properly.

4. Presentation of the Logs

Sometimes we may need to present the case study to high-level management before proceeding with the plan. So to simplify the presentation, we can use tools like Datadog and Splunk to get data in a format such as tabular, graphic, etc. so that other team members can understand.

5. Replicate the Architect with Proper Functionality

This is the most important part. End-to-end development is the only solution for smooth migration activity. We need to ensure standards here, such as maintaining core functionality, risk management, conveying data pattern changes to other associated clients, and ensuring user access, business processes, etc. The point 1 study can help us understand the behavior of systems to check on what modern tech we can land our migration on.

We can implement and plan using one of the migration methods I mentioned above in the blog.

6. End-to-end Testing

Once the legacy system is replicated to Modern Tech, we need to ensure that we have a User Acceptance Testing (UAT) environment to perform the system testing. It could be challenging if the respective legacy systems didn’t have a testing environment back in the day. We may need to call mock backend URLs to mock the behavior of services.

7. Before Moving to Production, do Pre-production Testing Properly

Only after successful UAT testing one can ensure the functionality and may think to move changes to production hassle-free. However, some points must be ensured, such as following standards and maintaining documentation. In standards, we need to ensure that no risk may lead to the failure of services in modern technology and that they are compatible properly.

In the documentation, we need to ensure that all service flows are appropriately documented and that testing is done according to the requirement gathering.

Legacy systems and their workings are among the most complex and time-consuming topics. But to make the job easier, one must put effort into it.

Understanding Unit Testing

Unit testing is a crucial aspect of software development, especially in complex applications like Android apps. It involves testing individual units of code, such as methods or classes, in isolation. This ensures the correctness of each component, leading to a more robust and reliable application.

Why Unit Testing in MVVM?

The Model-View-ViewModel (MVVM) architectural pattern is widely adopted in Android app development. It separates the application into three distinct layers:

• Model: Handles data logic and interacts with data sources.
• View: Responsible for the UI and user interactions.
• ViewModel: Acts as a bridge between the View and Model, providing data and handling UI logic.

Unit testing each layer in an MVVM architecture offers numerous benefits:

• Early Bug Detection: Identify and fix issues before they propagate to other parts of the app.
• Improved Code Quality: Write cleaner, more concise, and maintainable code.
• Accelerated Development: Refactor code and add new features with confidence.
• Enhanced Collaboration: Maintain consistent code quality across the team.

Setting Up the Environment

1. Android Studio: Ensure you have the latest version installed.
2. Testing Framework: Add the necessary testing framework to your app/build.gradle file:
   

   testImplementation 'junit:junit:4.13.2'
   androidTestImplementation 'androidx.test.ext:junit:1.1.5'
   androidTestImplementation 'androidx.test.espresso:espresso-core:3.5.1'

3. Testing Library: Consider using a testing library like Mockito or MockK to create mock objects for testing dependencies.

Unit Testing ViewModels

1. Create a Test Class: Create a separate test class for each ViewModel you want to test.
2. Set Up Test Dependencies: Use dependency injection frameworks like Dagger Hilt or Koin to inject dependencies into your ViewModel. For testing, use mock objects to simulate the behavior of these dependencies.
3. Write Test Cases: Write comprehensive test cases covering various scenarios:

• Input Validation: Test how the ViewModel handles invalid input.
• Data Transformation: Test how the ViewModel transforms data from the Model.
• UI Updates: Test how the ViewModel updates the UI through LiveData or StateFlow.
• Error Handling: Test how the ViewModel handles errors and exceptions.

Example:

@RunWith(AndroidJUnit4::class)
class MyViewModelTest {

    @Test
    fun `should update the UI when data is fetched successfully`() {
        // ... (Arrange)
        val viewModel = MyViewModel(mockRepository)

        // ... (Act)
        viewModel.fetchData()

        // ... (Assert)
        viewModel.uiState.observeForever { uiState ->
            assertThat(uiState.isLoading).isFalse()
            assertThat(uiState.error).isNull()
            assertThat(uiState.data).isEqualTo(expectedData)
        }
    }
}

Unit Testing Repositories

1. Create Test Classes: Create separate test classes for each Repository class.
2. Set Up Test Dependencies: Use dependency injection to inject dependencies into your Repository. For testing, use mock objects to simulate the behavior of data sources like databases or network APIs.
3. Write Test Cases: Write test cases to cover:

• Data Fetching: Test how the Repository fetches data from remote or local sources.
• Data Storage: Test how the Repository stores and retrieves data.
• Data Manipulation: Test how the Repository processes and transforms data.
• Error Handling: Test how the Repository handles errors and exceptions.

Example:

@RunWith(AndroidJUnit4::class)
class MyRepositoryTest {

    @Test
    fun `should fetch data from remote source successfully`() {
        // ... (Arrange)
        val mockApi = mock(MyApi::class.java)
        val repository = MyRepository(mockApi)

        // ... (Act)
        repository.fetchData()

        // ... (Assert)
        verify(mockApi).fetchData()
    }
}

Implementing SonarQube

SonarQube is a powerful tool for code quality and security analysis. Here’s a detailed guide on how to integrate SonarQube with your Android project:

1. Set Up SonarQube Server:

• Install SonarQube Server: Download and install the SonarQube server on your machine or a server.
• Configure SonarQube: Configure the server with database settings, user authentication, and other necessary parameters.
• Start SonarQube Server: Start the SonarQube server.

2. Configure SonarQube Scanner:

• Install SonarQube Scanner: Download and install the SonarQube Scanner.
• Configure Scanner Properties: Create a sonar-scanner.properties file in your project’s root directory and configure the following properties:
  

  sonar.host.url=http://localhost:9000
  sonar.login=your_sonar_login
  sonar.password=your_sonar_password
  sonar.projectKey=my-android-project
  sonar.projectName=My Android Project
  sonar.sources=src/main/java
  sonar.java.binaries=build/intermediates/javac/release/classes

3. Integrate SonarQube with Your Build Process:

• Gradle: Add the SonarQube Gradle plugin to your build.gradle file:
  

  plugins {
      id 'org.sonarsource.scanner-gradle' version '3.3'
  }

  Configure the plugin with your SonarQube server URL and authentication token.

• Maven: Add the SonarQube Maven plugin to your pom.xml file. Configure the plugin with your SonarQube server URL and authentication token.

4. Run SonarQube Analysis:

• Execute the SonarQube analysis using the SonarQube Scanner. This can be done manually or integrated into your CI/CD pipeline.

5. Analyze the Results:

• Once the analysis is complete, you can view the results on the SonarQube dashboard. The dashboard provides insights into code quality, security vulnerabilities, and potential improvements.

Implementing Test Coverage with Bitrise

Test coverage measures the percentage of your code that is covered by tests. It’s a crucial metric to assess the quality of your test suite. Here’s how to measure test coverage with Bitrise:

1. Configure Code Coverage Tool: Add a code coverage tool like JaCoCo to your project. Configure it to generate coverage reports in a suitable format (e.g., XML).
2. Add Code Coverage Step to Bitrise Workflow: Add a step to your Bitrise Workflow to generate the code coverage report. This step should execute your tests and generate the report.
3. Upload Coverage Report to SonarQube: Add a step to upload the generated code coverage report to SonarQube. This will allow SonarQube to analyze the report and display the coverage metrics.

Best Practices for Unit Testing

• Write Clear and Concise Tests: Use descriptive names for test methods and variables.
• Test Edge Cases: Consider testing scenarios with invalid input, empty data, or network errors.
• Use a Testing Framework: A testing framework like JUnit provides a structured way to write and run tests.
• Leverage Mocking: Use mocking frameworks like Mockito or MockK to isolate units of code and control their behavior.
• Automate Testing: Integrate unit tests into your CI/CD pipeline to ensure code quality.
• Review and Refactor Tests: Regularly review and refactor your tests to keep them up-to-date and maintainable.

By following these guidelines and incorporating unit testing into your development process, you can significantly improve the quality and reliability of your Android apps.

Metadata Extraction: IDMC vs. PowerCenter

When we talk about metadata extraction, IDMC (Intelligent Data Management Cloud) can be trickier than PowerCenter. Let’s see why.
In PowerCenter, all metadata is stored in a local database. This setup lets us use SQL queries to get data quickly and easily. It’s simple and efficient.
In contrast, IDMC relies on the IICS Cloud Repository for metadata storage. This means we have to use APIs to get the data we need. While this method works well, it can be more complicated. The data comes back in JSON format. JSON is flexible, but it can be hard to read at first glance.
To make it easier to understand, we convert the JSON data into a table format. We use a tool called jq to help with this. jq allows us to change JSON data into CSV or table formats. This makes the data clearer and easier to analyze.

In this section, we will explore jq. jq is a command-line tool that helps you work with JSON data easily. It lets you parse, filter, and change JSON in a simple and clear way. With jq, you can quickly access specific parts of a JSON file, making it easier to work with large datasets. This tool is particularly useful for developers and data analysts who need to process JSON data from APIs or other sources, as it simplifies complex data structures into manageable formats.

For instance, if the requirement is to gather Succeeded Taskflow details, this involves two main processes. First, you’ll run the IICS APIs to gather the necessary data. Once you have that data, the next step is to execute a jq query to pull out the specific results. Let’s explore two methods in detail.

Extracting Metadata via Postman and jq:-

Step 1:

To begin, utilize the IICS APIs to extract the necessary data from the cloud repository. After successfully retrieving the data, ensure that you save the file in JSON format, which is ideal for structured data representation.

Step 2:
Construct a jq query to extract the specific details from the JSON file. This will allow you to filter and manipulate the data effectively.

Windows:-
(echo Taskflow_Name,Start_Time,End_Time & jq -r ".[] | [.assetName, .startTime, .endTime] | @csv" C:\Users\christon.rameshjason\Documents\Reference_Documents\POC.json) > C:\Users\christon.rameshjason\Documents\Reference_Documents\Final_results.csv

Linux:-
jq -r '["Taskflow_Name","Start_Time","End_Time"],(.[] | [.assetName, .startTime, .endTime]) | @csv' /opt/informatica/test/POC.json > /opt/informatica/test/Final_results.csv

Step 3:
To proceed, run the jq query in the Command Prompt or Terminal. Upon successful execution, the results will be saved in CSV file format, providing a structured way to analyze the data.

Extracting Metadata via Command Prompt and jq:-

Step 1:
Formulate a cURL command that utilizes IICS APIs to access metadata from the IICS Cloud repository. This command will allow you to access essential information stored in the cloud.

Windows and Linux:-
curl -s -L -X GET -u USER_NAME:PASSWORD "https://<BASE_URL>/active-bpel/services/tf/status?runStatus=Success" -H "Accept: application/json"

Step 2:
Develop a jq query along with cURL to extract the required details from the JSON file. This query will help you isolate the specific data points necessary for your project.

Windows:
(curl -s -L -X GET -u USER_NAME:PASSWORD "https://<BASE_URL>/active-bpel/services/tf/status?runStatus=Success" -H "Accept: application/json") | (echo Taskflow_Name,Start_Time,End_Time & jq -r ".[] | [.assetName, .startTime, .endTime] | @csv" C:\Users\christon.rameshjason\Documents\Reference_Documents\POC.json) > C:\Users\christon.rameshjason\Documents\Reference_Documents\Final_results.csv

Linux:
curl -s -L -X GET -u USER_NAME:PASSWORD "https://<BASE_URL>/active-bpel/services/tf/status?runStatus=Success" -H "Accept: application/json" | jq -r '["Taskflow_Name","Start_Time","End_Time"],(.[] | [.assetName, .startTime, .endTime]) | @csv' /opt/informatica/test/POC.json > /opt/informatica/test/Final_results.csv

Step 3:
Launch the Command Prompt and run the cURL command that includes the jq query. Upon running the query, the results will be saved in CSV format, which is widely used for data handling and can be easily imported into various applications for analysis.

Conclusion
To wrap up, the methods outlined for extracting workflow metadata from IDMC are designed to streamline your workflow, minimizing manual tasks and maximizing productivity. By automating these processes, you can dedicate more energy to strategic analysis rather than tedious data collection. If you need further details about IDMC APIs or jq queries, feel free to drop a comment below!

Reference Links:-

IICS Data Integration REST API – Monitoring taskflow status with the status resource API

jq Download Link – Jq_Download

In the PC-IDMC conversion process, it can be challenging to gather detailed information about workflows. Specifically, we often need to determine:

• The number of transformations used in each mapping.
• The number of sessions utilized within the workflow.
• Whether any parameters or variables are being employed in the mappings.
• The count of reusable versus non-reusable sessions used in the workflow etc.

To obtain these details, we currently have to open each workflow individually, which is time-consuming. Alternatively, we could use complex queries to extract this information from the PowerCenter metadata in the database tables.

This section focuses on XQuery, a versatile language designed for querying and extracting information from XML files. When workflows are exported from the PowerCenter repository or Workflow Manager, the data is generated in XML format. By employing XQuery, we can effectively retrieve the specific details and data associated with the workflow from this XML file.

Step-by-Step Guide to Extracting Workflow Details Using XQuery: –

For instance, if the requirement is to retrieve all reusable and non-reusable sessions for a particular workflow or a set of workflows, we can utilize XQuery to extract this data efficiently.

Step 1:
Begin by exporting the workflows from either the PowerCenter Repository Manager or the Workflow Manager. You have the option to export multiple workflows together as one XML file, or you can export a single workflow and save it as an individual XML file.

Step 2:-
Develop the XQuery based on our specific requirements. In this case, we need to fetch all the reusable and non-reusable sessions from the workflows.

let $header := "Folder_Name,Workflow_Name,Session_Name,Mapping_Name"
let $dt := (let $data := 
    ((for $f in POWERMART/REPOSITORY/FOLDER
    let $fn:= data($f/@NAME)
    return
        for $w in $f/WORKFLOW
        let $wn:= data($w/@NAME)
        return
            for $s in $w/SESSION
            let $sn:= data($s/@NAME)
            let $mn:= data($s/@MAPPINGNAME)
            return
                <Names>
                    {
                        $fn ,
                        "," ,
                        $wn ,
                        "," ,
                        $sn ,
                        "," ,
                        $mn
                    }
                </Names>)
    |           
    (for $f in POWERMART/REPOSITORY/FOLDER
    let $fn:= data($f/@NAME)
    return          
        for $s in $f/SESSION
        let $sn:= data($s/@NAME)
        let $mn:= data($s/@MAPPINGNAME)
        return
            for $w in $f/WORKFLOW
            let $wn:= data($w/@NAME)
            let $wtn:= data($w/TASKINSTANCE/@TASKNAME)
            where $sn = $wtn
            return
                <Names>
                    {
                        $fn ,
                        "," ,
                        $wn ,
                        "," ,
                        $sn ,
                        "," ,
                        $mn
                    }
                </Names>))
       for $test in $data
          return
            replace($test/text()," ",""))
      return
 string-join(($header,$dt), "
")

Step 3:
Select the necessary third-party tools to execute the XQuery or opt for online tools if preferred. For example, you can use BaseX, Altova XMLSpy, and others. In this instance, we are using Basex, which is an open-source tool.

Create a database in Basex to run the XQuery.

Step 4: Enter the created XQuery into the third-party tool or online tool to run it and retrieve the results.

Step 5:
Export the results in the necessary file extensions.

Conclusion:
These simple techniques allow you to extract workflow details effectively, aiding in the planning and early detection of complex manual conversion workflows. Many queries exist to fetch different kinds of data. If you need more XQueries, just leave a comment below!

The 2024 InsureTech Connect (ITC) conference was truly exhilarating, with key takeaways impacting the insurance industry. Each year, it continues to improve, offering more relevant content, valuable industry connections, and opportunities to delve into emerging technologies.

This year’s event was no exception, showcasing the importance of personalization to the customer, tech-driven relationship management, and AI-driven underwriting processes. The industry is constantly evolving, and ITC displays the alignment of everyone within the insurance industry surrounding the same purpose.

The Road Ahead: Transformative Trends

As I reflect on ITC and my experience, it is evident the progression of the industry is remarkable. Here are a few key takeaways from my perspective that will shape our industry roadmap:

1. Personalization at Scale

We’ve spoken for many years about the need to drive greater personalization across our interactions in our industry. We know that customers engage with companies that demonstrate authentic knowledge of their relationship. This year, we saw great examples of how companies are treating personalization, not as an incremental initiative, but rather embedding it at key moments in the insurance experience, particularly underwriting and claims.

For example, New York Life highlighted how personalization is driving generational loyalty. We’ve been working with industry leading insurers to help drive personalization across the distribution network: carriers to agents and the final policyholder.

Success In Action: Our client wanted to integrate better contact center technology to improve internal processes and allow for personalized, proactive messaging to clients. We implemented Twilio Flex and leveraged its outbound notification capabilities to support customized messaging while also integrating their cloud-based outbound dialer and workforce management suite. The insurer now has optimized agent productivity and agent-customer communication, as well as newfound access to real-time application data across the entire contact center.

2. Holistic, Well-Connected Distribution Network

Insurance has always had a complex distribution network across platforms, partnerships, carriers, agents, producers, and more. Leveraging technology to manage these relationships opens opportunities to gain real-time insights and implement effective strategies, fostering holistic solutions and moving away from point solutions. Managing this complexity and maximizing the value of this network requires a good business and digital transformation strategy.

Our proprietary Envision process has been leading the way to help carriers navigate this complex system with proprietary strategy tools, historical industry data, and best practices.

3. Artificial Intelligence (AI) for Process Automation

Not surprisingly, AI permeated many of the presentations and demos across the session. AI Offers insurers unique decisioning throughout the value chain to create differentiation. It was evident that while we often talk about AI as an overarching technology, the use cases were more point solutions across the insurance value chain. Moreover, AI is not here to replace the human, but rather assist the human. By automating the mundane process activities, mindshare and human capital can be invested toward more value-added activity and critical problems to improve customer experience. Because these point solutions are available across many disparate groups, organizational mandates demand safe and ethical use of AI models.

Our PACE framework provides a holistic approach to responsibly operationalize AI across an organization. It empowers organizations to unlock the benefits of AI while proactively addressing risks.

Our industry continues to evolve in delivering its noble purpose – to protect individual’s and businesses’ property, liability, and financial obligations. Technology is certainly an enabler of this purpose, but transformation must be managed to be effective.

Perficient Is Driving Success and Innovation in Insurance

Want to know the now, new, and next of digital transformation in insurance? Contact us and let us help you meet the challenges of today and seize the opportunities of tomorrow in the insurance industry.

As new technologies become available within the digital space, businesses must adapt quickly by modernizing their legacy systems and harnessing the power of the cloud to stay competitive. Forrester’s 2024 report recognizes 42 notable providers– and we’re proud to announce that Perficient is among them.

We believe our inclusion in Forrester’s Application Modernization and Multicloud Managed Services Landscape, Q4 2024 reflects our commitment to evolving enterprise applications and managing multicloud environments to enhance customer experiences and drive growth in a complex digital world.

With the demand for digital transformation growing rapidly, this landscape provides valuable insights into what businesses can expect from service providers, how different companies compare, and the options available based on provider size and market focus.

Application Modernization and Multicloud Managed Services

Forrester defines application modernization and multicloud managed services as:

“Services that offer technical and professional support to perform application and system assessments, ongoing application multicloud management, application modernization, development services for application replacements, and application retirement.”

According to the report,

“Cloud leaders and sourcing professionals implement application modernization and multicloud managed services to:

• Deliver superior customer experiences.
• Gain access to technical and transformational skills and capabilities.
• Reduce costs associated with legacy technologies and systems.”

By focusing on application modernization and multicloud management, Perficient empowers businesses to deliver superior customer experiences through agile technologies that boost user satisfaction. We provide clients with access to cutting-edge technical and transformational skills, allowing them to stay ahead of industry trends. Our solutions are uniquely tailored to reduce costs associated with maintaining legacy systems, helping businesses optimize their IT budgets while focusing on growth.

Focus Areas for Modernization and Multicloud Management

Perficient has honed its expertise in several key areas that are critical for organizations looking to modernize their applications and manage multicloud environments effectively. As part of the report, Forrester asked each provider included in the Landscape to select the top business scenarios for which clients select them and from there determined which are the extended business scenarios that highlight differentiation among the providers. Perficient self-reported three key business scenarios that clients work with us out of those extended application modernization and multicloud services business scenarios:

• Infrastructure Modernization: We help clients transform their IT infrastructure to be more flexible, scalable, and efficient, supporting the rapid demands of modern applications.
• Cloud-Native Development Execution: Our cloud-native approach enables new applications to leverage cloud environments, maximizing performance and agility.
• Cloud Infrastructure “Run”: We provide ongoing support for cloud infrastructure, keeping applications and systems optimized, secure, and scalable.

Delivering Value Through Innovation

Perficient is listed among large consultancies with an industry focus in financial services, healthcare, and the manufacturing/production of consumer products. Additionally, our geographic presence in North America, Latin America, and the Asia-Pacific region was noted.

We believe that Perficient’s inclusion in Forrester’s report serves as another milestone in our mission to drive digital innovation for our clients across industries. We are proud to be recognized among notable providers and look forward to continuing to empower our clients to transform their digital landscapes with confidence. For more information on how Perficient can help your business with application modernization and multicloud managed services, contact us today.

Download the Forrester report, The Application Modernization And Multicloud Managed Services Landscape, Q4 2024, to learn more (link to report available to Forrester subscribers and for purchase).

In a move that has sparked intense discussion across the enterprise software landscape, Klarna announced its decision to drop both Salesforce Sales Cloud and Workday, replacing these industry-leading platforms with its own AI-driven tools. This announcement, led by CEO Sebastian Siemiatkowski, may signal a paradigm shift toward using custom AI agents to manage critical business functions such as customer relationship management (CRM) and human resources (HR). While mostly social media fodder at this point, this very public bet on SaaS replacement has raised important questions about the future of enterprise software and how Agentic AI might reshape the way businesses operate.

AI Agents – Impact on Enterprises

Klarna’s move maybe be a one-off internal pivot or it may signal broader shifts that impact enterprises worldwide. Here are three ways this transition could affect the broader market:

1. Customized AI Over SaaS for Competitive Differentiation Enterprises are always on the lookout for ways to differentiate themselves from the competition. Klarna’s decision may reflect an emerging trend: companies developing custom Agentic AI solutions to better tailor workflows and processes to their specific needs. The advantage here lies in having a system that is purpose-built for an organization’s unique requirements, potentially driving innovation and efficiencies that are difficult to achieve with out-of-the-box software. However, this approach also raises challenges. Building Agentic AI solutions in-house requires significant technical expertise, resources, and time. Not all companies will have the bandwidth to undertake such a transformation, but for those who do, it could become a key differentiator in terms of operational efficiency and personalized customer experiences.
2. Shift in Vendor Relationships and Power Dynamics If more enterprises follow Klarna’s lead, we could see a shift in the traditional vendor-client dynamic. For years, businesses have relied on SaaS providers like Salesforce and Workday to deliver highly specialized, integrated solutions. However, AI-driven automation might diminish the need for comprehensive, multi-purpose platforms. Instead, companies might lean towards modular, lightweight tech stacks powered by AI agents, allowing for greater control and flexibility. This shift could weaken the power and influence of SaaS providers if enterprises increasingly build customized systems in-house. On the other hand, it could also lead to new forms of partnership between AI providers and SaaS companies, where AI becomes a layer on top of existing systems rather than a full replacement.
3. Greater Focus on Data and Compliance Risks With AI agents handling sensitive business functions like customer management and HR, companies like Klarna must ensure that data governance, compliance, and security are up to the task. This shift toward Agentic AI requires robust mechanisms to manage customer and employee data, especially in industries with stringent regulatory requirements, like finance and healthcare. Marc Benioff, Salesforce’s CEO, raised these concerns directly, questioning how Klarna will handle compliance, governance, and institutional memory. AI might automate many processes, but without the proper safeguards, it could introduce new risks that legacy SaaS providers have long addressed. Enterprises looking to follow Klarna’s example will need to rethink how they manage these critical issues within their AI-driven frameworks.

AI Agents – SaaS Vendors Respond

As enterprises explore the potential of Agentic AI-driven systems, SaaS providers like Salesforce and Workday must adapt to a new reality. Klarna’s decision could be the first domino in a broader shift, forcing these companies to reconsider their own offerings and strategies. Here are three possible responses we could see from the SaaS giants:

1. Doubling Down on AI Integration Salesforce and Workday are not standing still. In fact, both companies are already integrating AI into their platforms. Salesforce’s Einstein and the newly introduced Agentforce are examples of AI-powered tools designed to enhance customer interactions and automate tasks. We might see a rapid acceleration of these efforts, with SaaS providers emphasizing Agentic AI-driven features that keep businesses within their ecosystems rather than prompting them to build in-house solutions. However, as Benioff pointed out, the key might be blending AI with human oversight rather than replacing humans altogether. This hybrid approach will allow Salesforce and Workday to differentiate themselves from pure AI solutions by ensuring that critical human elements—like decision-making, customer empathy, and regulatory knowledge—are never lost.
2. Building Modular and Lightweight Offerings Klarna’s move underscores the desire for flexibility and control over tech stacks. In response, SaaS companies may offer more modular, API-driven solutions that allow enterprises to mix and match components based on their needs. This would enable businesses to take advantage of best-in-class SaaS features without being locked into a monolithic platform. By offering modular systems, Salesforce and Workday could cater to enterprises looking to integrate AI while maintaining the core advantages of established SaaS infrastructure—such as compliance, security, and data management.
3. Strengthening Data Governance and Compliance as Key Differentiators As AI grows in influence, data governance, compliance, and security will become critical battlegrounds for SaaS providers. SaaS companies like Salesforce and Workday have spent years building trusted systems that comply with various regulatory frameworks. Klarna’s AI approach will be closely scrutinized to ensure it meets these same standards, and any slip-ups could provide an opening for SaaS vendors to argue that their systems remain the gold standard for enterprise-grade compliance. By doubling down on their strengths in these areas, SaaS vendors could position themselves as the safer, more reliable option for enterprises that handle sensitive or regulated data. This approach could attract companies that are hesitant to take the AI plunge without fully understanding the risks.

What’s Next?

Klarna’s decision to replace SaaS platforms with a custom AI system may represent a significant shift in the enterprise software landscape. While this move highlights the growing potential of AI to reshape key business functions, it also raises important questions about governance, compliance, and the long-term role of SaaS providers. As organizations worldwide watch Klarna’s big bet play out, it’s clear that we are entering a new phase of enterprise software evolution—one where the balance between AI, human oversight, and SaaS will be critical to success.

Problem Statement

In Talend, while generating output file if we need to add a column as number or column name with space or to include any special characters as column name in Talend it won’t allow directly by adding the below-mentioned column names in schema, will get the below mentioned error.

As number as column name:

As space in column name:

As special characters in column name:

Solution:

The above use case was implemented by simple Talend job and with the below steps.

Step 1: To use tFixedFlowInput component by providing the actual column names (number/special character/space) as highlighted below,

Step 2: To map the fields to the target file to populate the headers in the first line of the output.

Step 3: To load the actual source data which we need to load it in output target file will be done in step 3. The source data can be a Input File or any other stream of data. Here input file is used as a source for example.

Step 4: In order to avoid the header from actual file used and to pick the headers from the previous flow we need to use sequence and given condition to pick the records which has sequence more than the value 1 as below in tMap.

Step 5: To load the source data after tMap to the same target file using append operation on addition to the header load from the previous flow.

with the same concept we can replace the output component instead of tFileOutputDelimited to tFileOutputExcel for generating excel.

Result:

The output file is generated after the execution of the job and the given columns are loaded successfully as below in header of the target file.

APIs, or application programming interfaces, are essential to the dynamic world of digital transformation because they allow companies to communicate quickly and efficiently with their data and services. Consequently, effective management is essential to ensure these APIs function correctly, stay safe, and provide the desired benefits. This is where Google Cloud’s top-tier API management product, Apigee, comes into play.

What is Apigee?

Apigee is a great platform for companies that want to manage their APIs effectively. It really simplifies the whole process of creating, growing, securing, and implementing APIs, which makes things a lot easier for developers. One thing that stands out about Apigee is its flexibility; it can handle both external APIs that third-party partners can access and internal APIs used within the company. This makes Apigee a great option for businesses of all sizes. Moreover, its versatility is a significant benefit for those looking to simplify their API management. It also integrates nicely with various security layers, like Nginx, which provides an important layer of authentication between Apigee and the backend. Because of this adaptability, Apigee enhances security and allows for smooth integration across different systems, making it a reliable choice for managing APIs.

Core Features of Apigee

1. API Design and Development

Primarily, Apigee offers a unique suite of tools for developing and designing APIs. You can define API endpoints, maintain API specifications, and create and modify API proxies by using the Open API standard. Consequently, it becomes easier to design functional and compliant APIs with industry standards. Furthermore, this capability streamlines the development process and ensures that the APIs meet regulatory requirements. Thus, developers can focus on innovation while maintaining a strong foundation of compliance and functionality. Below is a flow diagram related to API Design and Development with Apigee:

2. Security and Authentication

Any API management system must prioritize security, and Apigee leads the field in this regard. It provides security features such as OAuth 2.0, JWT (JSON Web Token) validation, API key validation, and IP validation. By limiting access to your APIs to authorized users, these capabilities help safeguard sensitive data from unwanted access.

3. Traffic Management

With capabilities like rate limitation, quota management, and traffic shaping, Apigee enables you to optimize and control API traffic. This helps proper usage and maintains consistent performance even under high traffic conditions.

4. Analytics and Monitoring

You can access analytics and monitoring capabilities with Apigee, which offers insights into API usage and performance. You can track response times, error rates, and request volumes, enabling you to make data-driven decisions and quickly address any issues that arise.

5. Developer Portal

Apigee includes a customizable developer portal where API users can browse documentation, test APIs, and get API keys. This portal builds a community around your APIs and improves the developer experience.

6. Versioning and Lifecycle Management

Keeping an API’s versions separate is essential to preserving backward compatibility and allowing it to change with time. Apigee offers lifecycle management and versioning solutions for APIs, facilitating a seamless upgrade or downgrade process.

7. Integration and Extensibility

Apigee supports integration with various third-party services and tools, including CI/CD pipelines, monitoring tools, and identity providers. Its extensibility through APIs and custom policies allows you to tailor the platform to meet your specific needs.

8. Debug Session

Moreover, Apigee offers a debug session feature that helps troubleshoot and resolve issues by providing a real-time view of API traffic and interactions. This feature is crucial for identifying and fixing problems and is essential during the development and testing phases. In addition, this feature helps ensure that any issues are identified early on; consequently, it enhances the overall quality of the final product.

9. Alerts:

Furthermore, you can easily set up alerts within Apigee to notify you of critical issues related to performance and security threats. It is crucial to understand that both types of threats affect system reliability and can lead to significant downtime; addressing them promptly is essential for maintaining optimal performance.

10. Product Onboarding for Different Clients

Apigee supports product onboarding, allowing you to manage and customize API access and resources for different clients. This feature is essential for handling diverse client needs and ensuring each client has the appropriate level of access.

11. Threat Protection

Apigee provides threat protection mechanisms to ensure that your APIs can handle concurrent requests efficiently without performance degradation. This feature helps in maintaining API stability under high load conditions.

12. Shared Flows

Apigee allows you to create and reuse shared flows, which are common sets of policies and configurations applied across multiple API proxies. This feature promotes consistency and reduces redundancy in API management.

Benefits of Using Apigee

1. Enhanced Security

In summary, Apigee’s comprehensive security features help protect your APIs from potential threats and ensure that only authorized users can access your services.

2. Improved Performance

Moreover, with features like traffic management and caching, Apigee helps optimize API performance, providing a better user experience while reducing the load on your backend systems.

3. Better Visibility

Apigee’s analytics and monitoring tools give valuable insights into API usage and performance, helping you identify trends, diagnose issues, and make informed decisions.

4. Streamlined API Management

Apigee’s unified platform simplifies the management of APIs, from design and development to deployment and monitoring, saving time and reducing complexity.

5. Scalability

Finally, Apigee is designed to handle APIs at scale, making it suitable for both small projects and large enterprise environments.

Getting Started with Apigee

To get started with Apigee, follow these steps:

1. Sign Up for Apigee

Visit the Google Cloud website and sign up for an Apigee account. Based on your needs, you can choose from different pricing plans.

2. Design Your API

Use Apigee’s tools to design your API, define endpoints, and set up API proxies.

3. Secure Your API

Implement security policies and authentication mechanisms to protect your API.

4. Deploy and Monitor

Deploy your API to Apigee and use the analytics and monitoring tools to track its performance.

5. Engage Developers

Set up your developer portal to provide documentation and resources for API consumers.

In a world where APIs are central to digital innovation and business operations, having a powerful API management platform like Apigee can make a significant difference. With its rich feature set and comprehensive tools, Apigee helps organizations design, secure, and manage APIs effectively, ensuring optimal performance and value. Whether you’re just starting with APIs or, conversely, looking to enhance your existing API management practices, Apigee offers a variety of capabilities. Furthermore, it provides the flexibility necessary to thrive in today’s highly competitive landscape.