Hi Friends
Good Day
Hope you are doing good.
I have created this presentation for Idea, which aims to address a significant challenge in talent acquisition: efficiently processing a large volume of resumes within tight deadlines.
To develop this solution, I have utilized various AI/ML LLM models, including Cohere Embed, reinforcement learning (based on trial and error), and the Random Forest (decision trees) ML model, to analyze data and effectively process a greater number of resumes in a shorter time frame, ensuring the selection of more qualified and skilled candidates.
For coding the algorithms in Python, I am leveraging Chat GPT and Google Bard LLMs.
To articulate the Idea, the problem, the solution, and the marketing opportunities, I have employed the Lean Canvas Model, which provides a clear explanation of each section.
I encourage you to watch the video and share any questions you may have, as your feedback will be invaluable in enhancing my solution.
https://lnkd.in/dDwZWzfm
Thanks
hashtag#ResumeAutomation
hashtag#AITalentAcquisition
hashtag#GPTinRecruitment
hashtag#LeanCanvas
hashtag#MLResumeScreening
hashtag#TAOneAI
hashtag#RecruitmentTech
hashtag#HiringAutomation
hashtag#AIHiringTool
hashtag#IT
hashtag#Banking
hashtag#logistics
hashtag#EDI
hashtag#B2B
hashtag#Retail
hashtag#resume
hashtag#jobs
hashtag#opportunities
hashtag#latestjobs
hashtag#USA
hashtag#India
hashtag#Singapore
hashtag#Malaysia
hashtag#ITjobs
hashtag#LLM
hashtag#mechinelearning
hashtag#ML
hashtag#chatgpt
hashtag#googlebard
hashtag#googlegemini
hashtag#azure
hashtag#AWS
hashtag#SAP
hashtag#IDOC
hashtag#marketing
hashtag#recruitment
hashtag#itservices
hashtag#jobopenings
hashtag#jobopportunities
hashtag#chatgpt4
hashtag#googlebard
hashtag#googlegemini
hashtag#AzureLogicapps
Friday, October 23, 2026
Address a Significant Challenge in talent acquisition: efficiently processing a large volume of resumes within tight deadlines
Thursday, October 22, 2026
Day 8 — AI Can Help the BA Negotiate a Difficult Customer Conversation
Day 8 — AI Can Help the BA Negotiate a Difficult Customer Conversation
Imagine the customer says:
“We will not approve additional budget.”
Instead of arguing, the BA can use AI to model alternatives.
Scenario A
Full requirement
→ Full investment
→ Original timeline + additional time
Scenario B
Prioritized requirements
→ Lower investment
→ Earlier delivery
Scenario C
Phase 1 + Phase 2
→ Controlled investment
→ Business value delivered earlier
AI can help the BA perform:
What-if analysis
But the BA should never manipulate numbers to force customer approval.
The objective is:
Find the best business outcome within the customer's constraints.
That is negotiation.
Not estimation manipulation.
hashtag#BusinessAnalyst hashtag#Negotiation hashtag#AI hashtag#StakeholderManagement hashtag#BusinessValue
Tuesday, October 20, 2026
Day 10 — AI Can Turn a BA into a Business Problem Solver
Day 10 — AI Can Turn a BA into a Business Problem Solver
A customer may say:
“Our invoice processing is taking too long.”
A traditional BA may document:
“Invoice processing should be faster.”
An AI-enabled BA should investigate:
Where is the delay?
Order received
↓
Validation
↓
Mapping
↓
ERP processing
↓
Invoice generation
↓
EDI translation
↓
Transmission
↓
Customer acknowledgement
AI can help analyze:
Process logs
Transaction volumes
Failure patterns
Processing times
Exception reports
Operational tickets
The BA can then identify:
“The problem is not the EDI transmission. The major delay occurs during downstream ERP validation.”
That's a much more valuable BA contribution.
hashtag#AI hashtag#BusinessAnalyst hashtag#ProcessImprovement hashtag#B2B hashtag#DigitalTransformation
Thursday, October 15, 2026
Day 1 — The Future BA: From Requirement Collector to Business Advisor
Day 1 — The Future BA: From Requirement Collector to Business Advisor
A traditional BA primarily asks:
“What are your requirements?”
An AI-enabled BA asks:
“What business problem are we actually trying to solve?”
AI can help the BA:
Analyze customer documents
Extract business requirements
Identify missing requirements
Detect contradictions
Group requirements by business capability
Identify functional and non-functional requirements
Generate clarification questions
Identify assumptions and dependencies
Compare current vs future processes
But the BA remains responsible for understanding why the requirement exists.
The real transformation is:
Requirement Collector → Requirement Analyst → Business Advisor
AI does not make someone a good BA.
The BA's ability to ask the right questions + AI's ability to analyze information makes a powerful combination.
Wednesday, October 14, 2026
Day 11 — Don't Just Ask Customers What They Want. Recommend What They Need.
Day 11 — Don't Just Ask Customers What They Want. Recommend What They Need.
A powerful BA should not always wait for the customer to propose the solution.
Customer:
“We want a dashboard showing failed EDI transactions.”
BA investigation:
Failure volume → 8,000/month
Manual investigation → 20 minutes/transaction
Common failures → mapping + validation + partner configuration
Potential solution:
Automated EDI validation + error classification + partner-specific rules + operational dashboard
Now the BA isn't simply delivering the customer's requested dashboard.
The BA is addressing the underlying business problem.
This is where:
Domain Knowledge + Business Analysis + AI
becomes powerful.
hashtag#BusinessAnalyst hashtag#AI hashtag#EDI hashtag#BusinessTransformation hashtag#Innovation
Monday, October 12, 2026
🚀 Building an EDI Validator designed to simplify EDI testing
🚀 Building an EDI Validator designed to simplify EDI testing
After spending more than 20 years in the EDI/B2B integration industry across retail, logistics, finance, and enterprise integration projects, one question has consistently stayed with me:
Why do experienced EDI teams still spend so much time finding validation problems?
Throughout my career, I've repeatedly seen the same challenges across organizations of all sizes:
🔴 Trading partner testing cycles taking weeks instead of days
🔴 Manual verification of ISA/IEA, GS/GE, and ST/SE control numbers
🔴 Missing mandatory segments causing repeated rejections
🔴 Segment count mismatches discovered late in testing
🔴 Customer-specific business rules maintained in spreadsheets and documents
🔴 Teams using multiple tools, editors, and scripts to validate a single transaction
🔴 Valuable time spent debugging instead of delivering business value
As an EDI developer and integration consultant, EDI Tech Lead I often asked myself:
"Why are we still manually investigating problems that software should identify in seconds?"
Over the years, I worked with various EDI translators and integration platforms. While they provide excellent integration capabilities, I found that validation activities themselves often remain fragmented, time-consuming, and highly dependent on individual expertise.
A typical EDI validation process usually involves:
✅ Parsing transactions
✅ Verifying envelope structures
✅ Checking control numbers
✅ Validating segment counts
✅ Confirming mandatory segments
✅ Applying business rules
✅ Verifying trading partner requirements
✅ Preparing error reports
✅ Repeating the process after every correction
For many teams, this can consume hours or even days.
This recurring challenge became the primary motivation behind my personal project:
🚀 Building an EDI Validator designed to simplify EDI testing, partner onboarding, and transaction validation.
My goal is not to replace existing EDI platforms.
My goal is to provide a focused validation layer that can:
✔ Detect structural errors quickly
✔ Validate business rules automatically
✔ Support trading partner-specific requirements
✔ Produce meaningful error reports
✔ Reduce onboarding cycles
✔ Help EDI teams spend more time delivering solutions and less time debugging files
The platform currently supports:
• ISA/IEA, GS/GE, ST/SE validation
• Segment count validation
• Business rule validation
• Trading partner-specific validation
• Error severity classification
• Detailed validation reporting
• Validation history and analytics
I'm building this solution based on nearly two decades of real-world EDI implementation experience.
What has been the most time-consuming or frustrating part of EDI validation and partner onboarding in your experience?
hashtag#EDI hashtag#B2BIntegration hashtag#EDIValidation hashtag#X12 hashtag#EnterpriseIntegration hashtag#SupplyChain hashtag#Integration hashtag#BuildInPublic hashtag#SaaS
What if the customer is asking for the wrong solution?
AI-Powered Business Analyst Series | Post 16
🤔 What if the customer is asking for the wrong solution?
Let me share a situation that many Business Analysts might have experienced.
A customer approaches the team and says:
“We need a dashboard to track our failed transactions.”
The team starts discussing the dashboard design, required fields, reports, filters, and timelines.
Everyone is focused on delivering what the customer requested.
But wait a minute. 👇
Have we really understood the customer's problem?
What if the actual issue is not the lack of a dashboard?
What if hundreds of transactions are failing every day because of incorrect partner configurations, mapping errors, or missing validations?
A dashboard might help the customer see the problem. But will it actually help them solve the problem?
This is where I believe AI can make a real difference in Business Analysis.
Instead of immediately converting the customer's request into requirements, a BA can use AI to explore some deeper questions:
🔹 What is causing the transactions to fail?
🔹 Are there recurring patterns in the failures?
🔹 Which failures consume the most operational effort?
🔹 Can some errors be prevented before transactions are processed?
🔹 Can we automate error classification and suggest corrective actions?
🔹 Would improving the existing process deliver more value than building a new dashboard?
Of course, AI cannot simply assume the answers are correct. The BA must validate the findings using actual data, technical expertise, and discussions with the customer and delivery team.
But imagine the conversation changing from:
Customer: “Can you build us a dashboard?”
BA: “Yes, we can. But based on what we have discovered, there may also be an opportunity to reduce the failures themselves. Can we explore both options before deciding?”
Now the BA is not just documenting requirements.
The BA is helping the customer think about the business problem differently.
And there is another benefit.
The company may be able to deliver a better solution using its existing capabilities, reduce unnecessary development, and create more value for the customer.
That is a win for the customer and the service provider.
💡 My takeaway:
A good BA understands what the customer asks for.
A great BA investigates why the customer needs it.
An AI-enabled BA can go one step further by identifying patterns, exploring alternatives, and helping people make better-informed decisions.
AI should not replace the Business Analyst's thinking. It should help the BA ask better questions.
What is your opinion?
Have you ever seen a situation where the customer requested one solution, but the actual business problem required something completely different?
I would love to hear your experiences in the comments. 👇
#BusinessAnalyst #ArtificialIntelligence #BusinessAnalysis #DigitalTransformation #RequirementsEngineering #AIinBusiness
Sunday, October 11, 2026
Day 14 — AI + BA: From Customer Requirement to Approved Business Case
Day 14 — AI + BA: From Customer Requirement to Approved Business Case
Imagine the complete flow:
Customer Problem
↓
AI-assisted Requirement Discovery
↓
BA Validation
↓
Business Process Analysis
↓
Requirement Gap Analysis
↓
Company Capability Mapping
↓
Solution Options
↓
Effort Estimation
↓
Cost/Benefit Analysis
↓
Risk Analysis
↓
Timeline
↓
Customer Discussion
↓
Approval
↓
Implementation
↓
Measurement of Business Outcome
This is much more powerful than simply creating a requirements document.
The BA becomes the orchestrator of business value.
hashtag#BusinessAnalyst hashtag#AI hashtag#BusinessCase hashtag#RequirementsEngineering hashtag#DigitalTransformation
Friday, October 9, 2026
Day 13 — How to Present a Solution Within the Company's Budget
Day 13 — How to Present a Solution Within the Company's Budget
A good solution is not necessarily the most technically advanced solution.
It must balance:
Business Value
Customer Need
Technology Capability
Risk
Timeline
Budget
For example:
Solution A
Advanced platform
Investment: High
Time: 9 months
Solution B
Existing platform enhancement
Investment: Medium
Time: 5 months
Solution C
MVP + phased enhancement
Investment: Low initially
Time: 2 months for Phase 1
AI can help compare:
Cost vs Benefit vs Risk vs Timeline
The BA can then recommend the option that provides the best business value, not simply the most sophisticated technology.
hashtag#BusinessAnalyst hashtag#AI hashtag#BusinessValue hashtag#CostOptimization hashtag#DigitalTransformation
Tuesday, October 6, 2026
Saturday, October 3, 2026
How can AI help an EDI Validator validate messages against a customer’s specific requirements?
🚀 How can AI help an EDI Validator validate messages against a customer’s specific requirements?
I am trying to give explanation in more simplest way.
Traditional EDI validation usually checks whether an EDI message follows the standard X12 or EDIFACT syntax.
But in real-world B2B integration, that is only part of the problem.
A message can be technically valid EDI and still be wrong for a specific customer.
For example:
✅ The EDI 850 structure is valid
✅ Required segments are present
✅ Data types are correct
✅ Element lengths are correct
but the customer may have additional requirements such as:
❌ PO Number must follow a specific format
❌ Certain product codes are mandatory
❌ REF qualifiers must contain specific values
❌ Some segments are required only for particular order types
❌ Quantity/UOM combinations must follow customer rules
❌ Certain elements have customer-specific code lists
❌ Business rules may depend on values in other segments
Where AI can help 🤖
The EDI Validator can use AI to understand the customer implementation guide/specification and convert those requirements into machine-readable validation rules.
The flow could look like this:
Customer Specification / Implementation Guide
⬇️
AI Document Understanding
⬇️
Extract Segments, Elements, Qualifiers & Business Rules
⬇️
Create Customer-Specific Validation Rules
⬇️
EDI Message Validation
⬇️
Compare EDI against Customer Specification
⬇️
Detailed Validation Report
The AI doesn't need to replace deterministic EDI validation.
Instead, the best approach is to combine both:
Rules Engine + AI = Intelligent EDI Validation
The rules engine performs precise checks such as syntax, mandatory segments, data types, code values and relationships.
AI can help interpret complex customer specifications, identify business rules, explain validation failures and assist in generating customer-specific rules.
Example
Suppose a customer's specification says:
"For domestic orders, REF*IA must contain the customer's internal vendor number."
The EDI Validator could identify this requirement from the implementation guide and validate an incoming 850 accordingly.
If the message contains:
"REF*IA*12345"
the validator checks whether "12345" satisfies the customer's defined requirement.
If it fails, instead of simply saying:
"EDI validation failed."
the system could provide:
🔴 Customer Specification Violation
Segment: REF
Qualifier: IA
Issue: Vendor number does not match the customer's defined format/value requirement.
Specification Reference: Customer REF/IA rule
Recommended Action: Verify the vendor number provided in the REF*IA segment.
This makes the validator much more useful to EDI developers, B2B teams and business analysts.
hashtag#EDI hashtag#EDIVAlidator hashtag#AI hashtag#ArtificialIntelligence hashtag#B2BIntegration hashtag#EDIIntegration hashtag#X12 hashtag#EDIFACT hashtag#EDI850 hashtag#B2B hashtag#Integration hashtag#EDIStandards hashtag#SupplyChain hashtag#DigitalTransformation hashtag#GenAI hashtag#AIAutomation hashtag#BusinessIntegration hashtag#EDIConsulting hashtag#IntegrationArchitecture
Wednesday, March 11, 2026
Facets Electronic Data Interchange and How the transalation will work and corresponding details
Core Functionality
- Transaction Sets: Facets EDI typically handles standard X12 file layouts, including:
- EDI 834: Enrollment and disenrollment of members.
- EDI 837: Outbound and inbound claims submission.
- EDI 270/271: Eligibility inquiries and responses.
- EDI 835: Claim payment and remittance advice.
- Pre-Scrub Engines: These tools validate incoming EDI files for errors before they enter the Facets database, ensuring data integrity and reducing manual intervention.
- Interoperability: The system uses web services and APIs (specifically RESTful interfaces) to integrate Facets with third-party applications, care management platforms, and provider portals.
Key Benefits
- Automation: Reduces manual data entry by automating work routing and processing through configurable business rules.
- Real-Time Processing: Supports near real-time data publishing and synchronization for member accumulators (e.g., deductibles and out-of-pocket maximums).
- Accuracy and Compliance: Ensures that all data transfers meet HIPAA and ACA standards for security and privacy.
- Scalability: Designed to handle high-volume data for organizations serving anywhere from 100,000 to over 50 million members. Integration Tools
- Facets Open Access Solution: A suite that provides near real-time web services for data sharing with external systems.
- Enrollment Toolkit: Intelligently manages the receipt and correction of enrollment records to increase auto-enrollment success rates.
Common Facets Claim Processing Errors [4]
- Provider Record Not Found: Occurs when the NPI, Tax ID, or provider name in the 837 file does not match a record in the Facets database.
- Invalid Procedure Code: Triggered if the code submitted is not active or defined in the Facets reference tables for the date of service.
- Service Definition Error: Happens when the combination of codes (e.g., procedure vs. diagnosis) violates defined benefit rules. [5, 6]
Common EDI Transaction Set Issues
- EDI 834 (Enrollment): Failures often stem from member ID mismatches, incorrect relationship codes (e.g., marking a child as a spouse), or missing demographic data like date of birth.
- EDI 270/271 (Eligibility): Rejections (often in the AAA segment) typically point to identity mismatches or invalid provider credentials.
- EDI 835 (Payment): Issues include balancing errors where payment amounts do not reconcile with the original claim or missing remittance codes.
General Troubleshooting Steps
- Analyze System Logs: Review both internal Facets logs and your trading partner’s logs to differentiate between connectivity issues (e.g., SFTP/AS2 failures) and data layer issues.
- Verify Data Syntax: Use EDI mapping or translation tools to ensure the file conforms to X12 standards (e.g., no invalid characters like '#' or incorrect field lengths).
- Test Connectivity: Use diagnostic commands like
ping,traceroute, ortelnetto check for network latency or blocked firewall ports. - Check Configuration: Confirm that Sender/Receiver IDs and mailbox addresses in your ERP/Facets setup match current partner specifications to avoid routing errors.
Best Practices for Prevention
- Implement Pre-Scrubbing: Use automated validation to catch formatting and missing data errors before they hit the Facets core.
- Maintain Master Data: Regularly update provider and member master records in Facets to reduce "record not found" errors.
- Payer Companion Guides: Always refer to specific Payer Companion Guides for the unique rules of each trading partner.
Thursday, January 8, 2026
Check Your IPv4 and IPv6 of Public IP of your system
Public IP Checker
Thursday, May 1, 2025
Automate Resume Screening with TAOne.AI | Fast & Smart Talent Filtering Process 500+ Resumes in Hour
Thursday, January 2, 2025
Implementing EDI Integration Using Microsoft Azure Logic Apps
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# Implementing EDI Integration Using Microsoft Azure Logic Apps
This comprehensive guide provides a step-by-step approach to implementing EDI (Electronic Data Interchange) integration using Microsoft Azure Logic Apps. Azure Logic Apps is a cloud-based service designed to help automate workflows and integrate EDI transactions seamlessly with your systems and trading partners.
---
## **Step 1: Prerequisites** Before starting the implementation, ensure you have the following:
1. **Azure Subscription**: - Sign up for an Azure account if you don’t already have one. - Access the Azure Portal.
2. **Trading Partner EDI Specifications**: - Obtain the EDI implementation guide for the documents you will exchange (e.g., EDI 810, EDI 850).
3. **Existing Systems**: - Identify the systems (e.g., ERP, CRM) that will integrate with EDI workflows.
4. **Data Format**: - Define the data format (e.g., X12, EDIFACT, XML) based on trading partner requirements.
---
## **Step 2: Create a Logic App** 1. **Log in to Azure Portal**: - Navigate to the Azure portal and search for "Logic Apps."
2. **Create a New Logic App**: - Click "Create" and provide the following details: - **Resource Group**: Create or select an existing resource group. - **Name**: Name your Logic App (e.g., `EDI_Integration_Workflow`). - **Region**: Select the appropriate region for hosting.
3. **Open Logic App Designer**: - Open the Logic App in Designer mode to start building your workflow.
---
## **Step 3: Add EDI Integration Connector** Azure provides built-in connectors for EDI transactions, such as AS2, X12, and EDIFACT.
### **For X12 EDI** 1. **Set Up an Integration Account**: - Navigate to "Integration Accounts" in the Azure portal. - Create an Integration Account and link it to your Logic App.
2. **Upload Partner Agreements**: - Define trading partners and upload their details (e.g., X12 schemas, certificates, and agreements) into the Integration Account. - Add: - **Schemas**: Import X12 schema files for the EDI document types you are processing. - **Partners**: Add trading partner details (identifiers, roles, and agreements). - **Agreements**: Configure inbound and outbound agreements specifying EDI protocols and settings.
3. **Configure X12 Connector**: - In the Logic App Designer, search for "EDI X12" and add the X12 connector. - Choose "Receive X12 Message" or "Send X12 Message" based on the workflow.
---
## **Step 4: Design the Workflow**
### **Inbound EDI Workflow** 1. **Receive EDI Document**: - Add a trigger to start the Logic App, such as "When a file is added to Azure Blob Storage" or "Receive AS2 message."
2. **Decode EDI Message**: - Use the "EDI Decode" action to validate and parse the received EDI document. - Map the EDI segments to readable data (e.g., JSON, XML).
3. **Transform Data**: - Add a "Transform XML" action to convert the EDI message into the desired format for your system. - Use a predefined map or create one using Azure’s mapping tools.
4. **Send Data to System**: - Add an action to send the transformed data to your internal system (e.g., SQL Database, Dynamics 365).
### **Outbound EDI Workflow** 1. **Receive Data from System**: - Add a trigger to listen for new data in your system (e.g., "When an item is created in SQL Database").
2. **Transform Data**: - Use the "Transform XML" action to convert internal data into the required EDI format.
3. **Encode EDI Message**: - Use the "EDI Encode" action to package the data into an X12-compliant EDI document.
4. **Send EDI Document**: - Add an action to send the EDI document to the trading partner via AS2, FTP, or another protocol.
---
## **Step 5: Test the Integration** 1. **Enable Logging**: - Use Azure Monitor or Application Insights to track the execution of your Logic App.
2. **Perform Test Runs**: - Simulate inbound and outbound transactions using test data. - Verify that the EDI documents are generated, validated, and transmitted correctly.
3. **Fix Errors**: - Debug any errors using the Logic App’s run history and logs.
---
## **Step 6: Go Live** 1. **Deploy the Logic App**: - Ensure all configurations are in place and move the Logic App to production.
2. **Monitor Live Transactions**: - Use Azure’s monitoring tools to ensure smooth operation and address any issues promptly.
---
## **Step 7: Maintain and Optimize** 1. **Periodic Reviews**: - Review workflows to ensure compliance with updated trading partner requirements.
2. **Optimize Performance**: - Monitor latency and throughput, and adjust Logic App triggers and actions as needed.
3. **Add New Partners**: - Scale your solution by adding new trading partners or EDI document types.
---
By following this detailed roadmap for implementing EDI integration using Azure Logic Apps, you can streamline your business processes, ensure compliance with trading partner requirements, and achieve efficient and reliable electronic data exchange.
Monday, October 14, 2024
Introducing a rate limiter feature in IBM Sterling Integrator allows for comprehensive API functionality without the need to invest in additional API tools.
To activate and integrate the rate limiter feature in Sterling Integrator for comprehensive API functionality, follow these steps.To effectively deliver a service, it's essential to create a system that accepts input from clients and returns the appropriate output based on that input.
When we offer this service, it's important to pinpoint the client's IP address for any requests originating from outside our network.
To achieve the capability of identifying the client IP address within Sterling Integrator, we should adhere to the following steps.
To activate the Client IP feature, follow these steps: First, include the property client_ip_correlation_enabled=false in the jdbc.properties_platform_ifcbase_ext.in file.
Next, execute ./setupfiles.sh.
This feature captures the IP address of the client that initiates the request.
Certain clients require this functionality to comply with regulatory standards.
Before you enable the Client IP feature, ensure that your firewall is configured to permit the IP address to pass through the Sterling External Authentication Server.
We will now verify the available rate limit for the customer associated with the given IP address.
As developers, we will save this information in our database. Each time a request is received, we will assess the rate limit for that partner.
If the request falls within the allowed rate limit, it will be forwarded to the appropriate API service.
Additionally, we can implement another check to monitor the number of requests made by the partner within a defined time frame. For instance, we could allocate a limit of 1,000 requests per hour for a specific partner based on their IP address.
To put this into action, we will track the number of requests made by the partner.
If any conditions fail, we will provide the relevant error code and description to the partner. They will need to rectify the issue by upgrading their subscription with the service provider.
When we integrate this functionality into Sterling Integrator, we can incorporate rate limiting within a generic process. If the result is positive, the request will then be directed to the appropriate API service business process.
I recommend implementing API capabilities in Sterling Integrator rather than using specific API tools for small and medium business who is already using Sterling Integrator for their EDI integrations .Given the business capacity, Sterling Integrator can effectively expose API services to the external world. It offers robust error handling features and a clear understanding of error codes, making it particularly suitable for small and medium-sized businesses.
The Sterling Integrator Server offers enhanced functionality, equipped with a wider array of services and adapters, allowing us to implement simple functions without the need for coding.
Tracking requests and generating reports is a breeze with the Sterling File Gateway.
While this tool primarily focuses on managing EDI-based transactions, it can also be effectively utilized for API service implementations.
There is a wealth of Sterling technical resources available in the market.
One important consideration when using Sterling Integrator as an API endpoint is that it only supports XML-based transactions and requests, excluding JSON format. To address this limitation, we can create an alternative solution by leveraging the Java Task Service to develop a Java program that formats JSON.
1. One minor limitation of the API toolscurrently on the market is that implementing any functionality requires coding in a language chosen by the organization or developer.
Maintaining this code can also pose challenges within the organization.
Should there be any updates or changes to the service in the future, it may necessitate hiring new personnel with expertise in the original programming language or rewriting the functionality in a different language altogether.
Additionally, as a centralized access point, an API serves as a gateway that can attract the attention of hackers. If an API is breached, it can expose all connected applications and systems to potential threats.
Monday, September 30, 2024
Workato Automation Tool and main capabilities when compare to other iPAAS tools like Dell Boomi
Good Day
Hope you are doing good.
I would like to share my recent achievement: yesterday, I successfully completed the Workato Automation Pro I tool certification as part of my efforts to enhance my skill set.
Some brief introduction about Workato Automation Tool ,
Workato is an automation tool that helps businesses connect different apps and systems without needing a lot of coding. It allows you to create workflows, called "recipes," that automate tasks like sending data from one app to another or triggering actions based on events. For example, it can automatically update a spreadsheet when a new order is placed or send an email when a project status changes. Workato works with many popular apps like Salesforce, Slack, and QuickBooks, making it easier to streamline work processes and save time on repetitive tasks.
This tool boasts enhanced security features compared to another iPaaS solution, Boomi. Additionally, it offers a unique capability to retain the failed steps of a process during unexpected system failures, automatically resuming process executions once the system restarts. This means there's no need for us to manually check whether we need to reprocess any failed processes.
#integration #EDI #B2B #Workato #Salesforce #Banking #Finance #IToperations #automation #QuickBooks #insurance #Retail #API #AI #IBM #boomi #ipaas #saas
LinkedIn Post Link: [Insert your LinkedIn post link here]
Workato Automation Tool brief description about Workato automation tool in layman wordsWorkato is an automation tool that helps businesses connect different apps and systems without needing a lot of coding. It allows you to create workflows, called "recipes," that automate tasks like sending data from one app to another or triggering actions based on events. For example, it can automatically update a spreadsheet when a new order is placed or send an email when a project status changes. Workato works with many popular apps like Salesforce, Slack, and QuickBooks, making it easier to streamline work processes and save time on repetitive tasks.
Write a BOT Application using JAVA to take the blog posts from Blogger website login and post that blog post in the Twitter channel on scheduled base
### Overview
1. **Fetch blog posts from Blogger**: Use the Google Blogger API to retrieve blog posts.
2. **Post on Twitter**: Use the Twitter API to post the content.
3. **Schedule the task**: Use a scheduler like `java.util.Timer` or Spring Scheduler to post the blogs at regular intervals.
4. **OAuth Authentication**: Handle OAuth authentication for both Blogger and Twitter.
### Dependencies
To get started, you'll need the following dependencies:
1. **Google Blogger API client**: To interact with Blogger.
2. **Twitter API client**: Use Twitter4J for Twitter API integration.
3. **Scheduler**: Use `java.util.Timer` or Spring for scheduling.
4. **OAuth Libraries**: You’ll need OAuth libraries for both Google and Twitter.
Here’s an example with these steps using Java:
### 1. Add Maven Dependencies
First, add the necessary dependencies to your `pom.xml`:
```xml
You'll need to configure Google OAuth2 to fetch Blogger posts. You can get the credentials from the [Google Developer Console](https://console.developers.google.com/).
Here’s the code to authenticate and fetch the posts:
```java
import com.google.api.services.blogger.Blogger;
import com.google.api.services.blogger.model.Post;
import com.google.api.services.blogger.model.PostList;
import com.google.api.client.googleapis.auth.oauth2.GoogleCredential;
import com.google.api.client.http.javanet.NetHttpTransport;
import com.google.api.client.json.JsonFactory;
import com.google.api.client.json.jackson2.JacksonFactory;
import java.io.IOException;
import java.util.List;
public class BloggerAPIService {
private static final String APPLICATION_NAME = "BloggerPostBot";
private static final String BLOG_ID = "your-blog-id"; // Replace with your blog ID
private static Blogger bloggerService;
public static Blogger getBloggerService() throws IOException {
if (bloggerService == null) {
GoogleCredential credential = GoogleCredential
.fromStream(new FileInputStream("path/to/your/client_secret.json"))
.createScoped(Collections.singleton("https://www.googleapis.com/auth/blogger"));
bloggerService = new Blogger.Builder(new NetHttpTransport(), JacksonFactory.getDefaultInstance(), credential)
.setApplicationName(APPLICATION_NAME)
.build();
}
return bloggerService;
}
public static List
Blogger.Posts.List request = getBloggerService().posts().list(BLOG_ID);
PostList posts = request.execute();
return posts.getItems();
}
}
```
### 3. Post Blog Content on Twitter
You will need to configure Twitter OAuth keys (API Key, API Secret Key, Access Token, Access Token Secret) on the [Twitter Developer Platform](https://developer.twitter.com/).
Here’s how you can post the content using Twitter4J:
```java
import twitter4j.Twitter;
import twitter4j.TwitterException;
import twitter4j.TwitterFactory;
import twitter4j.conf.ConfigurationBuilder;
public class TwitterBot {
private static Twitter twitter;
public static Twitter getTwitterInstance() {
if (twitter == null) {
ConfigurationBuilder cb = new ConfigurationBuilder();
cb.setDebugEnabled(true)
.setOAuthConsumerKey("your-consumer-key")
.setOAuthConsumerSecret("your-consumer-secret")
.setOAuthAccessToken("your-access-token")
.setOAuthAccessTokenSecret("your-access-token-secret");
TwitterFactory tf = new TwitterFactory(cb.build());
twitter = tf.getInstance();
}
return twitter;
}
public static void postTweet(String content) {
try {
getTwitterInstance().updateStatus(content);
System.out.println("Successfully posted: " + content);
} catch (TwitterException e) {
e.printStackTrace();
}
}
}
```
### 4. Schedule the Posting
You can use `java.util.Timer` or Spring’s `@Scheduled` to post blogs at regular intervals.
Here’s an example using Spring’s Scheduler:
```java
import org.springframework.scheduling.annotation.Scheduled;
import org.springframework.stereotype.Component;
import java.util.List;
@Component
public class BloggerTwitterScheduler {
@Scheduled(cron = "0 0 * * * *") // Every hour
public void postBlogToTwitter() {
try {
List
for (Post post : blogPosts) {
String content = post.getTitle() + "\n" + post.getUrl();
TwitterBot.postTweet(content);
}
} catch (Exception e) {
e.printStackTrace();
}
}
}
```
### 5. Main Class
```java
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.ComponentScan;
@SpringBootApplication
@ComponentScan(basePackages = {"your.package.name"})
public class BloggerTwitterBotApplication {
public static void main(String[] args) {
SpringApplication.run(BloggerTwitterBotApplication.class, args);
}
}
```
### 6. Application Properties
Add your application properties in `application.properties`:
```properties
spring.main.web-application-type=none
```
### 7. Running the Application
1. Obtain the necessary OAuth credentials for Google Blogger and Twitter.
2. Run the application, and it will fetch the blog posts and post them on Twitter on a scheduled basis.
---
This is a simplified version. You may want to add error handling, logging, and edge cases (e.g., duplicate posts, scheduling intervals).
LinkedIn Post Link: [Insert your LinkedIn post link here]
Tuesday, September 17, 2024
Online Cross Browser Testing module , Test your website with any list browsers and OS favors and get instant feedback about your Website
Online Cross-Browser Testing
Select browsers and OS flavors to run your website tests.
Cross-browser testing is the process of testing a website or web application across multiple browsers to ensure consistent functionality, design, and user experience. Different browsers (such as Chrome, Firefox, Safari, and Edge) may interpret web code (HTML, CSS, JavaScript) differently, which can lead to variations in how a site is displayed or behaves.
The purpose of cross-browser testing is to identify these inconsistencies and address them, ensuring that the web application works as intended for all users, regardless of which browser they are using. It typically involves:
- **Checking for Layout Differences**: Ensuring that the design and user interface (UI) look consistent across different browsers.
- **Verifying Functionality**: Ensuring that key functions (e.g., buttons, forms, navigation) work properly in each browser.
- **Testing JavaScript/DOM**: Ensuring that interactive elements and scripts behave consistently.
- **Performance Testing**: Checking load times and performance differences across browsers.
- **Device Compatibility**: Ensuring that the website works properly on both desktop and mobile versions of browsers.
-
Inbound Flow: 1) The inbound, EDI data needs to be collected. 2) The collected data should be De-enveloped (removing the headers) to get t... -
Here’s a comparison of the latest IBM Sterling Integrator map editor vs. IBM Transformation Extender (ITX) map editor from a data-mapping p...
-
Encoding Conversion (From ISO8859_1 / ISO-8859_1 / ISO_8859_1 to UTF8 and vice versa) There is one service called “Encoding Conversion” in...
When we talk about solving EDI problems, explaining the concept is relatively easy.
Building a working solution around those concepts is the more interesting challenge.
With the EDI Validation Suite, the objective is not only to share knowledge about EDI validation but also to explore how those concepts can be converted into a practical and reusable platform.
In this video, I'm sharing a little of what is happening behind the scenes.
Rather than showing only the user interface, this video provides a glimpse into the technical implementation supporting the solution — including the backend services, APIs and development environment that make the validation workflow possible.
The goal is to build the solution step by step with a foundation that can eventually support:
➡️ EDI transaction validation
➡️ Standard validation rules
➡️ Business-rule validation
➡️ Trading-partner-specific requirements
➡️ Validation history and reporting
➡️ Dashboard analytics
➡️ API-based validation
➡️ Future enterprise and SaaS capabilities
This is still part of the product-building journey.
I'm intentionally sharing some of the implementation process because I want this initiative to be more than discussions, presentations or theoretical EDI concepts.
The objective is to convert real EDI implementation experience into a practical working solution.
Over the coming weeks, I'll share separate demonstrations covering different capabilities of the EDI Validation Suite in more detail.
If you're an EDI Developer, B2B Consultant, Business Analyst, Integration Architect, QA Engineer or someone managing trading-partner integrations, your feedback and suggestions are very welcome.
What capabilities would you expect from a modern EDI validation platform?
hashtag#EDI hashtag#B2B hashtag#EDIValidation hashtag#X12 hashtag#B2BIntegration hashtag#APIs hashtag#IntegrationArchitecture hashtag#SoftwareDevelopment hashtag#EnterpriseIntegration