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Collaboration Between PM & BA

19/12/2022

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As you might know, Project Manager & Business Analyst are key players in every project. To understand why their collaboration is the most important part of the project. And how to make a good partnership between them? Let’s dive in.

Firstly, have a look at basic terms:

Project & Product?

  • Project is temporarily formed to create a product
  • Product is something that delivers value. For example, a car, a tool, a software …

Project & Product Goals

  • The goal of the project is to finish on time, within cost, and provide the right product.
  • The right product must be valuable and meet business and customer needs.
  • It’s actually the same as the picture below

You can see, those factors are constraints and we need people to manage/maintain them, it’s time to involve Project Manager & Business Analyst in.

Project Manager & Business Analyst

  • PM is responsible for Time: making the schedule for the whole project base on the Scope, defining processes, managing Costs before and during the development phase, guiding others to perform the best, and keeping the project on track in any situation.
  • BA is mainly focusing on business needs and providing the proper functionalities that meet all requirements, working with PM & stakeholders to finalize the Scope, BA is also taking care of the product’s quality as the client’s expectation.

So:

  • Without BA, build wrong things.
  • Without PM, exceed budget, extend the schedule.
  • And without a great collaboration between them can lead a project to all the things above.

PM & BA overlap tasking

Let’s get back to the above picture, it’s easy to reveal that Project & Product is strongly dependent on each other. Imagine that, the client adds essential change requests to the product, and promptly it makes the project scope get bigger, the cost of building the product would be increased and it take longer to complete.

It’s just a popular example, but in actual work, other things can happen in many many ways, and then potentially to lead the project to fail.

To keep everything are under control, it’s necessary to have a tight collaboration between PM & BA, bellow are kind of works they have to share in daily activity:

  • Scope & Requirements Management
  • Communications Management
  • Risk Management
  • Stakeholder Management

Go ahead with the following sections to understand deeply how to perform a good work and how to collaborate on those items.

What & How to collaborate?

Scope & Requirements Management

As mentioned in the above section, requirement changes actually happen in every project, but without control procedure it would become Scope creep. Some change requests are essential, scope creep is a situation that generally considered harmful to the project.

To welcome the good and needed, and also avoid scope creep, all the changes have to be recognized, judged and managed carefully. The process to control them:

  • Identify change
  • Understand whether it provides business value
  • Discuss the impact on quality, budget, scope and schedule
  • Is it feasible?
  • Give options for stakeholders to choose

Following those steps makes everything clear and every provided selection is reasonable, therefore the team and stakeholders can easily make a decision. Further, it helps to build trust in one another by removing all concerns and worries.

Communications Management

Communication is vital within projects. PM & BA communicate to share understanding about requirement and intent, the priority, timeline, and also keep track on what the team are working on.

Since both PM & BA interact with the same stakeholders, many detailed conversations in different contexts, friction may arise. To avoid this, they have to make transparency on:

  • What am I working on?
  • What are my priorities today?
  • Who do I plan to interact with?
  • What are key messages that we need to collaborate on?

So that they can ensure consistent messages are being communicated to the team and stakeholders.
Beside communicating with each other, the equally critical part is to have this with team and stakeholders:

  • Listening to stakeholders – What do they need? What do they want? How do they feel?
  • Sharing understanding, knowledge, experience and also learning from them
  • Having a clear communication flow to engage appropriate stakeholders for each type of communication in the project.

That allow to connect better, build trust and stronger relationships with one another. Thus, contribute to project success.

Risk Management

When starting the project, PM & BA might feel no risk, but during the implementation phase, risks are always ever-present. So what kind of risk do they have to pay attention to?

  • Stakeholders involvement
  • Building wrong things
  • The risk to schedule

For example, if there is a complicated requirement, we need the client to involve in, but they can not because they’re busy(the project is just a low priority task in their daily activities), then we make an assumption, if our assumption is not true, it’s a big risk that affects on the output and schedule as well.

So PM & BA should have a process to manage all those kind of risk together and ensure the risks is well understood and under control:

  • Identify the risk factors
  • Measure the effect of each risk factor
  • Propose and take appropriate actions to mitigate the high-risk factors
  • Monitor the low-risk factors and repeat the process when situations come

Stakeholders Management

Continue with the example above. It’s sometimes hard to involve stakeholders due to the time and they might not understand how important they are in the project, so please let them know:

  • Role, responsibility and expectation
  • How their work connects with the project
  • Who do they have/should to work with

After clearing all the things above, PM & BA should keep the project team is coordinated by:

  • Always scheduling for stakeholders’ meetings – let them proactively in arranging their time.
  • Making the meeting agenda – let them well prepare for the meeting.
  • Combining meetings as much as possible – helps to save time, gives them a chance to interact and learn more from others. Through it, build better relationships and trust.
  • Sharing key messages before and after the meeting – make things transparent and let others feel comfortable to contribute.

Doing those would show stakeholders to see the value of their time and how it would be used. Working with the team helps them in other areas. PM & BA also ensure that the time is used well to maximize this.

Conclusion

The sign of the great collaboration between PM & BA is performing their work as the same person, understanding everything about the project and product. To do so, they have to clean up roles and responsibilities on each side, share their work and support each other. PM supports BA to know the terms of project management: time, cost, and scope. BA has to impart business needs, and client expectations to PM. The important thing is to spend enough time communicating to be able to respond to daily changes.

For external, define the working flow, and communication flow. Tactically involve stakeholders & project members in daily activities. Unique them in one team and lead them on how to collaborate with one another to achieve the project and product goals.

Reference

  • https://www.pmi.org/learning/library/business-analyst-project-manager-collaboration-6512
  • https://www.linkedin.com/learning/business-analyst-and-project-manager-collaboration

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Define Consistent Error Structures Good Practice: Use a standard error format so all errors are predictable and machine-readable.Example: {   "errorType": "ValidationError",   "message": "Invalid input: 'email' is missing",   "requestId": "12345-abcd" } Bad Practice: Avoid returning vague or unstructured errors that make debugging difficult. { "message": "Something went wrong", "error": true } Why It Matters: Structured errors make debugging easier by providing consistent, machine-readable information. They also improve communication with clients or downstream systems by conveying what went wrong and how it should be handled. 2. Use Custom Error Classes Good Practice: In Node.js, define custom error classes for clarity: class ValidationError extends Error {   constructor(message) {     super(message);     this.name = "ValidationError";     this.statusCode = 400; // Custom property   } } // Throwing a custom error if (!event.body.email) {   throw new ValidationError("Invalid input: 'email' is missing"); } Bad Practice: Use generic errors for everything, making identifying or categorizing issues hard.Example: throw new Error("Error occurred"); Why It Matters: Custom error classes make error handling more precise and help segregate application errors (e.g., validation issues) from system errors (e.g., database failures). 3. Include Contextual Information in Logs Good Practice: Add relevant information like requestId, timestamp, and input data (excluding sensitive information) when logging errors.Example: console.error({     errorType: "ValidationError",     message: "The 'email' field is missing.",     requestId: context.awsRequestId,     input: event.body,     timestamp: new Date().toISOString(), }); Bad Practice: Log errors without any context, making debugging difficult.Example: console.error("Error occurred"); Why It Matters: Contextual information in logs makes it easier to identify what triggered the error and where it happened, improving the debugging experience. Retry Logic Across AWS SDK and Other Services Retrying failed operations is critical when interacting with external services, as temporary failures (e.g., throttling, timeouts, or transient network issues) can disrupt workflows. Whether you’re using AWS SDK, third-party APIs, or internal services, applying retry logic effectively can ensure system reliability while avoiding unnecessary overhead. 1. Use Exponential Backoff and Jitter Good Practice: Apply exponential backoff with jitter to stagger retry attempts. This avoids overwhelming the target service, especially under high load or rate-limiting scenarios.Example (General Implementation): async function retryWithBackoff(fn, retries = 3, delay = 100) {     for (let attempt = 1; attempt <= retries; attempt++) {         try {             return await fn();         } catch (error) {             if (attempt === retries) throw error; // Rethrow after final attempt             const backoff = delay * 2 ** (attempt - 1) + Math.random() * delay; // Add jitter             console.log(`Retrying in ${backoff.toFixed()}ms...`);             await new Promise((res) => setTimeout(res, backoff));         }     } } // Usage Example const result = await retryWithBackoff(() => callThirdPartyAPI()); Bad Practice: Retrying without delays or jitter can lead to cascading failures and amplify the problem. for (let i = 0; i < retries; i++) {     try {         return await callThirdPartyAPI();     } catch (error) {         console.log("Retrying immediately...");     } } Why It Matters: Exponential backoff reduces pressure on the failing service, while jitter randomizes retry times, preventing synchronized retry storms from multiple clients. 2. Leverage Built-In Retry Mechanisms Good Practice: Use the built-in retry logic of libraries, SDKs, or APIs whenever available. These are typically optimized for the specific service.Example (AWS SDK): const DynamoDB = new AWS.DynamoDB.DocumentClient({     maxRetries: 3, // Number of retries     retryDelayOptions: { base: 200 }, // Base delay in ms }); Example (Axios for Third-Party APIs):Use libraries like axios-retry to integrate retry logic for HTTP requests. const axios = require('axios'); const axiosRetry = require('axios-retry'); axiosRetry(axios, {     retries: 3, // Retry 3 times     retryDelay: (retryCount) => retryCount * 200, // Exponential backoff     retryCondition: (error) => error.response.status >= 500, // Retry only for server errors }); const response = await axios.get("https://example.com/api"); Bad Practice: Writing your own retry logic unnecessarily when built-in mechanisms exist, risking suboptimal implementation. Why It Matters: Built-in retry mechanisms are often optimized for the specific service or library, reducing the likelihood of bugs and configuration errors. 3. Configure Service-Specific Retry Limits Good Practice: Set retry limits based on the service's characteristics and criticality.Example (AWS S3 Upload): const s3 = new AWS.S3({ maxRetries: 5, // Allow more retries for critical operations retryDelayOptions: { base: 300 }, // Slightly longer base delay }); Example (Database Queries): async function queryDatabaseWithRetry(queryFn) {     await retryWithBackoff(queryFn, 5, 100); // Retry with custom backoff logic } Bad Practice: Allowing unlimited retries can cause resource exhaustion and increase costs. while (true) {     try {         return await callService();     } catch (error) {         console.log("Retrying...");     } } Why It Matters: Excessive retries can lead to runaway costs or cascading failures across the system. Always define a sensible retry limit. 4. Handle Transient vs. Persistent Failures Good Practice: Retry only transient failures (e.g., timeouts, throttling, 5xx errors) and handle persistent failures (e.g., invalid input, 4xx errors) immediately.Example: const isTransientError = (error) =>     error.code === "ThrottlingException" || error.code === "TimeoutError"; async function callServiceWithRetry() {     await retryWithBackoff(() => {         if (!isTransientError(error)) throw error; // Do not retry persistent errors         return callService();     }); } Bad Practice: Retrying all errors indiscriminately, including persistent failures like ValidationException or 404 Not Found. Why It Matters: Persistent failures are unlikely to succeed with retries and can waste resources unnecessarily. 5. Log Retry Attempts Good Practice: Log each retry attempt with relevant context, such as the retry count and delay. async function retryWithBackoff(fn, retries = 3, delay = 100) {     for (let attempt = 1; attempt <= retries; attempt++) {         try {             return await fn();         } catch (error) {             if (attempt === retries) throw error;             console.log(`Attempt ${attempt} failed. Retrying in ${delay}ms...`);             await new Promise((res) => setTimeout(res, delay));         }     } } Bad Practice: Failing to log retries makes debugging or understanding the retry behavior difficult. Why It Matters: Logs provide valuable insights into system behavior and help diagnose retry-related issues. Summary of Best Practices for Retry logic AspectGood PracticeBad PracticeRetry LogicUse exponential backoff with jitter to stagger retries.Retry immediately without delays, causing retry storms.Built-In MechanismsLeverage AWS SDK retry options or third-party libraries like axios-retry.Write custom retry logic unnecessarily when optimized built-in solutions are available.Retry LimitsDefine a sensible retry limit (e.g., 3–5 retries).Allow unlimited retries, risking resource exhaustion or runaway costs.Transient vs PersistentRetry only transient errors (e.g., timeouts, throttling) and fail fast for persistent errors.Retry all errors indiscriminately, including persistent failures like validation or 404 errors.LoggingLog retry attempts with context (e.g., attempt number, delay,  error) to aid debugging.Fail to log retries, making it hard to trace retry behavior or diagnose problems. Logging Best Practices Logs are essential for debugging and monitoring Lambda functions. However, unstructured or excessive logging can make it harder to find helpful information. 1. Mask or Exclude Sensitive Data Good Practice: Avoid logging sensitive information like:User credentialsAPI keys, tokens, or secretsPersonally Identifiable Information (PII)Use tools like AWS Secrets Manager for sensitive data management.Example: Mask sensitive fields before logging: const sanitizedInput = {     ...event,     password: "***", }; console.log(JSON.stringify({     level: "info",     message: "User login attempt logged.",     input: sanitizedInput, })); Bad Practice: Logging sensitive data directly can cause security breaches or compliance violations (e.g., GDPR, HIPAA).Example: console.log(`User logged in with password: ${event.password}`); Why It Matters: Logging sensitive data can expose systems to attackers, breach compliance rules, and compromise user trust. 2.  Set Log Retention Policies Good Practice: Set a retention policy for CloudWatch log groups to prevent excessive log storage costs.AWS allows you to configure retention settings (e.g., 7, 14, or 30 days). Bad Practice: Using the default “Never Expire” retention policy unnecessarily stores logs indefinitely. Why It Matters: Unmanaged logs increase costs and make it harder to find relevant data. Retaining logs only as long as needed reduces costs and keeps logs manageable. 3. Avoid Excessive Logging Good Practice: Log only what is necessary to monitor, troubleshoot, and analyze system behavior.Use info, debug, and error levels to prioritize logs appropriately. console.info("Function started processing..."); console.error("Failed to fetch data from DynamoDB: ", error.message); Bad Practice: Logging every detail (e.g., input payloads, execution steps) unnecessarily increases log volume.Example: console.log(`Received event: ${JSON.stringify(event)}`); // Avoid logging full payloads unnecessarily Why It Matters: Excessive logging clutters log storage, increases costs, and makes it harder to isolate relevant logs. 4. Use Log Levels (Info, Debug, Error) Good Practice: Use different log levels to differentiate between critical and non-critical information.info: For general execution logs (e.g., function start, successful completion).debug: For detailed logs during development or troubleshooting.error: For failure scenarios requiring immediate attention. Bad Practice: Using a single log level (e.g., console.log() everywhere) without prioritization. Why It Matters: Log levels make it easier to filter logs based on severity and focus on critical issues in production. Conclusion In this episode of "Mastering AWS Lambda with Bao", we explored critical best practices for building reliable AWS Lambda functions, focusing on optimizing performance, error handling, and logging. Optimizing Performance: By reducing cold starts, using smaller deployment packages, lightweight runtimes, and optimizing VPC configurations, you can significantly lower latency and optimize Lambda functions. Strategies like moving initialization outside the handler and leveraging Provisioned Concurrency ensure smoother execution for latency-sensitive applications.Error Handling: Implementing structured error responses and custom error classes makes troubleshooting easier and helps differentiate between transient and persistent issues. Handling errors consistently improves system resilience.Retry Logic: Applying exponential backoff with jitter, using built-in retry mechanisms, and setting sensible retry limits optimizes that Lambda functions gracefully handle failures without overwhelming dependent services.Logging: Effective logging with structured formats, contextual information, log levels, and appropriate retention policies enables better visibility, debugging, and cost control. Avoiding sensitive data in logs ensures security and compliance. Following these best practices, you can optimize lambda function performance, reduce operational costs, and build scalable, reliable, and secure serverless applications with AWS Lambda. In the next episode, we’ll dive deeper into "Handling Failures with Dead Letter Queues (DLQs)", exploring how DLQs act as a safety net for capturing failed events and ensuring no data loss occurs in your workflows. Stay tuned! Note: 1. Provisioned Concurrency is not a universal solution. While it eliminates cold starts, it also incurs additional costs since pre-initialized environments are billed regardless of usage. When to Use:Latency-sensitive workloads like APIs or real-time applications where even a slight delay is unacceptable.When Not to Use:Functions with unpredictable or low invocation rates (e.g., batch jobs, infrequent triggers). For such scenarios, on-demand concurrency may be more cost-effective.

                13/01/2025

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                  Best Practices for Building Reliable AWS Lambda Functions

                  13/01/2025

                  317

                  Bao Dang D. Q.

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