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  • Understanding Technical Debt : Causes and Solutions

    An emerging perspective in modern software development, influenced by lean methodology and from works like The Goal, Lean Startup, and Project to Product, is that mistakes and experimentation are essential for learning. This often means releasing imperfect software into production, which naturally creates some technical debt. The initial shortcuts or compromises are the principal, invisible to users but clear to developers, while the long-term impact bugs, quality issues, and slower delivery is the interest. The key distinction is between deliberate, prudent debt incurred for speed and learning, versus reckless debt caused by carelessness. Rather than striving for perfection or rewarding sheer volume of code, successful teams focus on delivering incremental units of value, accepting manageable debt as part of an adaptive and iterative software process.

    For example, in a major banking initiative that was built on MongoDB, Kafka, AWS, and the Spring Framework technology stack and related java-based stack, technical debt accumulated rapidly due to shortcuts taken by the offshore vendor team under tight delivery timelines. Instead of carefully planning data models and adhering to MongoDB best practices, collections were loosely structured, queries became inefficient, document exceeding the limit supported and schema inconsistencies began to appear across services. Unit testing was often gamed or skipped to meet deadlines, leaving brittle codebase with hidden defects. Kafka was introduced for event streaming, but without proper design standards or validation pipelines, issues like message duplication, too many events that were not needed and processing delays surfaced. Over time, these gaps created mounting operational inefficiencies and raised long-term maintenance costs.

    Although an on-site technology team provided governance, the distributed offshore model made reviews largely reactive rather than preventative. By the time design flaws were identified, many had already been deployed into production, making remediation costly and disruptive. This resulted in mounting technical debt that surfaced as constant rework, frequent patching, and a noticeable decline in delivery velocity. Beyond the technical inefficiencies, the absence of consistent standards and robust quality controls posed risks to regulatory compliance and eroded customer confidence two non-negotiable priorities in the banking sector. Ultimately, this case illustrates how unmanaged technical debt in mission-critical financial systems can quietly erode both business agility and long-term system resilience.

    So technical debt is the implied cost of choosing a quick or easy solution today instead of a better, more sustainable one that might take longer to implement. Just like financial debt, it allows teams to move faster in the short term but creates a repayment burden later in the form of rework, reduced productivity, lack of flexibility for further extension and increased system fragility. It often arises from poor design, lack of testing, rushed development, or skipping best practices, and while some debt can be intentional and manageable, un-managed technical debt accumulates and can slow down innovation, increase risks, increase costs and make systems harder to maintain over time.

    Technical debt is often categorized by its origin and the awareness among the team as when it was incurred during the development life cycle. I will write about these later on. There are categories as how we classify debt and some are

    • Good Debt vs. Bad Debt : –
      • Good Debt : Debt taken on knowingly and strategically to achieve a clear, immediate business goal (e.g., shipping a feature quickly to beat a competitor). The team accepts the risk and plans to pay it back.
    • Deliberate vs. Accidental : –
      • Deliberate Debt : The team decides to take the shortcut (e.g., hard coding a value) to meet a deadline. This aligns with prudent debt.
      • Accidental Debt (or Unintentional): Debt that accumulates over time due to evolving understanding of the product, new business requirements, or learning that a previous design decision was simply incorrect. This is often the largest source of debt.

    Technical debt can be classified as

    • Process-Related Causes
      • Rushed development to meet tight deadlines.
      • Frequent scope or requirement changes without redesign.
      • Short-term fixes and workarounds prioritized over long-term solutions.
      • Lack of regular code reviews or quality assurance checkpoints.
      • Inadequate planning for scalability and maintainability.
    • People-Related Causes
      • Limited technical expertise or lack of training in tools/frameworks.
      • Poor communication between business and technical teams.
      • Misaligned priorities between stakeholders (e.g., speed vs. quality).
      • Inconsistent coding practices across distributed or offshore teams.
      • High turnover, leading to knowledge gaps and loss of context.
    • Technology-Related Causes
      • Incomplete or poor data modeling and architecture.
      • Skipping unit tests, integration tests, or automated testing.
      • Not following best practices for databases, frameworks, or cloud services.
      • Overly complex, bloated, or redundant code base.
      • Legacy system dependencies without modernization planning.
      • Insufficient or outdated documentation.

    Some of the business domain applications where I have seen very high technical debt are in

    • Banking and Financial Services
      • Applications related to Core banking systems, payment processing, credit risk engines.
      • Many banks rely on decades-old COBOL-based mainframe program integrated with newer systems (e.g., API’s, mobile apps). Rushed compliance updates, fragmented data models, and vendor-driven offshore development often leave behind fragile architectures.
    • Healthcare and Life Sciences
      • Applications related to Electronic Health Records (EHR), patient portals, insurance claims processing.
      • Systems are typically a patchwork of legacy software tied together with new cloud or AI modules. Strict compliance (HIPAA, GDPR) leads to quick-fix security patches, while poor interoperability standards create messy integrations across hospitals, labs, and insurers. Offshore Vendor Driven Development often leads to Technical Debt due to various reasons like gaps in skills, requirements misunderstanding etc.
    • Telecommunications
      • Billing systems, customer management platforms, network monitoring.
      • High user volumes force companies to add features quickly. Mergers and acquisitions introduce multiple legacy stacks, leading to duplicated logic and fragile middle-ware layers. Billing engines especially carry massive customization with poor documentation. Offshore Vendor Driven Development often leads to Technical Debt due to various reasons like gaps in skills, requirements misunderstanding etc.
    • Retail and E-Commerce
      • Inventory management, omnichannel order fulfillment, personalization engines.
      • Fast-moving competition drives teams to push out features without long-term design. Legacy ERP systems often fail to scale with cloud-based microservices, creating complex, high-maintenance integrations.

    Key Strategies that help to deal with Technical Debt are

    • Identify and Track Debt : – Maintain a “technical debt register” or backlog itemizing known issues.
    • Prioritize by Impact :- Tackle the debt that most affect business outcomes (e.g., security risks, customer experience).
    • Refactor Incrementally :-  Improve code, data models, or tests in small steps rather than waiting for big rewrites.
    • Adopt Testing & Automation :-  Use unit, integration, and regression testing with CI/CD pipelines to prevent new debt.
    • Set Standards & Best Practices :-  Enforce coding guidelines, architecture reviews, and documentation practices.
    • Communicate in Business Terms :-  Explain the cost of debt as slower delivery, higher risk, or lost revenue to gain stakeholder buy-in.

    Dealing with technical debt is less about eliminating it entirely and more about managing it strategically. Teams must acknowledge that some debt is intentional taken on to move quickly and should plan to repay it before it accumulates interest. By embedding refactoring into regular sprints, strengthening automated testing, and aligning teams on best practices, organizations can gradually reduce hidden risks while still delivering value. Importantly, leaders need to view technical debt not as a purely technical issue but as a business trade-off; when its impact is communicated in financial and customer terms, it becomes easier to secure time and resources for remediation.

    The cost of resolving technical debt can be significant, often consuming 20–30% of a project’s budget depending on its severity and how long the debt has been left un-managed. For example, minor issues such as missing unit tests or small refactors may take days or weeks to resolve, costing a fraction of the sprint. In contrast, large-scale debt—such as poor data modeling, outdated frameworks, or legacy integrations—can extend timelines by several months and add millions of dollars in remediation costs for enterprise projects. The longer the debt remains, the more “interest” it accrues: bugs take longer to fix, new features take longer to deliver, and maintenance costs grow exponentially. Industry studies suggest that organizations often spend up to 30% of their development time addressing technical debt rather than delivering new features, making proactive debt management essential to avoid ballooning project costs and delays.

    By solving technical debt, organizations gain both short-term efficiency and long-term resilience in their software systems. Reducing debt improves developer productivity, since clean, well-structured code base are easier to maintain, extend, and debug meaning less time wasted on workarounds and rework. It also strengthens system reliability and performance, as refactored architectures reduce bugs, downtime, and inefficiencies. From a business perspective, addressing technical debt lowers project costs by minimizing maintenance overhead, accelerates time-to-market for new features, and ensures smoother compliance with security and regulatory requirements. Just as importantly, it boosts team morale and collaboration, because developers spend more time innovating and less time fighting fragile code.

    References : –

    Sourcery. (2022, September 24). The impact of technical debt

    Martini, A., Besker, T., & Bosch, J. (2018). Technical debt tracking: Current state of practice.

  • Engineering Team Effectiveness: Part 1

    Some Engineering Teams function like finely tuned engines, consistently delivering success. Their communication is smooth, deadlines are met with ease, and challenges are faced directly. On the other hand, some teams struggle to hit their goals. Their communication is disorganized, messy and deadlines often feel overwhelming.  So, what sets the high-performing teams apart? . It usually comes down to a few key things having a clear plan, open communication, trust, and a shared sense of purpose. Some teams already have the rhythm down, while others are still working to find their groove.

    The great thing is, that rhythm can be learned. Even teams that struggle at first can build momentum with practice. In software engineering, this rhythm shows up in the way teams consistently create value by writing code, testing it, and releasing useful features to the world. Teams that do this well and often are considered effective. So, if we want to build great software, we first need to focus on building strong, effective engineering teams.

    I’ve witnessed how team dynamics can either drive a project to success or cause it to fall apart. Creating effective teams isn’t only about having the right technical skills it’s about building a culture rooted in collaboration, trust, and a common purpose. Team is a group connected by shared goals and responsibilities. Its members collaborate and hold each other accountable as they tackle problems and work toward success. When planning, reviewing progress, or making decisions, effective teams consider the strengths and availability of everyone not just one person. It’s this shared purpose that powers true teamwork.

    Google’s Project Aristotle uncovered some key dynamics that drive the success of software engineering teams and some of attributes of the that came out of that research are

    Psychological Safety

    Researchers in Google found this to be the single most important factor. It’s about how safe team members feel sharing their thoughts and ideas without worrying about criticism or backlash. When teams feel secure, they’re more willing to take risks and explore new ideas often leading to stronger results.

    Teams with high psychological safety : –

    • Have lower turnover rates
    • Make better use of the diverse ideas shared within the group
    • Generate more revenue and consistently hit sales targets
    • Are rated as highly effective by their leaders

    Signs your team may need to strengthen psychological safety:

    • Team members avoid giving or asking for constructive feedback.
    • People hesitate to share different viewpoints or ask basic questions.
    • Silence dominates meetings, with only a few voices regularly speaking up.
    • Mistakes are hidden rather than discussed and learned from.
    • Decisions get made quickly without much debate or input from everyone.

    Reflection questions for Team :

    • Do team members feel at ease brainstorming in front of one another ?
    • Can they admit mistakes or failures openly without feeling judged or excluded ?
    • Does everyone get a chance to speak in meetings, or do a few people dominate the conversation ?
    • Do people feel their ideas are valued, even if not all are adopted ?
    • Are disagreements handled respectfully, without fear of backlash ?
    • Do team members support each other when someone takes a risk or tries something new ?

    Dependability

    This is all about how much team members can count on one another to follow through finishing tasks and meeting deadlines as promised. When people trust each other to be reliable, the team naturally becomes more efficient and effective.

    Signs your team may need to strengthen dependability:

    • Limited visibility into project priorities or progress
    • Tasks or problems lack clear ownership, leading to diffusion of responsibility
    • Deadlines are often missed without explanation
    • Follow-ups are needed frequently to ensure work gets done

    Reflection questions for Team : –

    • When team members say they’ll complete something, do they follow through?
    • Do team members proactively communicate delays and take responsibility?
    • Are deadlines consistently met without last-minute scrambling?
    • Do people feel comfortable holding each other accountable?
    • Is work quality consistent, or do others often need to step in to fix issues?
    • Are responsibilities clearly defined so everyone knows who owns what ?

    Structure and Clarity

    It is about making sure everyone knows the team’s goals as well as their own roles and responsibilities. When expectations are clear, team members stay more focused, productive, and aligned with the bigger picture.

    Signs your team may need to strengthen structure and clarity : –

    • Team members are unclear about project goals or priorities.
    • Roles and responsibilities are not well defined, causing overlap or gaps.
    • People frequently ask, Who’s responsible for this ?
    • Tasks are started but left unfinished due to shifting direction.
    • Meetings end without clear next steps or ownership.
    • Progress is hard to measure because expectations aren’t specific.

    Reflection questions for Team :-

    • Do all team members clearly understand the team’s goals ?
    • Are individual roles and responsibilities well defined and documented ?
    • When new tasks arise, is it obvious who should take ownership ?
    • Are expectations and deadlines communicated in a way everyone understands ?
    • Do team members feel confident about what success looks like in their work ?
    • Is there a process for reviewing progress and adjusting priorities when needed ?

    Meaning

    This is about how much team members feel their work truly matters. When people see purpose in what they do, they’re more motivated, engaged, and committed to the team’s success.

    Signs your team may need to strengthen meaning : –

    • Team members treat tasks as routine checkbox work rather than purposeful contributions
    • Motivation and engagement drop, especially for repetitive or long-term projects
    • People rarely connect their work to personal values or the team’s mission
    • Conversations focus only on outputs (tasks completed) rather than outcomes (why it matters)
    • Team members show little enthusiasm when talking about their work

    Reflection questions for Team :-

    • Do team members feel their work has personal significance and aligns with their values ?
    • Are we regularly connecting day-to-day tasks to the bigger mission of the project or organization ?
    • Do people feel proud to share what they’re working on with others ?
    • Is the purpose of our work clear and consistently communicated by leadership ?
    • Do team members find opportunities for growth and fulfillment in what they do ?
    • Are we celebrating not just the “what” but also the “why” behind our achievements ?

    Impact

    This reflects how strongly team members believe their work makes a real difference whether for the organization or for society at large. When people feel their contributions have impact, they tend to be more committed, energized, and invested in the project’s success.

    Signs your team may need to strengthen impact:

    • Team members struggle to see how their work connects to larger goals.
    • Achievements go unnoticed or un celebrated.
    • People feel like they’re just checking boxes rather than driving real change.
    • Motivation drops when tasks seem disconnected from outcomes.
    • Success stories or customer feedback are rarely shared

    Reflection questions for Team :

    • Do team members understand how their work contributes to the organization’s success ?
    • Are individual and team achievements recognized and celebrated?
    • Do people feel their efforts make a difference to customers, colleagues, or society ?
    • Is leadership regularly communicating the broader purpose and value of the team’s work ?
    • Do team members feel proud to talk about their contributions outside of the team ?
    • Are we connecting day-to-day tasks to meaningful outcomes ?

    By focusing on these factors, software engineering teams can create an environment conducive to collaboration, innovation, and success.

    There are also other factors that influences the team dynamics like size of the team, adaptability, diversity, leadership and communication styles.

    References : –

    Google rework : https://rework.withgoogle.com/intl/en/guides/understanding-team-effectiveness

  • Understanding Full Loader in Data Engineering

    Data engineering is a practice which is focused on designing, building, and maintaining the systems and infrastructure that enable the collection, storage, transformation, and delivery of data for analysis and decision-making. It involves creating reliable data pipelines that extract information from various sources, clean and structure it, and make it accessible in formats suitable for analytics, reporting, and machine learning. 

    Common use case in data engineering is the full load pattern, an ingestion method that processes and loads the entire dataset during each execution. While effective, this approach can become resource-intensive depending on the size of the data being handled. The full load method is typically applied in scenarios where datasets lack fields or indicators to identify when a record were inserted or last updated, making incremental loading impractical. Although it is among the most straightforward ingestion patterns to implement, the full load approach carries potential pitfalls that require careful planning and consideration to ensure efficiency and reliability.

    In this scenario, the target data source of the data pipeline requires transformation jobs that depend on additional IOT device information from a third-party data provider. This dataset changes only a few times in a week and contains fewer than one million rows, making it a relatively slow-evolving entity. However, the challenge is that the data provider does not define a “last updated” or “created at” attribute or any time marker to identify which rows have changed since the last ingestion. This forces user to load the full dataset every time rather than loading just the changed dataset. Given these limitations, the Full Loader pattern becomes an ideal solution. Its simplest implementation follows a two-step Extract and Load (EL) process, where native command exports the entire dataset from the source and import it into the target system. This approach works especially well for homogeneous data stores, as no transformation is required during the transfer. Although it may not always be the most efficient method for large, rapidly changing datasets, it is effective for smaller, slowly evolving datasets ensuring completeness and consistency in the absence of change-tracking attributes. If the source and target data stores are of a similar type — for example, migrating data from PostgreSQL to another PostgreSQL database — intermediate transformations are generally unnecessary because the data structures are already aligned. However, when the source and target systems differ in nature, such as transferring data from a relational database (RDBMS) to a NoSQL database, data transformations are typically required to adjust the schema, format, and structure to fit the target environment.

    Full Loader implementations are typically designed as batch jobs that run on a regular schedule. When the volume of data grows gradually, this approach works well since the compute resources remain relatively stable and predictable. In such cases, the data loading infrastructure can operate reliably for extended periods without performance concerns.However, challenges arise when dealing with datasets that evolve more dynamically. For instance, if the dataset suddenly doubles in size from one day to the next, relying on static compute resources can cause significant slowdowns or even failures due to hardware limitations. To address this variability, organizations can take advantage of auto-scaling capabilities within their data processing layer. Auto-scaling ensures that additional compute resources are allocated automatically during spikes in data volume, maintaining performance and reliability while optimizing resource usage.

    Another important risk associated with the Full Loader pattern is the potential for data consistency issues because the process involves completely overwriting the dataset, a common strategy is to use a truncate and load operation during each run. However, this approach carries significant drawbacks. For example, if the ingestion job executes at the same time as other pipelines or consumers reading the dataset, users may encounter incomplete or missing data while the insert operation is still in progress. To mitigate this, leveraging transactions is the simplest and most effective solution, as they manage data visibility automatically. In cases where the data store does not support transactions, a practical workaround is to use an abstraction layer such as a database view, which allows you to update the underlying structures without exposing incomplete data to consumers.

    In addition to concurrency concerns, there is the risk of losing the ability to revert to a previous dataset version if issues occur after a full overwrite. Without versioning or backups, once the data is replaced, the previous state cannot be recovered. To safeguard against this, it is critical to maintain regular dataset backups or implement versioned storage strategies. This ensures that if unexpected problems arise, the system can roll back to a reliable earlier version, preserving both data integrity and operational continuity