Incremental Development Processes

🧨 Has Iteration Moved Upstream in the AI Era? Let’s talk about Incremental Layering — a new pillar of AI-first development. For decades, iteration ruled software: 🔁 Build something rough 🧪 Test 🛠️ Refine But the original idea often lost its spark — filtered through layers of specialists, slowed by unclear product-market fit. Now? AI puts the creator in control. With tools like ChatGPT, GitHub Copilot, and no-code platforms, creators can prototype fully formed ideas in minutes, not months. That changes everything. 🎨 Creative iteration has shifted to the front of the process. Visionaries can explore, test, and reshape ideas rapidly — before a single line of production code is written. 🧱 Meanwhile, developers transform iteration into incremental layering: Start with a functional prototype (core logic), then purposefully enhance with additional capabilities, UX refinements, and performance optimizations — layer by layer. Each layer builds on validated foundations rather than requiring complete reworks. 🚀 This is Enhanced Development in the AI Era: ✅ Focused iteration where it matters most ✅ Earlier product-market validation ✅ Compressed development cycles Yes, we still need to manage: ⚠️ Technical debt ⚠️ Scalability ⚠️ Overdesigning early AI gives us scaffolds aligned with the creator’s intent — a structured launchpad for refinement, not blind iteration. 🧠 AI development isn’t just a workflow — it’s a mindset. Incremental Layering is a core pillar of AI-First Development. Let’s build with vision, velocity, and product-market fit. 👇 Are you seeing this shift? Share an AI tool helping you bring your vision to life. #AI #AIFirst #VisionDrivenDevelopment #SoftwareDevelopment #ProductDesign #TechLeadership #NoCode #Innovation #BuildWithAI

I often talk to people in the data space who think that writing incremental pipelines is for power users. But that couldn’t be further from the truth 🙅♂️ Every data team should adopt incremental loads. Despite some challenges they pose with maintaining idempotency and handling late-arriving data, incremental pipelines lie at the heart of efficient data processing. That’s why I feel it’s crucial for more data engineers to understand how to read and write incremental pipelines. 👓 Two common techniques for reading data incrementally 1️⃣ Maximal Timestamp (used by dbt) How it works: Finds the latest processed timestamp in an existing table, and uses it to filter new data. Pros: No additional state needed beyond what’s already stored in the table itself. Cons: Queries become convoluted from dual operational modes (initial load vs. incremental); historical data gaps can be missed, and makes scaling difficult for large datasets without batching. 2️⃣ Date Partitions (used by Airflow / Hive) How it works: Relies on a scheduler to provide explicit date ranges for predictable queries and partitioned processing. Pros: Easier to manage backfills, scalable for batch processing, and supports fixing data gaps manually or programmatically. Cons: Requires additional state management to track processed intervals. ✍️ And two common techniques for writing data incrementally 1️⃣ Merge How it works: Matches rows between source and target based on a key, and updating and inserting it where needed. Pros: Ideal for minimizing file rewrites during small updates. Cons: Inefficient for large updates due to the overhead that comes with joining all new and existing records to avoid duplicates. 2️⃣ Insert Overwrite How it works: Rewrites entire partitions (e.g., daily folders) atomically, ensuring a clean slate. Pros: Reliable for large-scale backfills and late-arriving data; avoids duplicate data issues entirely. Cons: Inefficient for small updates, since it rewrites entire partitions, regardless of number of changes. Having experienced or observed all these approaches to incremental loading (when I worked at Netflix, for example, we were faithful to the insert overwrite method), I built SQLMesh, the framework underlying Tobiko’s data transformation tools, to preempt common shortcomings. ✨ #SQLMesh has both state (it understands what date ranges you’ve already processed) and scheduling (it knows when / how often things should run). With this, we: track time intervals to prevent data gaps and wasted compute allow our users to configure cron schedules per model, and automatically detect when models (such as those that insert records by unique key) can’t safely run in parallel. Data engineers have options when it comes to tools and techniques to implement incremental data loads. Read about them in more detail in my blogpost linked below 👇 and don’t let the perceived complexity hold you back. #DataEngineering #DataTransformation

For first-time startup founders, it's enticing to dive into the 'Go Big or Go Home' mindset. The temptation of creating a groundbreaking, million-dollar product can be enticing ….. leading many to invest months or even years in stealth mode to achieve that monumental success :( But what if there's a more realistic and effective way to drive the startup journey? 👉 Consider a more adaptive alternative → Iterative development. -Rather than placing all bets on a grand vision, this approach involves constructing your product in small, incremental steps. -At each stage, you gather invaluable feedback, enabling you to make adaptive changes based on real-world insights. Step-by-Step process: ~Start with a clear vision—define the problem, target audience, and goals. ~Construct a basic product with essential features. ~Collect feedback via user testing, surveys, or focus groups. ~Adapt your product based on the received feedback. ~Test again to assess the impact of changes. ~Repeat until your product aligns with your audience's needs. Benefits of this activity: ✅ Lower risk ✅ Increased efficiency ✅ Higher customer satisfaction ✅ Quicker time-to-market ✅ Improved flexibility The best part? No need to invest all your resources into one idea upfront. Ever thought about an iterative approach engaging customers from the beginning? Share your thoughts and experiences below. P.S.: Let go of perfectionism. Through iteration, learn, pivot swiftly and create a product that truly meets customer needs🔥! #startups #iterate #founders

💡Combining Design Thinking, Lean UX, and Agile A combination of Design Thinking, Lean UX, and Agile methodologies offers a powerful approach to product development—it helps balance user-centered design with efficient concept validation and iterative product development. 1️⃣ User-centered foundation (Design Thinking): Begin by understanding the needs, emotions, and problems of the end-users. ✔ Start by conducting user research to identify and understand user needs. ✔ Gather insights through direct interaction with users (e.g., through interviews, surveys, etc.). Spend time understanding users' behavior, focusing on "why" rather than "what" they do. ✔ After gathering research, prioritize the most critical user insights to guide your design focus. Create a 2x2 matrix to prioritize insights based on impact (high vs low business impact) and feasibility (easy vs hard to implement) ✔ Begin brainstorming potential solutions based on these prioritized insights and formulate a hypothesis. Encourage cross-functional collaboration during brainstorming sessions to generate diverse ideas. 2️⃣ Hypothesis-driven testing (Lean UX): Lean UX helps quickly validate key assumptions. It fits perfectly between Design Thinking's ideation and Agile's development processes, ensuring that critical hypothesis are validated with users before actual development started. ✔ Formulate a testable hypothesis around a potential solution that addresses the user needs uncovered in the Design Thinking phase. ✔ Conduct experiment—develop a Minimum Viable Product (https://lnkd.in/dQg_siZG) to test the hypothesis. Build just enough functionality to test your hypothesis—focus on speed and simplicity. ✔ Based on the experiment's outcome, refine or revise the hypothesis and repeat the cycle. 3️⃣ Iterative product development (Agile): Once the Lean UX process produces validated concepts, Agile takes over for incremental development. Agile's iterative sprints will help you continuously build, test, and refine the concept. Agile complements Lean UX by providing the structure for frequent releases, allowing teams to adapt and deliver value consistently. ✔ Break down work into small, manageable chunks that can be delivered iteratively. ✔ Embrace iterative development—continue refining your product through iterative build-measure-learn sprints. Keep the user feedback loop tight by involving users in sprint reviews or testing sessions. ✔ Gather user feedback after each sprint and adapt the product according to the findings. Measure user satisfaction and track usability metrics to ensure improvements align with user needs. 🖼️ Design thinking, Lean UX and Agile better together by Dave Landis #UX #agile #designthinking #productdesign #leanux #lean  

Incremental Processing in Lakehouse Incremental processing is an approach where only small, recent changes to data are processed, rather than processing large batches of data all at once. This method is particularly valuable in environments where data is continuously updated, enabling frequent and manageable updates. For example, take Uber - their ‘Trips’ database is crucial for providing accurate trip-related data. Previously, Uber relied on bulk uploads: ❌ Writing ~120 TB to Parquet every 8 hours ❌ The actual data changes would be less than 500 GB, but they’d still perform a full recomputation of downstream tables ❌ This resulted in data freshness delays of up to ~24 hours This is where Incremental Processing powered by Apache Hudi comes in. Here are some benefits of processing data incrementally: ✅ Efficiency Gains: Processing only recent changes reduces data volume and eases the burden on computational resources. ✅ Fresher Data: Systems can access up-to-date information almost in real time by minimizing delays. ✅ Cost Savings: Incremental updates eliminate the expense of frequent full recomputation. ✅ Simplified Debugging: Errors are limited to smaller increments, making them easier to identify and fix. Apache Hudi facilitates incremental processing using its Timeline, alongside features such as indexing, log merging, and incremental queries, making it indispensable for this style of data processing. For instance, in Spark SQL, you can execute an incremental query to process data from the earliest commit to the latest state using the following command: 𝘚𝘌𝘓𝘌𝘊𝘛 * 𝘍𝘙𝘖𝘔 𝘩𝘶𝘥𝘪_𝘵𝘢𝘣𝘭𝘦_𝘤𝘩𝘢𝘯𝘨𝘦𝘴(‘𝘥𝘣.𝘵𝘢𝘣𝘭𝘦’, ‘𝘭𝘢𝘵𝘦𝘴𝘵_𝘴𝘵𝘢𝘵𝘦’, ‘𝘦𝘢𝘳𝘭𝘪𝘦𝘴𝘵’); #dataengineering #softwareengineering