Best Practices for Data Management

Not a joke, many Data Engineers don’t fully understand the Medallion architecture or their caveats. Here’s a simple, crisp breakdown of the Medallion Architecture and why each layer matters: 🔹 Bronze (Raw Ingestion) - All incoming data lands here-> logs, JSON, CSV, streaming events - Data stays in its original form (think Delta Lake tables) - Use schema-on-read to keep raw JSON/XML (no forced schema yet) - Partition by ingest date/hour for fast file pruning - Add audit columns (ingest_timestamp, source_file, batch_id) for full traceability Why care? Bronze is your “source of truth.” You can recover, reprocess, or track every record. 🔹 Silver (Cleansed & Curated) - Cleaned, standardized view of Bronze data - Enforce data types, drop nulls, fill defaults (schema-on-write) - Use joins and dedupe logic (window functions help remove duplicates) - Add data profiling and constraints (NOT NULL, CHECK) to stop bad data early Why care? Silver gives you reliable, consistent tables for analytics, reports, and ML models. 🔹 Gold (Business Aggregations) - Highly curated, aggregated tables or dimensional models - Pre-compute metrics (daily active users, revenue by region) - Use Slowly Changing Dimension (SCD) for customer data - Partition and Z-order in Delta for super-fast queries Why care? Gold delivers high-performance datasets for BI tools and ML feature stores. Key Benefits Across Layers 1. Modularity & Maintainability – Keep ingestion, cleaning, and aggregation logic separate 2. Data Quality – Catch issues step by step 3. Scalability – Stream and batch workloads scale on their own 4. Governance & Lineage – Track every change with audit columns and Delta logs What else you would like to add here ? 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝟭:𝟭 𝗳𝗼𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲 → https://lnkd.in/gH4DeYb4 𝗔𝗧𝗦 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗲𝗱 𝗿𝗲𝘀𝘂𝗺𝗲 𝘁𝗲𝗺𝗽𝗹𝗮𝘁𝗲 → https://lnkd.in/g-iw7FaQ Gif -> Ilum ♻️ Found this useful? Repost it! ➕ Follow for more daily insights on building robust data solutions.

At a time of severe funding cuts in the humanitarian sector, data teams need to overhaul their ways of working. In resource-constrained times, humanitarian analytics will need to cost less, while continuing to deliver insights into essential needs. This will involve optimizing data acquisition, engaging with decision makers, a critical look at new technology. And more importantly, a renewed commitment to working together. If you're an analysis, here are some options on the way forward: ✅ Engage with your managers. Try to understand their priorities, their top information needs. And let go redundant data collection, as hard as that may be. ✅ Optimize data acquisition.  Review your sampling, collect some data less frequently. Consider collecting more data by mobile, which is cheaper. Be open about the trade-offs involved. ✅ Try modeling indicators. My colleagues here at WFP VAM have made strides in modeling and forecasting (link in comments). While this is not always a substitute for actuals, it can help guide a decision in these resource constrained times. ✅ Be realistic as you assess bringing on new data sources. My experience has shown however that fancy new data streams require time and resources to mainstream. Proceed with caution -- no silver bullets here. ✅ Work together: Connect with others to share data and insights in a way that’s responsible. Leverage open data. And of course, ensure your #data is accessible to others. After all, humanitarian data is a public good. Let me know your thoughts. Bonus: a picture from a focus group discussion during my early days as an #analyst. #LIPostingDayApril 

Do you think Data Governance: All Show, No Impact? → Polished policies ✓ → Fancy dashboards ✓ → Impressive jargon ✓ But here's the reality check: Most data governance initiatives look great in boardroom presentations yet fail to move the needle where it matters. The numbers don't lie. Poor data quality bleeds organizations dry—$12.9 million annually according to Gartner. Yet those who get governance right see 30% higher ROI by 2026. What's the difference? ❌It's not about the theater of governance. ✅It's about data engineers who embed governance principles directly into solution architectures, making data quality and compliance invisible infrastructure rather than visible overhead. Here’s a 6-step roadmap to build a resilient, secure, and transparent data foundation: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗼𝗹𝗲𝘀 & 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Define clear ownership, stewardship, and documentation standards. This sets the tone for accountability and consistency across teams. 2️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Implement role-based access, encryption, and audit trails. Stay compliant with GDPR/CCPA and protect sensitive data from misuse. 3️⃣ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 & 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Catalog all data assets. Tag them by sensitivity, usage, and business domain. Visibility is the first step to control. 4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Set up automated checks for freshness, completeness, and accuracy. Use tools like dbt tests, Great Expectations, and Monte Carlo to catch issues early. 5️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Track data flow from source to dashboard. When something breaks, know what’s affected and who needs to be informed. 6️⃣ 𝗦𝗟𝗔 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Define SLAs for critical pipelines. Build dashboards that report uptime, latency, and failure rates—because business cares about reliability, not tech jargon. With the rising AI innovations, it's important to emphasise the governance aspects data engineers need to implement for robust data management. Do not underestimate the power of Data Quality and Validation by adapting: ↳ Automated data quality checks ↳ Schema validation frameworks ↳ Data lineage tracking ↳ Data quality SLAs ↳ Monitoring & alerting setup While it's equally important to consider the following Data Security & Privacy aspects: ↳ Threat Modeling ↳ Encryption Strategies ↳ Access Control ↳ Privacy by Design ↳ Compliance Expertise Some incredible folks to follow in this area - Chad Sanderson George Firican 🎯 Mark Freeman II Piotr Czarnas Dylan Anderson Who else would you like to add? ▶️ Stay tuned with me (Pooja) for more on Data Engineering. ♻️ Reshare if this resonates with you!

📊 The data maturity gap isn’t where most people think it is. I’ve been digging into Data Orchard's State of the Sector Data Maturity 2025 report, based on five years of data from 1,200+ organisations across 56 countries 📊 A few findings really stood out 👇 • Only 6% of organisations are “mastering” data. Most are still learning or developing — and real progress takes years of sustained leadership focus, not a new dashboard 📉 • Size makes no difference. There’s no link between income and data maturity. Small organisations can do this well; big ones often struggle 🔍 • No sector has cracked it. Commercial and not-for-profit organisations score the same on average. Culture and leadership matter more than sector labels ⚖️ • Skills remain the biggest weakness — for the fifth year running. Not tools. Not ambition. Skills at leadership level and across the workforce 🧠 • Boards are a hidden bottleneck. Leadership is central to data maturity, yet many organisations lack data-literate trustees. Without digital confidence at board level, progress stalls. We need more Digital Trustees.🪜 • Openness accelerates improvement. Organisations that share insight, question assumptions, and work transparently build better data, better decisions, and more trust. Open working needs to become the norm. 🔓 💡 The takeaway? Data maturity isn’t a tech problem. It’s a leadership, governance, and capability challenge. If you care about impact, accountability, or using AI responsibly, the unglamorous work matters most: investing in people, improving data quality, and creating space to ask better questions. That’s where real transformation starts.

What happens when you vibe code with Claude 4.5 for an hour? You accidentally solve a $1M problem 🎯 Started playing around with an idea, and 60 minutes later had a fully functional PyPI package that turns expensive LLM API calls into free, self-hosted models. LLM Intercept is a proxy server that captures your LLM interactions and automatically formats them for fine-tuning smaller models. Think of it as your training data pipeline on autopilot. The workflow is beautifully simple: 1️⃣ Install via pip: pip install llm-intercept 2️⃣ Route your OpenAI-compatible API calls through the proxy 3️⃣ Automatically log and format all interactions in SQLite 4️⃣ Export clean datasets in Parquet or JSONL format 5️⃣ Fine-tune compact models like Liquid AI's LFM2 series (350M-2.6B params) 6️⃣ Deploy your custom model locally at zero marginal cost Key capabilities that make this compelling for model developers: ✅ Universal compatibility with OpenRouter, llama.cpp, and any OpenAI-compatible endpoint ✅ Full streaming support with SSE ✅ Function calling and tools support ✅ Web dashboard for monitoring and analysis ✅ Simple Password-protected admin interface ✅ Smart export with system prompts stripped Perfect for developers using permissive models like DeepSeek-V3.2 (MIT), Qwen3-235B (Apache 2.0), or GLM-4.5 (MIT) who want to build their own specialized models. Transform your expensive API dependencies into efficient, self-hosted solutions. The future of AI is small, specialized, and running on your own infrastructure. 🔗 GitHub: https://lnkd.in/g4whHmAu 📦 PyPI: pip install llm-intercept

I am coming out of a data equity advisory call and needed to say this out loud for my nonprofit friends (especially the ones in leadership roles): you can spend millions on dashboards, AI tools, and surveys, but none of it matters if the leadership isn’t willing to listen. The biggest barrier to data equity isn’t technology. It’s the human ego (can we call it leadership’s?). I have seen this come up a bunch of times: ● A donor survey revealed that BIPOC donors feel disconnected from the organization’s messaging, yet leadership sticks to the same fundraising strategies because “this is how we’ve always done it.” ● A staff engagement survey highlights burnout and pay inequities, but the leadership team dismisses it as “an HR issue” instead of a systemic one. ● A program evaluation finds that specific marginalized communities aren’t benefiting as intended, yet the org keeps funding the same initiatives instead of reallocating resources. When leaders ignore, dismiss, or downplay uncomfortable data, they don’t just lose insights—they lose trust. Does any of this ring a bell? ● Dismissing data because it challenges the narrative built forever. ● Avoiding specific questions because you are afraid of the answers. ● Gatekeeping decisions instead of inviting community voices into the progress work. Can we change this? Yes, we can. Our leaders can. You can… Without going into my essay-writing mode, here are three top-of-my-head ideas: ● Make data actionable, not performative. If you are collecting data but not using it to drive change (even if slow) + communicate about that change, you might be engaging in performative transparency. Start sharing with the community why and what you collect that data for. ● Engage with your data – multiple times in multiple ways. Data listening is not a one-time event. Build mechanisms for continuous engagement with staff, donors, and community members through your collected data. Ask questions to that data, see if you are asking the right things, right way, at the right time. ● Build a culture where data is both accessible for celebration and challenge. It is likely a harmful system if data is only accessed and accepted to celebrate without cultural self-awareness. Leaders must be open to questioning their own biases and redistributing decision-making power based on what the data reveals. Data equity starts with leadership and cultural accountability. Is there a time when data work revealed something uncomfortable in your work? Did you act on it? Report a data harm you witnessed here: https://lnkd.in/gjQuNxrP And then let’s talk. #nonprofits #nonprofitleadership #community

Treating data as a product is a necessity these days but the main question is: How do you operationalize it without adding more tools, more silos, and more manual work? There has been some confusion and process gaps around it especially when you're working with Databricks Unity Catalog. From contract to catalog, it's important for us to treat the data journey as a single process. Here, I'd like to talk about a practical user flow that organisations should adopt to create governed, discoverable, and mature data products using UC and contract-first approach. But before I begin with the flow, it's important to make sure that: ✅ Producers clearly define what they're offering (table schema, metadata, policies); ✅ Consumers know what to expect (quality, access, usage); ✅ Governance and lifecycle management are enforced automatically. That's why to do this, I'd like to divide the architecture into 3 parts: 👉 Data Contract Layer: To define expectations and ownership; 👉 UC Service Layer: API-driven layer to enforce contracts as code; 👉 UC Layer: Acting as Data & Governance plane. ☘️ The Ideal flow: 🙋 Step 1: Producer would define the schema of the table (columns, dtypes, descriptions) including ownership, purpose and intended use. 👨💻 Step 2: Producer would add table descriptions, table tags, column-level tags (e.g., PII, sensitive) and domain ownership rules. 🏌♂️ Step 3: Behind the scenes, the API service would trigger the table creation process in the right catalog/schema. Metadata would also be registered. 🥷 Step 4: Producer would include policies like: Who can see what? Which columns require masking? What's visible for which role? etc.. 😷 Step 5: Row/column filters and masking logic would be applied to the table. ⚡ Step 6: Once the table is live, validation would kick-in that would include schema checks, contract compliance, etc. 💡 Step 7: Just-in-Time Access would ensure consumers don't get access by default. Instead, access would be granted on demand based on Attribute Based Access Control (ABAC). The process, again, would be managed by APIs and no ad-hoc grants via UI. 👍 Step 8-9: All access and permission changes would be audited and stored. As soon as the consumer requests access to the table, SELECT permission would be granted based on approvals ensuring right data usage and compliance. 🔔 Step 10-11: Upon consumer request and based on the metrics provided, a Lakehouse Monitoring would be hooked-in to the table to monitor freshness, completeness, and anomalies. Alerts would also be configured to notify consumers proactively. ☑️ Step 12: The Lakehouse monitoring dashboard attached to the table would be shared with the stakeholders. 🚀 What do you get⁉️ -A fully governed & discoverable data product. -Lifecycle polices enforced for both producer and consumer. -Decoupled producer and consumer responsibilities. -Quality monitoring observability built-in. #Databricks #UnityCatalog #DataGovernance #DataContract #DataProducts

A schema changes. A column gets renamed. By 9 a.m., before the meeting half your dashboards are red. But you find out that from an angry VP. You don't have scalable data foundation, you have house of cards. Every data leader knows this pain: A schema changes. A column gets renamed. By 9 a.m., half your dashboards and models are red. By noon, finance stops trusting the data and goes back in excel. By Friday, your engineers are rebuilding validation logic again. If your data quality rules break every time your business changes you don’t have a scalable data foundation. You have a house of cards. Most companies think they have “data quality.” In reality, they have patchwork SQL scripts. -Hard-coded checks buried inside dbt models. -Manual fixes every time a column changes. -Quality rules tied to physical tables, not business logic. And then they wonder why every analytics or AI project collapses under the weight of constant schema drift. It’s not a tooling issue. It’s an architecture issue. You can’t scale trust if your validation logic from Joe who left 2-years ago with no documentation. That’s why leading data orgs are moving metadata-driven data quality systems that evolve as fast as your data does. Most found out their data is wrong from angry VP, but that's already too late. Here’s what that looks like in practice 1. Externalize your rules. Stop embedding validation logic inside pipelines. Move it into metadata - YAML, JSON, or catalog tables. Your rules should be read by the pipeline, not coded into it. When the schema changes, you update metadata, not 300 lines of SQL. 2. Tie rules to business domains, not tables. “Revenue must be > 0” belongs to the Finance domain, not one Snowflake table. When you ground rules in business logic, schema changes stop breaking them. When you tie rules to domains, becomes resilient to technical changes. 3. Empower stewards. Most data quality frameworks fail because everything lives inside engineering. The business never sees what’s being checked or when it fails, so they can’t fix what they caused. Need them seeing what’s being checked, agreeing on what “good” looks like once, and being alerted when their data fails. 4. Automate schema change detection. Have your lakehouse scan schema diffs daily. If a field changes or disappears, trigger an alert. If a new column appears, auto-suggest baseline checks. Your rules become self-healing, not brittle. 5. Accuracy Not Enough Nobody cares if you have 99% accurate data. Nobody cares how many test you ran and how frequent refresh time is. Get feedback, get trust constantly from the end-users using the data. As well know the dates. Dates where important meetings are, where data used and so on. You don’t build trust by writing more tests. You build trust by designing systems that don’t break every time your business evolves. That’s the difference between fragile analytics and AI-ready architecture.

Is “good enough” data really good enough? For 88% of MOps pros, the answer is a resounding no. Why? Because data hygiene is more than just a technical checkbox. It’s a trust issue. When your data is stale or inconsistent, it doesn’t just hurt campaigns; it erodes confidence across the org. Sales stops trusting leads. Marketing stops trusting segmentation. Leadership stops trusting analytics. And once trust is gone, so is the ability to make bold, data-driven decisions. Research tells that data quality is the #1 challenge holding teams back from prioritizing the initiatives that actually move the needle. Think of it like a junk drawer: If you can’t find what you need (or worse, if what you find is wrong), you don’t just waste time, you stop looking altogether. So what do high-performing teams do differently? → They schedule routine maintenance. → They establish ownership - someone is accountable for data processes. → They invest in validation tools - automation reduces the manual grind. → They set governance policies - because clean data only stays clean if everyone protects it. Build a culture where everyone values accuracy, not just the Ops team. Because clean data leads to clearer decisions and a business that can finally operate with confidence.

I've reviewed hundreds of financial models across 100+ clients. Most of them fail in the first 30 seconds. https://lnkd.in/eHYUr9Jc The numbers might be fine. But I open the file and see 47 tabs with names like "Sheet2_final_v3" and I already know what I'm dealing with. Assumptions buried in random cells. No flow. No structure. If I can't follow your model, nobody else will either. This is the same 9-part structure I use at my firm and teach to every fractional CFO I work with. → Drivers TabThis is the most important tab in your entire model. One place for every assumption. Revenue growth, headcount, tax rates. Change one input and the entire model updates. No hunting through tabs. → Source Data TabsRaw exports from QBO or your ERP. Keep them separate from your calculations. One formula pulls from here to populate everything else. → Error Check TabValidates that data made it from source to destination. Assets equal liabilities plus equity. Revenue ties across statements. Green means fine, red means stop. → Instructions TabMost people skip this. Don't. Which cells are editable, which tabs are read-only, what each color means. Your model will get passed around. Make it easy to audit. → Three Financial StatementsIncome statement, balance sheet, cash flow. All pulling from the drivers tab. Historicals and projections in one place. → Revenue TabYour most important forecast. Build it separately, link it back to drivers. Every business is different here, but the connection to the model stays the same. → Headcount TabYour largest expense needs its own schedule. Start dates, salaries, departments, prorated amounts. One mistake here and your cash forecast is off by six figures. → Balance Sheet SchedulesAR, AP, CapEx, debt. Waterfalls that show how balances move over time. These connect your P&L to your cash flow. → DashboardsThe view your board actually sees. KPIs, summary financials, budget vs actual. Everything else feeds into this. You can build your own following this structure, or grab a free template here: https://lnkd.in/eHYUr9Jc What does your model structure look like?