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.

Data Governance: Understand Key Focus Areas 🎯 𝐌𝐞𝐭𝐫𝐢𝐜𝐬 & 𝐊𝐏𝐈𝐬: 🔘 Data Quality: Measure accuracy, completeness, and consistency. 🔘 Stakeholder Satisfaction: Ensure data governance efforts meet stakeholder expectations. 🔘 Security: Track how well your data is protected against breaches. 🔘 Operational Efficiency: Assess the effectiveness of your data processes. 🔘 User Adoption: Gauge the extent to which data tools and processes are utilised. 🔘 Data Value: Quantify the business value derived from data. 🔘 Data Risk: Identify and mitigate potential data-related risks. 🔘 Compliance: Ensure adherence to relevant laws and regulations. 🔍 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 & 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬: 🔘 Data Quality Management: Maintain high standards for data accuracy and reliability. 🔘 Regulatory Compliance: Stay compliant with laws and regulations to avoid penalties. 🔘 Cost Efficiency: Optimize data-related costs for better financial management. 🔘 Data Stewardship: Assign responsibility for data management and policies. 🔘 Data Usability: Ensure that data is accessible and usable for stakeholders. 🔘 Data Transparency: Promote openness in data practices and policies. 🔘 Data Ethics: Uphold ethical standards in data collection and usage. 🔘 Decision-Making: Use data to inform strategic decisions. 🔘 Data Security: Protect data from unauthorised access and breaches. 🔘 Data Ownership: Clearly define who owns and is responsible for data. 🔘 Data Integrity: Maintain the accuracy and consistency of data over its lifecycle. 🔘 Data Auditing: Regularly review data and governance practices to ensure compliance and performance. 👥 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬: 🔘 Executive Leadership: Drive data governance strategy and ensure alignment with business goals. 🔘 Data Owners: Responsible for specific data assets and their quality. 🔘 Data Stewards: Manage data policies and quality. 🔘 Data Users: Utilise data for various business functions. 🔘 IT Departments: Support data infrastructure and security. 🔘 Legal and Compliance Teams: Ensure data governance practices comply with legal requirements. 🔘 Business Analysts: Analyse data to derive business insights. 🔘 External Partners: Collaborate on data sharing and governance. 🛠 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 & 𝐓𝐨𝐨𝐥𝐬: 🔘 Data Dictionary: Defines data elements and their meanings. 🔘 Data Catalogue: Organises data assets for easy discovery and access. 🔘 Metadata Management: Manages data about data for better understanding and use. 🔘 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 🔘 𝐏𝐨𝐥𝐢𝐜𝐲 𝐚𝐧𝐝 𝐑𝐮𝐥𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 🔘 𝐃𝐚𝐭𝐚 𝐋𝐢𝐧𝐞𝐚𝐠𝐞 🔘 Reporting Tools: Generate reports to monitor and manage data. 🔘 Governance Dashboards: Visualise key metrics and governance performance. 🔘 Audit and Compliance Tools: Ensure data governance policies and regulations are adhered to. #DataGovernance #DataQuality #Compliance #DataSecurity #DataEthics #DataIntegrity #DataManagement #AI #DataScience #DataStrategy

Let's talk about the elephant in the data room: You can't purchase your way to clean data. No tool, platform, or governance framework will magically fix your data quality issues. Only doing the work will. I've watched organizations pour thousands and even millions into cutting-edge data management tools and meticulously crafted governance frameworks. Yet years later, many are still grappling with the same problems: Data quality isn't where it needs to be. Data isn't documented. Data can't be connected. Why? Because the proponents of tools and frameworks are missing a core truth: Data quality is a human challenge at its heart. The real key to data quality lies in: ◾ How your teams communicate and collaborate and whether your departments even speak the same data language. ◾ How well your organization builds bridges between technical and business teams. ◾ Whether your employees understand why data quality matters and have meaningful incentives to care. To be clear: tools can help. But they won't create good data entry practices, foster cross-departmental collaboration, or build a culture of data ownership. And they certainly can't replace human judgment, no matter how "AI-powered" they claim to be. Real transformation begins with three fundamental questions: 1️⃣ Is the impact of data quality on the business understood in concrete terms, as in "value potential" and "value at risk" (not some abstract notion like "you need it for AI")? 2️⃣ Does everyone understand the impact of their role in data quality and the impact of data quality on their role? Again, this must be concrete and connected to daily work, not abstract like "it's important for the company." 3️⃣ Have you thoughtfully designed incentives for caring about data quality? (Or do you expect it to somehow emerge from everything else you're doing?) Building a culture of data stewardship means more than giving a few people fancy titles and occasionally inviting them for pizza. And measuring true quality requires looking beyond metrics and KPIs (after all, it's human nature to find ways to meet metrics, whether or not that achieves the actual goal). All too often, data quality is treated as "yes, it's important—among these other five priorities." That's a trap. It's either a priority or it isn't. The path to better data isn't paved with shortcuts. It requires rolling up your sleeves and doing the real work. When it comes to data quality, stop chasing silver bullets. Start investing in what truly matters: your people and the culture of quality they create. Either way, the results will speak for themselves.

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 

Dear data engineers, you’ll thank yourself later if you spend time learning these today: ⥽ SQL (Advanced) & Query Optimization > AI can help you write SQL, but only you can tune a query to avoid those nightmare full-table scans. ⥽ Distributed Data Processing (Spark, Flink, Beam, etc.) > When datasets grow beyond RAM, knowing Spark or Beam inside out is what lets you scale from gigabytes to terabytes. No AI prompt will save you from shuffling bottlenecks if you don’t get the fundamentals. ⥽ Data Warehousing (Snowflake, BigQuery, Redshift, etc.) > Modern warehouses change the game, partitioning, clustering, and streaming ingestion. Know how and when to use each, or you’ll pay for it (literally, in cloud bills). ⥽ Kafka, Kinesis, or Pub/Sub > Real-time pipelines live and die on event streaming. AI can set up a topic, but only experience teaches you how to avoid data loss, lag, and dead-letter nightmares. ⥽ Airflow & Orchestration > Scheduling DAGs, managing retries, and tracking lineage are what separate side-projects from production. Copilot won’t explain why your pipeline is missing yesterday’s data. ⥽ Parquet, Avro & Data Formats > Efficient formats are what make your pipelines affordable and fast. Learn how and when to use each. AI won’t optimize your storage costs. ⥽ Schema Evolution & Data Contracts > When teams change code, schemas break. Schema evolution is where production pipelines break. Practice versioning, validation, and enforcing data contracts. ⥽ Monitoring & Data Quality > “It loaded, but did it load right?” > AI can’t spot silent data drift or null spikes. Only real monitoring and quality checks will save your job. ⥽ ETL vs ELT > Sometimes you transform before loading, sometimes after. Understand tradeoffs: it’s money, time, and data accuracy. ⥽ Partitioning & Indexing > With big data, these two can make or break your pipeline speed. AI can suggest a partition key, but only hands-on will teach you why it matters. ⥽ SCDs, CDC & Data Versioning > Slowly Changing Dimensions, Change Data Capture, historical accuracy—know how to track what changed, when, and why. ⥽ Cloud Data Platforms (AWS, GCP, Azure) > Learn managed services, IAM, cost controls, and infra basics. Cloud AI tools are great, but you have to make them work together. ⥽ Data Lake Design & Governance > Not all data belongs in a warehouse. Know how to set up, secure, and govern a data lake, or your company will end up with a data swamp. ⥽ Data Privacy & Compliance > GDPR, CCPA, masking, encryption, one slip here, and it’s not just code review, it’s legal. ⥽ CI/CD for Data Pipelines & Git > Automated testing for data flows, rollback for broken jobs, versioning for reproducibility, learn this before a failed deploy ruins your week. Write those data pipelines, break schemas, tune storage, and trace why something failed in prod. That’s how you build instincts. AI will make you faster. But these fundamentals make you irreplaceable.

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!

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

After years building event-driven systems. Here are the top 4 mistakes I have seen: 1. Duplication Events often get re-delivered due to retries or system failures. Without proper handling, duplicate events can: • Charge a customer twice for the same transaction. • Cause duplicate inventory updates, messing up stock levels. • Create inconsistent or broken system states. Solution: • Assign unique IDs to every event so consumers can track and ignore duplicates. • Design event processing to be idempotent, ensuring repeated actions don’t cause harm. 2. Not Guaranteeing Order Events can arrive out of order when distributed across partitions or queues. This can lead to: • Processing a refund before the payment. • Breaking logic that relies on correct sequence. Solution: • Use brokers that support ordering guarantees (e.g., Kafka). • Add sequence numbers or timestamps to events so consumers can detect and reorder them if needed. 3. The Dual Write Problem When writing to a database and publishing an event, one might succeed while the other fails. This can: • Lose events, leaving downstream systems uninformed. • Cause mismatched states between the database and event consumers. Solution: • Use the Transactional Outbox Pattern: Store events in the database as part of the same transaction, then publish them separately. • Adopt Change Data Capture (CDC) tools to track and publish database changes as events automatically. 4. Non-Backward-Compatible Changes Changing event schemas without considering existing consumers can break systems. For example: • Removing a field might cause missing data for consumers. • Renaming or changing field types can trigger runtime errors. Solution: • Maintain versioned schemas to allow smooth migration for consumers. • Use formats like Avro or Protobuf that support schema evolution. • Add adapters to translate new schema versions into older ones for compatibility. "Every schema change is a test of your system’s resilience—don’t fail it." What other mistakes have you seen out there?

AI is no longer a pilot project for legal teams. It is already embedded in the tools many of us use every day. That makes the real challenge less about adoption and more about judgment. A few hard-earned lessons: • AI usually arrives bundled into platforms, not as a clean standalone decision. Treat it as an operational commitment, not a feature toggle. • Technical and data constraints matter more than demo performance. If the AI cannot integrate cleanly or respect your data boundaries, it will not scale. • Vendor maturity and AI maturity are not the same thing. Narrow, well-defined AI use cases tend to outperform ambitious, opaque ones. • Language and interface design are not cosmetic. If the system is imprecise, the AI built on top of it will be too. • Implementation timelines are almost always optimistic. AI does not fix messy data. It exposes it. • The most revealing question remains simple: if the AI fails, does the system still work? AI should accelerate legal judgment, not replace it. Teams that treat AI as additive, bounded, and governable are seeing durable value. Teams that chase novelty are still paying for it later. Curious how others are pressure-testing AI tools in their legal tech stack this year. I am Colin S. Levy and I serve as General Counsel at Malbek. I spend much of my time teaching, advising, and writing about how lawyers can work with technology in ways that are practical, responsible, and grounded in how legal work actually gets done. #legaltech #innovation #law #business #learning

ADF's Copy Activity Won’t Save You From Source Changes, Here’s Why You Need Schema Drift Handling If you’ve built data pipelines in Azure Data Factory, you know this: Copy Activity works great when your source schema is fixed. But what happens when… - A column is added? - A data type changes? - A column gets renamed or dropped? Your pipeline doesn’t break immediately but your data does. You’ll start missing columns in your destination Data mapping mismatches Silent failures that corrupt your data lake How I handle this in real-world projects: 1. Enable Schema Drift in Data Flows especially when working with semi-structured or CSV data 2. Always use Mapping Data Flows with dynamic column handling 3. Log your source metadata before ingestion to track unexpected changes over time 4. Set alerts on Copy Activity’s output schema mismatch Real lesson: Cloud pipelines don’t fail loudly they fail quietly when you ignore schema drifts. Plan for schema flexibility BEFORE it hits production. #DataEngineering #AzureDataFactory #AzureDataLake #SchemaMismatch #DataFlow #MappingDataFlow