Data Analysis Techniques For Engineers

I dug into the source code of this viral map of global energy infrastructure. Here's what I found. ⬇️ Brian Bartholomew (mention not working for some reason) built OpenGridWorks an interactive map unifying the world's entire power grid into one free explorable platform. 120K+ power plants. 2.7M line-miles of transmission. 800K+ substations. The rendering stack uses MapLibre GL JS with PM Tiles meaning the entire map loads fast, runs client-side, and handles massive vector tile datasets without slowing down. Brian stitched together over a dozen public sources into a single unified layer: EIA Form 860m for US power plant data HIFLD for transmission and substation geometry Global Energy Monitor for global plant tracking OpenStreetMap for base infrastructure WRI Aqueduct for water stress context Epoch AI for compute and energy demand trends IM3/PNNL for climate-energy modeling PeeringDB and TeleGeography for data center and network mapping AESO, USGS, US Census, NREL/AFDC, ITU, and IRS energy community data Plus OpenGridWorks' own derived datasets tying it all together. It's a full geospatial data integration project built on public data alone. Amazing work showing the energy infrastructure that keeps the world (and yes, AI) moving. 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing (link in comments)  📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

New dataset for energy modellers and analysts! This week, Iain Staffell and I released a major new update to Renewables.ninja. Our country-aggregated wind and solar datasets (in Europe) and country-aggregated weather and demand datasets (globally) now extend to December 2024, based on revised and updated models. Looking ahead, we’re working on a much larger upgrade with global, rebuilt, next-generation data, which will be available later this year... And also stay tuned for other additions and improvements coming later this year. You can explore the update here, integrate it into your workflows, and let us know what you create: https://lnkd.in/d3V64nsR

You've heard "garbage in, garbage out" a thousand times. But here's what that actually means: your fancy dashboard is only as good as the data behind it. Quantity is easy to measure—it's just Terabytes. But data quality? Quality is the hard part because it requires discipline, process, and ownership. Data quality and governance are no longer “nice-to-haves.” They define trust across the organization. → Growing demand due to privacy laws like GDPR and CCPA → Core skill required for roles like Data Engineer, Steward, and Architect → Tools like Collibra and Great Expectations now appear in almost every data job description Some numbers speak for themselves: → Data Quality Engineer roles growing 40%+ yearly → Governance Analysts earning around $80K–$120K → Chief Data Officers often crossing $200K+ Clean data isn’t just accuracy—it’s career growth and company credibility. What Good Data Quality Looks Like? Skip the theory. Here's what actually works: → Automated checks that catch issues before they spread → Validation rules that reject bad data at the source → Tracking where data comes from and where it goes → Alerts when something breaks (not after it's been broken for weeks) → Clear ownership so someone actually fixes problems Where in the real world it shows up? 👉This isn't abstract. Here's where data quality makes or breaks things: → Finance: Try explaining bad compliance data to auditors → Healthcare: Patient records need to be right, every time → Retail: Wrong inventory data means lost sales or wasted stock → ML projects: Your model is only as smart as your training data The Real Talk: Data quality feels boring until it's missing. Then suddenly everyone cares. It's not sexy work. Nobody celebrates when pipelines validate correctly. But it's the foundation everything else sits on. Gartner says organizations with formal data governance will see 30% higher ROI by 2026. As data engineers, that’s our call to design solutions that "𝘥𝘰𝘯’𝘵 𝘫𝘶𝘴𝘵 𝘮𝘰𝘷𝘦 𝘥𝘢𝘵𝘢, 𝘣𝘶𝘵 𝘮𝘰𝘷𝘦 𝘵𝘳𝘶𝘴𝘵." Honestly, I feel it's probably more if you count all the fires you don't have to fight. 👉 Folks I admire in this space - George Firican Dylan Anderson Piotr Czarnas 🎯 Mark Freeman II Chad Sanderson Here's a crisp guide on Data Quality & Governance for data engineers! 👇 What's the most annoying, recurring data quality issue you've had to fix lately? I'll go first: dates stored as strings. 🤦♂️

Dear #DataEngineers, No matter how confident you are in your SQL queries or ETL pipelines, never assume data correctness without validation. ETL is more than just moving data—it’s about ensuring accuracy, completeness, and reliability. That’s why validation should be a mandatory step, making it ETLV (Extract, Transform, Load & Validate). Here are 20 essential data validation checks every data engineer should implement (not all pipeline require all of these, but should follow a checklist like this): 1. Record Count Match – Ensure the number of records in the source and target are the same. 2. Duplicate Check – Identify and remove unintended duplicate records. 3. Null Value Check – Ensure key fields are not missing values, even if counts match. 4. Mandatory Field Validation – Confirm required columns have valid entries. 5. Data Type Consistency – Prevent type mismatches across different systems. 6. Transformation Accuracy – Validate that applied transformations produce expected results. 7. Business Rule Compliance – Ensure data meets predefined business logic and constraints. 8. Aggregate Verification – Validate sum, average, and other computed metrics. 9. Data Truncation & Rounding – Ensure no data is lost due to incorrect truncation or rounding. 10. Encoding Consistency – Prevent issues caused by different character encodings. 11. Schema Drift Detection – Identify unexpected changes in column structure or data types. 12. Referential Integrity Checks – Ensure foreign keys match primary keys across tables. 13. Threshold-Based Anomaly Detection – Flag unexpected spikes or drops in data volume or values. 14. Latency & Freshness Validation – Confirm that data is arriving on time and isn’t stale. 15. Audit Trail & Lineage Tracking – Maintain logs to track data transformations for traceability. 16. Outlier & Distribution Analysis – Identify values that deviate from expected statistical patterns. 17. Historical Trend Comparison – Compare new data against past trends to catch anomalies. 18. Metadata Validation – Ensure timestamps, IDs, and source tags are correct and complete. 19. Error Logging & Handling – Capture and analyze failed records instead of silently dropping them. 20. Performance Validation – Ensure queries and transformations are optimized to prevent bottlenecks. Data validation isn’t just a step—it’s what makes your data trustworthy. What other checks do you use? Drop them in the comments! #ETL #DataEngineering #SQL #DataValidation #BigData #DataQuality #DataGovernance

As data engineers, we often talk about scalability, performance, and automation — but there’s one thing that silently determines the success or failure of every pipeline: Data Quality. No matter how advanced your stack, if your data is inconsistent, incomplete, or inaccurate, your downstream dashboards, ML models, and decisions will all be compromised. Here’s a detailed list of 25 critical checks that every modern data engineer should implement 👇 🔹 1. Null or Missing Value Checks Ensure no essential field (like customer_id, transaction_id) contains missing data 🔹 2. Primary Key Uniqueness Validation Verify that key columns (like IDs) remain unique to prevent duplicate business entities or revenue double counting. 🔹 3. Duplicate Record Detection Detect duplicates across ingestion stages 🔹 4. Referential Integrity Validation Confirm that all foreign key relationships hold true 🔹 5. Data Type Validation Ensure incoming data matches schema definitions — no strings in numeric fields, no invalid dates. 🔹 6. Numeric Range Validation Catch impossible values (e.g., negative ages, >100% percentages, invalid ratings). 🔹 7. String Length & Pattern Checks Enforce length constraints and validate formats (emails, phone numbers, IDs) with regex rules. 🔹 8. Allowed Value / Domain Validation Ensure categorical columns only contain valid entries — e.g., gender ∈ {‘M’, ‘F’, ‘Other’}. 🔹 9. Business Rule Consistency Check rules like order_amount = item_price * quantity or revenue = sum(product_sales). 🔹 10. Cross-Column Consistency Validate logical dependencies — e.g., delivery_date ≥ order_date. 🔹 11. Timeliness / Freshness Checks Detect data delays and SLA breaches — especially important for near real-time systems. 🔹 12. Completeness Check Verify all partitions, expected files, or dates are present — no missing data slices. 🔹 13. Volume Check Against Historical Data Compare record counts or data sizes vs previous runs to detect anomalies in ingestion. 🔹 14. Statistical Distribution Checks Validate stability of metrics like mean, median, and standard deviation to catch silent drifts. 🔹 15. Outlier Detection Identify records that deviate significantly from normal ranges 🔹 16. Schema Drift Detection Automatically detect added, removed, or renamed columns — common in dynamic source systems. 🔹 17. Duplicate File Ingestion Check Prevent reprocessing of already-loaded files or data across multiple sources. 🔹 18. Negative / Invalid Value Checks Block impossible values like negative prices or zero quantities where not allowed. 🔹 19. Percentage / Total Consistency Check Ensure calculated percentages correctly sum to 100% or totals match constituent values. 🔹 20. Hierarchy Validation Validate hierarchical consistency. 🔹 21. Audit Column Consistency Confirm audit columns like created_by, updated_at, and load_date are properly populated. #DataEngineering #DataQuality #Databricks #ETL #DataPipelines #DataGovernance

Everyone’s posting their data analytics wins. Today, I'm sharing my losses. Courses didn't make me a data analyst. Real-world experience did with every failure along the way. Here’s my mistakes: • Scheduled a report with SQL errors that sent blank data to essential managers • Accidentally emailed key stakeholders the wrong file • Rushed a report with a critical formula mistake that had to be retracted and corrected • Updated a dashboard in production without proper testing, breaking visualizations for executive teams These failures taught me to: - Slow down when it matters most - Build consistent checks and processes - Test obsessively before releasing - Create safety nets for mistakes I owned those errors AND the required solutions. The truth? Every failure is an opportunity to grow. The best analysts I know aren't those who never make mistakes. Instead, it’s those who learn from them faster. What mistake taught you the most? Share below 👇 #DataAnalytics #FailForward #ProfessionalGrowth #DataLessons

Data governance is usually framed as a compliance problem. In reality, it’s a human one. Good data governance is about building trust. We were brought in to build the data platform for a compensation programme handling highly sensitive medical, legal and financial information. The technical requirements were substantial. → Zero trust architecture → Role based access controls → Infrastructure as Code for rapid deployment Case officers needed to make decisions about compensation claims. Those decisions depended entirely on having reliable, complete information. Vulnerable citizens needed to trust that their sensitive data was protected and their claims would be handled with dignity and accuracy. Before the platform existed, data was fragmented. Spreadsheets scattered across teams. Manual reconciliation consuming hours that should have been spent on casework. No single source of truth. What this meant in practice was → Case officers spent time cross-referencing files instead of supporting claimants → Data inconsistencies created delays and uncertainty → Citizens had no visibility of claim status or timelines Building a unified data platform was about giving case officers the reliable foundation they needed to do their jobs effectively. And it was about treating vulnerable people with the dignity they deserve by ensuring their information was handled with care, accuracy and transparency. When you unify case data and eliminate spreadsheet sprawl, you restore trust in a broken system. Good data governance enables people to do meaningful work. That is what matters. What is the human cost of poor data governance in your organisation? #DataGovernance #PublicSector #Trust

Over the past 10+ years, I’ve had the opportunity to author or contribute to over 100 #datagovernance strategies and frameworks across all kinds of industries and organizations. Every one of them had its own challenges, but I started to notice something: there’s actually a consistent way to approach #data governance that seems to work as a starting point, no matter the region or the sector. I’ve put that into a single framework I now reuse and adapt again and again. Why does it matter? Getting this framework in place early is one of the most important things you can do. It helps people understand what data governance is (and what it isn’t), sets clear expectations, and makes it way easier to drive adoption across teams. A well-structured framework provides a simple, repeatable visual that you can use over and over again to explain data governance and how you plan to implement it across the organization. You’ll find the visual attached. I broke it down into five core components: 🔹 #Strategy – This is the foundation. It defines why data governance matters in your org and what you’re trying to achieve. Without it, governance will be or become reactive and fragmented. 🔹 #Capability areas – These are the core disciplines like policies & standards, data quality, metadata, architecture, and more. They serve as the building blocks of governance, making sure that all the essential topics are covered in a clear and structured way. 🔹 #Implementation – This one is a bit unique because most high-level frameworks leave it out. It’s where things actually come to life. It’s about defining who’s doing what (roles) and where they’re doing it (domains), so governance is actually embedded in the business, not just talked about. This is where your key levers of adoption sit. 🔹 #Technology enablement – The tools and platforms that bring governance to life. From catalogs to stewardship platforms, these help you scale governance across teams, systems, and geographies. 🔹 #Governance of governance – Sounds meta, but it’s essential. This is how you make sure the rest of the framework is actually covered and tracked — with the right coordination, forums, metrics, and accountability to keep things moving and keep each other honest. In next weeks, I’ll go a bit deeper into one or two of these. For the full article ➡️ https://lnkd.in/ek5Yue_H

Ensuring data quality at scale is crucial for developing trustworthy products and making informed decisions. In this tech blog, the Glassdoor engineering team shares how they tackled this challenge by shifting from a reactive to a proactive data quality strategy. At the core of their approach is a mindset shift: instead of waiting for issues to surface downstream, they built systems to catch them earlier in the lifecycle. This includes introducing data contracts to align producers and consumers, integrating static code analysis into continuous integration and delivery (CI/CD) workflows, and even fine-tuning large language models to flag business logic issues that schema checks might miss. The blog also highlights how Glassdoor distinguishes between hard and soft checks, deciding which anomalies should block pipelines and which should raise visibility. They adapted the concept of blue-green deployments to their data pipelines by staging data in a controlled environment before promoting it to production. To round it out, their anomaly detection platform uses robust statistical models to identify outliers in both business metrics and infrastructure health. Glassdoor’s approach is a strong example of what it means to treat data as a product: building reliable, scalable systems and making quality a shared responsibility across the organization. #DataScience #MachineLearning #Analytics #DataEngineering #DataQuality #BigData #MLOps #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gUwKZJwN