标签：#data

Senior Solution Architect Viktor Vedmich shares how engineering leaders can maximize application performance using Valkey. He discusses the open-source Redis fork's 100% API compatibility, explores advanced caching strategies like lazy loading, and explains how to implement powerful data structures for real-time analytics, rate limiting, and session stores to solve the thundering herd problem. By Viktor Vedmich

CSV files are one of the most common formats for storing and exchanging data. Whether you’re working with logs, analytics data, application exports, or reports, there will likely come a time when you need to load CSV data into ClickHouse®. The good news is that ClickHouse® makes CSV ingestion straightforward and efficient. In this guide, you’ll learn how to create a table, prepare a CSV file, load CSV data into ClickHouse®, and verify that the data has been imported successfully. Why Use CSV Files with ClickHouse®? CSV (Comma-Separated Values) files are simple, portable, and supported by virtually every data platform. Common use cases include: Importing exported application data Loading historical datasets Migrating data from other databases Testing analytics workloads Sharing data between systems Because ClickHouse® is designed for high-performance analytics, it can efficiently process and query large CSV datasets once they are loaded into a table. Sample CSV File Let’s assume we have a file named employees.csv with the following contents: id,name,department,salary 1,Alice,Engineering,75000 2,Bob,Marketing,60000 3,Charlie,Finance,70000 This simple dataset will help demonstrate how to load CSV data into ClickHouse®. Step 1: Create a Table in ClickHouse® Before importing data, create a table that matches the structure of the CSV file. CREATE TABLE employees ( id UInt32, name String, department String, salary UInt32 ) ENGINE = MergeTree() ORDER BY id; This table contains four columns that correspond directly to the columns in our CSV file. Step 2: Load CSV Data into ClickHouse® There are several ways to import CSV data, but one of the most common methods is using the ClickHouse® client. Run the following command: clickhouse-client --query=" INSERT INTO employees FORMAT CSVWithNames" < employees.csv The CSVWithNames format tells ClickHouse® that the first row contains column headers. After executing the command, ClickHouse® will read the CSV file and insert the records

​We just deployed a major update to our SQL Server web sandbox, moving from an individual tool to a collaborative environment: ​✅ Group Rooms (Beta): Create private rooms using security tokens to chat and write code together in real time. ✅ Simulated Linked Servers (Beta): Connect with your friends' sandboxes to run cross-queries strictly in read-only mode to preserve performance. ✅ Gamification & XP: Earn experience points and unlock technical badges as you run queries or build database schemas. ✅ Social Layer: Manage your friends list and customize your public developer profile to showcase your achievements. ​Currently, all features are completely free to use during this phase of the platform. Try it out with your colleagues at tsqlsandbox.online and leave your feedback below!

PostgreSQL Error 2200D: invalid escape octet The 2200D: invalid escape octet error occurs in PostgreSQL when a bytea value contains an invalid escape sequence. This typically happens with the legacy escape format for binary data, where octet values must be represented as three-digit octal numbers in the range \000 to \377 . If the escape sequence falls outside this range or uses non-octal digits, PostgreSQL raises this error immediately. Top 3 Causes 1. Out-of-range octal values in bytea escape literals The bytea escape format only accepts octal values from \000 to \377 (decimal 0–255). Using values like \400 or non-octal digits like \9 will trigger this error. -- BAD: \400 exceeds valid octal range (max is \377) SELECT E ' \\ 400' :: bytea ; -- ERROR: invalid escape octet -- BAD: \9 is not a valid octal digit SELECT E ' \\ 9AB' :: bytea ; -- ERROR: invalid escape octet -- GOOD: valid octal escape sequences SELECT E ' \\ 377' :: bytea ; -- decimal 255 SELECT E ' \\ 101' :: bytea ; -- 'A' character SELECT E ' \\ 000' :: bytea ; -- null byte 2. Using escape format strings with hex output format Since PostgreSQL 9.0, the default bytea_output is hex . Applications that mix hex -format output back into escape -format input processing can generate malformed escape sequences. -- Check current bytea output format SHOW bytea_output ; -- GOOD: Use hex format (recommended for all new projects) SELECT ' \x DEADBEEF' :: bytea ; SELECT ' \x 48656C6C6F' :: bytea ; -- 'Hello' -- GOOD: Use encode/decode for safe conversions SELECT encode ( ' \x DEADBEEF' :: bytea , 'hex' ); -- output as hex string SELECT encode ( ' \x DEADBEEF' :: bytea , 'base64' ); -- output as base64 SELECT decode ( 'deadbeef' , 'hex' ); -- hex string → bytea SELECT decode ( 'SGVsbG8=' , 'base64' ); -- base64 → bytea 3. Incorrect escaping during data migration or manual SQL When migrating binary data from other databases (Oracle, MySQL) or writing raw INSERT statements manually, developers often confuse octal and

Introduction Excel is a powerful spreadsheet program developed by Microsoft that is used to calculate, organize and analyze data. It provides a way of turning raw data into meaningful insights through handling large datasets more efficiently from tracking sales and expenses to analyzing trends. Various Excel applications. Decision making: One of the major ways Excel is used in real-world data analysis is to support decision making. Companies collect large volumes of raw data everyday ranging from customer information, sales records to log records. This data is organized and cleaned by Excel into tables, charts and reports making it easier to derive insights and identify trends that help in decision making. Financial reporting: Excel is also widely used for financial reporting and budgeting. Businesses use it to record income and expenses, calculate profit margins and create financial predictions. By analyzing financial data, organizations are able to monitor their performance over time and plan better for future growth. Marketing performance: In addition to that, Excel can be used in market analysis. Marketing teams utilize Excel to track campaign and social media performance, customer engagement and product popularity. Insights derived from this data helps companies improve their marketing strategies and better understand consumer behavior. This past week I was introduced to several data cleaning features and formulas used in Excel to make analysis less nerve-wracking. For example, in stead of editing data cell by cell in the case of duplicate values, you can use the Find and Replace filter. Also, conditional formatting makes it easier to highlight specific cell ranges and erase duplicate values. Functions and formulas make it easy to obtain statistical and mathematical data. Learning Excel helps you look at data differently. Instead of data being just a bunch of texts, numbers or logs, data becomes something you can use to gain insights, make decisions, reveal pat

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

Last year, I examined RUM indexes within this series on multi-key indexing, demonstrating that they cannot substitute MongoDB's compound indexes for sorted queries. A year later, Microsoft has fixed this in the DocumentDB extension for PostgreSQL with an Extended RUM index that preserves the ordering of the keys, allowing an ordered scan rather than a bitmap scan. Let's revisit our pagination query to see how it performs now. I start a container with the latest DocumentDB (version v0.112-0 from May 26, 2026): docker run -d --name documentdb-local -p 10260:10260 -p 9712:9712 ghcr.io/documentdb/documentdb/documentdb-local:latest --username franck --password franck --start-pg I can connect to PostgreSQL on port 9712, where many extensions are installed, including the extended RUM index: docker exec -it documentdb-local psql -p 9712 postgres psql ( 17.10 ( Debian 17.10-1.pgdg13+1 )) Type "help" for help. postgres = # \dx List of installed extensions Name | Version | Schema | Description ------------------------- +---------+------------+------------------------------------------------------------ documentdb | 0.112-0 | public | API surface for DocumentDB for PostgreSQL documentdb_core | 0.112-0 | public | Core API surface for DocumentDB on PostgreSQL documentdb_extended_rum | 0.112-0 | public | DocumentDB Extended RUM index access method pg_cron | 1.6 | pg_catalog | Job scheduler for PostgreSQL plpgsql | 1.0 | pg_catalog | PL/pgSQL procedural language postgis | 3.6.3 | public | PostGIS geometry and geography spatial types and functions tsm_system_rows | 1.0 | public | TABLESAMPLE method which accepts number of rows as a limit vector | 0.8.2 | public | vector data type and ivfflat and hnsw access methods ( 8 rows ) postgres = # I can also connect to the MongoDB-compatible API: docker exec -it documentdb-local mongosh -u franck -p franck 'mongodb://localhost:10260/?tls=true&tlsAllowInvalidCertificates=true' Current Mongosh Log ID: 6a0b3b537d2a1c3471d1a7ba Connecting to: mo

From a massive DOGE data breach and the hacking of critical energy and water systems to the hack of an FBI surveillance system, here are the most damaging security incidents and data breaches of 2026.

Before this week, I thought Excel was just a fancy calculator with boxes. But after three days of my Data Science & Analytics course, I realise I was wrong. Really wrong. Excel is a spreadsheet tool used by millions of people from small business owners to data analysts at giant companies. And the best part? You don’t need to be a programmer to use it. You just need to know a few tricks. Here’s how Excel helps solve real-world problems using exactly what I learned in Week 1. 3 Real-World Ways Excel Is Used Business decisions with logic Managers use IF() statements to answer yes/no questions. Example: =IF(Sales>1000, "Bonus", "Needs Improvement"). One cell can decide who gets paid more. Cleaning messy data Real data is never clean. Marketing teams use Remove Duplicates, Find & Replace, and Text to Columns to fix hundreds of messy rows in seconds. No manual typing. Tracking deadlines and ages HR teams use DATEDIF() to calculate employee ages or years of service. TODAY() and NOW() keep reports automatically updated. No more “oh, I forgot to update the date.” 3 Excel Features I Learned This Week Remove Duplicates – One click, and Excel deletes repeated rows. Saved me from sending the same customer email twice. IFERROR() – Hides ugly errors like #DIV/0! and shows something friendly instead (e.g., “Check data”). Your boss will thank you. Sort & Filter – With AutoFilter, I can find all sales above $500 in one second. Then Custom Sort lets me sort by date and region together. My personal reflection Honestly? Learning Excel has changed how I see data. I used to look at a messy spreadsheet and feel lost. Now I see Remove Duplicates, Text to Columns, and TRIM() as tiny tools that bring order to chaos. Data isn’t scary anymore. It’s just a puzzle and Excel gives me the pieces. I’m only one week in. But I already feel like a junior data analyst in training.

Data analysis is at the heart of how we spot patterns and improve systems today. Tools like Python, SQL, Power BI, and Tableau are everywhere in the data world, but Excel has held its ground as the starting point for anyone getting into data work, and there is a reason for that. What is Excel? Excel is a spreadsheet built on a grid of rows and columns. You use it to organize, format, and calculate data. For analysts it is where messy raw data gets sorted out, numbers get worked through, and everything gets turned into something that actually makes sense to look at. Ways Excel is Used in Real-World Data Analysis 1. Data Cleaning Raw data is almost never clean. Names are misspelled, IDs get duplicated, spacing is off, values go missing. None of that is unusual, it is just the reality of working with real data. Before any analysis happens the data has to be honest, because if the data is wrong the results will be too. Functions like PROPER() and TRIM() are some of the basic tools that help get data into a state where you can actually work with it. 2. Financial Reporting Every business, big or small, needs to know where the money is going. Excel makes that straightforward. SUM() adds up a range of numbers, AVERAGE() finds the mean, and once the calculations are done the data can be turned into charts and dashboards that tell the story of the business clearly. Not everyone in the room is an analyst, but everyone can read a chart. 3. Business Decision Making Clean data presented well becomes a decision making tool. What do customers want? What is working? What needs to change? Sorting figures from highest to lowest or filtering by region can take thousands of rows and turn them into something focused and answerable. That is really what data is for, helping people make better calls. Excel Features I Have Learned and How They Apply Three features that have stood out to me are conditional formatting, data validation, and cell referencing. Conditional formatting highlights ce

For two years, our Sponsored Placements service booked limited ad inventory through Redis: a counter in Redis, a Redlock around the decrement, and a TTL key per hold. It oversold. Not catastrophically — consistently. 40–60 double-booked placements a month , each one a manual refund and an apology email to an advertiser. The root cause was never one bug. It was the architecture: two sources of truth that could not be made atomic with each other. The count lived in Redis; the ownership lived in SQL. No transaction spans both. The Redlock only ever protected the Redis half. The one mental shift SKIP LOCKED turns a contended table into a concurrent work queue. Instead of every request fighting over one counter, each request grabs different rows and ignores the ones someone else is holding. FOR UPDATE alone serializes — that's the experience that scares people off SQL locking. FOR UPDATE SKIP LOCKED is the opposite: a transaction that would have blocked instead skips the locked row and takes the next free one. One row per reservable unit, then: START TRANSACTION ; SELECT id FROM inventory_unit WHERE placement_id = 42 AND ( status = 'available' OR ( status = 'held' AND hold_expires_at < NOW ( 3 ))) -- self-healing expiry ORDER BY id LIMIT 2 FOR UPDATE SKIP LOCKED ; -- the whole trick UPDATE inventory_unit SET status = 'held' , reservation_id = 'uuid' , hold_expires_at = NOW ( 3 ) + INTERVAL 10 MINUTE WHERE id IN ( 1107 , 1108 ); INSERT INTO reservation (...) VALUES (...); COMMIT ; Two concurrent requests for the same pool lock different rows. Neither waits. The claim, the hold, and the reservation are one transaction — there is nothing to reconcile because there is nothing else. The numbers (8 weeks before vs 8 weeks after) Metric Redis + Redlock MySQL SKIP LOCKED Oversells / month 40–60 0 Reservation p95 210 ms 34 ms Reservation p99 540 ms 61 ms Throughput / instance ~600 RPS 1,400 RPS Lock-wait timeouts / day ~900 <5 Nightly reconciliation 9–14 min deleted Redis cluster

Query optimization has never been a solved problem. The moment you think your database is running efficiently, data volumes triple, access patterns shift, and suddenly your carefully tuned indexes are doing more harm than good. For decades, database engineers have relied on rule-based query planners — systems that follow deterministic logic to pick execution plans. That model is cracking under the weight of modern big data workloads. AI-driven query optimization is emerging as the answer, and it's already changing how high-scale systems handle billions of records in real time. This isn't about replacing the database administrator. It's about giving them — and the database itself — a fundamentally smarter toolset. Why Traditional Query Planners Hit a Wall Every relational database ships with a query planner: a component that reads your SQL, examines table statistics, and decides how to execute the query. PostgreSQL's planner, for instance, uses cost-based estimation to choose between sequential scans, index scans, hash joins, and nested loops. The system is elegant, and it works — until it doesn't. The problem is that cost-based planners operate on inherently stale statistics. They estimate cardinality (the number of rows a filter will return) based on histograms and samples collected at the last ANALYZE run. When data distributions drift — as they constantly do in real-world systems — those estimates go wrong, sometimes catastrophically. A planner that believes a filter will return 100 rows but actually gets 10 million will choose a completely wrong join strategy, turning a 200ms query into a 45-second disaster. Scale compounds this fragility. In big data environments running on distributed systems like Apache Spark, Trino, or BigQuery, a bad plan doesn't just waste one machine's resources — it cascades across hundreds of nodes, blowing through memory budgets and creating shuffle bottlenecks that ripple across the cluster. How AI Changes the Optimization Equation AI

SQL tuning is the process of making a database query run faster and cheaper — cutting response time while minimizing the system resources it burns. Here's the playbook I actually use, from "this query is slow" to "this query is fixed," with the traps that bite people in the middle. The loop Tuning is iterative. The shape is always the same: Identify the problem. Find the slow query (logs, profiler, or user feedback) and measure a baseline — execution time and resource usage. You can't claim an improvement you didn't measure. Analyze & rewrite. Review the SQL for redundant joins, unnecessary work, and complex subqueries. Tighten the WHERE , select only the columns you need, convert subqueries to joins where it helps. Read the execution plan. Understand how the engine actually runs the query; find inefficient join orders and needless full scans. Revisit indexes. Evaluate whether existing indexes help; add or restructure as needed. Consider schema changes. If a column is updated so often that indexing it hurts, split it out. Sometimes the model is the bottleneck. Tune settings/hardware if it comes to that. Re-test and repeat. Apply changes, re-check the plan, confirm the gain, monitor. Reading an execution plan The execution plan shows how the DB will run your query — table scans, index access, join methods. Read it well and you can pinpoint where the time goes. Most engines expose it: EXPLAIN (MySQL/PostgreSQL), EXPLAIN PLAN FOR (Oracle), SET SHOWPLAN_ALL ON (SQL Server). Operators to know: Full Table Scan — reads every row. Happens when there's no suitable index, or the query can't use one. Index Scan — scans via an index; usually cheaper than a table scan. Index Seek — jumps to specific key values; very efficient, reads only the rows it needs. Nested Loops / Hash Join / Merge Join — the three ways to join two tables (more below). Sort — orders data; excessive sorting is a common performance drag. Three numbers that matter: Cost — estimated resources a step will cons

AWS recently announced ExtendDB, a DynamoDB-compatible adapter that lets developers use the DynamoDB API with different storage backends, starting with PostgreSQL. The project supports existing SDKs and tools without modification, giving teams greater flexibility to run DynamoDB-style workloads outside of native DynamoDB while maintaining compatibility with current applications and workflows. By Renato Losio

The ghost is not you. But it echoes you. What survives beyond scattered archives? Beyond exported conversations and curated bookmarks? The stuff we never think to preserve: the photos that show how you see the world. The correspondence that maps who matters to you. The Long Echo toolkit has grown. PTK for photos. MTK for mail. But these are sources, not destinations. The destination is something stranger: longshade , a persona built from your data that can respond to questions you never answered. I'm going to invert the usual pattern here. Instead of tools first, philosophy later, I want to start with the philosophical destination and work backward to the data that feeds it. longshade: The Ghost That Speaks The Central Question What if your archive could respond? Not a chatbot trained on your data. Not a digital resurrection. Something more careful: a voice that carries your patterns, your interests, your way of seeing the world. That's longshade. Right now it's spec-only (no implementation yet). It defines what it would mean to synthesize a conversable persona from personal archives. The Ghost Metaphor "The ghost is not you. But it echoes you." This framing matters. longshade isn't about immortality or resurrection. It's about preservation with a kind of agency. The echo can answer questions you never answered, using patterns you established. It speaks in your voice without claiming to be you. The distinction is important: Resurrection claims to recreate the person Simulation claims to predict the person Echo acknowledges it carries patterns, not identity An echo is honest about what it is. It responds because you left enough traces to inform a response, not because it is you. Voice vs. Personality longshade extracts voice , not personality. Your actual phrases. Your vocabulary. Your reasoning patterns. Your recurring metaphors. The way you explain things, not the things you might explain. I noticed something working with conversation archives: user messages are th

DuckDB 1.5.3 & Quack Protocol Release; PostgreSQL File Descriptor Tuning Today's Highlights This week's database news highlights significant advancements for DuckDB, including a feature-packed 1.5.3 release and the innovative Quack client-server protocol. We also delve into a critical PostgreSQL performance tuning guide on managing file descriptors. DuckDB 1.5.3: Not an Ordinary Patch Release (DuckDB Blog) Source: https://duckdb.org/2026/05/20/announcing-duckdb-153.html DuckDB has announced the release of version v1.5.3, a "patch release" that, despite its designation, delivers a substantial upgrade to the ecosystem. While the core DuckDB engine sees limited bugfixes, the true power of this release lies in the significantly upgraded extensions that ship alongside it. These extensions introduce a wealth of new features that enhance DuckDB's capabilities across various data processing tasks, making it much more than a routine update. Key among the new features is the integration of the Quack client-server protocol, which is highlighted as a major advancement. This allows DuckDB instances to communicate and operate in more distributed, concurrent environments, expanding its utility beyond purely embedded scenarios. Developers are encouraged to explore the updated extensions for improved functionality, ranging from new data formats to enhanced analytical operations. This release underscores DuckDB's commitment to continuous innovation through its modular extension system, providing users with powerful new tools without requiring major core engine overhauls for every new feature. Comment: This release is a great example of how DuckDB's extension model brings rapid innovation. Developers should check the extension changelogs, as that's where the real new features are. Quack: The DuckDB Client-Server Protocol (DuckDB Blog) Source: https://duckdb.org/2026/05/12/quack-remote-protocol.html The DuckDB team has introduced Quack, a new client-server protocol designed to enable s

Alzheimer's disease affects over 55 million people worldwide, yet the precise molecular changes happening inside individual brain cells remain poorly understood. I wanted to dig into that question - not at the tissue level, but at single-cell resolution. So I built a full scRNA-seq analysis pipeline in Python using Scanpy, working with a publicly available dataset of 63,608 nuclei from human prefrontal cortex tissue (sourced from CZ CELLxGENE). The donors spanned three Braak stages: 0 (cognitively normal), 2 (early Alzheimer's), and 6 (severe Alzheimer's). Here's what I found and how I found it. The Dataset The data came from a study on the molecular characterisation of selectively vulnerable neurons in AD. It covers the superior frontal gyrus, a prefrontal region known to be hit hard by neurodegeneration - and includes seven major brain cell types: Glutamatergic neurons GABAergic neurons Oligodendrocytes OPCs (oligodendrocyte precursor cells) Astrocytes Microglia Endothelial cells 31,997 genes. 63,608 cells. Three disease stages. A lot to work with. The Pipeline 1. Quality Control No dataset is clean out of the box. I filtered cells to keep only those with between 200 and 6,000 detected genes, and excluded anything with more than 20% mitochondrial gene content (high mitochondrial reads usually signal a dying or damaged cell). This removed around 2,809 low-quality cells. 2. Normalisation Library sizes were normalised to 10,000 counts per cell, followed by log1p transformation, standard practice that makes cells comparable regardless of how deeply they were sequenced. I then identified 5,607 highly variable genes to focus the downstream analysis. 3. Dimensionality Reduction PCA (50 components) → neighbourhood graph (10 neighbours, 20 PCs) → UMAP embedding. The UMAP is where the biology starts to become visible. All seven cell types separated into distinct clusters, with clear separation between neuronal subtypes and glial populations. 4. Differential Expression For t

A scraper can pass every check you wrote and still be wrong about the one thing you actually care about: how much it collected. No exception. No 500. No broken row. Exit code 0, logs green, every field valid. And the set on disk is a quarter of what the site actually has. I have run scrapers in production enough times to stop trusting a green run on its own, and this is the failure that taught me to count. TL;DR A paginated source can serve fewer rows than it claims and never throw — page caps, hidden offset limits, infinite scroll that "ends" early. Your status check (200), schema check (valid row), and byte check (you got data) all pass. None of them counts records. The tell: declared total vs unique ids collected. Or, when there's no declared total, the page that quietly repeats an earlier page. Below is a 40-line probe you can run right now. On a source that caps at 1,500 of a declared 4,000, it returned VERDICT: INCOMPLETE (missing 2500 rows) . This is a completeness check, not a correctness check. Different layer, different bug. What actually goes wrong You write the loop everyone writes. Walk ?page=1 , ?page=2 , keep going until a page comes back empty. Stop. Save. Done. The source has other plans. It says it has 4,000 records — the count is right there in the envelope, or in a "Showing 4,000 results" line in the HTML. But it only ever hands out real data for the first 30 pages. Page 31 doesn't error. It doesn't return empty either. It returns page 1 again. Still HTTP 200. Still 50 valid rows. Your loop has no reason to stop, so it grinds on until its own page budget runs out, collects a pile of rows, and exits clean. You now have 5,000 rows in hand and feel great about it. Looks like plenty. The catch: only 1,500 are unique. The page cap fed you the same first page over and over, and those duplicates hid the shortfall behind a big-looking row count. That is the exact shape of "50 rows passed every check while 4,000 existed" — the scraper saw a lot of rows an

Introduction Excel is a spreadsheet application developed by Microsoft that helps users organize, analyze and visualize data. It is used by businesses, organizations, researchers and students worldwide because it makes working with data easier and more efficient. Business Decision Making One of the ways Excel is used in real-world data analysis is in supporting business decision-making. Companies collect data such as customer information, financial transactions and sales records. Excel helps in organizing and analyzing this data using tools such as formulas and PivotTables. This makes it easier to identify trends and patterns in business performance, such as which products to stock and when to restock them. For example, a supermarket can analyze the monthly sales in Excel to identify the best-selling products and ensure that they remain in stock. Marketing Performance Excel is also used to analyze marketing performance. Businesses use it to track data from marketing campaigns such as website visits, social media engagement and sales conversions. This information is organized using charts and reports, which help evaluate which strategies are producing the best results. This allows companies to allocate their resources more effectively and improve future campaigns based on data rather than assumptions. As a result, Excel plays an important role in helping businesses understand their customers and improve the effectiveness of their marketing efforts. Financial Reporting Excel is widely used in financial reporting. It helps businesses to organize and analyze financial statements such as income statements, cash flow reports and balance sheets. It is also used to record transactions, calculate totals, and generate summaries that show the financial health of the business. By using built-in formulas and functions, accountants can quickly compute profits, expenses, taxes and forecasts with a high level of accuracy. Excel also allows the creation of financial charts and dashb