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Hi Folks , In this article, I'm going to share my learnings in SQL . Install MySQL Workbench editor from **MySQL Downloads **found in internet. After Installation, Open MySQL Workbench Click on '+' icon to create new sql editor Click on import icon present on top in sql editor workbench After importing, See the data, schema tables present in left side panel We see the sql editor workspace, result Grid,output view. How to display data present in result table ? Use SELECT '*' as Keyword FROM table_name Ex: SELECT * FROM moviesdb.movies; Instead of moviesdb.movies we can use movies as name. Follow the steps below: Go to the left panel where movie data present, select movie data table -> Right click on default schema it highlights in bold -> Then, Write query as, SELECT * FROM movies Method - 2 USE moviesdb SELECT * FROM movies; Here we're using ' USE ' Keyword How can we check how many rows are present after writing query ? Just go to output view present in MySQL Workbench editor (present below to the result Grid) you can see there (or) Check with Excel sheet using filter option to check how many rows are present. Suppose we're writing query but we don't know recieved output is right or wrong. We can check with our excel sheet data using filter option if it matches with our output rows then our query is correct this is how we can debug and check ✅ output. There are some inbuilt clauses present in SQL 1.WHERE 2.COUNT 3.DISTINCT (for unique values) 4.LIKE 5.% - Wildcard Search Ex: %movies% Gives the movies data present anywhere in the sentence %movies - Returns movies data present starting at the sentence %movies - Returns of movies present at last in the sentence Ctrl + Scroll UP - Zoom in SQL Editor Database - Collection of rows and columns in tabular format with schema it has duplicate ids, unique ids in different formats like excel, .sql etc. How to save all written queries ? Write all queries at one place and highlight each query, do the next steps mentioned below,

Every backtest has to answer a boring question: when the strategy says "buy," what price does it actually get? Most backtesting frameworks answer this question badly by default, and the badness is almost always in the strategy's favor. Here are the four assumptions that do the most damage, roughly in order of how often they show up. Mid-price fills If your backtest fills orders at the midpoint of the bid-ask spread, you are assuming you trade for free. You don't. A market order pays at least half the spread to cross it; a marketable limit order pays something close to that too, once you're honest about how often it actually gets hit versus sitting unfilled while the market moves away. Mid-price fills are the single most common way a backtest manufactures edge that doesn't exist, because the effect compounds with trade frequency — a strategy that trades often looks great on mid-price fills and mediocre-to-negative once it pays the spread on every round trip. Zero slippage Slippage is the gap between the price your signal fired at and the price your order actually executed at, and it's not just a queuing artifact — it's partly information. If your strategy is buying because something changed, other participants are reacting to the same thing, and the price you wanted is often gone by the time your order reaches the book. A backtest with zero slippage is quietly assuming the market waits for you. Unlimited size at the touch Backtests routinely assume you can execute your full position size at the best bid or ask, no matter how large the order is relative to the visible size there. In practice, a large order walks the book, and the average fill price is worse than the touch price by an amount that depends on how thin the book is. This one is invisible until you try to size up, which is exactly when a strategy that looked fine in testing starts bleeding. Commissions omitted or averaged Commissions and fees are usually small per trade and therefore easy to skip or fold in

Cohere has launched Parse 5, a multimodal foundation model designed to extract structured data from complex enterprise documents. The 2.3-billion-parameter system converts visually rich PDFs into Markdown while providing bounding box coordinates for visual grounding. It has been evaluated against over 2,000 enterprise pages, achieving an average score of 79.2 in key performance areas. By Olimpiu Pop

Is a blank cell signal, or just missing? Sometimes an empty cell is the most informative thing in the row. The trouble is that you usually only know which case you're in by reading the data dictionary — and that doesn't scale to 800 columns named f_0347 . So we measure it instead, then check the answer against the literature. Ames housing · 1,460 sales · 79 columns · 19 of them contain blanks "Drop any column that's more than 70% missing." I've written that line into more pipelines than I can count. On Ames it deletes four columns — and three of them have real price signal sitting in the gap. The blanks in this dataset are structural . A blank GarageQual doesn't mean the value was lost; it means the house has no garage. A blank Alley means no alley access. The emptiness is the measurement. That's easy to see here because the columns have English names and a published data dictionary. It is not easy to see on a vendor feed of anonymised features, which is what most real projects look like. So the question worth answering isn't "does missingness carry signal" — it's can you tell, without knowing what the column means? 0.41 R² from the blank/not-blank pattern alone — every value discarded 1.00 AUC recovering the garage blanks from other columns' values ±0.9% Total spread across five strategies — inside a ±1.5% CV noise band 1 · A blank cell has a price tag Start with the crude check: does sale price differ between rows where a column is blank and rows where it isn't? Columns that go blank on the same rows describe one fact, so the five garage columns collapse into one. Fig 1. Median sale price, blank rows vs. valued rows. No garage is a $68k median discount on a $163k median house. Note the sign flip: houses that have an alley or fence are the cheaper ones — those features mark older, denser blocks. "Blank = worse" is not a rule you can assume. Then the harder test. Throw away every value in the table and keep only a 19-column matrix of True / False — was this cell emp

An identity theft search site claimed to have more than 150 million driver's license photos stolen from an ID verification service. The crime site has now shut down.

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can compute quartiles by hand, build the standard outlier fence from them, and run that fence over any column to get back a short list of rows worth looking at. On the sixteen orders below, one mistyped quantity gets flagged automatically while every honest large order stays inside the fence. Here is what to actually do today. On the column you care about most, get four numbers: the 25th percentile, the 75th, their difference, and 1.5 times that difference added to the 75th. Anything above that last number is a row to open and read. It is one query, and it turns "is this data clean" into a list of specific rows. The short version: a percentile is a value with a known share of the data below it. The interquartile range is the width of the middle half. Values more than one and a half of those widths beyond the middle half get flagged. The fence is easier to see than to read, so it gets the picture. The original carries a diagram here. In words: A horizontal line with a row of small filled dots along it, spaced unevenly and thinning out towards the right. A tall rectangle is drawn around the dots in the middle of the row, covering the central half of them, with a thick vertical bar inside it. The rectangle's left edge is labelled Q1, its right edge Q3, and the bar inside it median. From each edge of the rectangle a horizontal whisker line runs outward to a short vertical cap, reaching the furthest dot on that side that still lies within range. To the right of the right-hand cap stands a tall dashed vertical line labelled fence, drawn one and a half rectangle-widths beyond the rectangle's right edge, with a small double-headed measuring arrow underneath showing that distance against the rectangle's own width. One lone dot sits well to the right of that dashed line, drawn as a hollow ring instead of a filled dot, so it reads as picked out rather than belonging with the rest. Every oth

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can say what a p-value measures in one sentence, compute one by hand with no distribution theory at all, and name the four things people routinely claim a p-value says that it does not. The worked example is nine real orders where two regions differ by 168.50, and the answer comes out of counting rather than out of a table. Here is what to actually do today. Any time you are about to report that two groups differ, write down the two group sizes first. If either is under about twenty, a p-value will almost certainly come back large no matter how real the difference is, and the honest report is the difference, the sizes, and an interval, not a verdict. The short version: a p-value is the share of results at least as extreme as yours that you would get if the thing you are testing had no effect at all. Small means your result would be unusual under nothing-happening. It does not mean the effect is large, and it does not mean the effect is real. That definition is doing a lot of work in one sentence, so it gets the picture. The original carries a diagram here. In words: A histogram built from small dots, one dot per outcome, arranged in ten vertical columns of different heights standing on a horizontal baseline. The columns rise from one dot at the far left to a peak of twenty-seven dots just right of centre, then fall away to a single dot at the far right, giving the whole shape a rounded hump centred slightly left of the middle of the picture. Two vertical dashed lines cut down through the shape, one on the left of the hump and one on the right, placed symmetrically about the hump's centre. The dots lying in the two tails beyond those lines are drawn in a darker, warmer shade, and the dots in the bulk between them are drawn in a lighter blue, so the tails stand out from the middle. In the two columns the dashed lines pass through, the darker dots are stacked at the bottom of the co

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can build a moving average by hand, choose a window length on purpose rather than by habit, explain the lag it introduces, and spot the one data problem that silently corrupts every moving average you will ever compute. On the sixteen weeks below, the raw series wobbles with a standard deviation of 237. A three-week average brings that to 101, and a five-week to 62. Here is what to actually do today. Add a moving average to your busiest weekly or daily chart, using a window that matches your cycle: 7 for daily data with a weekday pattern, 4 or 13 for weekly, 12 for monthly. Then plot it on top of the raw series rather than instead of it, so the reader can see both the noise and the trend. The short version: a moving average replaces each point with the average of it and the points around it. Wobble cancels out, trend survives, and the price is that the smoothed line reacts late. The effect is easier to see than to describe, so it gets the picture. The original carries a diagram here. In words: A line chart with two lines drawn on the same axes over sixteen evenly spaced points. The first line is thin and violently jagged, swinging from near the top of the chart down to near the bottom and back again several times, with one especially deep trough about a quarter of the way along and one especially tall spike just past the middle. The second line is thicker and starts two points later than the first. It stays in a narrow band through the middle of the chart, rising and falling only gently, and never comes close to either the peaks or the troughs of the jagged line. Where the jagged line spikes to its highest point, the thick line rises only slightly and does so one point later than the spike. The word actual labels the jagged line near its left end, and the phrase three-week average labels the thick line. Every number on this page is real. The same sixteen orders used across these

By Michael Nocito , data analyst · Published August 9, 2026 By the end of this page you can compute both averages by hand, say in one sentence which one your question needs, and show what each does when one number in your data is wrong. On the sixteen orders below, a single mistyped quantity moves the mean by 337.50 and the median by 30. That gap is the whole reason both words exist. Here is what to actually do today. Next to every average you report, put the median beside it and look at the two numbers together. If they are close, report the mean and move on. If they are far apart, you have found something worth saying out loud, and this page is about what. The short version: the mean shares the total out equally, so every value pulls on it. The median is the middle value once you sort, so only the position of a value matters, not its size. That difference is easiest to see rather than read, so it gets the picture. The original carries a diagram here. In words: Two horizontal number lines, one above the other, drawn on the same scale. On the top line, sixteen small filled dots sit in a loose cluster across the left and middle of the line. Two markers sit almost on top of each other just past the centre of that cluster: a solid triangle pointing up, labelled mean, and a short vertical bar, labelled median. On the bottom line the same sixteen dots appear, except one dot from the middle of the cluster has moved far to the right and off the end of the line, shown by an arrow leaving the line through a small break mark at the right edge. The median bar on the bottom line has barely shifted from where it was on the top line, moving only a hair to the right. The mean triangle on the bottom line has slid a long way to the right, roughly ten times further than the median moved, and a horizontal dotted guide connects its old position on the top line to its new position on the bottom line to show the size of the slide. Every number on this page is real. One sixteen-row order

Why “Behind the Scenes” Isn’t Optional in Machine Learning There’s a version of Machine Learning that treats models as interchangeable black boxes: Import the library. Call .fit() . Tune a few hyperparameters. Ship it. It works — until it doesn’t. When a model fails, the real question isn’t just “How do I fix it?” It’s “Why is it failing in the first place?” The engineers who can answer that question are the ones who understand what’s happening beneath the API. Not because knowing the internals is intellectually satisfying — although it is — but because that understanding is what separates applying a model from engineering one . Knowing that Gradient Descent exists is not the same as understanding why it becomes preferable to closed-form solutions as problems scale. Knowing that Softmax outputs sum to one is not the same as understanding why that alone doesn’t make its outputs true probabilities. Knowing what a technique does is useful. Understanding why it works, when it works, and what assumptions it depends on is what allows you to adapt it when the problem doesn’t look like the textbook example. And you can’t meaningfully customize something you don’t understand. That’s the idea behind #MLUnboxed — a series where I’ll break down Machine Learning and Statistics concepts, including many that are already well documented, but without stripping away the mechanics that make them work. The goal isn’t to reinvent established ideas. It’s to understand them deeply enough to use them intelligently. This series is for people who already know the “what” and are ready to spend more time with the “why.” If that sounds like you, let’s get into it.

Preventing Cache Penetration in Spring Boot Using Redis and Bloom Filters Cache penetration occurs when high-frequency requests query non-existent keys, bypassing the Redis cache completely and hitting the relational database directly. Here is how we set up a Bloom Filter guard layer in front of Redis and PostgreSQL. 1. The Bloom Filter Guard Concept A Bloom Filter is a space-efficient probabilistic data structure that tests whether an element is definitely NOT in a set or MIGHT be in a set. @Component public class CachePenetrationGuard { private final BloomFilter < String > accountFilter ; public CachePenetrationGuard () { // Expected insertions: 500,000, False positive probability: 0.01 (1%) this . accountFilter = BloomFilter . create ( Funnels . stringFunnel ( StandardCharsets . UTF_8 ), 500000 , 0.01 ); } public void registerKey ( String accountId ) { accountFilter . put ( accountId ); } public boolean mightContain ( String accountId ) { return accountFilter . mightContain ( accountId ); } } 2. Service Layer Verification Before querying Redis or PostgreSQL, verify with the Bloom Filter: @Service public class AccountService { private final CachePenetrationGuard guard ; private final RedisTemplate < String , AccountDto > redisTemplate ; private final AccountRepository repository ; public AccountDto getAccount ( String accountId ) { // Step 1: Bloom filter pre-check if (! guard . mightContain ( accountId )) { return null ; // Instant rejection, saves DB from unnecessary lookups } // Step 2: Redis lookup AccountDto cached = redisTemplate . opsForValue (). get ( "acc:" + accountId ); if ( cached != null ) return cached ; // Step 3: DB fetch and cache populate AccountDto dbResult = repository . findByAccountId ( accountId ); if ( dbResult != null ) { redisTemplate . opsForValue (). set ( "acc:" + accountId , dbResult , Duration . ofMinutes ( 30 )); } return dbResult ; } } 3. Summary Combining Bloom Filters with TTL jitter in Redis shields backend databases from cache

Most data engineers write pipelines the way most people write shell scripts: run it, eyeball the output, ship it. That works right up until a schema changes upstream, a null slips through a join, or someone "fixes" a transformation and silently breaks three downstream tables. By then the bug isn't your problem anymore — it's a bad number in someone's dashboard. Software engineers solved this problem decades ago with automated testing. Data engineering has been slower to adopt the habit, partly because our code touches messy external reality (files, databases, clusters) in a way a typical web app doesn't. But that's exactly why testing matters more here, not less. This article is a practical, DE-flavored crash course in pytest — the dominant Python testing framework — plus the patterns you actually need for pandas, Polars, and PySpark pipelines. Why bother testing a data pipeline? A few concrete failure modes that tests catch before production does: A column gets renamed upstream and your join silently produces all-null matches instead of erroring. A "cleaning" function that's supposed to drop duplicates accidentally drops valid rows too. A date-parsing function works on your local machine's locale and breaks in the CI environment. A refactor changes an aggregation from sum to mean and nobody notices until finance asks why revenue looks 90% smaller. None of these require exotic testing techniques. They require the habit of writing small, deterministic checks against small, deterministic inputs — which is exactly what pytest is built for. Where pytest fits — and where it doesn't Before diving in, it's worth being precise about scope, because "testing a data pipeline" actually covers two different questions, and conflating them is a common source of confusion: Is my code correct? Given a known input, does the transformation logic produce the right output? This is a property of your code , and it doesn't change based on what day it is or what a source system decided to

A new identity theft service launched on the dark web this week is selling digital scans of more than 153 million drivers licenses from people in the United States and Canada. Based on interviews with individuals whose licenses are available for purchase on this service, it appears to be siphoning images collected by a widely-used identity verification company based in Louisiana. KrebsOnSecurity also has learned that the New Orleans field office of the Federal Bureau of Investigation (FBI) today launched an official inquiry into the source of the images.

Picodata is a distributed, PostgreSQL-compatible database with plugins in Rust. Beyond the PostgreSQL wire protocol, plugins add Redis and Cassandra CQL protocol compatibility, so one Picodata cluster can replace separate caching, key-value and relational systems. It is open source and self-hosted. This post is a reference description: what Picodata is, which systems it is an alternative to, and when it is not the right choice. Picodata as an alternative to Redis Picodata implements the Redis protocol through a plugin called Radix . Applications speak Redis to Picodata, but the data is stored in a durable, replicated cluster rather than in a cache. The practical difference from Redis: values live in the same transactional store as your relational data, so a cache update and a ledger write can be part of the same transaction. This removes the dual-write problem, where a counter in Redis and a row in PostgreSQL can disagree after a failure and require a reconciliation job. Durability is WAL-based rather than best-effort. Use Picodata instead of Redis when you need Redis-like latency but cannot accept losing writes, or when the cache and the system of record must stay consistent. Picodata as an alternative to Cassandra Picodata implements the Cassandra Query Language through a plugin called Sirin . Applications issue CQL against Picodata. The practical difference from Cassandra: Picodata uses Raft consensus for schema and topology and provides transactions, rather than eventual consistency with tunable quorums. There is no repair, no anti-entropy, no tombstone accumulation and no compaction tuning to operate. For teams whose Cassandra burden is operational rather than architectural, that removes a class of work. Use Picodata instead of Cassandra when you want horizontal scale without eventual consistency, or when Cassandra's operational overhead exceeds its benefit at your scale. Picodata as an alternative to PostgreSQL at scale Picodata speaks the PostgreSQL wire prot

Disclaimer: This is a side project, not a production story. The slow-query problem is real, but the database is synthetic data I generated to make it show up on demand. I have no connection to PgCache. Everything here is in a repo you can clone and run. I tested version 0.6.2. A handful of dashboard queries on one of my projects were fine for a year and then weren't: count users by tier, revenue grouped by country, best-selling products per category. Nothing exotic, just aggregates and joins over tables that had gotten big. The usual fixes didn't sit right with me. A materialized view means picking a refresh interval and serving slightly stale numbers in between. Redis in front of Postgres means writing and maintaining code that knows which cache entries to throw away on every write. A read replica just runs the same slow query on another machine. PgCache offers a different trade. It's a proxy that talks the Postgres wire protocol, so your app connects to it as if it were the database. It caches reads. And instead of expiring entries on a timer, it follows Postgres's replication stream and refreshes a cached result when the rows behind it change. That stream is the same feed Postgres uses to copy data to a standby server , a running log of every insert, update, and delete. The "no timers, no manual invalidation" part is the interesting claim. Here's how it held up. A database big enough to be slow First I needed a database where "slow" was real and not a rounding error. I wrote a seed script for a small e-commerce schema and filled it to about 16 million rows: Table Rows Notes users 1,000,000 10 countries; tiers 50% free / 33% pro / 17% enterprise products 2,000 10 categories orders 5,000,000 four statuses, random totals, spread over two years order_items 10,000,000 about two per order I added indexes on every foreign key and on every column the test queries filter or group by. That was on purpose. I wanted to compare PgCache against a Postgres that had been tuned p

What Rebuilding Kafka From Scratch Actually Teaches You There's a gap between using a system and understanding it. Most engineers never close that gap, and honestly, most of the time that's fine. Kafka works. Topics, producers, consumers, pull the levers, ship the data. Done. But then you hit a weird latency spike, or a consumer group stalls in a way that doesn't match the docs, or replication starts behaving like it has feelings. And suddenly "I know the terminology" doesn't cut it anymore. That's exactly why this rebuild post is worth your time. The Abstraction Tax Every framework you use charges you an abstraction tax. The tax isn't the dependency. It's the mental model debt you carry when something goes wrong and you don't know what layer to blame. Kafka's tax is particularly sneaky because its concepts sound simple: topics are channels, partitions are buckets, offsets are counters. You can get productive fast. And then that simplicity starts lying to you. Why does lag spike when throughput looks fine? Why does adding consumers past the partition count do nothing? Why does a rebalance tank your throughput for 30 seconds? These aren't Kafka quirks. They're direct consequences of how the log is actually structured, consequences that become obvious the second you implement it yourself. What the Rebuild Exposes When you write the log append yourself, the offset model stops being abstract. An offset isn't just a cursor, it's a byte position in a segment file. Consumers aren't "reading from a partition," they're replaying a structured log from a known position. Replication isn't a background checkbox, it's a follower explicitly fetching and acknowledging write positions. A few things that tend to click when you go through this kind of exercise: Segment files and retention , Kafka doesn't delete old messages by scanning. It deletes whole segment files once they're past the retention boundary. If you've ever been surprised by how Kafka handles disk, this is why. Why par

What building a warehouse management system taught me about the data operational software leaves behind — and the engineering it takes to make that data trustworthy. The second that outlives itself A picker holds a handheld scanner, points it at a carton, and pulls the trigger. There's a beep. They type 10, confirm, and move to the next location. The whole thing takes about a second. For a long time I thought of my job as making that second work. I built the screen, the endpoint behind it, the repository behind that. My definition of done was that the user completed the workflow, the API returned success, and the right rows landed in the database. What changed my thinking was noticing what was still there afterwards. The screen closes, the session ends, the app ships a new version, the picker changes jobs, the device is replaced. The row stays — and the row isn't a record of a UI interaction. It's a durable claim about the physical world: at this time, this person, on this device, ten units of this product moved. The application is the instrument. The data is the measurement. A measurement is only ever worth what the instrument's precision allows. This article is about the gap between those two definitions of done, and the specific decisions — retry semantics, timestamps, identity, status codes, conflict resolution — that determine which side of it you land on. Almost all of them get made by application developers, inside feature work, long before anyone tries to analyze anything. What warehouse owners actually do with this data now Worth being concrete about the stakes first, because "data quality matters" is the kind of statement everyone agrees with and nobody acts on. What's changed isn't that owners suddenly became analytical. It's that operational systems started producing enough granular, attributed, time-stamped movement data that previously unanswerable questions became answerable. Inventory accuracy is a working-capital decision. Stock you can't trust is s

A raw sold-listings search is not automatically a usable comp set. Search for a phone and you may also get cases, chargers, broken screens, empty boxes, and nearby models. Search for a camera lens and you may get caps, adapters, or a different focal length. If those rows go directly into a median, the result can describe the search noise instead of the product. I wanted a larger measurement than a single convenient example, so I ran a fixed 100-product study through CompSniper, the sold-price API I own. The goal was not to prove that an automated classifier is always correct. The goal was narrower: Measure what the production relevance cleaner removed and how the product-level median changed on one predeclared sample. The protocol I selected the products before making the first request: 20 smartphones and tablets 20 gaming and computing products 20 cameras and lenses 20 audio and music products 20 collectibles and luxury products Every search used the same settings: Marketplace: ebay.com Sold window: 2026-06-02 through 2026-08-31 Page: 1 Requested rows: 240 Sort: ended recently Condition: any Relevance cleaning: enabled Each relevance-enabled response contained the raw sample count and raw median captured before classification, followed by the cleaned rows and deterministic price summary from the same fetched page. That meant one production request per product, not separate raw and cleaned fetches. All 100 requests succeeded with unique request IDs. The headline results Across the study: 19,220 priced raw rows were parsed 11,942 priced rows remained after cleaning 7,278 rows were classified out The weighted removal rate was 37.87% 34 of 100 product medians changed by at least 10% 15 of 100 changed by at least 25% 11 of 100 changed by at least 50% The direction was not always upward: 73 medians increased 21 medians decreased 6 medians stayed unchanged That is important. The cleaner is not instructed to raise prices. It tries to retain listings for the requested produ

The company, which distributes medicines and medical devices to hospitals and healthcare practices across the U.S., said it was hacked and expects intermittent service degradation.

Adding an index stops working eventually. Here's what we did when a nationwide logistics platform's core table crossed a billion rows — and the parts nobody warns you about. There's a specific moment in a backend engineer's life when the usual advice stops working. A query gets slow. You check the execution plan, you add an index, it gets fast again. This works for years. It works so reliably that it starts to feel like a law of nature. Then one day you add the index and nothing happens. Or worse — the index takes six hours to build, locks the table while it does, and the query is still slow at the end of it. That's roughly where we were on a nationwide logistics platform processing tens of thousands of orders a day. The tracking events table — one row per scan, per parcel, per status change — had crossed a billion rows. Every parcel generated a dozen or more events on its journey. The table only ever grew. This is what we did about it, and more usefully, what nobody told us beforehand. First: are you sure you need this? Partitioning is not a performance trick you reach for when a query feels sluggish. It carries real operational cost, and most tables that people want to partition should just be indexed properly. Some honest signals that you're actually at the boundary: Your indexes no longer fit comfortably in memory, so index reads hit disk Index maintenance — REINDEX, VACUUM, ANALYZE — takes so long you can't schedule it Deleting old data is impossible in practice, because a DELETE of a hundred million rows will destroy your write throughput for hours Your queries almost always filter on a single obvious dimension, usually time That last one matters more than the rest. Partitioning only helps if your access pattern lines up with how you split the data. If your queries hit every partition anyway, you have added complexity and gained nothing. For us the alignment was clean: nearly every query on the events table was scoped to a date range. Operations dashboards loo