DataMosaicCompare it properly.
Files, tables and databases, compared step by step, from the formula to the query.
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RSS feedMicrosoft Spreadsheet Compare: how to use it, where it stops
Which copies of Office have it, the steps from either door, what the results mean, and the job it was never built for.
Inventory reconciliation process for a store chain, in seven steps
Most guides reconcile one warehouse against one system. A chain has two systems per store, sales posting overnight and stock in transit, and the process has to allow for all three.
Data reconciliation in ETL: counts, sums and hashes for every load
A load can finish green and still be two rows short. Balance every batch by count and by sum, fingerprint it with a hash, and make the job fail when it doesn't add up.
Inventory accuracy formula: three measures on one count
One store, ten lines, one count. Worked out three ways, it scores 50%, 94.8% and 90.9%, and the honest answer depends on the question you are asking.
Compare data between two databases: SQL Server to Oracle in 3 steps
Tills on SQL Server, the ERP on Oracle, one invented day of sales: totals first, then a row hash both engines agree on, and the five type traps that make equal rows look different.
Compare two columns with VLOOKUP, XLOOKUP or Power Query
Three ways to match two lists on a key, run on one invented price file, and the two traps that make a lookup say a row is missing when it isn't.
Compare two tables in SQL for differences: three queries that work
EXCEPT both ways, a full outer join on the key, and a hash per row, run on one invented day of sales, with the NULL traps that make each one lie.
Compare two CSV files for differences: in Excel, Python and PowerShell
When the rows come in a different order, match them on a key first. Three ways to do it, and the four things that make identical files look different.
How to compare two Excel files and highlight differences
Three ways, from one formula to a lookup on a key, and how to tell which one your files need.
Data reconciliation: techniques, and a worked retail example
Counts, totals, keys, fields, hashes and grain: what each technique catches and misses, worked through on one chain’s Monday morning.