Tall arrays provide a way to work with data backed by a datastore that can
have millions or billions of rows. You can create tall numeric arrays, cell
arrays, categoricals, strings, datetimes, durations, or calendar durations,
and you can use any of these tall types as variables in a tall table or tall
timetable. Many operations and functions work the same way with tall arrays
as they do with in-memory MATLAB® arrays, but most results are evaluated only when you request
them explicitly using
gather. MATLAB automatically optimizes the queued calculations by minimizing
the number of passes through the data. For more information, see Tall Arrays for Out-of-Memory Data .
For more information about integrating with big data systems or compiling tall array algorithms, see Extend Tall Arrays with Other Products.
|Create tall array|
|Create datastore for large collections of data|
|Collect tall array into memory after executing queued operations|
|Write tall array to local and remote locations for checkpointing|
|Define execution environment for mapreduce or tall arrays|
|Control random number generation for tall arrays|
|Transform array by applying function handle to blocks of data|
|Reduce arrays by applying reduction algorithm to blocks of data|
|Apply moving window function to blocks of data|
|Apply moving window function and block reduction to padded blocks of data|
Learn about tall arrays and perform an example calculation.
How to leverage deferred execution of tall arrays to optimize performance of calculations.
Extract, assign, and view portions of a tall array.
This example shows how to calculate grouped statistics of a tall timetable containing power outage data.
Visualization techniques for tall arrays.
List of products that enhance capabilities of tall arrays.
Development guide for authoring custom algorithms to operate on tall arrays.