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Dagster Data Engineering Glossary:


Data Caching

Store expensive computation results so they can be reused, not recomputed.

Definition of caching

Caching is the process of storing the result of an expensive computation so that it can be reused in future instead of being calculated each time it is needed. This is a very important concept in data engineering, particularly when dealing with large datasets or complex transformations.

The main purpose of caching is to increase performance and reduce the computational load on your system. It reduces the time required for data processing tasks by storing frequently accessed data or results of expensive computations in a cache memory, which is a high-speed storage layer.

If you are doing a data-intensive task like running a complex SQL query on a huge dataset, the first time you run it, it might take a considerable amount of time. But if you cache the result, the next time you run the same query, instead of actually running the computation again, you can just retrieve the result from the cache. This way, you can drastically reduce the execution time for the repeated task.

Example of caching in Python

In Python, there are several ways to implement caching. The simplest one is to use the functools.lru_cache decorator from the Python Standard Library. LRU stands for Least Recently Used, and the functools.lru_cache decorator implements a LRU caching algorithm for your functions.

Here's a very basic example:

Let's consider a function that calculates the n th Fibonacci number, which is a common example for demonstrating the use of caching. The Fibonacci sequence is a series of numbers where a number is the sum of the two preceding ones. The sequence looks like this: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, ...

The recursive approach to calculating the nth Fibonacci number is quite inefficient because it ends up recalculating smaller Fibonacci numbers multiple times. Let's see how much we can speed it up by using caching.

Here's the Fibonacci function without caching:

def fib_no_cache(n):
    if n < 2:
        return n
    else:
        return fib_no_cache(n-1) + fib_no_cache(n-2)

# Let's see how long it takes to calculate the 35th Fibonacci number
import time
start = time.time()
print(fib_no_cache(35))
end = time.time()
print('Time without caching:', round(end - start,2), 'seconds')

Running this on your local computer might yield this type of output:

9227465
Time without caching: 1.87 seconds

And here's the same function with caching using the functools.lru_cache decorator:

from functools import lru_cache
import time

@lru_cache(maxsize=None)
def fib_with_cache(n):
    if n < 2:
        return n
    else:
        return fib_with_cache(n-1) + fib_with_cache(n-2)

# Let's see how long it takes to calculate the 35th Fibonacci number
start = time.time()
print(fib_with_cache(35))
end = time.time()
print('Time with caching:', round(end - start,6), 'seconds')

Which will yield something like this:

9227465
Time with caching: 2.8e-05 seconds

Note: These times will vary based on the hardware and other running processes on your machine.

Clearly, the version with caching is significantly faster, especially as n increases. This is because with caching, each Fibonacci number is only calculated once and then stored for future use, whereas the version without caching recalculates each Fibonacci number many times.

For more advanced caching needs, such as distributed caching or disk-based caching, you might need to use more sophisticated tools, such as joblib for disk caching or Redis and Memcached for distributed caching.

Remember that caching is not always the best solution and it's not free. Caching uses memory or disk space to store results, and it can also make your code more complex, and debugging more time consuming. You should use caching wisely and only for operations that are actually expensive and are likely to be repeated with the same input.


Other data engineering terms related to
Data Management:
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Append

Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.
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Archive

Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. Store data for long-term retention and compliance.
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Augment

Add new data or information to an existing dataset to enhance its value.
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Auto-materialize

The automatic execution of computations and the persistence of their results.
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Backup

Create a copy of data to protect against loss or corruption.
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Batch Processing

Process large volumes of data all at once in a single operation or batch.
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Categorize

Organizing and classifying data into different categories, groups, or segments.
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Checkpointing

Saving the state of a process at certain points so that it can be restarted from that point in case of failure.
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Deduplicate

Identify and remove duplicate records or entries to improve data quality.
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Deserialize

Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
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Dimensionality

Analyzing the number of features or attributes in the data to improve performance.
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Encapsulate

The bundling of data with the methods that operate on that data.
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Enrich

Enhance data with additional information from external sources.
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Export

Extract data from a system for use in another system or application.
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Graph Theory

A powerful tool to model and understand intricate relationships within our data systems.
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Idempotent

An operation that produces the same result each time it is performed.
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Index

Create an optimized data structure for fast search and retrieval.
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Integrate

Combine data from different sources to create a unified view for analysis or reporting.
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Lineage

Understand how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
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Linearizability

Ensure that each individual operation on a distributed system appear to occur instantaneously.
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Materialize

Executing a computation and persisting the results into storage.
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Memoize

Store the results of expensive function calls and reusing them when the same inputs occur again.
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Merge

Combine data from multiple datasets into a single dataset.
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Model

Create a conceptual representation of data objects.
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Monitor

Track data processing metrics and system health to ensure high availability and performance.
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Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.
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Parse

Interpret and convert data from one format to another.
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Partition

Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.
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Prep

Transform your data so it is fit-for-purpose.
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Preprocess

Transform raw data before data analysis or machine learning modeling.
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Primary Key

A unique identifier for a record in a database table that helps maintain data integrity.
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Replicate

Create a copy of data for redundancy or distributed processing.

Scaling

Increasing the capacity or performance of a system to handle more data or traffic.
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Schema Inference

Automatically identify the structure of a dataset.
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Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.
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Secondary Index

Improve the efficiency of data retrieval in a database or storage system.
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Software-defined Asset

A declarative design pattern that represents a data asset through code.
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Synchronize

Ensure that data in different systems or databases are in sync and up-to-date.
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Validate

Check data for completeness, accuracy, and consistency.
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Version

Maintain a history of changes to data for auditing and tracking purposes.
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