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Divide data into smaller subsets for improved performance.

Data partitioning definition:

Partitioning is a technique that helps data engineers and ML engineers organize data and the computations that produce that data. Partitioning also makes data pipelines more performant and cost-efficient by letting them operate on subsets of data instead of all of it at once. For more information on partitioning, read Partitions in Data Pipelines.

Partitioning considerations in data engineering:

If you are new to partitioning in data pipelines, here are some key things to consider:

  • Choose the appropriate partition key: The partition key should be chosen based on the most common types of queries that will be performed on the data. A good partition key will ensure that the data is distributed evenly across partitions and minimize the need for shuffling data during query processing.
  • Avoid over-partitioning: Over-partitioning can negatively impact query performance as it increases the number of small partitions that need to be processed. It can also result in increased storage requirements and complexity. A good rule of thumb is to limit the number of partitions to 10,000 or less.
  • Use a consistent partitioning scheme: Consistency in the partitioning scheme ensures that the same data is stored in the same partition across different runs of the pipeline. This can help to reduce the complexity of data processing and improve query performance.
  • Use partition pruning: Most modern data processing systems support partition pruning, which allows queries to scan only the relevant partitions instead of scanning the entire dataset. This can significantly improve query performance, especially when dealing with large datasets.
  • Consider data skew: Data skew can occur when the data is not evenly distributed across partitions, resulting in some partitions processing significantly more data than others. This can lead to longer query times for those partitions and can impact overall query performance. Techniques such as data shuffling or using a different partition key can be used to mitigate data skew.
  • Monitor partition usage: It is important to monitor partition usage to identify any hotspots or data skew issues that may impact query performance. Regularly analyzing the partition usage can help to identify opportunities for optimization and fine-tuning the partitioning scheme.
  • Plan for future growth: As the data grows, the partitioning scheme may need to be adjusted to ensure optimal query performance. It is important to plan for future growth and be prepared to adjust the partitioning scheme as needed.

A simple example of partitioning data in Python:

Let's consider a very simple example where we want to partition a list of integers into two partitions, say one for even numbers and one for odd numbers. Here's a simple Python script that does that:

def partition_data(data):
    even_partition = []
    odd_partition = []

    for num in data:
        if num % 2 == 0:

    return even_partition, odd_partition

# Now let's use the function with some data
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

even_partition, odd_partition = partition_data(data)

print("Even Partition: ", even_partition)
print("Odd Partition: ", odd_partition)

When you run this script, it would print:

Even Partition:  [2, 4, 6, 8, 10]
Odd Partition:  [1, 3, 5, 7, 9]

So in effect we have partitioned our dataset into two, based on a business rule. The sum of all of your partitions should constitute your entire dataset with no duplicate entries. The most common form of partitioning would be by date (data partitioned by day, week, or month for example), but other partitioning approaches are also used.

Other data engineering terms related to
Data Management:


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


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


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


Select, organize and annotate data to make it more useful for analysis and modeling.


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


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


Enhance data with additional information from external sources.


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


Create an optimized data structure for fast search and retrieval.


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


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


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


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

Named Entity Recognition

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


Interpret and convert data from one format to another.


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


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


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

Schema Mapping

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


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


Check data for completeness, accuracy, and consistency.


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