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Scaling

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

Data scaling definition:

In data engineering, scaling refers to the process of increasing the capacity or performance of a system to handle more data or traffic. There are two primary approaches to scaling a system: horizontal scaling and vertical scaling.

Horizontal Scaling:

Horizontal scaling, also known as “scaling out”, involves adding more resources or machines to a system to increase its capacity. This approach involves distributing the load across multiple machines in a cluster or network, with each machine handling a portion of the workload. Horizontal scaling is typically achieved by adding more machines or instances of a service to a system, which can be done dynamically based on demand. Horizontal scaling provides the ability to handle larger workloads, but it requires additional management and coordination to ensure that the workload is distributed evenly across all the machines in the cluster.

Vertical Scaling:

Vertical scaling, also known as “scaling up”, involves increasing the capacity or performance of a single machine by adding more resources such as CPU, RAM, or storage. This approach involves upgrading the hardware or infrastructure of a machine to increase its processing power and capability. Vertical scaling is typically achieved by adding more CPU cores, RAM, or storage to an existing machine. Vertical scaling can provide increased performance and capability for a single machine, but it may have limitations due to the physical constraints of the hardware, such as the maximum number of cores or amount of memory that can be installed.

The main differences between horizontal and vertical scaling can be summarized as follows:

  • Horizontal scaling involves adding more machines to a system, while vertical scaling involves upgrading the hardware of a single machine.
  • Horizontal scaling provides the ability to handle larger workloads by distributing the load across multiple machines, while vertical scaling provides increased performance and capability for a single machine.
  • Horizontal scaling requires additional management and coordination to ensure that the workload is distributed evenly across all the machines in the cluster, while vertical scaling does not require additional management overhead.

The choice between horizontal and vertical scaling depends on the specific needs and requirements of a system. Horizontal scaling is typically preferred for systems that require high availability and fault tolerance, while vertical scaling is preferred for systems that require high performance and low latency.


Other data engineering terms related to
Data Management:

Archive

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

Augment

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.

Backup

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

Curation

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

Deduplicate

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

Dimensionality

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

Enrich

Enhance data with additional information from external sources.

Export

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

Index

Create an optimized data structure for fast search and retrieval.

Integrate

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

Memoize

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

Merge

Combine data from multiple datasets into a single dataset.

Mine

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

Model

Create a conceptual representation of data objects.

Monitor

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.

Parse

Interpret and convert data from one format to another.

Partition

Divide data into smaller subsets for improved performance.

Prep

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

Preprocess

Transform raw data before data analysis or machine learning modeling.

Replicate

Create a copy of data for redundancy or distributed processing.

Schema Mapping

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

Synchronize

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

Validate

Check data for completeness, accuracy, and consistency.

Version

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