Data Partitioning

    What is data partitioning?

    Data partitioning is the process of logically and/or physically partitioning data into segments that are more easily maintained or accessed. It is used to improve performance, manageability and scalability in database systems and data processing.

    Data Partitioning Types

    An effective data partitioning strategy has many benefits to a recruitment team. Often data partitioning can be done in software such as the Eclipse Recruitment Software. There are multiple ways to partition data in order to efficiently manage, access, and secure candidate data, job postings, and other information necessary for recruitment.

    Vertical partitioning

    Vertical partitioning is a kind of data partitioning, meaning that it is a technique to partition a data set, but is not a strategy. Vertical partitioning operates by dividing data into a table with multiple vertical sections based on columns. Each vertical partition contains a subset of columns from the original table.

    Horizontal partitioning

    Horizontal partitioning data means that a table is partitioned into multiple horizontal partitions, also known as shards, based on rows. As with vertical partitioning, horizontal partitioning contains a subset of rows from the original table. Both horizontal and vertical partitioning may have their data stored on separate and smaller databases or servers.

    Range partitioning

    Range partitioning data divides data into partitions based on ranges of values, such as age range partitioning, date range, or alphabetical ranges. Range partitioning is usually used to store data from a period of time, such as historical data.

    List partitioning

    List partitioning is a tool that divides a table into partitions based on discrete values in a column or set of columns. The partitions in list partitioning are assigned based off of a specific list of values, such as regions, roles, etc. It allows for a closer control over the data distribution when compared to range partitioning.

    Hash partitioning

    Hash partitioning divides data into partitions based on a hashing algorithm applied to one or more of the columns in a table. This data partitioning criteria ensures an even distribution of data and is often used for data distribution in a distributed database environment.

    Composite partitioning

    Composite partitioning combines multiple partitioning tools to create nested or hierarchical partitions. This technique for partitioning data allows for a customised approach at each different level of the partitioning hierarchy.

    How does data partitioning help make the entire process of recruitment easier?

    Utilising data partitioning strategies within recruitment helps organise and segregate candidate data, job postings, and other information into different partitions within a database partitioning or software system. There are a number of benefits to using data partitioning within the process of recruiting:

    Candidate Segmentation

    In recruiting you’ll often need to organise various pieces of data so you can easily manage relevant talent pools for different roles. Data partitioning allows you to segment various pieces of data based on different criteria, such as job role, department, location, or hiring status. It allows you to take a more targeted approach for candidate sourcing, screening, and engagement.

    Data security and access control

    Data partitioning allows recruitment agencies to implement granular access controls and security measures to enhance security for sensitive data and improve security across the entire database. This granular partitioning strategy also allows for enhanced compliance with GDPR regulations and minimises the risk of unauthorised access or data breaches.

    Scalability & Performance

    Utilising data partitioning strategies helps recruitment agencies improve their system performance and scalability by distributing data across multiple partitions or devices. It helps recruitment software handle large volumes of data more effectively, reduce the latency, and improve query performance. Partitioning also helps to smooth data management tasks, such as data backup, data recovery, and data archiving. It allows companies to manage the partitioned data independently and scale their resources, such as upgrading to multiple servers, as and when it is needed for their agency strategy.

    Is it worth partitioning data?

    In conclusion, data partitioning is a powerful technique used to enhance the performance, manageability, and scalability of large datasets in database systems and data processing environments. Functional partitioning allows users to divide a large dataset into smaller, more manageable pieces and allows for more efficient query processing, easier data management, and better system scalability.