Centralised Data Repositories

Data needs to be consistent and comparable to offer you good value; so what can centralised data repositories do? Here we explain the types and options for data repositories, and the benefits of cohesive data sets for enterprise businesses.

Types of data repositories

Data repositories come in various types, each suited for different purposes and use cases. Each type of data repository serves specific purposes and has distinct advantages, making them suitable for different scenarios in data management and analysis. Here are some common types:

  • Databases: relational databases use structured tables to store data and support SQL for querying. Examples include MySQL, PostgreSQL, and Oracle Database. They are ideal for structured data and complex queries. NoSQL Databases are designed for unstructured or semi-structured data. They offer flexible schemas and scalability. Examples include MongoDB (document-based), Cassandra (wide-column), and Redis (key-value).
  • Data Warehouses: these are specialised repositories designed for analytics and reporting. They consolidate data from different sources for querying and analysis, using structured data. Examples include Amazon Redshift, Google BigQuery, and Snowflake.
  • Data Lakes: data lakes store vast amounts of raw data in its native format until needed. They can handle structured and unstructured data, which is beneficial for big data analytics. Technologies like Apache Hadoop and AWS S3 are commonly used for data lakes.
  • File storage systems: these repositories store files in a hierarchical file structure and are often used for document management or digital asset storage. Examples include traditional file systems and cloud storage services like Google Drive and Dropbox.
  • Content Management Systems (CMS): These platforms store and manage digital content, often used for websites. They can help organise, edit and publish various content types. Popular examples include WordPress, Joomla and Drupal.
  • Distributed file systems: these are designed to store data across multiple machines, ensuring redundancy and scalability. Examples include Hadoop Distributed File System (HDFS) and Google File System (GFS).
  • Graph databases: these are optimised for storing and querying data that is interconnected, using nodes and edges to represent relationships. Examples include Neo4j and Amazon Neptune. They are particularly useful in social networks, recommendation system, and fraud detection.
  • In-Memory Data Stores: these repositories keep data in RAM for faster read and write access, making them ideal for real-time applications. Examples include Redis and Memcached.
  • Object Storage: used primarily for storing unstructured data and large files, object storage is architected for scalability and accessibility. Examples include Amazon S3 and Google Cloud Storage.

 

 

  

 

Data repository vs database

A data repository and a database serve different purposes in data management, although they are often related and can overlap in their functions. Here’s a comparison of the two:

A data repository is a broader term that refers to any centralised location where data is stored, managed, and accessed. It can encompass various data storage systems. Data repositories can include databases, data lakes, data warehouses, file storage systems, content management systems, and more. Essentially, any place where data is stored can be considered a data repository. In terms of structure, data repositories can accommodate structured, unstructured, and semi-structured data, often without strict schema requirements. This flexibility allows for a diverse range of data types and formats.

The primary purpose of a data repository is to serve as a storage solution, allowing for data collection, preservation, and retrieval. They often facilitate broader use cases such as data aggregation, sharing, and analysis. Data repositories are used for a variety of purposes, including research data storage, archiving, data sharing across departments, and big data analytics.

Meanwhile, a database is a specific type of data repository that is designed to store and manage structured data. It provides a systematic way to organise, retrieve, and manipulate this data. Databases come in different types, including be relational (using tables and SQL) or NoSQL (document-based, key-value, graph and others), each with its specialisation for organising data. Databases usually have a predefined schema, which enforces organisation and data integrity, allowing for complex queries and transactions. The main goal of a database is to facilitate efficient access and manipulation of data.

Databases are geared toward transactional processing, ensuring data consistency and reliability. Databases are typically used for applications that require frequent read/write operations, such as transaction systems, inventory management, and in customer relationship management (CRM).

In summary, all databases are data repositories, but not all data repositories are databases. A data repository can store various formats and types of data, while a database focuses specifically on structured data and often employs specific schema and querying capabilities. Understanding the distinction can help you choose the right system for your specific data management needs.

How to get accessible, shared data

Using a central repository for shared information should be straightforward and uncomplicated. Regardless of the application, accessing centralised data should be as easy as logging in and retrieving information without concerns about accessibility, security, or downtime. A centralised repository will help you:

  • Manage your data effectively
  • Organise and store your data properly
  • Cite your data by providing a persistent identifier
  • Enhance the discoverability of your data
  • Increase the value of your data for both current and future research
  • Ensure the long-term preservation of your data

How to link databases

Database integration involves consolidating data from various, disparate sources to create a single, authoritative version that can be shared and managed throughout your organisation. This process can encompass existing databases as well as other sources such as web services or different input methods. In many instances, this means merging several existing databases into a unified resource.

Alternatively, it may involve maintaining separate databases while establishing a single platform to access, view, and manage the information. Regardless of the approach, the primary objective is to enhance information sharing, improve efficiency, and support better decision-making across the organisation.

Integration also plays a crucial role in ensuring the validity, integrity, and security of your data, as well as optimising the performance of any tools that rely on that data.

How Pimcore deals with data

Pimcore is a contemporary and holistic data management system. Pimcore data objects, which represent the PIM component of the platform, are built on class definitions that define their structure and attributes. These objects can manage a wide range of structured data types, including but not limited to products, categories, individuals, customers, news articles, orders and blog posts.

At the core of Pimcore’s data management are data objects, which represent structured data based on the predefined classes. These objects can encompass a variety of data types, ensuring all relevant information is stored in a unified format.

  • Data modelling: Pimcore uses a robust data modelling framework that allows users to create custom data structures through classes and attributes. This enables businesses to define their specific data needs, whether it’s for products, customers or other entities.
  • Centralised data repository: Pimcore acts as a centralised repository for all data assets, including product information, digital assets, and customer data. This centralisation simplifies data access and management across different platforms and departments.
  • Version control: Pimcore includes built-in version control for data, allowing users to track changes and revert to previous versions if necessary. This feature ensures data integrity and accountability in collaborative environments.
  • Data integration: the Pimcore platform supports data integration from various sources, including external APIs and other databases. This capability allows businesses to consolidate data from multiple systems, ensuring consistency and completeness.
  • Data Enrichment: users can enrich data by adding additional attributes, tags, or metadata, which enhances the value of the information stored in Pimcore. This enrichment supports better categorisation and searchability.
  • Search and filtering: Pimcore provides powerful search capabilities, enabling users to quickly find and filter data based on specific criteria. This enhances usability and efficiency, especially when dealing with large datasets.
  • Data governance and security: the platform includes features for data governance, ensuring that data quality and security are maintained. User permissions and roles can be defined to control access to sensitive information.
  • Multi-channel delivery: Pimcore’s data management extends beyond storage; it facilitates the seamless delivery of content and data across multiple channels, such as websites, mobile apps and eCommerce platforms.
  • Reporting and analytics: Integrated analytics and reporting tools enable users to gain insights from their data, track performance metrics, and make informed decisions based on real-time information.

By providing these capabilities, Pimcore effectively handles data in a way that supports businesses in managing their information assets, streamlining workflows and enhancing overall operational efficiency.

As Pimcore Silver Partners, we have helped Aussie companies like yours to centralise and coordinate their data. Contact us to learn more.

 

 

Related questions

How to ensure data is comparable?

It's important to maintain consistency when integrating multiple databases. In the examples we’ve discussed so far, we’ve dealt with small datasets and relatively simple schemas. However, this won't always be the case. There will often be situations where values for similar attributes across different databases are incompatible. In such instances, you'll need to use data transformation to ensure compatibility.

For example, this is necessary when one of your existing data sources stores a certain attribute as a string while another source records the same attribute as a numerical value. This issue could arise with customer phone numbers across different platforms. Instead of modifying the schema or set up of an existing database, which could disrupt other tools that depend on that data, you can instead apply a rule or expression to transform these values whenever they are passed between databases.

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