What Are Data Ponds?
A data pond is a component of larger data ecosystems that store, regulate and transform data for organizations. It forms an element of widespread data networks that may include data lakes, puddles and oceans. As a part of this data network, data ponds collect a variety of single-project or departmental datasets, known as puddles, into a central warehouse.
Understanding Data Ponds in Relation to Other Data Systems
To understand data ponds, you must know their relationship to other data systems such as the following:
Data Puddles
Puddles are advanced big data systems with extensive capabilities for single-purpose use and efficient business unit performance. They’re intended for use within a singular team, project or department. Data loaded into data puddles comes from one team or project, serves that singular purpose, and provides insight for isolated performance.
Data Lakes
Lakes are large-scale solutions that serve whole organizations. They centralize data types from different sources, departments, projects and other organization-wide platforms into one system. Data lakes can store these variant datasets whether raw, semi-structured or structured, making them valuable data governance and management resources.
Data Oceans
Data oceans are a step above data lakes, providing a more integrated data management method. Where lakes store and process variant data in different structural stages, data oceans aim to integrate data for interconnected, enterprise-wide accessibility. With data oceans, different users across your organization can access insights across units, generally in a standardized format.
How Data Ponds Work
Data ponds exist alongside these diverse data infrastructures. They are a collection of puddles from various projects, teams and departments. You can create a data pond by gathering puddles through warehouse offloading, ETL offloading or organic composition as business units upload their data.
Data Ponds vs Data Lakes
Although similar in their broad data collection, data ponds and lakes have four core differences.
- Data ponds follow a more organic, less organized collection and storage of data, whereas lakes include systemic data organization and analysis.
- IT teams and data engineers often manage ponds. Lakes have self-regulating capabilities that enable them to extract, transform and load data.
- Users who want to use pond data often have to request permissions and assistance from IT teams. Lakes enable users to locate and use data independently.
- Pond data use is often project-based, while data lakes are a comprehensive storage platform for all organizational data.
These features make data ponds ideal for smaller-scale, targeted applications and access to interdepartmental data for specific projects or uses. Their benefits include low costs, as IT teams handle scaling and some aspects of the data management process.
Data Pond Architecture
Data ponds consist of three primary components — ingestion ponds, platforms for different data types and archives. The system loads data from sources, keeps it in distinct app, analog and text ponds, and archives it. This design simplifies access to data according to type and gives IT managers control of processing and formatting.
Data Pond Challenges and Solutions
A few challenges of utilizing data ponds include:
- Limited data coverage
- High IT team or data engineer involvement
- Minimal processing and data management capabilities
To overcome these challenges, organizations can view data ponds as parts of their data control and storage infrastructure — not the entirety. Ponds can be efficient solutions when used together with systems like data lakes and warehouses. Implement consolidated and standardized solutions like lakes to house diverse datasets, improve efficiency and accessibility, and streamline processes like data transformation.
To ensure quality data across your data network, you can also employ advanced automation and AI technology to validate, integrate and maintain your data for organization-wide applications.
Build Quality-First Data Systems
Creating an interconnected and comprehensive data network with diverse capabilities can empower you to enhance data quality, management, access and use. Request a demo to learn how Anomalo can optimize your data ecosystem.
