How operational data stores (ODS) and data warehouses work together
See the pros and cons of operational data stores (ODS) and data warehouses. They can also work together. Get details here.
How Operational Data Stores (ODS) and Data Warehouses Work Together

Data lacks value until organizations can gain business intelligence and insights from it. Most businesses find it challenging to transform and maximize the value of an organization’s data. Data storage options must hold company data and be available for querying as needed. Why? Statistics indicate that fast and easy data access increases business performance by up to 21%.

Two storage options are operational data stores (ODS) and data warehouses. Although one cannot replace the other, both storage options offer pros and cons for various business use cases.

This article differentiates between ODS and data warehouses, citing cases where one approach edges out the other and explaining how an integrated solution involving both options makes a more robust data storage solution for your business.

What is an operational data store (ODS)

An operational data store is a staging environment for ingesting, storing, and preparing data for operational and analytical uses. It’s referred to as a staging area because it’s not usually the final destination for an organization’s data. Instead, it is a cost-effective storage option that presents an up-to-date holistic view of data from various sources.

ODS is a repository containing a snapshot of the most current view of consolidated data from multiple transactional systems, making it ideal for performing operational reporting and real-time sub-second queries.

Drawbacks of ODS

Although ODS is a valuable addition to business intelligence, it has some limitations:

  1. Difficult to scale: Relational database technologies are foundational blocks used to build most operational data stores. Relational database technologies are challenging to scale as they’re built to scale on single servers to maintain their mapping integrity. Hence, scaling requires larger servers which is a lengthy and expensive process. Therefore, an increase in volume could mean more expenses in running costs.
  2. Lower concurrency: An ODS performance largely depends on the workload and design. ODS works best for running smaller queries at a given time. If many people access an ODS simultaneously, it causes low performance.
  3. Not ideal for real-time API serving: ODS does not offer real-time API reporting, thus making it unsuitable for real-time data applications like risk analysis and fraud detection.
  4. High Latency: ODS exists on relational database systems, which cannot handle large amounts of data while operating at high performance. Thus, ODS can’t handle operations demanding low latency like autonomous and trading applications.

Operational data stores vs. data warehouses

Operational data store
Data warehouse
Staging area; the middle ground between data sources and the final data location, like a data warehouse.
Final landing location for data; an ODS often feeds data here
Query complexity
Simple, high-granular queries; high performance
More complex, summary-level queries based on stored aggregated data
Data loading frequency
Data is constantly overwritten, usually within a few hours or days
Static, non-volatile data
Easier, and therefore less expensive to set up than an on-prem data warehouse; roughly equivalent to a cloud data warehouse
Large volumes of data and can be expensive to set up, unless using a cloud data warehouse
Use cases
Active day-to-day operations requiring fast business operations queries for quick insights and incident reporting
Excellent for archiving data, historical analysis for analyzing trends over time, and data audits
Data types
Works on processed and transformed data
Structured, semi-structured, and unstructured data
Characteristics of an operational data store
  • Fast, sub-second queries: Because ODS only reflects the most current view of transactional data, it is excellent at performing quick queries.
  • Data volatility: ODS is volatile and gets overwritten as soon as new data exists, usually within minutes.

Operational data stores become practical for handling transactional data; for instance, an e-commerce store takes transactional data like a customer, billing, purchase, and delivery data. A customer support team member may need an updated look into purchase info at the end of the day to know the total number of goods purchased.

Characteristics of a data warehouse
  • High-volume data storage: Data warehouses can house up to petabytes of data, making them ideal for storing massive amounts of data.
  • Static non-volatile data: Data warehouse data are fixed and updated periodically over time. This feature makes data warehouses great for data loading and retrieval.
  • Historical analysis: Because data warehouses store data over long periods, it is ideal for performing historical research analysis and identifying trends.

Data warehouses are a type of database built for online analytical processing. For example, large organizations looking to identify winning strategies and improve their current product offerings may analyze data warehouse data to identify insights.

Why ODS and data warehouses work together

While ODS and data warehouses work excellently in some use cases, one does not eliminate the need for the other as each performs differently and both work together excellently to form an integral solution. Therefore, organizations may use both an ODS and a data warehouse in a tiered architecture to obtain maximum benefits from each store.

This tiered architecture works for several business use cases, including:

  1. Mergers and acquisitions: In cases where an organization gets merged or acquired, an ODS will be handy in displaying the updated state of company data, while the data warehouse provides a holistic view into company data archives and historical insights.
  2. E-commerce stores: E-commerce stores access customer records like name, billing address, order, and order dates. An ODS in a tiered architecture will be beneficial in helping customer support to track customer order status at a particular time or date and identify product performance over time from historical data warehouse data.

Therefore, organizations can combine an ODS and a data warehouse to create a more integrated solution that improves latency, performance, and availability to enable a robust data analytics solution. To get started, organizations need to employ a good design and data integration strategy that ensures easy data flow between ODS and the data warehouse.


Making data easily accessible ensures excellent benefits for organizations. But, first, organizations must choose a solution that guarantees data availability when and where needed. Operational data stores and cloud data warehouses are examples of such storage solutions.

Operational data stores act as a snapshot for organization data and have the most updated integrated data at any point. This feature makes them excellent for troubleshooting business errors and real-time reporting.

For large data stores like data warehouses, the data volume and complex data structure make subsecond queries difficult. However, data warehouses are excellent for complex queries and analyses of historical data.

StreamSets DataOps Platform enables organizations to build, run, monitor, and manage the smart data pipelines that deliver data to your ODS or data warehouse.


Accelerate decision-making with analytics-ready data

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