What is an Operational Data Store?

An operational data store (ODS) is a centralized data repository that collects and stores data from multiple transactional systems for real-time analytics. Organizations have different software systems for CRMs, accounting, sales, and so on that contain data about other aspects of their operations. An operational data store combines data from various sources to create a snapshot of the organization’s current operations. It continuously integrates data in its original format and updates it in real time. Business intelligence tools can access the operational data store for real-time processing.

Organizations implement an operational data store mainly because it can integrate data from multiple sources in real time. Otherwise, reporting across individual systems gives a siloed and incomplete view of business operations. For example, you can use the sales CRM to find the list of new customers. However, only the accounting system knows the order value of each customer. You must look at data from both systems to find the list of newly signed-up high-value accounts today. An operational data store lets you do just that for advanced real-time operational intelligence. Benefits include the following:

Enhanced decision making

Operational data stores give managers and decision-makers a holistic understanding of the business. Real-time insights from an ODS can lead to more informed and timely decisions. The organization can respond faster to changing market conditions and customer needs. For example, a retail company can offer discounts on products close to finishing as soon as stock levels fall below a certain threshold.

Improved data quality

An ODS can implement uniform data quality checks across multiple systems. It can identify and correct errors and remove duplications. Improved data quality ensures that the information used for analysis and decision-making is accurate and reliable, leading to better outcomes.

Increased operational efficiency

An ODS streamlines the data flow within an organization, reducing the time and effort required to access and analyze data from different systems. This efficiency speeds up operational processes and reduces the workload on IT departments. Teams can access the data they need quickly, enabling faster response times and boosting overall productivity.

Flexibility and scalability

Operational data stores are designed to be flexible and scalable to accommodate the evolving data needs of an organization. They can scale to integrate new data streams with minimal changes, ensuring the data architecture remains robust and capable of supporting the organization’s objectives.

The operational data store architecture has several components that work together for real-time data capture.

Data ingestion layer

Data ingestion components in the operational data store collect data from various source systems—this could be your operational systems, transactional databases, and external data feeds like social media platforms or third-party APIs. The data sources push the data into the ODS using change data capture mechanisms.

Change data capture (CDC) is a method to capture incremental changes in source systems, such as new entries, updates to existing entries, or deletions. The source databases are monitored for changes; only the changed data is automatically transferred to the ODS. This approach minimizes the volume of data that needs to be moved at any given time, reduces the load on network resources, and improves the efficiency of the data ingestion process.

Data ingestion components use API calls, direct database connections, or other mechanisms to input the data changes into the ODS.

Data transformation layer

Components in this layer use predefined mappings, data models, and time-sensitive business rules to transform the ingested data into a standard format. They may also optimize the data for faster processing and retrieval. For example:

• Partition large datasets to optimize storage utilization.
• Create indexes on frequently queried attributes.
• Cache frequently accessed data mechanisms.

The goal is to integrate data, providing a unified and coherent view of operational data while maximizing retrieval and reporting efficiency.

Data presentation layer

Components in this layer make data accessible and understandable to business users, enabling them to derive insights and make informed decisions based on real-time data. User-friendly interfaces, with visualization and query tools, allow business users to interact independently with the ODS. They can run complex queries to get a complete view of current operational data. For example, you can do the following:

• Create structured reports that present relevant business metrics in a readable format.
• Generate interactive charts, graphs, and dashboards that make complex data more accessible to understand at a glance.
• Explore data sets and uncover patterns, trends, and insights without needing extensive technical skills.

The layer supports automatic operational reporting on schedule or report generation on demand.

Data governance layer

Components in this layer support overall data management to meet governance and compliance requirements. It includes:

• Access controls, encryption, and other data security mechanisms to prevent unauthorized access.
• Audit trails for data traceability, compliance, and governance.
• Data quality processes—such as validation, profiling, and monitoring—to maintain data integrity.
• A metadata repository to store information about the data sources, mappings, transformations, and other metadata.

The mechanisms ensure that data usage in the operational data stores aligns with the organization’s overall data strategy.

Data engineering includes other technologies like relational databases, data warehouses, data lakes, data marts, etc. The technologies can be broadly categorized into transactional and analytical technologies.

Transactional technologies are designed to manage and store day-to-day business transactions in real-time. They are optimized for operations that involve high volumes of data writing and reading with minimal latency. In contrast, analytical technologies are engineered to facilitate the analysis and reporting of data. They are optimized for data reading and complex queries, supporting the aggregation, summarization, and analysis of large data volumes from various sources.

An ODS acts as a bridge between transactional and analytical technologies. It takes transactional data as input and prepares it for analytics. However, it may not offer the full depth of data analysis like other analytical technologies.

Data warehouse

A data warehouse is a central repository of information that can be analyzed to make more informed decisions. It can store data from everywhere, including historical data for archiving. In contrast, an operational data store only provides a limited amount of information for a limited time. For example, the ODS might store sales records only for the past 12 hours.

In a data pipeline, the ODS acts as another data source for the data warehouse. Traditional data warehouses did not have the capability for fast, real-time analytics that an ODS has. However, modern data warehouses provide high-speed, real-time data availability on transaction data—eliminating the need for an ODS altogether.

Data mart

A data mart is a data storage system that contains information specific to an organization’s single business unit. It includes a small and selected part of the data that the company stores in a more extensive storage system.

Data marts extract subject-oriented information from a data warehouse, while operational data stores send information into the data warehouse for processing. Data marts offer historical information you can analyze, but an ODS provides an updated view of current operations. For example, you can use a data mart to identify sales patterns for the past quarter but receive hourly sales figure updates from the ODS.

While operational data stores are excellent for real-time snapshot reporting, they cannot provide in-depth data warehouse analytics. You cannot run complex queries or analyze historical data for advanced use cases.

Operational data stores can also be high-maintenance and unstable. Data integrations may break, or the system may not function as expected if data source formats change. Scaling an ODS is also challenging and sometimes impossible.

Overcoming limitations with zero-ETL

Zero-ETL is a set of integrations that lets you run analytics on transactional data directly within your data warehousing solution. A zero-ETL integration enables you to ingest data from multiple operational systems directly into your enterprise data warehouse and present it for analytics almost instantly. There is no requirement to stage the streaming data for transformation or use an operational data store. You get more accurate and timely insights for use cases like real-time dashboards, optimized gaming experiences, data quality monitoring, and customer behavior analysis—all without investing in multiple solutions.

Amazon Redshift is a fully managed analytics service that uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes. It uses AWS-designed hardware and machine learning to deliver the best price performance at any scale. With an AI-powered, massively parallel processing (MPP) architecture and zero-ETL approach, you can unify all your data for powerful analytics, near real-time use cases, and AI/ML applications.

Amazon Aurora is a modern relational database service offering performance and high availability at scale, fully open-source MySQL- and PostgreSQL-compatible editions, and a range of developer tools for building serverless and machine learning (ML)-driven applications.

Amazon Aurora zero-ETL integration with Amazon Redshift enables near real-time analytics and machine learning (ML) using Amazon Redshift on petabytes of transactional data. Within seconds of transactional data being written into Amazon Aurora, zero-ETL seamlessly makes the data available in Amazon Redshift, removing the need to build operational data stores.

AWS manages the work needed to set up, operate, and scale the operational data store and data warehouse on your behalf, freeing you to focus on business processes.

Get started with operational data stores on AWS by creating a free account today.