To better understand the difference between an operational data store (ODS) and a data warehouse, it is best to clarify that an ODS is not a replacement or substitute for a data warehouse. While an ODS is often an intermediary or staging area for a data warehouse, the ODS differs in that its data is overwritten and changes frequently. In contrast, a data warehouse contains static data for archiving, storage, historical analysis, and reporting.
However, an ODS and a data warehouse have much in common as they both import and consolidate data from disparate sources. These sources provide a key function for analysis and reporting, but it is important to distinguish the nuances between the two to decide whether to deploy one integrated data solution or combine them within a tiered data architecture to deliver the most business intelligence (BI) for your organization.
A Fortune 100 P&C insurer in the US found it challenging to manage operations efficiently with slow development life cycles, limited data processing capability, and heavy dependence on IT. They were looking for low-cost infrastructure and analytics solutions as they migrated their existing applications to event-based architecture.
Knowing there was a better way, they set out to deploy an intelligent, state-of-the-art ODS and analytics solution. To learn how we migrated their existing applications to event-based architecture, read this case study on deploying a next-gen operational data store.
A data warehouse is a system used for reporting and data analysis that acts as the central repository of data integrated from disparate sources. Data warehouses store unstructured, structured, and semi-structured data to offer organizations a single source of truth (SSOT) for long-term strategic planning.
Most data warehouses include the following elements:
A relational database (RDB) to store large amounts of business data related to customers, orders, or products.
An extraction, loading, and transformation (ELT) solution used to prepare big data for statistical analysis, reporting, and data mining capabilities.
Client-side visualization tools for presenting data to business users.
Advanced data warehouses often include sophisticated applications that generate actionable information by applying data science and artificial intelligence (AI) algorithms.
Data warehouses can be deployed on-premises, in the cloud, or in a hybrid cloud environment. Most data warehouses are hosted on a cloud service, which offers a more scalable and cost-effective solution to on-premises infrastructures. The most popular cloud data warehouse options include:
1. Amazon Redshift is a fully managed, AWS cloud-based data warehousing platform. Redshift is an excellent choice for enterprises that have an existing relational database management system (RDBMS), such as MySQL, PostgreSQL, and Oracle DB.
2. Azure SQL Data Warehouse is a Microsoft managed petabyte-scale service with controls to manage compute and storage independently. It is best for users looking to pause the compute layer while persisting the data to reduce operational costs in a pay-as-you go environment.
3. Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for interactive analysis of massive datasets. Google offers integrated machine learning and business intelligence tools, such as BigQuery ML and BigQuery BI Engine to support advanced analytics capabilities.
4. SAP Data Warehouse Cloud is a SAAS cloud solution that includes data integration, database, data warehouse, and analytics capabilities to help organizations build a data-driven enterprise.
5. Snowflake is an ANSI-standard SQL columnar store database designed for big data analytics. Snowflake is best suited for organizations running complex queries, doing data analytics, or big data science.
One of the major drawbacks of a data warehouse is its non-volatile nature, meaning the data is read-only and requires cleansing. This leads to time variance, which means that data warehouse updates are performed in scheduled batches, leading to the possibility of dated reporting.
For this reason, many organizations choose to implement an ODS as a staging area to integrate operational data for day-to-day functioning.
An operational data store is a cost-effective solution to the non-volatile nature of data warehouses. An ODS does not require the same type of transformations as a data warehouse. Since an ODS can only store structured data, the data remains in its existing schema, making it more like a data lake, which uses the schema-on-write approach.
In this sense, the ODS acts as a repository that stores a snapshot of an organization's most current data, making it easier for users to diagnose problems before searching through component systems. For example, an ODS allows service representatives to immediately query a transaction to answer:
Where is the customer’s package currently located?
Why is the transaction not going through?
What steps can I take to further troubleshoot this problem?
Since the staging area receives operational data from transactional sources in near-real-time, the burden is offloaded from the transactional systems by only providing access to current data that is being queried. This makes an ODS the ideal solution for those looking for a 360-degree view of information connected to current data records to make faster business decisions.
Traditional ODS solutions typically suffer from high latency because they are based on either relational databases or disk-based NoSQL databases. These systems simply cannot handle large amounts of data and provide high performance simultaneously.
The limited scalability of traditional systems also leads to performance issues when multiple users access the data store at the same time. As such, traditional ODS solutions cannot provide real-time API services for accessing systems of record.
In short, it depends on your use cases and the amount of data being analyzed. If your organization anticipates an overwhelming amount of client, employee, and customer account information, then an ODS solution should be integrated with a data warehouse system.
Mergers or acquisitions are another factor to consider for creating a tiered architecture. To enable a central view of current and historical data across multiple source systems, combining an ODS and data warehouse will offer the most relevant snapshot of the entire enterprise.
In this digital age, where the cloud promises much freedom and flexibility, a robust IT infrastructure uniting cloud, analytics, and big data platforms will drive business excellence for your enterprise.
Trianz’ analytics team can develop and deploy an ODS solution with minimal disruption to your overall business processes. By deploying a next-gen operational data store using Hortonworks Data Platform (HDP) on AWS EC2, Hadoop deployment on AWS Simple Storage Service (S3), Elastic Block Store (EBS), and AWS EC2 Instance Store, the transformation will remedy slow development life cycles, limited data processing capabilities, and heavy dependence on IT.
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