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A data mart is a simple form of data warehouse focused on a single subject or line of business. With a data mart, teams can access data and gain insights faster, because they don’t have to spend time searching within a more complex data warehouse or manually aggregating data from different sources.
A data mart provides easier access to data required by a specific team or line of business within your organization. For example, if your marketing team is looking for data to help improve campaign performance during the holiday season, sifting through and combining data scattered across multiple systems could prove costly in terms of time, accuracy, and ultimately, money.
Teams forced to locate data from various sources most often rely on spreadsheets to share this data and collaborate. This usually results in human errors, confusion, complex reconciliations, and multiple sources of truth—the so-called “spreadsheet nightmare.” Data marts have become popular as a centralized place where the necessary data is collected and organized before reports, dashboards, and visualizations are created.
Data marts, data lakes, and data warehouses serve different purposes and needs.
A data warehouse is a data management system designed to support business intelligence and analytics for an entire organization. Data warehouses often contain large amounts of data, including historical data. The data within a data warehouse usually is derived from a wide range of sources, such as application log files and transactional applications. A data warehouse stores structured data, whose purpose is usually well-defined.
A data lake allows organizations to store large amounts of structured and unstructured data (for example, from social media or clickstream data), and to immediately make it available for real-time analytics, data science, and machine learning use cases. With a data lake, data is ingested in its original form, without alteration.
The key difference between a data lake and a data warehouse is that data lakes store vast amounts of raw data, without a predefined structure. Organizations do not need to know in advance how the data will be used.
A data mart is a simple form of a data warehouse that is focused on a single subject or line of business, such as sales, finance, or marketing. Given their focus, data marts draw data from fewer sources than data warehouses. Data mart sources can include internal operational systems, a central data warehouse, and external data.
A data mart dedicated to a team or specific line of business offers several benefits:
Business teams are striving to become more agile and data-driven to guide strategy and improve day-to-day decision-making, yet they typically struggle to turn an ever-growing mountain of data into insights. CFOs spend on average 2.24 hours per day sifting through spreadsheets. Although business teams usually turn to IT for help, IT teams may have a hard time keeping up with business users’ demands for increased access to more disparate data sources, larger volumes of data, and faster query times.
Setting up data marts can also be a concern for IT teams already burdened with a heavy workload, because they need to manage those data marts on an ongoing basis and ensure data security. Moving data marts to the cloud helps alleviate concerns of both business and IT teams by moving administration and security tasks to the cloud service provider which decreases the need for manual intervention and lowers operational costs.
Oracle provides a complete and self-service solution that allows business teams to get the deep, trustworthy, data-driven insights they need to make quick decisions.
Business teams can quickly combine all necessary data across different sources and formats, including spatial and graph, in a converged database to drive secure collaboration around a single source of truth provided by data marts. Analysts can easily leverage self-service data tools and embedded machine learning—with zero coding required—to accelerate data loading, transformation, and preparation, automatically find patterns and trends, make predictions, and gain insights based on data with transparent lineage.
Governed and secure, Oracle’s solution makes it possible for IT to reduce risks. IT teams additionally can rely on a simple, reliable, and repeatable approach for all data analytics requests from business departments, greatly improving productivity.
Oracle Autonomous Data Warehouse intelligently automates provisioning, configuring, securing, tuning, scaling, patching, backing up, and repairing. This eliminates nearly all the manual and complex tasks that can introduce human error. Built-in data tools enable simple, self-service data loading, data transformation, business modeling, and automatic insights for data marts. DBAs can shift their efforts from routine database administration to new application designs, and helping business departments achieve their goals. Business users in finance, HR, and marketing can be empowered with secure data access and consistently high query performance for any number of concurrent users, even at peak times. Autonomous Data Warehouse automatically scales according to workload needs, without any downtime.
View the highlights from Oracle Live: New Autonomous Data Warehouse Innovations: