With 1.145 trillion MB of data being created every day, businesses are spoilt for choice when it comes to accessing data that can provide them with insights for strategic decision-making. However, the very volume is a deterrent too. Since it is a mix of structured and unstructured data, they have to be stored differently and in different formats. As a result, locating relevant data from various sources and pulling them together in one place for a unified view can be a challenge. Users often use Excel spreadsheets to migrate data and collaborate. While creating challenges of complex reconciliations and creating multiple sources of truth, it is also prone to confusion and human errors.
Data Mart simplifies access to relevant data by creating a smaller version of the data warehouse serving a single functional unit of an organization. It is controlled by the particular functional unit and accesses a subset of the data stored in the data warehouse. A data warehouse pulls together data from multiple sources whereas a Data Mart draws data only from a few sources.
The data marts are smaller than the data warehouses, flexible, and can be built and maintained more easily. They provide users with responses to queries quickly and also facilitate data summaries, which is difficult in data warehouses due to the larger volume and range of data stored there.
With technological evolution and the availability of tools for distributed computing and virtualization, physical data marts are being replaced by virtual data marts that offer the same features in an agile manner.
The virtual data marts are built on an abstraction and federation layer in the Data Virtualization Platform. As a result, the complexities of the Big Data stores are hidden and data from these stores are integrated with other data from within the enterprise.
As businesses continue to have a substantial part of their data in traditional relational databases as well, queries are federated across relational and non-rational databases to provide a single data store.
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Like physical data marts, virtual data marts provide the following key benefits:
Data Blending: Blends relational and non-relational data from multiple sources
Data Transformations and Aggregations: Extract Transform and Load (ETL) tools used to build data marts transform raw data and aggregate to a higher granular level on the.
Unified View: By blending data from different sources and presenting them in a unified format, virtual data marts provide a single source of truth essential for reporting and analytics.
Integration with Visualization Tools: The unified data can be integrated with BI tools for visualizations.
In addition to the above, virtual data marts also score above physical data marts in the following aspects, including:
Agility: Speedy data structure creation and delivery to provide blended high-volume data along with transformations ranging from simple to complex. Functions enabling complex data preparation, cleaning, and data manipulation can be added to the tool. It should also make available reusable user-defined functions (UDFs).
Cost and Time-Effective: Faster and cost-effective creation, upgradation, and maintenance
Extensibility: New sources or one-time data sources can be used to quickly extend the existing marts or lakes. Its capability for detailed lineage and impact analysis helps to identify the data sources and the transformation that have been applied to the data. The data can be defined, described, tagged, and classified to make it a searchable data catalog.
Streaming and Real-Time Analytics: Data virtualization facilitates real-time analytics with centralized data security features at the granular level.
Performance: Query performance improves due to data being stored in server memory. The virtual data marts enable access to different types of data sources such as SQL databases, Hadoop files, spreadsheets, flat files, packaged applications, messaging devices, and social media networks.
Some of the many use cases of virtual data marts include:
Resource Management: Providing each department with a customized repository ensures a balanced utilization of resources by the different functional units. This helps to improve the effective and efficient use of resources.
Data Analytics with Greater Focus: As the data used for analytics is relevant for each functional unit, the outcome of the analytics is also focused and provides clearer insights.
Quick Turnaround: Virtual data mart generally can be implemented faster and marts would be created without physical data bases.
Secure Access to Selective Data: Virtual Data Marts enable organizations to allow users access to selective data based on their needs and authorization, thus ensuring data security.
Indium Software, a leading provider of data engineering solutions, is a Denodo partner. We leverage the Denodo data virtualization capabilities to deliver virtual data marts based on the user requirements. Our team of technology experts with domain expertise build data marts that deliver a holistic view of relevant data for drawing clear insights.
Using Denodo’s Logical Data Warehouse (LDW), we enable data federation, create virtual databases, and decentralize data warehouses to provide different functions access to different data sets based on their needs. As a result, businesses can experience:
● ROI in six months
● Improved business user productivity
● Decreasing cost of development resources
● Quicker access to data for ETL processes
● Access in real-time to integrated data across multiple data sources within an organization, without any replications
By Uma Raj
By Uma Raj
By Abishek Balakumar
Indium Software is a leading digital engineering company that provides Application Engineering, Cloud Engineering, Data and Analytics, DevOps, Digital Assurance, and Gaming services. We assist companies in their digital transformation journey at every stage of digital adoption, allowing them to become market leaders.