A Denodo virtualization project typically classifies the
project duties of the primary implementation team into four Primary roles.
Denodo Data Virtualization Project Roles
Data Virtualization Architect
Denodo Platform Administrator
Data Virtualization Developer
Denodo Platform Java Programmer
Data Virtualization Internal Support Team
Project Team Member Alignment
While the denodo project is grouped into security permissions and a set of duties, it is import to note that the assignment of the roles can be very dynamic as to their assignment among project team members. Which team member who performs a given role can change the lifecycle of a denodo project. One team member may hold more than one role at any given time or acquire or lose roles based on the needs of the project.
virtualization Project Roles Duties
The knowledge, responsibilities, and duties of a denodo data
virtualization architect, include:
A Deep understanding of denodo security features
and data governance
Define and document5 best practices for users,
roles, and security permissions.
Have a strong understanding of enterprise
Defines data virtualization architecture and
Guides the definition and documentation of the
virtual data model, including, delivery modes, data sources, data combination,
The knowledge, responsibilities, and duties of a Denodo Platform
Denodo Platform Installation and maintenance, such as,
Installs denodo platform servers
Defines denodo platform update and upgrade policies
Creates, edits, and removes environments, clusters, and servs
Manages denodo licenses
Defines denodo platform backup policies
Defines procedures for artifact promotion between environments
Denodo platform configuration and management, such as,
Configures denodo platform server ports
Platform memory configuration and Java Virtual Machine (VM) options
Set the maximum number of concurrent requests
Set up database configuration
Specific cache server
Authentication configuration for users connecting to denodo platform (e.g., LDAP)
Secures (SSL) communications connections of denodo components
Provides connectivity credentials details for clients tools/applications (JDBC, ODBC,,,etc.)
Configuration of resources.
Setup Version Control System (VCS) configuration for denodo
Creates new Virtual Databases
Create Users, roles, and assigns privileges/roles.
Execute diagnostics and monitoring operations, analyzes logs and identifies potentials issues
Manages load balances variables
The Data Virtualization Developer role is divided into the
the knowledge, responsibilities, and duties of a Denodo Data
Virtualization Developer, by sub-role, Include:
The denodo data engineer’s duties include:
Implements the virtual data model construction
Importing data sources and creating base views,
Creating derived views applying combinations and
transformations to the datasets
Writes documentation, defines testing to eliminate
development errors before code promotion to other environments
The denodo business developer’s duties include:
Creates business vies for a specific business
area from derived and/or interface views
Implements data services delivery
The denodo application developer’s duties include:
Creates reporting vies from business views for
reports and or datasets frequently consumed by users
Denodo Platform Java
The Denodo Platform Java Programmer role is an optional,
specialized, role, which:
Creates custom denodo components, such as data sources, stored procedures, and VDP/iTPilot functions.
Implements custom filters in data routines
Tests and debugs any custom components using Denodo4e
Internal Support Team
The denodo data virtualization internal support team’s duties
Access to and knowledge of the use and trouble
of developed solutions
Tools and procedures to manage and support
project users and developers
Denodo provides some general Virtual Dataport naming
convention recommendations and guidance.
First, there is the general guidance for basic Virtual Dataport object
types and, secondly, more detail naming guidance recommends.
Data virtualization is a data management approach that allows retrieving and manipulation of data without requiring technical data details like where the data is physically located or how the data is formatted at the source. Denodo is a data virtualization platform that offers more use cases than those supported by many data virtualization products available today. The platform supports a variety of operational, big data, web integration, and typical data management use cases helpful to technical and business teams. By offering real-time access to comprehensive information, Denodo helps businesses across industries execute complex processes efficiently. Here are 10 Denodo data virtualization use cases.
1. Big data analytics
Denodo is a popular data virtualization tool for examining large data sets to uncover hidden patterns, market trends, and unknown correlations, among other analytical information that can help in making informed decisions.
2. Mainstream business intelligence and data warehousing
Denodo can collect corporate data from external data sources and operational systems to allow data consolidation, analysis as well as reporting to present actionable information to executives for better decision making. In this use case, the tool can offer real-time reporting, logical data warehouse, hybrid data virtualization, data warehouse extension, among many other related applications.
3. Data discovery
Denodo can also be used for self-service business intelligence and reporting as well as “What If” analytics.
4. Agile application development
Data services requiring software development where requirements and solutions keep evolving via the collaborative effort of different teams and end-users can also benefit from Denodo. Examples include Agile service-oriented architecture and BPM (business process management) development, Agile portal & collaboration development as well as Agile mobile & cloud application development.
5. Data abstraction for modernization and migration
Denodo also comes in handy when reducing big data sets to allow for data migration and modernizations. Specific applications for this use case include, but aren’t limited to data consolidation processes in mergers and acquisitions, legacy application modernization and data migration to the cloud.
6. B2B data services & integration
Denodo also supports big data services for business partners. The platform can integrate data via web automation.
7. Cloud, web and B2B integration
Denodo can also be used in social media integration, competitive BI, web extraction, cloud application integration, cloud data services, and B2B integration via web automation.
8. Data management & data services infrastructure
Denodo can be used for unified data governance, providing a canonical view of data, enterprise data services, virtual MDM, and enterprise business data glossary.
9. Single view application
The platform can also be used for call centers, product catalogs, and vertical-specific data applications.
10. Agile business intelligence
Last but not least, Denodo can be used in business intelligence projects to improve inefficiencies of traditional business intelligence. The platform can develop methodologies that enhance outcomes of business intelligence initiatives. Denodo can help businesses adapt to ever-changing business needs. Agile business intelligence ensures business intelligence teams and managers make better decisions in shorter periods.
With over two decades of innovation, applications in 35+ industries and multiple use cases discussed above, it’s clear why Denodo a leading platform in data virtualization.
Today, a business heavily depends on data to gain insights into their processes and operations and to develop new ways to increase market share and profits. In most cases, data required to generate the insights are sourced and located in diverse places, which requires reliable access mechanism. Currently, data warehousing and data virtualization are two principal techniques used to store and access the sources of critical data in a company. Each approach offers various capabilities and can be deployed for particular use cases as described in this article.
A data warehouse is designed and developed to secure host historical data from different sources. In effect, this technique protects data sources from performance degradation caused by the impact of sophisticated analytics and enormous demands for reports. Today, various tools and platforms have been developed for data warehouse automation in companies. They can be deployed to quicken development, automate testing, maintenance, and other steps involved in data warehousing. In a data warehouse, data is stored as a series of snapshots, where a record represents data at a particular time. In effect, companies can analyze data warehouse snapshots to compare data between different periods. The results are converted into insights required to make crucial business decisions.
Moreover, a data warehouse is optimized for other functions, such as data retrieval. The technology duplicates data to allow database de-normalization that enhances query performance. The solution is further deployed to create an enterprise data warehouse (EDW) used to service the entire organization.
Features of a Data Warehouse
A data warehouse is subject-oriented, and it is designed to help entities analyze data. For instance, a company can start a data warehouse focused on sales to learn more about sales data. Analytics on this warehouse can help establish insights such as the best customer for the period. The data warehouse is subject oriented since it can be defined based on a subject matter.
A data warehouse is integrated. Data from various sources is first out into a consistent format. The process requires the firm to resolve some challenges, such as naming conflicts and inconsistencies on units of measure.
A data warehouse in nonvolatile. In effect, data entered into the warehouse should not change after it is stored. This feature increases accuracy and integrity in data warehousing.
A data warehouse is time variant since it focuses on data changes over time. Data warehousing discovers trends in business by using large amounts of historical data. In effect, a typical operation in a data warehouse scans millions of rows to return an output.
A data warehouse is designed and developed to handle ad hoc queries. In most cases, organizations may not predict the amount of workload of a data warehouse. Therefore, it is recommendable to optimize the data warehouse to perform optimally over any possible query operation.
A data warehouse is regularly updated by the ETL process using bulk data modification techniques. Therefore, end users cannot directly update the data warehouse.
Advantages of Data Warehousing
The primary motivation for developing a data warehouse is to provide timely information required for decision making in an organization. A business intelligence data warehouse serves as an initial checkpoint for crucial business data. When a company stores its data in a data warehouse, tracking it becomes natural. The technology allows users to perform quick searches to be able to retrieve and analyze static data.
Another driver for companies investing in data warehouses involves integrating data from disparate sources. This capability adds value to operational applications like customer relationship management systems. A well-integrated warehouse allows the solution to translate information to a more usable and straightforward format, making it easy for users to understand the business data.
The technology also allows organizations to perform a series of analysis on data.
A data warehouse reduces the cost to access historical data in an organization.
Data warehousing provides standardization of data across an organization. Moreover, it helps identify and eliminate errors. Before loading data, the solution shows inconsistencies to users and corrects them.
A data warehouse also improves the turnaround time for analysis and report generation.
The technology makes it easy for users to access and share data. A user can conduct a quick search on a data warehouse to find and analyze static data without wasting time.
Data warehousing removes informational processing load from transaction-oriented databases.
Disadvantages of Data Warehousing
While data warehousing technology is undoubtedly beneficial to many organizations, not all data warehouses are relevant to a business. In some cases, a data warehouse can be expensive to scale and maintain.
Preparing a data warehouse is time-consuming since it requires users to input raw data, which has to be achieved manually.
A data warehouse is not a perfect choice for handing unstructured and complex raw data. Moreover, it faces difficulties incompatibility. Depending on the data sources, companies may require a business intelligence team to ensure compatibility is achieved for data coming from sources running distinct operating systems and programs.
The technology requires a maintenance cost to continue working correctly. The solution needs to be updated with latest features that might be costly. Regularly maintaining a data warehouse will need a business to spend more on top of the initial investment.
A data warehouse use can be limited due to information privacy and confidentiality issues. In most cases, businesses collect and store sensitive data belonging to their clients. Viewing it is only allowed to individual employees, which limits the benefits offered by a data warehouse.
Data Warehousing Use Case
There are a series of ways organizations use data warehouses. Businesses can optimize the technology for performance by identifying the type of data warehouse they have.
A data warehouses can be used by an organization that is struggling to report efficiently on business operations and activities. The solution makes it possible to access the required data
A data warehouse is necessary for an organization where data is copied separately by different divisions for analysis in spreadsheets that are not consistent with one another.
Data warehousing is crucial in organizations where uncertainties about data accuracy are causing executives to question the veracity of reports.
A data warehouse is crucial for business intelligence acceleration. The technology delivers rapid data insights to analysts at different scales, concurrency, and without requiring manual tuning or optimization of a database.
Data virtualization technology does not require transfer or storage of data. Instead, users employ a combination of application programming interfaces (APIs) and metadata (data about data) to interface with data in different sources. Users use joined queries to gain access to the original data sources. In other words, data virtualization offers a simplified and integrated view to business data in real-time as requested by business users, applications, and analytics. In effect, the technology makes it possible to integrate data from distinct sources, formats, and locations, without replication. It creates a unified virtual data layer that delivers data services to support users and various business applications.
Data virtualization performs many of the same data integration functions, that is, extract, transform, and load, data replication, and federation. It leverages modern technology to deliver real-time data integration with agility, low cost, and high speed. In effect, data virtualization eliminates traditional data integration and reduces the need for replicated data warehouses and data marts in most cases.
Capabilities and Benefits of Data Virtualization
There are various benefits of implementing data virtualization in an organization.
Firstly, data virtualization allows access and leverage of all information that helps a firm achieve a competitive advantage. The solution offers a unified virtual layer that abstracts the underlying source complexity and presents disparate data sources as a single source.
Data virtualization is cheaper since it does not require actual hardware devices to be installed. In other words, organizations no longer need to purchase and dedicate a lot of IT resources and additional monetary investment to create on-site resources, similar to the one used in a data warehouse.
Data virtualization allows speedy deployment of resources. In this solution, resource provisioning is fast and straightforward. Organizations are not required to set up physical machines or to create local networks or install other IT components. Users have a single point of access to a virtual environment that can be distributed to the entire company.
Data virtualization is an energy-efficient system since the solution does not require additional local hardware and software. Therefore, an organization will not be required to install cooling systems.
Disadvantages of Data Virtualization
Data virtualization creates a security risk. In the modern world, having information is a cheap way to make money. In effect, company data is frequently targeted by hackers. Implementing data virtualization from disparate sources may give an opportunity to malicious users to steal critical information and use it for monetary gain.
Data virtualization requires a series of channels or links that must work in cohesion to perform the intended task. In this cases, all data sources should be available for virtualization to work effectively.
Data Virtualization Use Cases
Companies that rely on business intelligence require data virtualization for rapid prototyping to meet immediate business needs. Data virtualization can create a real-time reporting solution that unifies access to multiple internal databases.
Provisioning data services for single-view applications, such as in customer service and call center applications require data virtualization.