Which Version Control Systems Are supported by denodo Virtualization 7.0?

Using Version Control is a denodo Virtual DataPort (VDP) recommended best practice. Version 7.0 of denodo virtualization supports three Version Control Systems (VCS):

  • Microsoft Team Foundation Server (TFS) 2010 or later
  • Apache Subversion (1.7), and
  • Git

Related References:

Denodo Data Virtualization Project Roles

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

Role To 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.

Denodo virtualization Project Roles Duties

Data Virtualization Architect

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 data/information assets
  • Defines data virtualization architecture and deployments
  • Guides the definition and documentation of the virtual data model, including, delivery modes, data sources, data combination, and transformations

Denodo Platform Administrator

The knowledge, responsibilities, and duties of a Denodo Platform Administrator, Include:

  • 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

Data Virtualization Developer

The Data Virtualization Developer role is divided into the following sub-roles:

  • Data Engineer
  • Business Developer
  • Application Developer

the knowledge, responsibilities, and duties of a Denodo Data Virtualization Developer, by sub-role, Include:

Data Engineer

The denodo data engineer’s duties include:

  • Implements the virtual data model construction view by
    • Importing data sources and creating base views, and
    • Creating derived views applying combinations and transformations to the datasets
  • Writes documentation, defines testing to eliminate development errors before code promotion to other environments

Business Developer

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
  • Writes documentation

Application Developer

The denodo application developer’s duties include:

  • Creates reporting vies from business views for reports and or datasets frequently consumed by users
  • Writes documentation

Denodo Platform Java Programmer

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

Data Virtualization Internal Support Team

The denodo data virtualization internal support team’s duties include

  • Access to and knowledge of the use and trouble of developed solutions
  • Tools and procedures to manage and support project users and developers

Denodo Virtual Dataport (VDP) naming Convention Guidance

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.      

Denodo Basic Virtual Dataport (VDP) Object Prefix Recommendations

  • Associations Prefix: a_{name}
  • Base Views Prefix: bv_{name}
  • Data Sources Prefix: ds_{name}
  • Integration View Prefix: iv_{name}
  • JMS Listeners Prefix: jms_{name}
  • Interfaces Prefix: i_{name}
  • Web Service Prefix: ws_{name}

Virtual Dataport (VDP) High-Level Project Structure

Different layers are identified when creating logical folders hierarchies within each Data Virtualization project.  The recommended high-Level project folders are:

Connectivity

  • Connectivity, where related physical systems, data sources, and base views are part of this folder.

Integration

  • Integration views include the combinations and transformations views for the next layers. Not directly consumed views at this level.

Business Entities

  • Business Entities include Canonical business entities exposed to all users.

Report Views

  • Report Views include Pre-built reports and analysis frequently consumed by users.

Data Services

  • Data Services include web services for publishing views from other levels. Can contain views need for data formatting and manipulation.

Associations

  • This folder stores associations.

JMS listeners

  • This folder stores JMS listeners

Stored procedures

  • This folder stores custom stored procedures developed using the VDP API.

Denodo Knowledge Base VDP Naming Conventions

Additional more detailed naming convention and Virtual Dataport organization guidance are available in the donodo Community Knowledge Base, under Operations

Knowledge Base Virtual Dataport (VDP) Naming Conventions Online Page

Virtual Dataport (VDP) Naming Conventions Downloadable PDF

denodo Virtualization – Useful Links

Here are some denodo Virtualization references, which may be useful.

Reference Name Link
denodo Home Page https://www.denodo.com/en/about-us/our-company
denodo Platform 7.0 Documentation https://community.denodo.com/docs/html/browse/7.0/
denodo Knowledge Base and Best Practices https://community.denodo.com/kb/
denodo Tutorials https://community.denodo.com/tutorials/
denodo Express 7.0 Download https://community.denodo.com/express/download
Denodo Virtual Data Port (VDP) https://community.denodo.com/kb/download/pdf/VDP%20Naming%20Conventions?category=Operation
JDBC / ODBC drivers for Denodo https://community.denodo.com/drivers/
Denodo Governance Bridge – User Manual https://community.denodo.com/docs/html/document/denodoconnects/7.0/Denodo%20Governance%20Bridge%20-%20User%20Manual

Related References

Using Logical Data Lakes

Today, data-driven decision making is at the center of all things. The emergence of data science and machine learning has further reinforced the importance of data as the most critical commodity in today’s world. From FAAMG (the biggest five tech companies: Facebook, Amazon, Apple, Microsoft, and Google) to governments and non-profits, everyone is busy leveraging the power of data to achieve final goals. Unfortunately, this growing demand for data has exposed the inefficiency of the current systems to support the ever-growing data needs. This inefficiency is what led to the evolution of what we today know as Logical Data Lakes.

What Is a Logical Data Lake?

In simple words, a data lake is a data repository that is capable of storing any data in its original format. As opposed to traditional data sources that use the ETL (Extract, Transform, and Load) strategy, data lakes work on the ELT (Extract, Load, and Transform) strategy. This means data does not have to be first transformed and then loaded, which essentially translates into reduced time and efforts. Logical data lakes have captured the attention of millions as they do away with the need to integrate data from different data repositories. Thus, with this open access to data, companies can now begin to draw correlations between separate data entities and use this exercise to their advantage.

Primary Use Case Scenarios of Data Lakes

Logical data lakes are a relatively new concept, and thus, readers can benefit from some knowledge of how logical data lakes can be used in real-life scenarios.

To conduct Experimental Analysis of Data:

  • Logical data lakes can play an essential role in the experimental analysis of data to establish its value. Since data lakes work on the ELT strategy, they grant deftness and speed to processes during such experiments.

To store and analyze IoT Data:

  • Logical data lakes can efficiently store the Internet of Things type of data. Data lakes are capable of storing both relational as well as non-relational data. Under logical data lakes, it is not mandatory to define the structure or schema of the data stored. Moreover, logical data lakes can run analytics on IoT data and come up with ways to enhance quality and reduce operational cost.

To improve Customer Interaction:

  • Logical data lakes can methodically combine CRM data with social media analytics to give businesses an understanding of customer behavior as well as customer churn and its various causes.

To create a Data Warehouse:

  • Logical data lakes contain raw data. Data warehouses, on the other hand, store structured and filtered data. Creating a data lake is the first step in the process of data warehouse creation. A data lake may also be used to augment a data warehouse.

To support reporting and analytical function:

  • Data lakes can also be used to support the reporting and analytical function in organizations. By storing maximum data in a single repository, logical data lakes make it easier to analyze all data to come up with relevant and valuable findings.

A logical data lake is a comparatively new area of study. However, it can be said with certainty that logical data lakes will revolutionize the traditional data theories.

Related References

10 Denodo Data Virtualization Use Cases

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.

What Is Machine Learning?

Machine Learning
Machine Learning

Machine learning is Artificial Intelligence (AI) which enables a system to learn from data rather than through explicit programming.  Machine learning uses algorithms that iteratively learn from data to improve, describe data, and predict outcomes.  As the algorithms ingest training data to produce a more precise machine learning model. Once trained, the machine learning model, when provided data will generate predictions based on the data that taught the model.  Machine learning is a crucial ingredient for creating modern analytics models.