The denodo “Base Layer” in the Logical Data Warehouse (LDW) can be thought of as the Data Staging local layer in a more traditional data warehouse (DW) development pattern. The Base layer is the level at which the source system data structures are transformed into denodo field types and the source data structures are rendered as created in base views (bv).
Base views (bv), the first step in virtualizing data, are the denodo structures reflecting the source system structure and the second step behind the data source connection and, therefore, are essential elements for the other layers of the Logical Data Warehouse (LDW). To provide some guidance to facilitate the usefulness and performance of base views here are some best practices:
Use consistent Object Naming conventions. It is strongly recommended that the denodo standard naming conventions be used.
Import and or create the Primary Keys (PK), Foreign Keys (FK), and Associations.
Have Statistics Collection been set and include all critical fields?
Base views, as a rule, should not be cached unless absolutely necessary for reasons of performance.
Create indexes on Primary Keys (PK), Surrogate keys, and Foreign Keys (FK)
Create performance Indexes to mirror sources system to improve performance.
Populate view Metadata properties describing the type and the nature of the data which the view contains. Note: if you have a governance team, they may want to manage the metadata in the denodo data catalog.
Retain the original table name (applying naming convention prefix and field names to facilitate data Lineage traceability.?
Use denodo tools against tables, where possible, rather than (Manual) SQL views, Database views, or Stored Procedure. Denodo cannot rewrite or optimize these objects.
Field metadata should be annotated with “Not Used” if the field is always null, blank, or empty. This saves time and labor when working with levels and researching data issues
Enterprises and cloud computing become more integrated and
essential for gain or maintain a competitive advantage through big data and
Analytics. Cloud is now essential in improving operations efficiency and
synergy. To optimize the enterprise architecture with the cloud, there are a
few strategic questions need to be considered;
how much cloud business does your enterprise need?
what cloud strategy best meets your enterprise operational and security needs?
do private, public clouds, or hybrid cloud fit in your enterprise’s information
workload deployment strategy?
fit in the enterprise’s information workload deployment strategy?
is A Multi-cloud Strategy?
This probably is the point where the narrative should
introduce the principle of multi-cloud. A multi-cloud is an approach to cloud
computing which seeks to optimize enterprise costs, Return-On-Investment (ROI),
and enabling big data analytics, which is already evolving the information
workload deployment strategy of many organizations. Multi-cloud has already
affected the major software and Software-As-A-Service (SaaS) providers, which
have been rapidly evolving their application suites to enable this new
reality. As recently as this week, IBM
announced that they had moved its Cloud-native software architecture.
Time To Consider A Multi-Cloud Strategy For Your Enterprise?
Multi-cloud is a cloud computing strategy seeks to align from
different cloud providers capability to optimize different business operations
and technical requirements. A multi-cloud strategy can be a way to reduce the
dependence upon more traditional software vendors and or on a single cloud
Of A Multi-Cloud Strategy
The advantages of a multi-cloud enterprise information
workload deployment strategy are:
enterprise can still operate even if one or more of the clouds providers goes
offline or encounter other difficulties.
can avoid vendor lock-in since the enterprise’s data is stored on different clouds
service providers and could be migrated if need be.
can provide a reduction in the scales of data breach vulnerability since
breaching one cloud does not provide access to the entire data of your
enterprise, even if your organization has not implemented hybrid-cloud
(private/public) strategy because all the data simply isn’t all housed one cloud.
multi-cloud solutions are customizable. Every enterprise can select what works
best in order to achieve optimal efficiency.
Of The Multi-Cloud
The multi-cloud enterprise information workload deployment
strategy has downsides as well. For instance:
across the multi-cloud providers may require more planning, relationship
management, and strategic oversight.
implementations, while reducing the potential scale of any one security breach,
it does provide more than one potential breach point to be monitored, managed,
Based on your enterprise’s industry, use of big data technologies, information security needs and the use information analytics to gain or maintain a competitive advantage and or comparative advantage, a multi-cloud enterprise information workload deployment strategy has a place in optimizing your enterprises technical and information strategy. Especially when your multi-cloud strategy includes a hybrid-cloud (public/private) as a major pillar in your cloud strategy.
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
What Is a Logical
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
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.
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
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
To create a Data
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.
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.
The 360-degree view of
the consumer is a well-explored concept, but it is not adequate in the digital
age. Every firm, whether it is Google or Amazon, is deploying tools to
understand customers in a bid to serve them better. A 360-degree view demanded
that a company consults its internal data to segment customers and create
marketing strategies. It has become imperative for companies to look outside
their channels, to platforms like social media and reviews to gain insight into
the motivations of their customers. The 720-degree view of the customer is
further discussed below.
What is the
720-degree view of the customer?
A 720-degree view of the customer refers to a
three-dimensional understanding of customers, based on deep analytics. It
includes information on every customer’s level of influence, buying behavior,
needs, and patterns. A 720-degree view will enable retailers to offer relevant
products and experiences and to predict future behavior. If done right, this
concept should assist retailers leverage on emerging technologies, mobile
commerce, social media, and cloud-based services, and analytics to sustain
lifelong customer relationships
What Does a
720-Degree View of the Customer Entail?
Every business desires to cut costs, gain an
edge over its competitors, and grow their customer base. So how exactly will a
720-degree view of the customer help a firm advance its cause?
Social media channels help retailers interact
more effectively and deeply with their customers. It offers reliable insights
into what customers would appreciate in products, services, and marketing
campaigns. Retailers can not only evaluate feedback, but they can also deliver
real-time customer service. A business that integrates its services with social
media will be able to assess customer behavior through tools like dislikes and
likes. Some platforms also enable customers to buy products directly.
Customer analytics will construct more detailed customer profiles by
integrating different data sources like demographics, transactional data, and
location. When this internal data is added to information from external
channels like social media, the result is a comprehensive view of the customer’s
needs and wants. A firm will subsequently implement more-informed decisions on
inventory, supply chain management, pricing, marketing, customer segmentation,
and marketing. Analytics further come in handy when monitoring transactions,
personalized services, waiting times, website performance.
The modern customer demands convenience and
device compatibility. Mobile commerce also accounts for a significant amount of
retail sales, and retailers can explore multi-channel shopping experiences. By
leveraging a 720-degree view of every customer, firms can provide consumers
with the personalized experiences and flexibility they want. Marketing
campaigns will also be very targeted as they will be based on the transactional
behaviors of customers. Mobile commerce can take the form of mobile
applications for secure payment systems, targeted messaging, and push
notifications to inform consumers of special offers. The goal should be to
provide differentiated shopper analytics.
Cloud-based solutions provide real-time data across multiple channels, which illustrates an enhanced of the customer. Real-time analytics influence decision-making in retail and they also harmonize the physical and retail digital environments. The management will be empowered to detect sales trends as transactions take place.
The Importance of
the 720-Degree Customer View
Traditional marketers were all about marketing
to groups of similar individuals, which is often termed as segmentation. This technique
is, however, giving way to the more effective concept of personalized
marketing. Marketing is currently channeled through a host of platforms,
including social media, affiliate marketing, pay-per-click, and mobile. The
modern marketer has to integrate the information from all these sources and
match them to a real name and address. Companies can no longer depend on a
fragmented view of the customer, as there has to be an emphasis on
personalization. A 720-degree customer view can offer benefits like:
Firms can improve customer acquisition by
depending on the segment differences revealed from a new database of customer
intelligence. Consumer analytics will expose any opportunities to be taken
advantage of while external data sources will reveal competitor tactics. There
are always segment opportunities in any market, which are best revealed by
real-time consumer data.
Marketers who rely on enhanced digital data can
contribute to cost management in a firm. It takes less investment to serve
loyal and satisfied consumers because a firm is directing addressing their
needs. Technology can be used to set customized pricing goals and to segment
New Products and
Real-time data, in addition to third-party information, have a crucial impact on pricing. Only firms with a robust and relevant competitor and customer analytics and data can take advantage of this importance. Marketers with a 720-degree view of the consumer across many channels will be able to utilize opportunities for new products and personalized pricing to support business growth
The first 360 degrees include an enterprise-wide
and timely view of all consumer interactions with the firm. The other 360
degrees consists of the customer’s relevant online interactions, which
supplements the internal data a company holds. The modern customer is making
their buying decisions online, and it is where purchasing decisions are
influenced. Can you predict a surge in demand before your competitors? A
720-degree view will help you anticipate trends while monitoring the current
View and Big Data
Firms are always trying to make decision making
as accurate as possible, and this is being made more accessible by Big Data and
analytics. To deliver customer-centric experiences, businesses require a
720-degree view of every customer collected with the help of in-depth analysis.
Big Data analytical capabilities enable monitoring
of after-sales service-associated processes and the effective management of
technology for customer satisfaction. A firm invested in being in front of the
curve should maintain relevant databases of external and internal data with
global smart meters. Designing specific products to various segments is made
easier with the use of Big Data analytics. The analytics will also improve
asset utilization and fault prediction. Big Data helps a company maintain a
clearly-defined roadmap for growth
It is the dream of every enterprise to tap into
customer behavior and create a rich profile for each customer. The importance
of personalized customer experiences cannot be understated in the digital era.
The objective remains to develop products that can be advertised and delivered
to customers who want them, via their preferred platforms, and at a lower
The private cloud concept is running the cloud software architecture and, possibly specialized hardware, within a companies’ own facilities and support by the customer’s own employees, rather than having it hosted from a data center operated by commercial providers like Amazon, IBM Microsoft, or Oracle.
private (internal) cloud may be a one or more of these patterns and may be part
of a larger hybrid-cloud strategy.
Home-Grown, where the company has built its own software and or hardware could infrastructure where the private could is managed entirely by the companies’ resources.
Commercial-Off-The-Self (COTS), where the cloud software and or hardware is purchased from a commercial vendor and install in the companies promises where is it is primarily managed by the companies’ resources with licensed technical support from the vendor.
Appliance-Centric, where vendor specialty hardware and software are pre-assembled and pre-optimized, usually on proprietary databases to support a specific cloud strategic.
Hybrid-Cloud, which may use some or all of the about approaches and have added components such as:
Virtualization software to integrate, private-cloud, public-cloud, and non-cloud information resources into a central delivery architecture.
Public/Private cloud where proprietary and customer sensitive information is kept on promise and less sensitive information is housed in one or more public clouds. The Public/Private hybrid-cloud strategy can also be provision temporary short duration increases in computational resources or where application and information development occur in the private cloud and migrated to a public cloud for productionalization.
In the modern technological era, there are a variety of cloud patterns, but this explanation highlights the major aspects of the private cloud concept which should clarify and assist in strategizing for your enterprise cloud.
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.
Cloud computing is a service driven model for enabling ubiquitous, convenient, on demand network access to a shared pool computing resources that can be rapidly provisioned and released with minimal administrative effort or service provider interaction.