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International Journal of Computer Trends and Technology Volume 71 Issue 9, 1-6, September 2023
ISSN: 2231–2803 / https://doi.org/10.14445/22312803/IJCTT-V71I9P101 © 2023 Seventh Sense Research Group®
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Original Article
Evolution of Enterprise Data Warehouse: Past Trends and
Future Prospects
Sivakumar Ponnusamy
Senior Data Engineer, Cognizant Technology Solutions, Richmond, VA, USA.
Received: 03 July 2023 Revised: 18 August 2023 Accepted: 01 September 2023 Published: 15 September 2023
Abstract - Data Warehousing has evolved over the past few decades primarily due to the exponential growth of data that
traditional system is unable to handle and secondly due to technological advancement, which makes it feasible to have real-
time data and cloud technology which provides unlimited storage and scalability. The journey for these changes started with
the MIS (Management Information system) when data integration from various IT systems was possible. In the next stages,
data repositories come into demand, and warehousing modernizes with the assistance of data mart mechanisms. The
emergence of new tools and software used for the same has also given rise to Modern cloud-based SaaS data processing
systems. Data lakes and data lakehouses have transformed the systems, providing greater autonomy and enabling the
processing of larger volumes of data to generate insights for decision-making. The future of Datawarehouse will be based on
AI and Machine Learning, which would be helpful with infrastructure scalability, cost savings, and agility, as well as
increasing the reliability and usability of the data as well.
Keywords - Data warehouse, ETL, DataLakeHouse, Bigdata, Machine learning.
1. Introduction
Data warehousing has been subjected to alternation and
innovation as the world of data has gone through changes in
terms of volume and variety of the information collected.
Technological advancement is the key, urging users and
developers to work on the new data models. One of the
common examples of such is the high usage of cloud
computing, which has changed the complete IT
infrastructure. In the same way, the data warehousing
techniques have also changed with the transformation in the
methodologies to manage it. According to the research
presented by Nambiar and Mundra (2022), advancements in
cloud computing, IoT, and data analytics have caused
changes in data warehousing over time. The need described
by businesses can also be resolved with the help of smart
tools and techniques. Businesses require data warehousing
solutions that can scale to meet their performance needs. As
data volumes increase, systems must handle larger query
workloads and promptly deliver results. According to the
findings of D. Subotić (2015), multiple factors have led to
the evolution of the data warehousing methodologies and
techniques. Mainly, data volume and variety of the data
needed to be stored is the key element. Data warehousing
systems must evolve to accommodate larger and more
diversified datasets, including structured, semi-structured and
unstructured data, as the amount of data generated by
businesses and individuals continues to expand dramatically
[2].
Dhaouadi et al. (2022) have said that the need for real-
time insights has prompted data warehousing to develop to
facilitate real-time data processing and analytics. This
requires technologies that process and analyze data as it is
generated, enabling businesses to make faster decisions. The
adoption of cloud computing has changed data warehousing
in addition to the other contributing elements since it offers
scalable, affordable, and adaptable solutions. Cloud-based
data warehouses eliminate the need for up-front
infrastructure investments by enabling organizations to scale
their resources up or down in response to demand. Data
warehouses can now handle and analyze data more
effectively thanks to advancements in hardware, including
faster processors, greater memory capacity, and high-speed
storage devices [3].
2. Literature Review
2.1. MIS
MIS was the major intervention in the world of data
warehousing and IT integration. Businesses relied on MIS
systems to generate basic reports and gain insights into their
data. These systems were typically file-based and lacked the
capabilities to handle large volumes of data. Mishra et al.
(2015) described in their research that MIS systems laid the
groundwork for structured data management practices. They
introduced the idea of arranging data into clearly defined
fields and tables, consistent with data warehousing's
structured nature. It was simpler to gather, store, and retrieve
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2
data from numerous sources using this method. It pulled data
from various departments within an organization to generate
reports and provide insights. This need for data integration
and consolidation paved the way for the data warehousing
concept, where data from different sources is brought
together into a central repository for analysis [4].
Varajão et al. (2022) have given the justification for the
rise of MIS and caused the rise of data warehousing with its
assistance. They stated that MIS systems emphasized the
importance of historical data for decision-making. Data
warehouses built upon this principle by storing historical
data over time, enabling trend analysis, historical
comparisons, and predictive modeling. This system is
primarily focused on generating predefined reports and basic
analytics. Data warehousing expanded on this concept by
providing more advanced reporting and analytics
capabilities, allowing users to perform complex queries, drill
down into data, and create custom reports. More
sophisticated Business Intelligence (BI) tools were
developed with the evolution of data warehousing. These
tools allow users to create interactive dashboards and
visualizations and perform ad-hoc queries for deeper
insights. MIS is also significant in usage, highlighting the
need for accurate and consistent data for effective decision-
making. Data warehousing solutions adopted data quality
practices to ensure data accuracy, integrity, and consistency,
improving overall data reliability [5].
2.2. Data Warehouses
After the MIS, the computing technologies further
transformed into more modern technologies, which were
excessively beneficial for the data warehousing processes.
Data warehousing emerged as a new concept in the late
1980s and early 1990s. A Data warehouse is subject-
oriented, integrated, time-variant, non-volatile data collection
used to support management decision-making processes.
Data warehouses are centralized, integrated repositories that
store structured data from various sources. They are designed
to support analytical processing and provide a historical data
view. Technologies like Online Analytical Processing
(OLAP) and Extract, Transform, Load (ETL) became
essential components of data warehousing solutions. Online
analytical processing (OLAP) allows data analysis from
different viewpoints.
OLAP provides the benefit of faster decision-making
and an integrated view of data. OLAP system operates in 3-
main types-MOLAP (Multi-dimensional OLAP), ROLAP
(Relational OLAP) and HOLAP (Hybrid OLAP). Data
modelling is data representation in a Data warehouse or
OLAP cube. It stores multidimensional data as Star or
Snowflake schema. Star schema consists of Facts and a
Dimension table. The fact table contains numerical facts
related to business processes, which refers to the Dimension
table via foreign keys. Snowflake schema is an extension of
star schema where some dimension table leads to one or
more secondary dimension tables [6]. Business people
perform basic analytical operations with OLAP cubes, such
as slice, dice, Rollup, Drill down and Pivot.
2.3. Data Marts
Data marts are subsets of data warehouses that focus on
specific business departments or user groups. They are
designed to provide faster access to relevant data for
particular use cases, making it easier for end-users to obtain
the information they need without querying the entire data
warehouse. As David Loshin (2013) stated in his book about
data marts, he presented his opinion on data warehousing.
Data marts and data warehouses differ mainly because they
serve different purposes.
Data warehouses are generally used for exploratory
analysis, while data marts are for formalized reporting and
specific drill-down investigations. As data marts focus on the
goals and needs of a specific department, they contain
smaller amounts of data, but that data is highly relevant to
the department's operation. Different departments might
require different data mart structures due to their unique
analytical or reporting needs [9,10].
Following the research presented by Edward M. Leonard
(2011), the role of data marts comes after the traditional data
warehouse techniques. Data marts are specialized structures
developed from a data warehouse and designed to organize
data for specific business purposes. This customization
makes data marts a vital tool for addressing the unique data
needs of different departments or business units.
Three are 3-type of data marts differ based on their
relationship to the Data warehouse and source system, which
feeds DataMart or Data warehouse. Dependent data marts are
subsets which get loaded from the Enterprise data warehouse
(top-down approach or Bill Inmon model). Independent data
marts are standalone DataMart, which serves specific
business domains and are combined to form Enterprise
Datawarehouse (Bottom-up Approach or Ralph Kimball
model). Hybrid data marts combine data from existing data
warehouses and other Operational Data stores (ODS).
Moreover, the availability of numerous reporting tools
makes data warehouses user-friendly. These tools empower
individuals to extract data by themselves instead of waiting
for others to distribute it, enhancing the efficiency of data
analysis and decision-making processes. The author further
emphasized the critical role of data warehouses and data
marts in enabling Business Intelligence by facilitating data
access, organization, and analysis. The ability to create data
marts from a data warehouse and to use diverse reporting/BI
tools makes data warehouses invaluable for the Business and
management community to make strategic business decisions
[11].
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2.4. BigData
As time passed, data volumes exploded, and traditional
data warehouses faced challenges in handling the sheer scale
and diversity of data. Santoso and Yulia (2017) have stated
how combining big data technology with data warehouses
can aid the decision-making process for university
management by turning raw data into actionable insights. Big
Data is defined by 3V’s- Volume, velocity and variety. Data
generated from social media, IOT sensors, and weblogs are a
few examples of Big data. It can be structured, semi-
structured or unstructured data. There are valuable insights
that can be derived from Bigdata, such as customer
sentiments and market insights, which is impossible without
implementing a big data solution. Apache Hadoop is mainly
for storage, and MapReduce, Spark and other technologies
are used for processing. Apache Hive is a distributed, fault-
tolerant Data warehouse system that enables big data
analytics. The paper concludes by pointing out the need for
future developments and the implementation of institutional
projects involving Big Data [7,8].
2.5. NoSQL Database
The need for the data fields had increased and required
major changes in the data management domain. NoSQL
databases became more popular as big data increased and
more adaptable data models were required. These databases,
like MongoDB, Cassandra, AWS DynamoDB, Couchbase
and Hbase, provide horizontal scaling and schema flexibility,
making them perfect for some large data applications. The
research offered by the article from Sokolova et al. (2019)
states a redesign of a database management system for a
retail business company. Originally based on a traditional
data model, the database system is migrated to a hybrid
model that combines SQL and NoSQL databases. Adding the
NoSQL database enhances the system's flexibility,
scalability, and efficiency. NoSQL databases store data in a
single data format, including a JSON document, rather than
the traditional table structure of a relational database. NoSQL
is also a type of distributed database, meaning that
information is copied and stored on various servers, which
can be remote or local; hence, it provides data availability
and reliability. Some main types of NoSQL databases are
key-value, document, graph, In-memory, wide-column and
search. NoSQL databases are very good at horizontal scaling
with high performance and availability. The paper also
discusses the architecture of this redesigned system and its
functionality, emphasizing the benefits brought by the hybrid
approach, combining both SQL and NoSQL databases [12].
2.6. Modern Cloud Data System
Cloud computing revolutionized data warehousing by
offering scalable, flexible, and cost-effective solutions.
Cloud-based data warehouses like Snowflake, Amazon
Redshift and Azure Synapse Analytics became popular
choices for organizations seeking to offload infrastructure
management and scale their data processing based on
demand. Bhatti and Rad (2017) have described cloud
computing in the way that the significant shift in the
Information Technology industry from traditional relational
databases to cloud databases over the last 40 years. The
cloud is particularly suitable for data-intensive applications,
such as storing and mining large datasets and commercial
data [13].
Applications supported by cloud databases are diverse
and adaptable, with many value-based data management
applications like banking, online reservation, e-commerce,
and inventory management being developed. However, while
these databases support key features like Atomicity,
Consistency, Isolation, and Durability (ACID), their use in
the cloud is not straightforward. The paper's objective was to
investigate the pros and cons of databases commonly used in
cloud systems and to examine the challenges associated with
developing cloud databases. Data security is the main issue
with moving into a cloud-based Data warehouse as we
cannot store NPI data in plain text; hence, it requires added
complexity, such as tokenization, before storing confidential
data in the cloud. Some of the challenges stated include the
security risks, reliability of the cloud system for operations,
and higher costs possess the challenges [14,15].
2.7. Data Lake and Data Lakehouse
Data lakes emerged as an alternative approach to
traditional data warehousing. A data lake is a centralized
repository that can store both structured and unstructured
data in its raw form. The concept of a Data lakehouse
combines data lakes with some elements of data
warehousing, aiming to bridge the gap between data
engineering and data analytics by providing features like
ACID transactions, data indexing, and support for SQL
queries directly on raw data. The modern data architecture
ensures the combination of elements from both a data
warehouse and a data lake. It is known as a "data lakehouse,"
which is a hybrid of data warehouses and data lakes. This
new concept seeks to break down silos between data
engineers and data scientists to foster a collaborative
environment, ultimately enabling more effective data
analysis [16].
Further, details were given to explain the procedure on
which data lakes and lake houses operate. The data lake
house is designed to handle both structured and unstructured
data, providing a unified platform for data engineers and data
scientists to work together. Previously, these two roles
tended to operate in separate domains, with data engineers
mostly working with structured data in data warehouses and
data scientists preferring data lakes for their versatility in
handling both structured and unstructured data. The
datalakehouse merges these domains, eliminating duplication
of effort and speeding up the process of finding value in the
data [17].
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Such a mechanism also brings improvements in data
management. It is capable of handling diverse types of data,
including structured, semi-structured, and unstructured data,
all in a cost-effective manner. This ability, combined with
data diversity, reduces the risk of data loss and enhances data
recovery and availability. The lake house paradigm fosters an
environment conducive to not only descriptive and predictive
reporting but also prescriptive reporting, which provides
advice on potential outcomes and next steps. Faster access to
shared, secure, and connected data enables businesses to
align with modern analytics and gain insights more quickly.
Moreover, it supports the need for faster development
and productization, essential for businesses seeking to extract
value from their data scientists. With data scientists spending
less time on data preparation, they can focus more on
modeling data and deriving insights from it. This agility and
speed are key for organizations wishing to mature their
business reporting and analytics practices. The lakehouse
provides a conducive environment for machine learning and
AI operations. With data's increasing volume and diversity,
organizations leverage machine learning and AI to analyze
and interpret data effectively. The lakehouse offers a "data
playground" for data scientists, allowing them to build
advanced analytics models using large quantities of
structured and unstructured data [18].
2.8. Real-time Data Processing
The need for data processing technologies has been
enhanced with the intervention of online businesses and the
boom of usage in enterprises. With the demand for real-time
analytics, modern data systems have focused on providing
real-time data processing capabilities. Technologies like
Apache Kafka, Apache Flink, and Apache Spark Streaming
allow for real-time data ingestion, processing, and analytics
[19].
2.9. Data Governance
As data becomes more critical, the need for proper data
governance and security measures has grown. Organizations
now focus on implementing robust data governance
frameworks to ensure data privacy, compliance, and data
quality. The introduction to the concepts related to data
governance has enhanced the data warehousing techniques,
which has resulted in the minimization of data-related
research and also made the way for the governance, its usage,
and utility that can be gained. Research presented by
Abraham et al. (2019) stated that data governance refers to
managing data to enhance its value and minimize data-
related costs and risks. In the context of data governance, a
data warehouse could be an essential tool since it offers a
structured repository for storing and analyzing data, which
could be crucial in implementing effective data governance
strategies. This can enable organizations to maintain data
quality, consistency, security, and privacy, which are
significant components of data governance. Nonetheless, for
specific references to data warehousing, it might be
beneficial to review other parts of the text or a different text
[20].
2.10. AI and Machine Learning Integration
The trend of development and installation of modern
tools and technologies are a part of daily targets for large
firms. As plenty of data is available, and not just commercial
enterprises, non-profit organization have made their
dependence upon the data. Nowadays, The decision-making
process depends entirely on the data available, which could
be qualitative or quantitative. There has been a push to
integrate AI and machine learning capabilities into data
warehousing solutions in recent years. This integration
enables businesses to leverage advanced analytics, predictive
modeling, and machine learning algorithms to gain deeper
insights from their data. A research paper presented by Sizwe
et al. (2015) examines the role of knowledge engineering in
enhancing organizational capabilities and adapting to
unpredictable market environments. It stresses the
importance of transforming collected data into real-time
information to support successful decision-making [21] and
delivers timely results. As data becomes increasingly crucial,
data governance practices have gained prominence, ensuring
data quality, security, and compliance. The integration The
authors emphasize the necessity of integrating artificial
intelligence into data warehousing and data mining. The
integration of data warehousing holds promise as machine
learning and AI algorithms drive advanced analytics,
automated data preparation, intelligent query optimization,
and even more accessible user interactions through natural
language. The authors emphasize the necessity of integrating
artificial intelligence into data warehousing and data mining.
The integration of AI can help in analyzing and interpreting
vast amounts of data, which can be a complex and
challenging process. The research aims to explore suitable
techniques, technologies, and trends to facilitate this
integration, providing an insightful overview of data
warehousing and data mining. It also aims to highlight the
techniques and limitations of analyzing and interpreting large
amounts of data. In this context, AI and machine learning can
be crucial tools for making sense of vast, complex data sets
and extracting meaningful insights [22].
3. Future Prospects
The potential for future data warehousing relies on
implementing advanced analytical steps. Data warehouses
will leverage machine learning and AI algorithms to provide
more advanced analytics and predictive insights. ML models
can be trained on historical data to make predictions and
recommendations, enabling organizations to predict trends
and make informed decisions. There is also a possibility that
Machine learning algorithms can be used to automate the
process of data preparation, including data cleaning,
transformation, and integration. This will reduce the manual
effort required to structure and format data for analysis. The
Sivakumar Ponnusamy et al. / IJCTT, 71(9), 1-6, 2023
5
generation and ease of access to AI have made everything
easier in this world. So comes the data warehousing as well,
as it is expected that AI-powered query optimization will
become more sophisticated, automatically selecting the most
efficient query execution plans based on data distribution,
workload patterns, and system performance. AI and Machine
Learning applications in Data warehousing will help
optimize resource allocation & usage, thereby reducing
operational costs for Datawarehouse. Data warehouses will
integrate NLP capabilities to enable users to query and
interact with data using natural language. This will make data
analysis more accessible to a wider range of users, including
business users without much technical expertise, thus
reducing the learning curve for querying data [23].
4. Conclusion
The evolution of data warehousing has been a dynamic
journey driven by the increasing volume, velocity, and
variety of data. Technological advancements, including cloud
computing, big data solutions, and AI integration, have
played pivotal roles in shaping the modern data warehousing
landscape. This evolution has empowered businesses to
make more informed decisions, gain competitive advantages,
and enhance their overall operations. Research has
highlighted the extensive factors influencing data
warehousing changes over time, reflecting the growing needs
of businesses that are now effectively addressed through
smart tools and techniques. The scalability imperative is ever
more relevant, with data volumes continually on the rise. In
response, data warehousing solutions have adapted to handle
larger query workloads efficiently by incorporating AI and
machine learning capabilities into data warehousing. This has
marked a transformative phase, enabling deeper insights and
automation of various processes. The future of data
warehousing holds promise as machine learning and AI
algorithms drive advanced analytics, automated data
preparation, intelligent query optimization, and even more
accessible user interactions through natural language
processing. As technology continues to advance, data
warehousing's evolution is poised to remain aligned with
business needs, enabling organizations to harness data's full
potential for strategic decision-making and innovation.
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