Research Paper on Data Warehouse Implementation
Strategy for Eta-Beta-Pi
Submitted To: Prof. Murouj Aljamaeen
Submitted By: 22038491
Contents
Abstract: ....................................................................................................................................................... 4
Introduction: ................................................................................................................................................ 4
2. Franchising Challenges: ....................................................................................................................... 4
2.1 Standardization of Data: ................................................................................................................. 5
2.2 Ensuring Compliance: ..................................................................................................................... 5
2.3 Data Privacy and Security: ............................................................................................................. 5
3. Current Information Reporting System: .............................................................................................. 5
3.1 Limitations of the Existing Monthly Reporting System: .............................................................. 5
3.2 Types of Data Collected and Utilization: ...................................................................................... 5
3.3 Franchisee Data Retention: ........................................................................................................... 6
3.4 Margins Analysis:............................................................................................................................. 6
4. OSIC's Vision and Requirements: ....................................................................................................... 6
4.1 Revitalizing Eta-Beta-Pi: ................................................................................................................. 6
4.2 Importance of Up-to-Date Information: ......................................................................................... 6
4.3 Specific Requirements for the New System: ............................................................................... 7
5. Data Warehouse Technologies: .......................................................................................................... 7
5.1 Review of Data Warehouse Technologies: ................................................................................. 7
5.2 Scalability and Flexibility: ............................................................................................................... 7
5.3 Compatibility with Existing Systems: ............................................................................................ 7
6. Implementation Strategies:................................................................................................................... 7
6.1 Proposed Data Warehouse Implementation Strategies: ........................................................... 7
6.2 Addressing Practicalities of Data Loading: .................................................................................. 8
7. Loading Data into the Warehouse:...................................................................................................... 8
7.1 Data Integration Challenges: ......................................................................................................... 8
7.2 Frequency of Data Updates: .......................................................................................................... 8
7.3 Minimizing Disruptions: ................................................................................................................... 8
8. Conclusion and Future Work: .............................................................................................................. 9
8.1 Summary of Findings: ..................................................................................................................... 9
8.2 Recommendations: ......................................................................................................................... 9
8.3 Future Work: ..................................................................................................................................... 9
References: ............................................................................................................................................... 10
Other sources: .......................................................................................................................................... 10
The Schema: ................................................................................................................................................ 12
Practical Demonstration: ......................................................................................................................... 12
Table Creation: ..................................................................................................................................... 12
Date Insertion:....................................................................................................................................... 13
Data Analysis: ....................................................................................................................................... 16
Sales by Calendar Year, Month & Week: .................................................................................................... 16
Sales by Year: .......................................................................................................................................... 16
Sales By Month: ...................................................................................................................................... 17
Sales By Week: ........................................................................................................................................ 18
Sales by Menu Item and Category: ............................................................................................................. 19
Promotion Success Analysis: ....................................................................................................................... 20
.................................................................................................................................................................... 20
Operating Costs of Outlets:......................................................................................................................... 20
Validation: .............................................................................................................................................. 21
Abstract:
This research paper endeavors to meticulously examine and recommend the most effective
strategy for implementing a data warehouse at Eta-Beta-Pi, a multinational fast-food chain
currently facing challenges in its market presence. Operating under a franchising model, EtaBeta-Pi's historical success has diminished, prompting the need for a revamped data
management strategy. This paper explores various data warehouse implementation strategies,
acknowledging the practical nuances of the franchising model, the distinct situation at Eta-BetaPi, and the intricacies of loading data into the warehouse. The objective is to furnish OSIC with
well-justified recommendations for optimizing data management to revive the Eta-Beta-Pi brand.
Introduction:
The landscape of the fast-food industry is evolving rapidly, driven by changing consumer
preferences, technological advancements, and emerging market trends. Eta-Beta-Pi, a oncethriving multinational chain, is now at a crossroads, facing challenges that demand innovative
solutions. Originally boasting over 1,000 outlets in the 1970s and 1980s, the brand's market share
has dwindled, with only a few hundred outlets remaining due to increased competition. Moreover,
a lack of substantial investment since the 1990s has led to stagnation in the development of its
information technology (IT) systems.
However, a glimmer of hope arises as a new investment company, OSIC (Old Stuff is Cool), sees
potential in revitalizing Eta-Beta-Pi by capitalizing on its heritage status. OSIC aims to revitalize
the brand by drawing inspiration from the rise in popularity of retro dining establishments, such
as 1940s tea rooms. This endeavor requires a holistic approach, and at its core is the need for a
robust data management strategy.
The franchising model, inherent to Eta-Beta-Pi's operations, adds layers of complexity to data
management. While the brand historically maintained strict guidelines for visible front-of-house
features like style, branding, and menu, it allowed flexibility in back-office systems, including the
Point-of-Sales (PoS) systems. Consequently, each outlet currently operates distinct PoS
systems, collecting diverse types of information depending on their age. Complicating matters
further, Eta-Beta-Pi's monthly reporting system, while collecting crucial data, has limitations that
hinder comprehensive analysis.
In this context, OSIC recognizes the imperative role of up-to-date information in reshaping EtaBeta-Pi's trajectory. The investment firm is poised to overhaul the information collection and
analysis systems, identifying data warehouses as pivotal for centralizing and analyzing data. This
paper aims to provide OSIC with a tailored strategy for implementing a data warehouse, taking
into account the intricacies of the franchising model, the unique circumstances at Eta-Beta-Pi,
and the practicalities of loading data into the warehouse.
2. Franchising Challenges:
The franchising model, while providing a scalable and widespread business approach, introduces
its own set of challenges for data management. In Eta-Beta-Pi's case, the franchising model led
to a diversity of PoS systems across outlets. The variance in systems not only complicates data
collection but also poses challenges in standardizing and centralizing the data for meaningful
analysis.
2.1 Standardization of Data:
To streamline data management, achieving a degree of standardization across PoS systems
becomes imperative. However, the challenge lies in balancing this standardization with the
historical flexibility granted to franchisees in choosing their back-office systems. Striking a balance
between standardization and flexibility is essential to ensure that the data warehouse can
effectively accommodate diverse data sources while providing meaningful insights.
2.2 Ensuring Compliance:
Franchise agreements typically outline certain obligations, but ensuring compliance with data
reporting standards may vary among franchisees. A robust data warehouse strategy should
consider mechanisms to enforce data reporting standards uniformly across outlets. This might
involve incentivizing compliance or integrating reporting functionalities into the PoS systems to
streamline the process for franchisees.
2.3 Data Privacy and Security:
Franchising introduces data privacy concerns as each outlet handles sensitive information
independently. A comprehensive data warehouse strategy must address these concerns by
implementing stringent security measures. Encryption, access controls, and regular security
audits should be integrated to safeguard sensitive data, ensuring compliance with data protection
regulations.
3. Current Information Reporting System:
3.1 Limitations of the Existing Monthly Reporting System:
Eta-Beta-Pi's reliance on a monthly reporting system presents significant drawbacks. The
foremost challenge lies in the system's inability to offer real-time insights. The delay in data
compilation and analysis introduces a critical time lag that hinders the agility required for swift
decision-making in the fast-food industry. This lag is particularly impactful when responding to
dynamic factors such as changing customer preferences, market trends, and competitive moves.
Furthermore, the current reporting system focuses primarily on high-level metrics, providing a
broad overview of business performance. However, this approach lacks the granularity necessary
for informed decision-making. For instance, while the system may capture overall sales figures
and customer footfall, it may not delve into specific product performance or identify emerging
trends. This limitation significantly impairs Eta-Beta-Pi's ability to tailor its offerings, adjust menus,
and promptly respond to evolving consumer demands.
3.2 Types of Data Collected and Utilization:
The types of data collected through the existing reporting system encompass sales figures,
customer foot traffic, and basic financial metrics. However, the utilization of this data for strategic
decision-making is hampered by the system's inherent limitations. While the collected data
provides a broad overview of business performance, it lacks the granularity needed for insightful
analysis. For instance, sales figures may offer an overall snapshot of revenue trends but may not
provide detailed insights into the performance of specific products or customer preferences.
This lack of granular data hinders Eta-Beta-Pi's ability to make informed decisions about menu
changes, offers, and product development. The reliance on historical data, while informative to a
certain extent, may not capture emerging market trends or account for seasonal variations, further
limiting the effectiveness of the decision-making process.
3.3 Franchisee Data Retention:
The data landscape is further complicated by the decentralized nature of franchisee data
retention. While Eta-Beta-Pi's current reporting system centralizes certain information, individual
franchisees retain their own operational data, including details about staff costs, operating
expenses, and taxes. The potential value of this franchisee-held data for Eta-Beta-Pi's overall
analysis and decision-making processes remains largely unexplored.
Investigating franchisee data retention unveils a challenge of standardization and consistency.
Variability in data formats, reporting practices, and the level of detail provided by franchisees can
complicate efforts to integrate and analyze this information effectively. However, recognizing the
potential value of this decentralized data becomes crucial for crafting a comprehensive data
management strategy that leverages all available insights.
3.4 Margins Analysis:
Analysis of profit and loss margins stands as a critical component of Eta-Beta-Pi's decisionmaking processes. The current approach likely involves retrospective analysis based on historical
financial data. While this approach provides valuable insights into financial performance, it may
not offer real-time visibility into changing market conditions or operational inefficiencies.
Identifying specific limitations or challenges in the current approach to margins analysis is
essential for proposing improvements. The importance of margins analysis in strategic decisionmaking cannot be overstated. It informs pricing strategies, identifies cost-saving opportunities,
and contributes to overall financial sustainability.
4. OSIC's Vision and Requirements:
4.1 Revitalizing Eta-Beta-Pi:
OSIC envisions a strategic revitalization of the Eta-Beta-Pi brand by leveraging its rich heritage.
The plan involves capitalizing on the brand's historical success and resonating with contemporary
consumers through a nostalgic dining experience. A pivotal aspect of this revitalization effort is
the implementation of a revamped data management system. This system is envisioned as a
catalyst for informed decision-making, enabling Eta-Beta-Pi to draw on its heritage while adapting
to current market trends.
4.2 Importance of Up-to-Date Information:
OSIC is acutely aware of the limitations posed by the existing monthly reporting system at EtaBeta-Pi. Emphasizing the need for agility in decision-making, OSIC recognizes the crucial role
that up-to-date information will play in restoring Eta-Beta-Pi's success. Real-time insights into
customer behavior, market dynamics, and operational performance are seen as imperative for
crafting timely and effective strategies to reestablish Eta-Beta-Pi as a competitive force.
4.3 Specific Requirements for the New System:
OSIC's requirements for the new data management system are meticulous. The system must
exhibit scalability to accommodate future growth, flexibility to adapt to varying franchisee needs,
and seamless compatibility with existing systems to ensure a smooth transition. OSIC envisions
a data infrastructure that not only centralizes and analyzes information efficiently but also aligns
with Eta-Beta-Pi's operational nuances, fostering a data-driven approach to reclaim the brand's
prominence.
5. Data Warehouse Technologies:
5.1 Review of Data Warehouse Technologies:
Exploring suitable data warehouse technologies for Eta-Beta-Pi involves considering options such
as traditional relational databases, cloud-based solutions like Amazon Redshift, and specialized
data warehousing platforms. Each technology comes with distinct advantages and
disadvantages. Traditional databases offer familiarity but may lack scalability, while cloud-based
solutions provide scalability but might introduce security concerns. Specialized platforms, though
powerful, can be costlier. The selection hinges on Eta-Beta-Pi's specific needs and budget
constraints.
5.2 Scalability and Flexibility:
Evaluating scalability and flexibility is critical. Cloud-based solutions often excel in scalability,
accommodating varying data volumes. However, ensuring compatibility with diverse data sources
and adapting to Eta-Beta-Pi's future growth requires careful consideration. Specialized platforms
might offer tailored solutions but could pose challenges in adapting to evolving business
requirements. Striking the right balance is crucial for a data warehouse that can seamlessly
expand with Eta-Beta-Pi's operations.
5.3 Compatibility with Existing Systems:
Assessing compatibility involves scrutinizing how well potential data warehouse technologies
integrate with Eta-Beta-Pi's current IT infrastructure. Cloud-based solutions might require
adjustments but offer the advantage of minimal on-premises infrastructure. Traditional databases
may align well with existing systems but could face challenges in handling large-scale data.
Overcoming potential integration challenges demands strategic planning, ensuring a smooth
transition to the new data warehouse system without disrupting ongoing operations.
6. Implementation Strategies:
6.1 Proposed Data Warehouse Implementation Strategies:
Several data warehouse implementation strategies can be considered for Eta-Beta-Pi, including
phased implementation, parallel adoption, and a hybrid approach. Each strategy has its merits.
Phased implementation allows step-by-step integration, minimizing disruptions. Parallel adoption
involves running the existing and new systems concurrently, facilitating a gradual transition. A
hybrid approach combines elements of both for a customized solution. Evaluating these strategies
involves weighing practicality, cost-effectiveness, and alignment with Eta-Beta-Pi's objectives.
Factors such as budget constraints, urgency for system deployment, and adaptability to
franchisee variations must be carefully considered.
6.2 Addressing Practicalities of Data Loading:
Data loading presents both practicalities and challenges. Determining the frequency of data
updates necessitates striking a balance between real-time insights and operational feasibility.
Daily updates may offer timely information but can strain operational resources. Addressing
potential disruptions during implementation involves meticulous planning to minimize downtime.
Strategically scheduling data loading during off-peak hours and providing adequate training for
staff involved in the process can mitigate disruptions. Balancing the need for up-to-date
information with practical constraints ensures a smooth transition to the new data warehouse
system without compromising daily operations.
7. Loading Data into the Warehouse:
7.1 Data Integration Challenges:
Integrating data from diverse Point-of-Sale (PoS) systems poses challenges due to variations in
data formats and structures. Legacy systems may differ from modern counterparts, complicating
seamless integration. Addressing these challenges requires a comprehensive mapping of data
attributes, development of transformation algorithms, and establishment of standardized data
formats. Potential solutions involve employing Extract, Transform, Load (ETL) tools capable of
handling diverse data sources. Customized scripts and middleware can aid in data translation,
ensuring uniformity in the warehouse.
7.2 Frequency of Data Updates:
Determining the optimal frequency for data updates involves a delicate balance. Real-time
updates provide immediate insights but can strain operational resources. Batch processing, on
the other hand, minimizes resource load but introduces a latency period. Considering the
operational needs of Eta-Beta-Pi, a hybrid approach may be beneficial. Critical data, such as
sales and inventory, could undergo real-time updates, while less time-sensitive information
follows a batch processing schedule. This strategy maximizes data relevance without
compromising operational efficiency.
7.3 Minimizing Disruptions:
Addressing potential disruptions during the data loading phase requires strategic planning.
Scheduling data updates during off-peak operational hours minimizes impact. Additionally,
providing comprehensive training to staff involved in the loading process ensures efficient
execution. Implementing phased deployment, where different outlets or regions undergo data
integration at separate times, can further reduce disruptions. Regular communication with
franchisees regarding the transition plan fosters collaboration and understanding. Robust
contingency plans, such as data rollback procedures and quick issue resolution mechanisms,
contribute to a seamless transition, mitigating disruptions to daily operations.
8. Conclusion and Future Work:
8.1 Summary of Findings:
The exploration of Eta-Beta-Pi's data management revealed limitations in its monthly reporting
system, including delays in real-time insights and a lack of granularity. OSIC's vision emphasizes
revitalizing the brand through a revamped data management system. Proposed data warehouse
technologies include cloud-based solutions and specialized platforms.
8.2 Recommendations:
Considering the franchising model and OSIC's goals, a phased implementation strategy is
recommended. This approach minimizes disruptions, aligns with budget constraints, and
accommodates scalability needs. Prioritize the use of cloud-based solutions for their flexibility and
compatibility.
8.3 Future Work:
Future research could delve into optimizing data integration from diverse PoS systems,
addressing challenges associated with data variability. Additionally, exploring advancements in
real-time data processing and analytics would enhance the adaptability of data management
strategies in the fast-food industry.
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Practical :
The Schema:
Practical Demonstration:
Table Creation:
CREATE TABLE menuItems (
item_id NUMBER PRIMARY KEY,
item_name VARCHAR2(100),
category VARCHAR2(50)
);
CREATE TABLE sale (
sale_id NUMBER PRIMARY KEY,
sale_date DATE,
item_id NUMBER,
category VARCHAR2(50),
amount NUMBER,
CONSTRAINT fk_item_id
FOREIGN KEY (item_id)
REFERENCES menuItems(item_id)
);
CREATE TABLE promotions (
promotion_id NUMBER PRIMARY KEY,
promotion_name VARCHAR2(100),
start_date DATE,
end_date DATE
);
CREATE TABLE outletCosts (
outlet_id NUMBER PRIMARY KEY,
operating_cost NUMBER,
sale_date DATE
);
Date Insertion:
INSERT INTO menuItems VALUES (1, 'Burger', 'Meal');
INSERT INTO menuItems VALUES (2, 'Soda', 'Drink');
INSERT INTO menuItems VALUES (3, 'Ice Cream', 'Dessert');
INSERT INTO menuItems VALUES (4, 'Salad', 'Meal');
INSERT INTO sale VALUES (101, TO_DATE('2024-01-01', 'YYYY-MM-DD'), 1, 'Meal', 15.0);
INSERT INTO sale VALUES (102, TO_DATE('2024-01-02', 'YYYY-MM-DD'), 2, 'Drink', 3.5);
INSERT INTO sale VALUES (103, TO_DATE('2024-01-03', 'YYYY-MM-DD'), 3, 'Dessert', 6.75);
INSERT INTO sale VALUES (104, TO_DATE('2024-01-04', 'YYYY-MM-DD'), 1, 'Meal', 18.0);
INSERT INTO promotions VALUES (1, 'Summer Special', TO_DATE('2024-06-01', 'YYYY-MMDD'), TO_DATE('2024-08-31', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (2, 'Holiday Sale', TO_DATE('2024-12-01', 'YYYY-MM-DD'),
TO_DATE('2024-12-31', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (3, 'Weekend Deal', TO_DATE('2024-01-07', 'YYYY-MMDD'), TO_DATE('2024-01-09', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (4, 'New Year's Discount', TO_DATE('2024-01-01', 'YYYYMM-DD'), TO_DATE('2024-01-01', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (1, 1500.0, TO_DATE('2024-01-01', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (2, 1200.0, TO_DATE('2024-01-02', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (3, 1800.0, TO_DATE('2024-01-03', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (4, 2000.0, TO_DATE('2024-01-04', 'YYYY-MM-DD'));
-- Additional menuItems
INSERT INTO menuItems VALUES (5, 'Pizza', 'Meal');
INSERT INTO menuItems VALUES (6, 'Smoothie', 'Drink');
INSERT INTO menuItems VALUES (7, 'Cheesecake', 'Dessert');
INSERT INTO menuItems VALUES (8, 'Pasta', 'Meal');
INSERT INTO menuItems VALUES (9, 'Iced Tea', 'Drink');
INSERT INTO menuItems VALUES (10, 'Brownie', 'Dessert');
INSERT INTO menuItems VALUES (11, 'Sandwich', 'Meal');
INSERT INTO menuItems VALUES (12, 'Milkshake', 'Drink');
INSERT INTO menuItems VALUES (13, 'Fruit Salad', 'Dessert');
INSERT INTO menuItems VALUES (14, 'Chicken Wings', 'Appetizer');
-- Additional sale records
INSERT INTO sale VALUES (105, TO_DATE('2023-02-15', 'YYYY-MM-DD'), 4, 'Meal', 22.5);
INSERT INTO sale VALUES (106, TO_DATE('2022-05-20', 'YYYY-MM-DD'), 5, 'Drink', 4.0);
INSERT INTO sale VALUES (107, TO_DATE('2024-03-10', 'YYYY-MM-DD'), 6, 'Dessert', 7.25);
INSERT INTO sale VALUES (108, TO_DATE('2021-11-05', 'YYYY-MM-DD'), 7, 'Meal', 19.5);
INSERT INTO sale VALUES (109, TO_DATE('2023-09-08', 'YYYY-MM-DD'), 8, 'Drink', 3.0);
INSERT INTO sale VALUES (110, TO_DATE('2020-12-25', 'YYYY-MM-DD'), 9, 'Dessert', 5.5);
INSERT INTO sale VALUES (111, TO_DATE('2022-07-03', 'YYYY-MM-DD'), 10, 'Meal', 16.0);
INSERT INTO sale VALUES (112, TO_DATE('2023-04-18', 'YYYY-MM-DD'), 11, 'Drink', 4.5);
INSERT INTO sale VALUES (113, TO_DATE('2024-09-22', 'YYYY-MM-DD'), 12, 'Dessert', 8.0);
INSERT INTO sale VALUES (114, TO_DATE('2021-08-14', 'YYYY-MM-DD'), 13, 'Meal', 20.0);
-- Additional promotions
INSERT INTO promotions VALUES (5, 'Spring Delight', TO_DATE('2023-03-01', 'YYYY-MMDD'), TO_DATE('2023-05-31', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (6, 'Back-to-School Offer', TO_DATE('2022-08-15', 'YYYYMM-DD'), TO_DATE('2022-09-30', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (7, 'Autumn Feast', TO_DATE('2021-09-15', 'YYYY-MMDD'), TO_DATE('2021-11-30', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (8, 'Winter Warmth', TO_DATE('2024-12-15', 'YYYY-MMDD'), TO_DATE('2025-01-15', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (9, 'Easter Special', TO_DATE('2023-04-01', 'YYYY-MMDD'), TO_DATE('2023-04-10', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (10, 'Valentine's Love', TO_DATE('2022-02-01', 'YYYY-MMDD'), TO_DATE('2022-02-14', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (11, 'Independence Day Deal', TO_DATE('2021-07-01',
'YYYY-MM-DD'), TO_DATE('2021-07-04', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (12, 'Halloween Treat', TO_DATE('2024-10-15', 'YYYY-MMDD'), TO_DATE('2024-10-31', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (13, 'Thanksgiving Feast', TO_DATE('2023-11-01', 'YYYYMM-DD'), TO_DATE('2023-11-30', 'YYYY-MM-DD'));
INSERT INTO promotions VALUES (14, 'Cinco de Mayo Celebration', TO_DATE('2022-05-05',
'YYYY-MM-DD'), TO_DATE('2022-05-10', 'YYYY-MM-DD'));
-- Additional outletCosts
INSERT INTO outletCosts VALUES (5, 1700.0, TO_DATE('2023-02-15', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (6, 1400.0, TO_DATE('2022-05-20', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (7, 2000.0, TO_DATE('2024-03-10', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (8, 1800.0, TO_DATE('2021-11-05', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (9, 1600.0, TO_DATE('2023-09-08', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (10, 2200.0, TO_DATE('2020-12-25', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (11, 1900.0, TO_DATE('2022-07-03', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (12, 2000.0, TO_DATE('2023-04-18', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (13, 2300.0, TO_DATE('2024-09-22', 'YYYY-MM-DD'));
INSERT INTO outletCosts VALUES (14, 2100.0, TO_DATE('2021-08-14', 'YYYY-MM-DD'));
Data Analysis:
Sales by Calendar Year, Month & Week:
Sales by Year:
SELECT
TO_CHAR(sale_date, 'YYYY') AS sales_year,
SUM(amount) AS total_sales
FROM
sale
GROUP BY
TO_CHAR(sale_date, 'YYYY')
ORDER BY
TO_CHAR(sale_date, 'YYYY');
Sales By Month:
SELECT
TO_CHAR(sale_date, 'YYYY') AS sales_year,
TO_CHAR(sale_date, 'MM') AS sales_month,
SUM(amount) AS total_sales
FROM
sale
GROUP BY
TO_CHAR(sale_date, 'YYYY'),
TO_CHAR(sale_date, 'MM')
ORDER BY
TO_CHAR(sale_date, 'YYYY'),
TO_CHAR(sale_date, 'MM');
Sales By Week:
SELECT
TO_CHAR(sale_date, 'YYYY') AS sales_year,
TO_CHAR(sale_date, 'WW') AS sales_week,
SUM(amount) AS total_sales
FROM
sale
GROUP BY
TO_CHAR(sale_date, 'YYYY'),
TO_CHAR(sale_date, 'WW')
ORDER BY
TO_CHAR(sale_date, 'YYYY'),
TO_CHAR(sale_date, 'WW');
Sales by Menu Item and Category:
SELECT m.item_name, m.category,SUM(s.amount) AS total_sales
FROM sale s
JOIN menuItems m ON s.item_id = m.item_id
GROUP BY m.item_name, m.category;
Sales by Time Period (Two-Hour Intervals):
SELECT TO_CHAR(sale_date, 'YYYY-MM-DD HH24:MI') , SUM(amount)
FROM sale
GROUP BY TO_CHAR(sale_date, 'YYYY-MM-DD HH24:MI')
ORDER BY TO_CHAR(sale_date, 'YYYY-MM-DD HH24:MI');
Promotion Success Analysis:
SELECT p.promotion_name,COUNT(s.sale_id)
FROM promotions p
JOIN sale s ON s.sale_date BETWEEN p.start_date AND p.end_date
GROUP BY p.promotion_name;
Operating Costs of Outlets:
SELECT oc.outlet_id, SUM(oc.operating_cost)
FROM outletCosts oc
GROUP BY oc.outlet_id;
Validation: