Exploring OLAP in simple words with examples and scenarios

What is OLAP? OLAP stands for Online Analytical Processing. Think of it as a high-powered magnifying glass for data. It allows businesses to look at their data from different perspectives and dimensions, turning raw data into insightful information.

Online analytical processing (OLAP) is a technology that organizes large business databases and supports complex analysis. It can be used to perform complex analytical queries without negatively affecting transactional systems.

Simple Example: Imagine you run a chain of toy stores. You have sales data for every store, every day, for multiple years, and for thousands of toys. Now, you want to answer questions like:

• Which store had the most sales last December?
• Which toy was the best-seller during the summer across all stores?
• How did toy sales this year compare to the previous year?

With OLAP, you can "slice and dice" the data to answer these questions quickly.

 Lets sum it up in technical terms:

 "The databases that a business uses to store all its transactions and records are called online transaction processing (OLTP) databases. These databases usually have records that are entered one at a time. Often they contain a great deal of information that is valuable to the organization. The databases that are used for OLTP, however, were not designed for analysis. Therefore, retrieving answers from these databases is costly in terms of time and effort. OLAP systems were designed to help extract this business intelligence information from the data in a highly performant way. This is because OLAP databases are optimized for heavy read, low write workloads."

 

Why We Use OLAP:

1. Speed: OLAP databases are optimized for querying, making the retrieval of complex data faster.
2. Flexibility: Users can look at the data from multiple dimensions. In our toy store example, dimensions could be time (years, months), locations (store branches), or products (different toys).
3. Ad-hoc Queries: Unlike traditional databases where you might need predefined queries, OLAP allows spontaneous data exploration.

What Purpose It Solves:

1. Informed Decision Making: Businesses can make decisions based on data-driven insights.
2. Identifying Patterns: By analyzing data trends, businesses can capitalize on opportunities or address potential challenges.
3. Enhanced Productivity: Since OLAP tools are user-friendly, even non-tech staff can explore data without relying on the IT department.

What Happens Without OLAP:

1. Slower Insights: Traditional databases might not handle complex queries as efficiently.
2. Limited Perspectives: Without the ability to "slice and dice" data, businesses might miss out on key insights.
3. Dependency on IT: Non-tech employees might constantly need IT's help to pull and analyze data.

 

Use Cases:

1. Business Reporting: Companies can generate monthly, quarterly, or annual reports to see performance metrics.
2. Trend Analysis: Businesses can identify which products are becoming more popular over time.
3. Budgeting and Forecasting: By analyzing past spending and revenue, companies can set future budgets.

In Conclusion: OLAP is like a Swiss Army knife for data. It offers a multifaceted view of information, ensuring businesses not only see the complete picture but also understand it. In today's competitive landscape, having an OLAP system is not just an advantage; it's a necessity for data-driven decision-making.

 

OLAP vs. OLTP: Differences, Relationship, and Choosing the Right One

What are OLAP and OLTP?

• OLAP (Online Analytical Processing): It's like the "brain" of the data world. OLAP focuses on complex queries and involves vast amounts of data to assist in decision-making processes.
• OLTP (Online Transaction Processing): Think of this as the "heartbeat." OLTP manages daily transactions and ensures smooth business operations.

Key Differences:

1. Purpose:
• OLAP: Used for data analysis and decision-making. It answers questions like "Which region had the highest sales last year?"
• OLTP: Manages daily operations. It processes actions like placing an order or updating inventory.
2. Database Design:
• OLAP: Typically has a star or snowflake schema, making it efficient for complex queries.
• OLTP: Usually follows a relational model, ensuring data integrity and fast transaction processing.
3. Data Volume:
• OLAP: Deals with large amounts of historical data.
• OLTP: Manages current transactional data, which is comparatively smaller.
4. Query Complexity:
• OLAP: Complex, involving aggregations across multiple tables.
• OLTP: Simple and quick, typically affecting only one record at a time.

Relationship Between the Two:

• Data in OLTP systems often gets transferred to OLAP systems for analysis. You can think of OLTP as the frontline data gatherer, while OLAP is the analyzer.
• OLTP handles the business's day-to-day operations, but once the data is old or needs analyzing, it's sent to OLAP systems.

When to Choose What:

1. E-Commerce Store:
• Scenario: Processing a customer's order.
• Choose: OLTP, because it's a daily transaction.
• Scenario: Analyzing the year's most popular products.
• Choose: OLAP, as it requires aggregating past sales data.
2. Bank:
• Scenario: A customer transferring money to another account.
• Choose: OLTP, as it's a real-time transaction.
• Scenario: The bank wants to analyze the spending patterns of customers over the past five years.
• Choose: OLAP, as it's a complex, data-heavy query.
3. Hospital:
• Scenario: Updating a patient's current health details during a visit.
• Choose: OLTP, for immediate data entry and retrieval.
• Scenario: Analyzing the efficiency of a treatment plan over the past decade.
• Choose: OLAP, to get insights from historical data.

Conclusion: While OLAP and OLTP have different purposes, they're two sides of the same coin. OLTP ensures that businesses run smoothly day-to-day, while OLAP provides the insights needed for growth and improvement. Choosing between them depends on the task at hand: immediate transaction or deep analysis.

 

 

 

 

Scenario:

A multinational retail company wants to analyze its sales data. This data includes transaction details, product details, customer information, and store information. The primary objective is to understand sales performance, customer behaviors, and product popularity.

1. Data Ingestion:

Data Sources:

• Transaction Data: Details of every sale made, including product ID, store ID, customer ID, date of purchase, and amount.
• Product Data: Details of each product, including product ID, product name, category, and price.
• Customer Data: Customer ID, name, age, gender, and location.
• Store Data: Store ID, location, and size.

All this data is ingested into Azure Blob Storage from various point-of-sale systems, inventory systems, and customer management systems.

2. Data Movement:

Using Azure Data Factory:

• Data from different sources is moved into Azure Synapse Analytics.
• Basic cleaning is performed: removing duplicate entries, filling missing values, and correcting format inconsistencies.

3. Data Processing in Azure Synapse Analytics:

• Transformation: Converting product prices from different currencies to a standard currency (e.g., USD).
• Aggregation: Summing up total sales for each product, each store, and each region.
• Calculation: Calculating the average sale amount, profit margins for products, and customer lifetime value.

4. Data Modeling in Azure Analysis Services:

• Hierarchies:
• Time Hierarchy: Year → Quarter → Month → Day.
• Product Hierarchy: Category → Sub-category → Product Name.
• Location Hierarchy: Country → State/Province → City → Store.
• Metrics Defined: Total Sales, Average Sale, Total Profit, Number of Transactions.
• Relationships: Between tables, like connecting transaction data to product data using product ID, allowing us to see which products have the highest sales.

5. Data Visualization in Power BI:

With the OLAP model ready:

• A business user can see which products are the top sellers in Q1 in the USA.
• Drill-down to see top sellers in New York in January.
• Slice-and-dice data to compare sales of male vs. female customers.
• Observe trends, like a sudden increase in sales of winter products during December.

Summary:

So, in our example, raw transactional data from stores worldwide gets transformed into actionable insights. The company can easily understand its performance metrics at various granularities (like global sales vs. sales in a specific store on a specific day) and act on these insights to improve its business strategies.

Scenario:

A large hospital chain is aiming to improve patient care and optimize its operations. They want to analyze patient records, treatment histories, equipment usage, and staff schedules. Their primary objective is to understand patient recovery patterns, resource allocation, and staff efficiency.

1. Data Ingestion:

Data Sources:

• Patient Records: Details like patient ID, name, age, ailment, admission date, and discharge date.
• Treatment Data: Records of treatments prescribed, including medicine, surgeries, therapies, and their durations.
• Equipment Usage Data: Which equipment was used, for which patient, duration, and date.
• Staff Data: Staff ID, specialization, shifts, and leave records.

All these data sets are ingested into Azure Blob Storage from various hospital management systems.

2. Data Movement:

Using Azure Data Factory:

• Data is moved into Azure Synapse Analytics.
• Basic cleaning: Removing any incorrectly entered records, ensuring data privacy by masking certain patient details, and resolving discrepancies in drug names or equipment names.

3. Data Processing in Azure Synapse Analytics:

• Transformation: Converting diverse date formats to a standard one (e.g., YYYY-MM-DD).
• Aggregation: Counting the number of patients treated for each ailment, summing up equipment usage hours, and calculating staff working hours.
• Calculation: Determining average recovery time for each ailment, calculating equipment downtime, and computing staff overtime.

4. Data Modeling in Azure Analysis Services:

• Hierarchies:
• Time Hierarchy: Year → Month → Week → Day.
• Disease Hierarchy: Disease Category (like Cardiovascular) → Specific Disease (like Heart Attack) → Complications (if any).
• Staff Hierarchy: Department → Role → Individual Staff Member.
• Metrics Defined: Number of Patients, Average Recovery Time, Equipment Usage Hours, Staff Working Hours.
• Relationships: Connecting patient records with treatment data to understand which treatments are most effective for specific ailments.

5. Data Visualization in Power BI:

With the OLAP model in place:

• A hospital manager can analyze the average recovery time of patients suffering from cardiovascular diseases in the past year.
• Drill down to see the recovery patterns of heart attack patients specifically.
• Cross-examine data to check the efficiency of specific treatments or therapies on recovery times.
• Understand patterns, like increased hospital admissions during flu seasons.

Summary:

In this example, diverse hospital data, ranging from patient records to staff schedules, gets transformed into actionable insights. Hospital management can gain a deeper understanding of patient care patterns, resource allocation efficiency, and potential areas of improvement. This not only optimizes hospital operations but also enhances patient care quality.