User avatar DWH basics: basics of the architecture of OLAP system

In today's era of data-driven decision-making, organizations are increasingly turning to Data Warehousing (DWH) and Online Analytical Processing (OLAP) systems as indispensable tools for unlocking valuable insights from their data. This topic aims to provide a comprehensive understanding of the architecture of OLAP systems within the broader context of data warehousing. By digging into the central components, storage models, types of OLAP, and the role of the OLAP engine, we will explore how these systems empower businesses to extract actionable intelligence and drive informed decisions.

Fundamentals of data warehousing

A Data Warehouse (DWH) can be described as a central repository for storing large volumes of data from various sources in a structured and organized manner. Think of it as a "data treasure chest" where data is collected, cleaned, transformed, and stored for the primary purpose of making it easily accessible for analysis and decision-making.

Online Analytical Processing (OLAP) is like a magic tool for slicing and dicing data. It's a technology that allows you to play with your data in a super flexible and interactive way as if you were handling a multi-dimensional puzzle.

Imagine you have a data cube, where each side represents different aspects of your data, like time, products, and regions. OLAP lets you spin, pivot, and drill through this cube to get the exact data insights you need. It's like having a data playground where you can explore, analyze, and discover hidden patterns effortlessly.

In addition to OLAP, there is also OLTP (Online Transaction Processing). It's like the cashier at your favorite coffee shop. When you order your daily caffeine fix, the cashier quickly processes your payment, updates the inventory of coffee beans, and prints your receipt — all in real time.

So, OLTP databases are all about handling everyday transactions in businesses. They're super speedy, and designed for tasks like adding new customers, updating inventory, and recording sales as they happen. It's like the fast-paced action in the front lines of a store, where data is in constant motion, changing with every customer order, shipment, or reservation.

In this topic, we will concentrate our attention on OLAP, but it's vital to know about other types of databases.

Key components of OLAP architecture

OLAP (Online Analytical Processing) architecture consists of several key components that work together to enable efficient data analysis. We will talk in more detail about each one of them.

Data integration and ETL

Data integration and ETL (Extract, Transform, Load) are fundamental processes in the world of data warehousing and business intelligence. They involve the collection, transformation, and loading of data from various sources into a data warehouse for analysis. Here's a closer look at each step:

Extracting data from various sources:

Extraction is the process of retrieving data from multiple source systems, which can include databases, spreadsheets, flat files, web services, and more. You can extract data from a relational database or from csv file.

Simple select, extracting data from a table.

-- Extract data from a customer table
SELECT * FROM customer;

Cleansing and transforming data:

Transformation involves manipulating and preparing the extracted data to meet the requirements of the target data warehouse. Data transformation ensures that the data is consistent, clean, and in the right format for analysis.

Data cleansing: This process involves identifying and correcting errors and inconsistencies in the data, such as missing values, duplicates, and inaccuracies. For instance, if you think you have duplicate records in your data, and you're using the delete function to exclude these records, you are occupied with a data cleansing process.

-- Identify and remove duplicate records from a table
DELETE FROM orders
WHERE order_id NOT IN (
    SELECT MIN(order_id)
    FROM orders
    GROUP BY customer_id, order_date
);

Data integration: Different data sources may have varying data structures and naming conventions. Transformation includes integrating data from these sources into a unified format.

-- Join customer and order data to create a unified dataset
SELECT c.customer_id, c.name, o.order_id, o.order_date, o.total_amount
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id;

Data enrichment: Sometimes, additional data from external sources is added to enhance the existing data. For example, adding geographical information to customer data.

Data aggregation: Aggregation involves summarizing and aggregating data at different levels of granularity. This is important for analytical queries.

Loading data into the data warehouse:

Loading is the final step where the transformed and cleansed data is loaded into the data warehouse. The data is stored in a way that facilitates efficient querying and reporting.

Data warehouse models: Data can be loaded into various data warehouse models, including star schemas, snowflake schemas, and more. These models determine how data is organized and related within the warehouse. We will talk about them more precisely, later.

-- Insert data into a fact table in a star schema
INSERT INTO fact_sales (date_id, product_id, store_id, sales_amount)
SELECT d.date_id, p.product_id, s.store_id, SUM(o.total_amount)
FROM sales o
INNER JOIN date_dimension d ON o.order_date = d.date
INNER JOIN product_dimension p ON o.product_id = p.product_id
INNER JOIN store_dimension s ON o.store_id = s.store_id
GROUP BY d.date_id, p.product_id, s.store_id;

Data storage models

Star Schema:

Think of the Star schema like a star. In the center, you have a central fact table that contains the core business data, such as sales transactions. This fact table is surrounded by dimension tables, which are like the points of the star. Each dimension table represents a specific aspect or characteristic of the data, like customers, products, or time.

Snowflake schema:

The Snowflake schema is an extension of the Star schema. Picture a Star schema, but the dimension tables are more normalized. This means that the data in dimension tables is organized into multiple related tables. It looks like a snowflake because the branches of the star (the dimension tables) have more branches, like snowflakes.

For example, instead of having a single "customer" dimension table, you might have separate tables for customer demographics, addresses, and orders. This structure can save storage space and reduce data redundancy. However, it can also make queries a bit more complex because you need to join multiple tables to get all the required information.

Galaxy schema:

Now, imagine a Galaxy schema as a collection of multiple interconnected Star schemas. Each Star schema represents a specific area or domain of your data, like sales, inventory, or finance. These Star schemas are interconnected through shared dimension tables.

For example, in an e-commerce business, you might have a Star schema for sales, another for inventory, and yet another for customer support. All of them share common dimension tables like time and products. This way, you can independently analyze each domain's data while also performing cross-domain analysis when needed. It's like having multiple stars in your data universe.

Briefly, these schema models help structure data in a data warehouse, making it easier to analyze and gain insights. The choice between Star, Snowflake, or Galaxy depends on your specific data analysis needs and the trade-offs you're willing to make in terms of storage efficiency and query complexity.

Types of OLAP

OLAP types satisfy various data analysis needs. OLAP excels in performance, ROLAP in flexibility, and HOLAP combines the strengths of both. The choice depends on the complexity of your data and the speed of analysis you require. Let's talk about them in more detail.

1. MOLAP (Multidimensional OLAP):

Think of MOLAP as a well-organized library. Imagine you have a vast collection of books (data) neatly arranged on shelves. Each book represents a data point, and the shelves categorize them by different dimensions, like time, geography, or product categories. You can easily navigate through these shelves (dimensions) to find the information you need.

In MOLAP, data is stored in multidimensional cubes, making it efficient for complex queries. These cubes have predefined hierarchies and measures. It's like having different layers of data where you can drill down into details or roll up to see summaries. This type of OLAP is known for its fast query performance, making it suitable for tasks like data analysis and reporting.

2. ROLAP (Relational OLAP):

Imagine ROLAP as a well-structured filing cabinet. Instead of cubes, your data is organized into tables, similar to a relational database. Each table represents a dimension, and relationships between tables allow you to link data across dimensions.

In ROLAP, data is stored in a relational database, typically a star or snowflake schema. While it may not be as fast as MOLAP for certain types of queries, ROLAP is highly flexible and scalable. It's like having a dynamic filing system where you can easily add new files (data) and rearrange them to suit changing needs.

3. HOLAP (Hybrid OLAP):

Think of HOLAP as a combination of the best features from both MOLAP and ROLAP. Imagine you have a versatile toolkit. HOLAP systems offer the option to store summary data in multidimensional cubes (like MOLAP) while keeping detailed data in relational tables (like ROLAP).

This hybrid approach provides the best of both worlds. You get the speed of MOLAP for aggregations and the flexibility of ROLAP for detailed data exploration. It's like having a toolkit with specialized tools for different tasks, ensuring efficient data storage and retrieval.

OLAP engine and processing

The OLAP engine and processing are the backbone of OLAP systems. They ensure that you can interact with your data in a way that's fast, flexible, and insightful, whether you're analyzing sales figures, customer demographics, or any other dataset.

Imagine the OLAP engine as the "brain" behind your data analysis operations. It's like a master conductor in an orchestra, orchestrating the flow of data for efficient analysis.

• Aggregation and precomputation: Think of this as preparing a summary or digest of your data in advance. Imagine you're planning a road trip and you precalculate the total distance, average speed, and expected arrival time before you start driving. This way, you don't need to recalculate these values every time you check your progress. In OLAP, aggregation and precomputation involve summarizing data at various levels (like monthly sales totals) in advance to speed up queries. It's like having a map ready for your journey, so you can quickly get the information you need.

• Query optimization techniques: Picture this as finding the fastest route to your destination. When you have a complex question or need specific data from your dataset, the OLAP engine uses query optimization techniques to figure out the most efficient way to get you the answer. It's like a GPS that considers traffic, road conditions, and shortcuts to provide you with the quickest path. In OLAP, query optimization involves selecting the best algorithms, indexing strategies, and data retrieval methods to ensure your queries run as fast as possible.

In summary, the OLAP engine is the conductor of your data orchestra, orchestrating the aggregation and precomputation of data summaries and optimizing your queries for efficient data analysis. It's all about making your data analysis journey smoother and faster.

Front-end client tools

Imagine these tools as your personal data detectives. They help you uncover insights from your data and present them in a way that's easy to understand.

• Reporting and visualization tools: Think of these as your storytellers. Reporting tools are like journalists who gather data and compile it into structured reports. Visualization tools are like artists who turn data into colorful and interactive charts, graphs, and maps. Together, they create informative and visually appealing stories from your data. It's like reading a well-illustrated book that makes complex information simple to grasp.

• Ad-hoc query tools: Picture these tools as your data explorers. They allow you to ask questions directly to your data, just like having a conversation. You can dig deep into your dataset, filter, pivot, and drill down to find answers to specific questions. It's like having a chat with a knowledgeable friend who can provide instant answers. Ad-hoc query tools empower you to investigate your data without needing pre-made reports or fixed queries.

So to put in a few words front-end client tools are your data companions. Reporting and visualization tools present your data as compelling stories, while ad-hoc query tools enable you to have interactive conversations with your data, helping you uncover valuable insights and make informed decisions.

Conclusion

In our exploration of OLAP architecture within data warehousing, we've uncovered key concepts:

• Data warehousing: A central repository for organized data, vital for analysis and decision-making.

• OLAP's magic: OLAP empowers flexible, interactive data exploration across multiple dimensions.

• ETL process: Data integration, extraction, transformation, and loading are fundamental for data readiness.

• Data storage models: Star, Snowflake, and Galaxy schemas.

• Types of OLAP: MOLAP, ROLAP, and HOLAP.

• OLAP engine: Efficient data processing and query optimization for swift insights.

• Client tools: Reporting, visualization, and ad-hoc query tools empower users.

The significance of OLAP architecture in decision-making is profound. It transforms data into actionable insights, guiding organizations towards informed choices, and facilitating growth and innovation. OLAP is the compass in today's data-driven landscape, helping organizations navigate complexity and drive success through data-driven decisions.

Let's consolidate our knowledge by passing several test tasks!