Unit 03: LO 1 - 1.5 - Data-base, -warehouse, -lake

Analyse the differences between a database, data warehouse and a data lake

They are all databases to start

Analyse

Break the subject down into separate parts and examine each part. Show how the main ideas are related and why they are important. Reference to current research or theory may support the analysis.

Assessment

Report

This is an example of Join. This is standard for a database. What is common between both (tables) gets displayed.

Asda keeps records of all its chocolate stock and can connect in real-time to part of the Cadbury & Nestle's Databases to pull down real-time prices of each chocolate bar. A manager at Asda can you use the data warehouse to produce reports for their line manager outlining the current profits and losses based on chocolate.

A real estate company that advertises the vacant homes, has a database that connects to the gov.uk, Facebook and another real estate company's databases (all public) to produce a report about how many homes in the UK are selling for fewer than £100,000. Real estate agents can access the Data Warehouse reports to then give that information to their clients.

Differences:

database, for example:

generic storage system, that stores data for a specific purpose

data lake, for example:

stores big amounts of unstructured, raw data, the purpose of which may not yet be realised

data warehouse, for example:

will store vast amounts of structured big data

Link below to full story

Choosing the Right Data Storage Solution: Database vs. Data Lake vs. Data WarehouseThe world today is data-driven and businesses face the challenge of managing and extracting value from large volumes of data. As a result, various data storage solutions have emerged to address dif…

https://factor-bytes.com/2023/06/25/choosing-the-right-data-storage-solution-database-vs-data-lake-vs-data-warehouse/

Power BI Main Screen

Similarities:

Data Storage:
- All three serve as storage systems for large volumes of data.
Structured Data:
- They can handle structured data, which is organized and follows a specific format.
Querying:
- Users can query and retrieve information from these systems.

Differences:

Purpose:
- Database: Primarily used for transactional processing. It's like a digital filing cabinet for storing and retrieving individual records quickly.
  Microsoft Access or SQL Server is a relational database management system
- Data Warehouse: Designed for analysis and reporting. It's like a digital library where data is organized and optimized for efficient querying and reporting.
  Azure Synapse Analytics is a cloud-based data warehousing service
- Data Lake: Stores massive amounts of raw data, structured and unstructured, and is used for big data analytics and machine learning. It's like a vast reservoir where you can store any type of data without much structuring.
  Azure Data Lake Storage is a scalable and secure data lake solution
Data Type:
- Database: Typically stores structured data, like tables with rows and columns.
- Data Warehouse: Also stores structured data but often from various sources, and it may involve some level of data transformation for analysis.
- Data Lake: Stores structured, semi-structured, and unstructured data in its raw form, providing flexibility for diverse data types.
Schema:
- Database: Has a well-defined schema, which means the structure of the data is fixed and known in advance.
- Data Warehouse: Has a schema, but it's often more flexible, allowing for changes as business needs evolve.
- Data Lake: Typically has a schema-on-read approach, meaning the data structure is applied when you access and use the data, giving more flexibility in handling different types of data.
Processing Speed:
- Database: Designed for quick data transactions, ensuring high speed.
- Data Warehouse: Balanced between transactional and analytical processing, offering good speed for reporting and analysis.
- Data Lake: Suited for batch processing and big data analytics, may have a longer processing time for complex analyses.

Database

A database is like your school locker where you keep only what you need for the day—textbooks, notes, and maybe a snack.

Characteristics:

Specific Purpose: Designed for specific tasks like transactional processing.
Structured: Usually stores structured data that can be easily indexed and queried.
Real-Time: Good for real-time operations such as updating records instantly.

Typical Use Cases:

Storing customer information in a retail setting.
Managing student grades and attendance in educational institutions.

Data Lake

Imagine a massive playground. You can bring anything you like—balls, frisbees, or even your pet. But, it's so big that if you're not careful, you might lose track of what you brought.

Characteristics:

Raw Data: Capable of storing large volumes of raw, unstructured data.
Flexible: The purpose of the data might not be known at the time of storage.
Low Cost: Often cheaper per unit of storage, suitable for massive data sets.

Typical Use Cases:

Storing IoT sensor data for future analysis.
Archiving large volumes of log files or social media posts.

Data Warehouse

Think of a data warehouse as a big library archive. The books are sorted and categorized for very specific research purposes, but it's not a place you'd go to borrow a light read.

Characteristics:

Structured: Designed for structured or semi-structured data.
Query-Optimized: Built for complex queries and analytics.
Batch-Updated: Not generally designed for real-time data updates.

Typical Use Cases:

Business analytics and reporting.
Aggregating data from different departments for a comprehensive view.

Comparison Analysis

Data Type: Databases are usually for structured data, Data Lakes for unstructured data, and Data Warehouses for structured or semi-structured data.
Purpose: Databases are more transactional and suited for specific, real-time tasks. Data Lakes are more flexible and exploratory. Data Warehouses are geared towards in-depth analysis and queries.
Cost: Databases and Data Warehouses often have higher costs due to specialized software and hardware. Data Lakes are generally cheaper for storing large volumes of data.
Flexibility: Data Lakes offer the most flexibility since they can store raw data without a predetermined schema, unlike Databases and Data Warehouses.

By understanding these distinctions, you can better identify which data storage solution is the most appropriate for your specific needs.

From within Microsoft Power BI

Screenshot from within Microsoft Power BI

Power BI Datasets, Datamarts, Lakehouses, Warehouses, and Kusto Query Language (KQL) Databases—relate to data storage and querying, but they serve different purposes and are designed for different scenarios. Here's a breakdown of what each is and how they differ:

Power BI Datasets:

What it is: A Power BI Dataset is essentially a collection of tables, relationships between them, and queries used to fetch data. The dataset can include data from one or many sources.
Use Case: Designed to be used within the Power BI ecosystem for creating reports and dashboards.
Storage: Usually, data is not stored within the dataset but is refreshed from its original source, although import mode allows for storing data.
Query Language: DAX (Data Analysis Expressions) is commonly used for querying.

Datamarts:

What it is: A Datamart is a subject-oriented database and is essentially a subset of a data warehouse. It focuses on specific business areas like sales or finance.
Use Case: Created to provide business users with a simpler way to access data relevant to their department.
Storage: Data is stored in structured form, usually following a star or snowflake schema.
Query Language: SQL is commonly used for querying.

Lakehouses:

What it is: A Lakehouse combines features of both data lakes and data warehouses. It can handle structured and unstructured data.
Use Case: Designed to be an all-in-one solution for analytics, machine learning, and real-time analytics.
Storage: Data can be stored in both structured and unstructured formats.
Query Language: Usually SQL or a SQL-like query language.

Warehouses:

What it is: A Data Warehouse is a large, centralized database used for reporting and data analysis. It combines data from many sources.
Use Case: Typically used for business reporting and analysis.
Storage: Data is stored in a structured format, typically organized for query efficiency.
Query Language: SQL is commonly used for querying.

KQL Database (Kusto Query Language):

What it is: Kusto is a big data analytics cloud platform optimized for ad-hoc data exploration. It uses Kusto Query Language (KQL) for querying.
Use Case: Mainly used for real-time analytics and telemetry scenarios, like application monitoring or troubleshooting.
Storage: Data can be stored in structured or semi-structured formats.
Query Language: Uses Kusto Query Language (KQL) for querying.

In summary:

Power BI Datasets are specifically tailored for building BI reports.
Datamarts are smaller, more focused versions of Data Warehouses.
Lakehouses try to bring the best features of data lakes and data warehouses into a single platform.
Warehouses are large, structured storage solutions designed for complex queries and analytics.
KQL Databases are optimized for real-time analytics and ad-hoc queries.

Each has its own use case, storage considerations, and preferred query language.

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