From Data Chaos to Clarity: A Comprehensive Guide to Building Your Data Warehouse and Data Lake

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Kuldeep Pal
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18 min read

What is a Data Warehouse?

A data warehouse is a central repository of data that is used to support business decision-making activities. It is a large, scalable, and reliable database that stores historical data from multiple sources. The data warehouse is designed to enable business analysts and other users to easily access and analyze the data for reporting, analysis, and decision-making.

Data warehouses are typically used by organizations that need to analyze large amounts of data from different sources to make strategic decisions. Some common examples of industries that use data warehousing include retail, finance, healthcare, and manufacturing.

Data Warehouse Modeling

Data warehouse modeling is the process of designing the structure of the data warehouse. The goal of modeling is to create a schema that can efficiently store and retrieve data, while also providing the flexibility needed to support different types of analysis and reporting.

There are three main types of data warehouse modeling techniques:

  1. Dimensional Modeling: Dimensional modeling is the most commonly used modeling technique for data warehousing. It organizes data into "facts" and "dimensions." Facts are the measurements or metrics that are being analyzed, such as sales revenue or customer count. Dimensions are the attributes that provide context for the facts, such as time, location, or product.

  2. Entity-Relationship (ER) Modeling: ER modeling is a technique used to model relationships between data entities. In data warehousing, ER modeling is often used to model the relationships between different data sources that will be combined in the data warehouse.

  3. Data Vault Modeling: Data Vault modeling is a technique that is used to model complex data structures, such as those found in large enterprise data warehouses. Data Vault models are designed to be highly scalable and adaptable to changing business needs.

Data Warehouse Architecture

Data warehouse architecture refers to the overall design and structure of the data warehouse. There are two main types of data warehouse architecture:

  1. Traditional Architecture: Traditional data warehouse architecture consists of three layers: the bottom layer is the data source layer, where the data is extracted from various sources; the middle layer is the data warehouse layer, where the data is transformed and stored; and the top layer is the presentation layer, where users can access the data for reporting and analysis.

  2. Modern Architecture: Modern data warehouse architecture is based on cloud computing and uses a distributed architecture that can scale horizontally as needed. In a modern data warehouse architecture, data is extracted from various sources and loaded into a cloud-based data storage system, where it is processed and analyzed using cloud-based tools and services.

Data warehousing and modeling are complex topics that require a deep understanding of database design, data structures, and data analysis techniques. However, with the right tools and techniques, organizations can create powerful data warehouses that enable them to make strategic decisions based on data-driven insights. I hope this introduction has been helpful in getting you started on your journey to learning more about data warehousing and modeling.

The decision to use a data warehouse or a data lake depends on the specific needs of an organization and the types of data it is collecting.

When to Use a Data Warehouse

A data warehouse is typically used when an organization needs to analyze and report on structured data from multiple sources. Structured data is data that is organized into a specific format, such as tables or spreadsheets. Data warehouses are designed to support analytical queries, data visualization, and business intelligence reporting. Some common reasons to use a data warehouse include:

  • The data is structured and can be easily categorized into dimensions and facts.

  • The data needs to be aggregated, summarized, and analyzed to provide insights into business performance.

  • The data is historical and needs to be stored for long periods of time.

  • The organization needs to enforce strict data governance and ensure data quality.

When to Use a Data Lake

A data lake is typically used when an organization needs to store and analyze large volumes of unstructured or semi-structured data. Unstructured data is data that does not have a predefined structure, such as images, audio, video, text documents, social media posts, and machine-generated logs. Data lakes are designed to support advanced analytics, machine learning, and artificial intelligence. Some common reasons to use a data lake include:

  • The data is unstructured or semi-structured and needs to be stored in its raw form for future analysis.

  • The data is generated by a variety of sources and needs to be integrated and analyzed to gain insights into business performance.

  • The organization needs to support advanced analytics, such as machine learning and AI, that require large volumes of data.

  • The organization needs to experiment with new data sources and analysis techniques without committing to a specific data model.

In summary, data warehouses are best suited for structured data that needs to be organized and analyzed for reporting and decision-making, while data lakes are best suited for unstructured or semi-structured data that needs to be stored in its raw form and analyzed for advanced analytics and machine learning.

SCD stands for Slowly Changing Dimensions. It is a technique used in data warehousing to track changes to data over time. When implementing SCDs, it is important to consider how different types of changes to data will be handled. There are three types of SCDs:

  1. Type 1 SCD: In a Type 1 SCD, the old data is simply overwritten with new data. This means that historical data is not maintained and there is no record of how the data has changed over time. This approach is appropriate when historical data is not important or when the data changes frequently and rapidly.

  2. Type 2 SCD: In a Type 2 SCD, a new record is created for each change to the data. This means that historical data is maintained, and a new record is added to the database each time the data changes. This approach is appropriate when historical data is important and needs to be preserved for reporting and analysis.

  3. Type 3 SCD: In a Type 3 SCD, a separate column is added to the table to track the most recent change to the data. This means that historical data is maintained, but only the most recent change is recorded. This approach is appropriate when historical data is important, but only the most recent change is needed for reporting and analysis.

In summary, SCDs are used to track changes to data over time in a data warehouse. Type 1 SCDs overwrite old data with new data, Type 2 SCDs maintain historical data by adding a new record for each change, and Type 3 SCDs track only the most recent change to the data. The choice of SCD type depends on the specific needs of the organization and the type of data being stored.

The standard SCD types are commonly categorized into three types: Type 1, Type 2, and Type 3, as explained in my previous answer. However, it is worth noting that there are other variations of SCDs that can be used depending on the specific requirements of the organization. Some of these variations include:

  • Type 0 SCD: This type of SCD is used to maintain a complete and unchanging history of the data. In other words, data is never updated, only inserted. This approach is appropriate when the data being tracked is static and historical changes are not relevant.

  • Type 4 SCD: This type of SCD is also known as a hybrid SCD, as it combines elements of Type 1 and Type 2 SCDs. In a Type 4 SCD, the current values of the data are stored in one column, and the historical values are stored in another column. This approach is appropriate when it is important to track changes to the data over time, but the number of historical versions of the data needs to be limited.

  • Type 6 SCD: This type of SCD combines elements of Type 1, Type 2, and Type 3 SCDs. In a Type 6 SCD, a separate column is used to track changes to the data, and historical data is also maintained in separate columns. This approach is appropriate when it is important to maintain a complete history of the data, but the number of versions of the data needs to be limited.

It is important to note that the decision to use a particular type of SCD should be based on the specific requirements of the organization and the data being tracked.

SCD (Slowly Changing Dimensions) types are a set of techniques used in data warehousing to track changes to data over time. The standard SCD types are categorized into three types: Type 1, Type 2, and Type 3, as explained in my previous answer. SCDs higher than Type 3 are not common and are usually considered as variations of the standard types.

SCD Type 4, also known as a hybrid SCD, combines elements of Type 1 and Type 2 SCDs, while Type 6 combines elements of Type 1, Type 2, and Type 3 SCDs. There is no widely accepted industry standard for SCDs beyond Type 3, and any higher SCD types are usually customized variations based on the specific needs of the organization.

Therefore, SCD Type 5 is not a widely recognized SCD type, and if it exists, it would be a customized variation that is specific to an organization's needs.

Here are some examples of each SCD type from the real world:

  1. Type 1 SCD: In a Type 1 SCD, the old data is simply overwritten with new data. An example of Type 1 SCD is the customer's contact information in a customer relationship management (CRM) system. When a customer changes their phone number, the old phone number is replaced with the new one, and historical data is not preserved.

  2. Type 2 SCD: In a Type 2 SCD, a new record is created for each change to the data. An example of Type 2 SCD is the historical sales data in a retail store. When a customer buys a product, a new record is created in the sales table with the date of purchase, the product purchased, and the customer information. If the product information changes later, a new record is created in the product table with the updated information, and the historical sales data remains intact.

  3. Type 3 SCD: In a Type 3 SCD, a separate column is added to the table to track the most recent change to the data. An example of Type 3 SCD is the customer's credit limit in a credit card system. When a customer's credit limit changes, the old credit limit is stored in a historical column, and the new credit limit is stored in the current column. This approach allows for easy reporting on the current credit limit, while still maintaining historical data.

  4. Type 4 SCD: In a Type 4 SCD, the current values of the data are stored in one column, and the historical values are stored in another column. An example of Type 4 SCD is the employee's job title in an organization. When an employee's job title changes, the new job title is stored in a current column, while the old job title is stored in a historical column. This approach allows for easy reporting on the current job title, while still maintaining historical data.

  5. Type 6 SCD: In a Type 6 SCD, a separate column is used to track changes to the data, and historical data is also maintained in separate columns. An example of Type 6 SCD is the product's price in an e-commerce system. When a product's price changes, a new record is created in the price table with the updated information, and the historical price information is stored in a separate historical column. This approach allows for easy tracking of changes over time, while still maintaining historical data and limiting the number of versions of the data.

Here are some examples of cloud-based data warehousing solutions:

  1. Amazon Redshift: Amazon Redshift is a fully managed, petabyte-scale data warehouse service that runs on Amazon Web Services (AWS). It provides fast querying and analysis of data using SQL and offers seamless integration with other AWS services such as S3, Glue, and Data Pipeline. Redshift also provides various features such as automated backups, data encryption, and data compression.

  2. Google BigQuery: Google BigQuery is a cloud-based data warehousing solution that provides fully-managed, scalable, and cost-effective data storage and querying services. BigQuery offers seamless integration with other Google Cloud Platform services, such as Cloud Storage, Dataflow, and Dataproc. It also supports standard SQL and provides features such as automated backups, real-time data streaming, and data encryption.

  3. Microsoft Azure SQL Data Warehouse: Azure SQL Data Warehouse is a cloud-based data warehousing solution that provides scalable storage and query performance. It integrates seamlessly with other Azure services such as Azure Data Factory, Azure Stream Analytics, and Azure Databricks. It also offers features such as data compression, data encryption, and automated backups.

  4. Snowflake: Snowflake is a cloud-based data warehousing platform that offers fully-managed data warehousing services for enterprises. It is designed to handle large volumes of data and provide high-speed data processing and querying. Snowflake integrates with other cloud-based services such as AWS, Azure, and Google Cloud Platform. It also offers features such as automatic scaling, data encryption, and data sharing.

These are just a few examples of cloud-based data warehousing solutions, and there are many other solutions available in the market as well.

Note that this table is not exhaustive and there may be additional features or differences between these cloud-based data warehousing solutions that are not included here. Additionally, the choice of a data warehousing solution depends on the specific needs and requirements of an organization, so it's important to evaluate each solution in the context of the use case.

Note that these prices are subject to change and may vary depending on the specific region and usage pattern. Additionally, each cloud provider may offer different pricing models, such as reserved capacity pricing or custom pricing for large-scale usage. It's important to review the pricing details carefully and estimate the costs based on your specific use case before choosing a cloud-based data warehousing solution.

Determining the least costly cloud-based data warehousing solution depends on your specific use case and requirements. Each of the solutions we discussed has its own pricing model, which is based on various factors such as storage, compute, data transfer, and querying.

Generally, Google BigQuery is known for its low cost, as it offers a pay-as-you-go pricing model with no minimum charges for storage, compute, and data transfer. Additionally, BigQuery charges only for the amount of data scanned during querying, making it a cost-effective solution for organizations with sporadic or unpredictable querying needs.

However, the cost of each data warehousing solution depends on several factors, such as the volume of data, the complexity of queries, the frequency of data transfers, and the size of the compute resources required. Therefore, it's important to estimate the cost of each solution based on your specific use case before choosing a data warehousing solution.

Data Governance:

One important aspect to consider in both data warehousing and data lakes is data governance. Data governance refers to the overall management of the availability, usability, integrity, and security of the data used in an organization. It includes policies, procedures, and standards for managing data, as well as assigning ownership, responsibility, and accountability for data management.

In the case of data warehousing, data governance is important to ensure that the data stored in the data warehouse is accurate, consistent, and up-to-date, and that it can be trusted for business intelligence and decision-making purposes. This involves establishing data quality standards, data lineage, and data security protocols, as well as monitoring and auditing data usage.

Similarly, in the case of data lakes, data governance is important to ensure that the data stored in the lake is properly managed and secured, and that it can be used effectively for various analytical and data science tasks. This involves defining data policies and standards, managing metadata, and enforcing access controls to ensure that the data in the lake is used appropriately.

In summary, data governance is a critical aspect of both data warehousing and data lakes, and it's important to establish effective data governance practices to ensure that the data is managed properly and can be trusted for decision-making and analytical purposes.

In data warehousing, there are three types of schemas that are commonly used:

Star Schema:

The star schema is the most commonly used schema in data warehousing. It consists of a central fact table that is connected to multiple dimension tables through foreign keys. The fact table contains the quantitative measures that are being analyzed, while the dimension tables provide context to the measures. The star schema is simple, easy to understand, and efficient for querying large volumes of data.

Snowflake Schema:

The snowflake schema is an extension of the star schema that allows for more complex hierarchies in the dimensions. In a snowflake schema, the dimension tables are normalized, meaning that the data is stored in multiple related tables instead of one denormalized table. This can reduce data redundancy and improve data integrity, but it can also increase the complexity of querying the data.

Galaxy Schema:

The galaxy schema is a hybrid schema that combines the star schema and the snowflake schema. It is used when there are multiple fact tables that share common dimensions. In a galaxy schema, the dimensions are normalized in a snowflake schema, but the fact tables are denormalized into a single table like in a star schema. This can improve query performance by reducing the need for complex joins between tables.

Overall, each schema has its own advantages and disadvantages, and the choice of schema depends on the specific requirements and characteristics of the data being analyzed. The star schema is simple and efficient for querying large volumes of data, while the snowflake schema provides more flexibility and data integrity. The galaxy schema is used when there are multiple fact tables that share common dimensions.

Sure, here are a few more types of schemas that are used in data warehousing:

Fact Constellation Schema:

The fact constellation schema, also known as the fact and dimension grouping schema, is used when there are multiple fact tables that are not related to each other. In this schema, each fact table is connected to its own set of dimension tables, and the dimension tables may be shared between multiple fact tables.

Slowly Changing Dimension (SCD) Schema:

As we discussed earlier, the SCD schema is used to track changes in dimension data over time. There are different types of SCDs (Type 0, Type 1, Type 2, Type 3, etc.), each with its own method of tracking changes.

Bridge Schema:

The bridge schema is used when there is a many-to-many relationship between two dimension tables. In this schema, a bridge table is created to connect the two dimension tables through their shared values. The bridge table contains the foreign keys from both dimension tables and any additional attributes that describe the relationship between the two dimensions.

Junk Dimension:

The junk dimension is used to combine multiple low-cardinality attributes into a single table. This can help reduce the number of dimension tables in the data warehouse and improve query performance.

Hybrid Schema:

The hybrid schema, as the name suggests, is a combination of two or more schema types. For example, a hybrid schema could combine the star schema and snowflake schema, or the star schema and fact constellation schema, depending on the specific data requirements and analytical needs.

Overall, the choice of schema depends on the specific data requirements and analytical needs of the organization, and there may be cases where a custom schema needs to be designed to meet these needs.

Here are some examples of each type of schema:

Star Schema:

A common example of a star schema is a retail sales data warehouse. The fact table would contain the sales transactions, such as date, store, product, quantity, and price. The dimension tables would include attributes such as product, store, date, and customer.

Snowflake Schema:

An example of a snowflake schema is an e-commerce data warehouse. The fact table would contain information about the orders placed by customers, such as order date, product, quantity, and price. The dimension tables would include attributes such as product, customer, and location. In this case, the location dimension could be further normalized into tables for city, state, and country.

Galaxy Schema:

An example of a galaxy schema is a healthcare data warehouse. The fact tables could include information about patient visits, medications prescribed, and lab results. The dimension tables could include attributes such as patient, provider, date, and location, which would be shared between the fact tables.

Fact Constellation Schema:

An example of a fact constellation schema is a financial services data warehouse. The fact tables could include information about trades, positions, and securities. Each fact table would be connected to its own set of dimension tables, such as trade date, security, account, and market.

SCD Schema:

An example of an SCD schema is a customer relationship management (CRM) data warehouse. The dimension tables could include attributes such as customer, product, and location, and the SCDs would track changes to these attributes over time. For example, the Type 2 SCD could track changes to customer addresses or product names.

  • Bridge Schema:

An example of a bridge schema is a data warehouse for an online retailer. The dimension tables could include attributes such as customer and product, and the bridge table would connect these tables through their shared values. For example, the bridge table could contain information about which customers have purchased which products.

  • Junk Dimension:

An example of a junk dimension is a data warehouse for a transportation company. The dimension tables could include attributes such as shipment type, shipment mode, and shipment size. These attributes could be combined into a single junk dimension table to simplify the schema and improve query performance.

  • Hybrid Schema:

An example of a hybrid schema is a manufacturing data warehouse. The star schema could be used to track production metrics, such as production date, product, and quantity, while the fact constellation schema could be used to track inventory levels by product and location.

Conclusion:

In conclusion, both data warehousing and data lakes are essential tools for organizations to store and manage their data effectively. Data warehousing is a structured approach that involves the consolidation of data from different sources into a centralized repository, which is optimized for analytical processing. It enables organizations to generate insights from their data to make informed decisions.

On the other hand, data lakes provide a more flexible approach to data storage, allowing organizations to store both structured and unstructured data in their native format. They offer faster access to data and are ideal for organizations that require real-time analysis of their data. Data lakes also provide greater scalability and cost-effectiveness compared to data warehouses.

In summary, while data warehousing is best suited for structured data that requires strict governance and standardized processing, data lakes are ideal for storing and analyzing large volumes of data of different types, including unstructured data. Depending on an organization's specific data requirements, both data warehousing and data lakes can be used in tandem to achieve optimal results.