Exploring data warehouse essentials for data engineers

Data warehouses are the backbone of modern data analytics. They are a combination of intricate architecture, careful data modeling, and effective processes that ensure data is not only stored but is also accessible, consistent, and meaningful. Let’s look at these essentials in the next subsections.

Architecture

It’s similar to knowing the blueprints of a complicated building to comprehend the architecture of a data warehouse. We’ll break down the different layers that comprise a typical data warehouse architecture in this section.

The source layer is where data originates in a data warehouse and is in its original, unmodified state. A variety of data types, including flat files, external APIs, and databases, can be included in this layer. Making sure this layer is both easily and securely accessible for data ingestion is the first task facing you as a data engineer. After being extracted, the data moves to the staging area, a short-term storage space meant to hold it while it goes through the crucial Extract, Transform, Load (ETL) procedure. In this case, you may encounter issues such as inconsistent data originating from different sources.

The center of the data warehouse architecture is undoubtedly the ETL layer. Here, data is standardized, cleaned, formatted, enriched, and aggregated. For instance, you may have to standardize comparable measurements, such as pounds and kilograms, into a single unit or impute missing values. The data is moved into the data storage layer after transformation. This layer is where you’ll use data modeling techniques because it’s optimized for efficient querying. Data is frequently arranged using well-known schemas such as star or snowflake schemas to enhance query performance.

Lastly, end users can interact with the warehouse data through the data presentation layer. This layer could have summarized reports, compiled tables, and dashboards. During this last stage, as a data engineer, you frequently work with data analysts to make sure that the data presented is clear and easy to understand in addition to meeting business requirements.

Here are the different stages of the ETL process in more detail:

• The ETL process: The ETL process is a key part of data warehousing, and it’s crucial to understand this when working with data at scale. Here’s an explanation of each step:
  • Extract: The first step in the ETL process involves extracting data from various sources. The data may come from relational databases, flat files, web APIs, and numerous other places. The main challenge in this step is dealing with the different formats, schemas, and quality of data. This requires developing connections to these various systems, understanding their data models, and extracting the needed data in a way that minimally impacts the performance of the source systems.
  • Transform: In the transformation step, the raw data extracted from the source systems is cleaned and transformed into a format that’s suitable for analytical purposes. This often involves a variety of transformations, such as the following:
    • Cleaning: Cleaning is the process of dealing with null or missing values, getting rid of duplicates, or fixing values that are out of range
    • Standardizing: Data conversion to a common measurement system, text value standardization (for example, mapping true/false and yes/no to a common Boolean representation), and date and time format standardization are examples of standardizing
    • Enrichment: This could involve adding extra data, such as calculating new measures or adding geo-coordinates for addresses
    • Reformatting: This could include reorganizing the data into different formats, such as splitting or merging columns or altering the data types
    • Aggregation: This phase could involve computations such as averaging total sales by area

  The aim is to ensure the quality and consistency of data, making it easier to work with in the data warehouse.

• Load: The load step involves writing the transformed data to the target database, which is often a data warehouse. This step needs to be carefully managed to ensure data integrity and minimize the impact on the system, which needs to remain available to users during the loading process.

  There are generally two types of loading strategies:

  • Full load: As the name suggests, the entire dataset is loaded in one batch. This is typically done when a new ETL process is established.
  • Incremental load: Here, only data that has changed since the last load is updated. This is a more common and efficient approach, especially when dealing with large volumes of data.

  In some systems, the loading process also involves building indexes and creating partitioning schemes to improve query performance.

  The ETL process is at the heart of data engineering, and understanding it is critical for managing and maintaining robust data pipelines. It’s through this process that raw data is converted into meaningful information that can be used for business analysis and decision-making.

• Data modeling: The process of creating a visual representation of how data will be stored in a database is known as data modeling. It is a conceptual representation of the associations between various data objects, the rules governing these associations, and the data objects themselves. Data modeling facilitates the visual representation of data and ensures that it complies with legal requirements, business rules, and governmental directives.

  Data elements are defined and organized using data models, along with their relationships to one another and to the characteristics of real-world entities. This can be done for a number of purposes, such as database data organization, software structure creation, or even network architecture creation. There are several types of data models:

  • Conceptual data model: In data modeling, this is the highest level of abstraction. Without going into technical details, it provides a broad overview of interactions between various entities in the business domain. Before delving into the technical specifics, a data engineer may develop a conceptual model to convey to stakeholders the general structure of the data and make sure everyone is in agreement.
  • Logical data model: Through the addition of structure and limitations, such as data types and table relationships, this model expands upon the conceptual model. It is not dependent on any one technology, even though it has more detail than the conceptual model. The maintenance of data integrity and relationships during a data warehouse migration from one platform to another, such as from on-premises to the cloud, depends heavily on the logical data model, which acts as an intermediary step.
  • Physical data model: All of the implementation details unique to each database are included in the physical data model. It outlines the methods for storing, indexing, and retrieving data while optimizing performance. In order to optimize query performance, you may need to decide whether to use columnar storage or how to partition tables.

When establishing or optimizing a data warehouse, having a thorough understanding of the subtle differences between these various models can help you make better decisions. Your decision will affect how well data can be queried, how simple it is to understand, and how easily it can be modified to meet changing requirements, regardless of whether you are building new models from scratch or altering ones that already exist.

As we proceed, let's concentrate on the nuances of schemas, which are an additional essential component that support data modeling in the creation of an effective data warehouse.

Schemas

Within a data warehouse, schemas serve as the structural design frameworks that impact how data is arranged, saved, and accessed. They are just as important in determining a data warehouse’s usefulness and effectiveness as architectural plans are in determining a building’s appearance and functionality. Now, let’s explore the primary schema types that are frequently used in data warehousing.

Two main types of schemas that you will encounter in data engineering are star and snowflake schemas. Let’s look at these in more detail:

• Star schema: With a central fact table that is directly connected to multiple dimension tables, this is the most basic type of dimensional model. Because a star schema offers a simple method of data organization and permits the joining of fewer tables, it is typically simple to comprehend and effective for querying. For instance, a star schema’s simplicity and performance can often come in handy if you’re tasked with developing a quick reporting solution for a small-to-medium-sized business.

  The following diagram displays the general architecture of a star schema:

Figure 12.1 – Example of a star schema

Now let’s understand the architecture seen in the preceding diagram in the following bullets:

• Fact tables: Fact tables hold the data to be analyzed, and they summarize an enterprise’s business activities or transactions. These are quantifiable data or metrics that can be analyzed. For example, in a retail business, a fact table might include fields such as items sold, the total amount of sales, the number of items sold, and so on.
• Dimension tables: Dimension tables contain the textual context of measurements captured in fact tables. They contain details used to query, filter, or classify facts. For example, a dimension table for customers may contain fields such as Customer Name, Address, Email, and so on.

• Snowflake schema: A snowflake design divides dimension tables into more normalized tables by normalizing them, in contrast to a star schema. Although this reduces data redundancy, query performance suffers as a result of the additional joins required to obtain the same data. If you are working with a complex database where normalization and data integrity are more important than query speed, you may choose to use a snowflake schema.

  The following diagram provides an example of a snowflake schema:

Figure 12.2 – Example of a snowflake schema

Schemas have an impact on data modeling, ETL procedures, and the storage layer itself. Finding the right schema frequently requires striking a balance between query performance optimization and data redundancy reduction. Your decision will be influenced by a number of variables, including the particular business requirements, the type of data, and the anticipated workload from queries.

Understanding the distinctions and ramifications of these schema types gives you access to an additional level of knowledge that is crucial for data warehousing. With this knowledge, you can make well-informed decisions that will ultimately affect how well data is stored and retrieved, which will affect how well your organization’s business intelligence (BI) initiatives succeed.