ETL (extract, transform, load) is widely used by modern businesses to move and prepare data from multiple sources into a single storage system or cloud-based data lake.
Data warehouse ETL is a process that extracts, transforms, and loads data into a single targeted data warehouse. In fact, the ETL process offers multiple benefits, such as increased security, improved data quality, and accessibility.
Here, we’ll break down for you the ETL process in a data warehouse, its workflow, phases, and the characteristics of the staging area.
What Is ETL Process In A Data Warehouse?
The ETL process is a vital method in data warehousing: it stands for extract, transform, and load. It has steps that gather data from multiple sources, refine that data, and store it in a centralized data warehouse for analysis and reporting.
ETL starts with the extract phase. This is where data is collected from various origins—such as databases, cloud storage, APIs, or flat files. This step is vital for ensuring appropriate data is captured for further processing.
Next comes the transform stage, which involves cleansing, enriching, and restructuring the extracted data to meet the requirements of the target data warehouse schema. During this phase, data may be aggregated, normalized, or formatted to ensure consistency and accuracy.
Finally, the load phase transfers the transformed data into the data warehouse, making it readily available for querying and analysis. This is essential for integrating data from disparate sources to gain comprehensive insights.
Detailed Breakdown of the ETL Process in Data Warehousing
Step 1 – Data Extraction
Data extraction is the first step of data warehouse ETL. It includes phases like locating and extracting the initial data and is essential for cloud-based data migration and integration.
Enterprise integration platforms offer ETL tools for this purpose. These can help you connect to almost all API sources, provide code-based support, and enable reverse ETL for data extraction purposes.
Extraction Methods In Data Warehouse
The data warehouse ETL extraction process can include various methods, such as logical and physical extraction. Logical extraction in data warehouses usually applies to mobile devices, like cell phones and laptops. It uses APIs (Application Programming Interfaces) to extract all the data from the source in a single go.
We can further divide this data warehouse ETL process into full and incremental extraction. Full-logical extraction is the complete data extraction from a single storage system, such as an app, a cloud platform, or other software, without the need for a tracking source system. On the other hand, incremental-logical extraction is when, after the initial extraction, you extract all the new, added, or altered information.
In contrast, physical extraction focuses on the deleted or outdated but relevant files. Physical extraction can be online or offline, depending on whether you extract the data within or outside the source.
Extracting Changed Data
The changed data extraction process is usually an automated ETL process conducted after the initial load into the data warehouse. During the extraction process of changed data, you can apply different extraction methods, such as scraping, elimination, cleansing, or audit columns.
In this case, your ETL tools will help you tailor predefined algorithms, classes, or clusters to extract new or altered data, transform it, and load it into the targeted data warehouse. This is also true for cloud data warehouses. If you want automatic and instant syncing of changed data, you should turn to ETL and integration tools.
Step 2 – Data Transformation
Data transformation is the second step of the data warehouse ETL process. Depending on the techniques and tools, implementing this step can differ. The following’s a brief example of the data transformation implementation process.
How To Implement Transformation?
Data transformation is tightly connected to the automated ETL process. You must implement various transformation tools to transform the data from the operational source format to the desired target data warehouse format.
The transformation process consists of a few simple steps: discover data, map data, generate code, execute code, and review. Other optional operational actions may include cleansing, filtering, breaking down columns, and removing duplicates.
Step 3 – Data Loading
Data loading is the last step of the data warehouse ETL process. In this phase, you load the data into the storage system or the data lake. It’s essential to ensure that the loading methods are correct and have as little scope as possible.
There are two main aspects to note—refreshing and updating data. Depending on the scope of the changes, you can refresh when you write new data in the targeted warehouse, while you can implement updates if there are changes in the existing data pool.
Flat Files In ETL
Flat files contain data in plain text format and are among the most common data objects of the data warehouse ETL process. Here are the two most common cases of processing flat files in ETL—processing fixed length and delimited flat files.
Processing Fixed Length Flat Files in Data Warehousing
Contrary to relational databases, fixed-length flat files contain fixed-width data tables. They use ordinal positions you’d later utilize to identify the fields in the record. They are great for smaller databases with a few requirements and configuration options. However, it’s essential to gather them in a single data pool from which you can manage their attributes, such as type, representation, value, length, and nullness.
Processing Delimited Flat Files in Data Warehousing
Delimited flat files are data that have marks at the end of each particular record. These marks can be commas, tabs, or semicolons. For example, comma-separated values (CSVs) are stored in tabular form, while the tab-delimited ones are stored in separate fields where each record is connected to a single line.
Purpose Of Staging Area
The staging area or the landing zone acts as an intermediary storage or data pool during the data warehouse ETL process. The staging area is crucial because it interconnects the source and the targeted system, holds raw data, makes space for transitory transformational tables, and enables easy source system queries.
Designing The Staging Area
You can design an external staging area (like cloud-based systems) for complex transformation, huge chunks of data, and real-time processing. On the other hand, you can design an internal staging area (like the modern unified cloud data warehouses) to reduce the complexity, index tables, separate data in “marts,” and create “views. When you design the staging area, you should keep an eye on a few design aspects:
- The ETL team must own the staging area;
- Users shouldn’t have access to the staging area;
- ETL tools and operations should be able to operate the staging area’s files and data;
- You shouldn’t use the staging area for generating reports.
When you design or choose a staging area, you should be sure that it can provide you with data integration tools that support cleansing, aggregation, and replication features.
ETL Process Flow
The five steps of the data ETL process flow are extraction, cleaning, transformation, loading, and analysis. A frequent example of the ETL process flow within a company would be connecting to multiple sources, including CRMs and ERPs, extracting batches of files, copying data to the staging area, transforming, and loading the data into the targeted warehouse.
Advantages of ETL Process in Data Warehousing
The ETL process in data warehousing has numerous advantages. These advantages are pivotal if you’re seeking to leverage data for strategic decision-making and analytics.
Here’s why:
1. Centralized Data Management
ETL enables the consolidation of data from various sources into a single data warehouse. This approach gives a unified view of your data, creating easier access and analysis.
In addition, it simplifies reporting and helps ensure stakeholders are working with the same information.
2. Improved Data Quality
Through the transformation phase of ETL, you can clean, validate, and enrich data with ETL. Therefore, this process eliminates errors, duplicates, and inconsistencies—resulting in higher data quality.
Remember, improved data quality enhances the reliability of analyses and reporting, so you can make informed decisions.
3. Enhanced Performance
ETL processes are optimized for performance; this allows for efficient data extraction, transformation, and loading.
You can therefore schedule ETL jobs during off-peak hours to minimize the impact on operational systems. This flexibility will guarantee data is readily available for analysis without wasting resources.
4. Scalability
ETL processes can scale as your data volumes grow to accommodate increasing data without a loss in performance. This scalability is vital for rapid growth or changes in data sources and types.
5. Historical Data Tracking
ETL allows you to maintain historical data in your data warehouses. This benefit is essential for trend analysis, forecasting, and regulatory compliance.
If you preserve historical records, you can make better predictions based on past performance.
6. Integration of Diverse Data Sources
ETL process in data warehousing enables the integration of data from various sources—including relational databases, cloud applications, and flat files.
This is crucial if you must analyze data from multiple systems to gain comprehensive insights.
Disadvantages of ETL Process in Data Warehousing
Although the ETL process in data warehousing provides numerous benefits—there are also disadvantages.
Your organization should consider these when implementing it in their data warehousing strategies:
1. Complexity and Resource Intensity
ETL processes can be complex and resource-intensive; they require significant time and effort to design, implement, and maintain. This can be a hindrance if your organization has limited technical expertise.
2. Potential Data Latency
ETL processes may introduce latency—especially if data is extracted in batches instead of in real-time. As a result, this delay can affect the timeliness of insights, which is challenging if you rely on immediate data for decisions.
3. High Initial Setup Costs
The initial setup costs for ETL tools and infrastructure can be substantial. You must invest in software, hardware, and skilled personnel to effectively implement and manage ETL processes.
For smaller businesses or tighter budgets, this investment may be prohibitive.
4. Risk of Data Loss or Corruption
During the ETL process, there is a risk of data loss or corruption—particularly if proper validation and error-handling measures are not in place.
However, ensuring data integrity throughout extraction, transformation, and loading is critical to ensure successful outcomes.
5. Maintenance Challenges
Maintaining ETL processes can be challenging as data sources evolve or change. For instance, you must monitor and update your ETL workflows to accommodate new data requirements, which can be resource-intensive.
6. Limited Flexibility
ETL processes can be less flexible than other data integration methods—such as ELT (Extract, Load, Transform). In situations where real-time data access and analysis are critical, the traditional ETL approach may fail to meet your needs.
The main things to consider when learning about the ETL process are the phases, various approaches and techniques, and the importance of staging areas as intermediaries. Understanding these phases, approaches, and staging area considerations will help you effectively manage Modern Data Pipelines.