What is ETL?

ETL stands for Extract, Transform, Load. It is a process that involves three main steps:

1. Extract: Data is extracted from various source systems. These sources can include databases, flat files, APIs, or other data repositories. Extracting data is crucial as the initial step in transforming and loading it into data warehouses for analytics and business intelligence.
2. Transform: The extracted data is then transformed. This transformation process can involve cleaning the data, enriching it, converting it into a different format, aggregating it, applying business rules to it, or using data mapping as a necessary technique for combining multiple data sources based on correlating information.
3. Load: Finally, the transformed data is loaded into a target data store, typically a data warehouse or data lake, where it can be used for analysis and reporting.

Example of ETL

Imagine a company that maintains a website with a comparison of book prices across different shops. To achieve this goal we have to gather data from various websites, some of them can expose those data in API, and some of them will probably have to be scraped. After gathering those data we will have to transform them because each website can have a different price format, spelling of the book name, etc.

Structured ETL pipelines improve the stability and speed of data analysis, thereby facilitating compliance with regulations and supporting business intelligence efforts.

The ETL process for this company might look like this:

1. Extract: Pull book data from the exposed endpoints or scrape them.
2. Transform: Convert data into the same format. This process may contain:

• Flattening the data so that in each record we have one book
• Dropping the records that do not meet our criteria (for instance, records without a price)
• Converting prices to the same format and currency
• Adding a cell with the metadata of the source of the record
• Applying the upfront enforced data warehouse format conventions

3. Load: Load the transformed data into a data warehouse where they can be easily queried

What is ELT?

ELT stands for Extract, Load, Transform. This process also involves three steps but in a different order:

1. Extract: Data is extracted from various source systems, similar to the ETL process.
2. Load: The extracted data is immediately loaded into a target data warehouse, allowing for raw data to be directly available for subsequent transformations. This highlights the critical role of the target data warehouse in the ELT process, enabling efficient data management and analytics.
3. Transform: The transformation of data occurs after it has been loaded into the target system. This transformation process utilizes the computing power of the cloud data warehouse to perform the necessary transformations, highlighting its importance in modern data analytics and business intelligence.

Example of ELT

Using the same example, a website comparing book prices, the ELT process might look like this:

1. Extract: Pull book data from the exposed endpoints or scrape them.
2. Load: Load all the raw sales data directly into a data lake. In this step, we want to have as raw data as possible, so regarding scraping, we can download the whole HTML of the website. Data lakes are emerging solutions that allow businesses to load vast amounts of raw data for improved analytics and data-driven decision-making.
3. Transform: Perform the necessary transformations (listed in the previous process) on each file with raw data and present it to the final user in a desired way

Key Differences Between ETL and ELT

‍Processing Location

ETL: Data transformation happens before loading the data into the target system. Traditional data warehouses often face constraints and limitations in the ETL process, necessitating the transformation of data in a staging area to address these challenges before loading it into the target system for analysis. This means the transformation process typically occurs on a separate ETL server or engine (like spark or flink). The crucial thing to notice is that the desired data warehouse format (i.e. the shape of facts and dimensions tables) needs to be applied almost immediately after the extraction. In this way, the load process is usually smooth and fast as it operates on a curated and often pre-calculated dataset. Businesses need to manage both structured and unstructured data for effective analysis and reporting.

ELT: Data is extracted and stored in a data warehouse almost as-is. Data transformation happens at the very end of the process, often in a direct correlation with an ad-hoc, specific user request. On a timeline, the transformation can happen weeks or even months after loading the data into the target system. The transformations leverage the processing power of the data warehouse or data lake and can be more time and resource-consuming than in ETL.

Data Processing Time

‍ETL: Since data is processed upfront, it can lead to delays in data availability (from an overall perspective). However, the ability to process data efficiently in different contexts ensures that once processed, data are usually accessed faster because they are already in close-to-final form.

ELT: Since data is loaded first without enforcing schema, it is available for transformation and analysis almost immediately. This allows data scientists and business analysts to access and work with data earlier in the pipeline, alleviating bottlenecks associated with traditional ETL methods and enabling quicker decision-making. However, the time of accessing a particular data piece can be longer than in ETL because it needs to be transformed before the presentation.

Flexibility in Data Transformation

ETL: Require more upfront design and planning as we have to enforce schema from the beginning. Changes to transformation logic can necessitate changes to the ETL pipeline, which can be complex and time-consuming.

ELT: Offers greater flexibility as we don’t have to enforce schema upfront. Since data is loaded in its raw form, transformations can be modified or added later without the need to re-engineer the entire pipeline. Data analytics plays a crucial role in processing and analyzing large datasets for business intelligence, allowing decision-makers to derive insights quickly and efficiently. ELT can handle various data types, including structured or unstructured data, making it ideal for cloud-based data warehousing and data lake environments.

Complexity & Maintenance

ETL: Can be complex to maintain. Managing the ETL server, ensuring data consistency, and handling errors during the transformation process can require significant effort. Traditional ETL tools are better suited for structured data, while newer ELT methodologies can efficiently handle unstructured data.

ELT: Simpler maintenance in some aspects because transformations are handled within the data warehouse environment, reducing the need for separate transformation infrastructure.

Cost

ETL: Costs can be higher due to the need for dedicated ETL tools and infrastructure. However, this can be offset by not needing as powerful a data warehouse, since the data is pre-transformed.

ELT: This can be cost-effective, especially with cloud data warehouses that offer pay-as-you-go pricing. The major cost consideration is the processing power used for transformations within the data warehouse.

‍Reprocessing

ETL: Reprocessing can sometimes be impossible when the source is changed. For example, in the case of our book company, if we are scraping data from the online store and the table structure is changed so our xpaths do not work anymore, our pipelines will fail and don't save any data. If we don't manage to fix this on time, all data or part of it may permanently disappear from the system.

ELT: Reprocessing is almost always possible when the source is changed. In the case of our book company, even if the table structure is changed and our xpaths do not work anymore, we still have raw data in the data lake. A simpler pipeline is less prone to errors.

Choosing Between ETL and ELT for Data Warehouse

When to Use ETL

1. Complex Data Transformations:
   • Scenario: When data needs significant transformation before it is loaded into the data warehouse.
   • Reason: ETL tools are designed for complex data transformations and ensure data is in the correct format before loading.
   • Examples: Aggregations, joins, data cleansing, or applying business rules.
2. Data Compliance and Security:
   • Scenario: When data must be transformed to meet regulatory compliance before storage.
   • Reason: Sensitive data can be masked or encrypted during the transformation phase, ensuring compliance before loading.
   • Examples: GDPR or HIPAA requirements where data anonymization is needed.
3. Resource Optimization:
   • Scenario: When minimizing load on the data warehouse is necessary.
   • Reason: Transformations are handled on a separate ETL server, reducing the compute load on the data warehouse.
   • Examples: Legacy systems where data warehouse resources are limited.
4. Existing ETL Infrastructure:
   • Scenario: When an organization already has a robust ETL infrastructure.
   • Reason: Leveraging existing ETL tools and processes can be more efficient and cost-effective.
   • Examples: Established data pipelines and skilled ETL team in place.
5. Incremental Data Loads:
   • Scenario: When handling incremental data loads and change data capture.
   • Reason: ETL processes can be optimized to handle deltas and changes efficiently before loading.
   • Examples: Daily updates or transactional data where only new or changed records are processed.

When to Use ELT

1. Large Data Volumes:
   • Scenario: When dealing with large volumes of data that need to be processed quickly.
   • Reason: Loading data first and leveraging the data warehouse's processing power is more efficient.
   • Examples: Big data environments with petabytes of data.
2. Real-Time Data Processing:
   • Scenario: When real-time or near-real-time data processing is required.
   • Reason: ELT allows for immediate loading and near real-time transformation and analysis.
   • Examples: Real-time analytics for IoT data or streaming data.
3. Scalability:
   • Scenario: When the ability to scale up or down quickly is important.
   • Reason: Cloud-based data warehouses can scale resources dynamically to handle varying workloads.
   • Examples: Seasonal data spikes or unpredictable data growth.
4. Simpler Data Transformations:
   • Scenario: When data transformations are relatively simple and can be handled within the data warehouse.
   • Reason: Using the data warehouse's native processing capabilities simplifies the pipeline.
   • Examples: Basic data cleaning, type conversions, or filtering.
5. Flexibility and Agility:
   • Scenario: When the flexibility to change transformation logic quickly is needed.
   • Reason: ELT allows for transformations to be adjusted without reloading data, providing agility.
   • Examples: Rapidly changing business requirements or iterative data modeling.
6. Cost Efficiency:
   • Scenario: When cost efficiency is a priority, especially in cloud environments.
   • Reason: Pay-as-you-go pricing for cloud data warehouses can reduce costs compared to maintaining separate ETL infrastructure.
   • Examples: Startups or companies with tight budgets.

Hybrid Approaches

In some scenarios, a hybrid approach might be beneficial. For example, initial data extraction and basic transformations (e.g., data cleaning) could be done using ETL, followed by loading the data into a data warehouse for further transformation and analysis using ELT techniques.

Conclusion

Understanding the differences between ETL and ELT is crucial for making informed decisions about data integration strategies. ETL, with its sequential approach of extracting, transforming, and then loading data, is ideal for scenarios requiring significant preprocessing or compliance with data regulations. On the other hand, ELT's approach of extracting, loading, and then transforming data within the target system is well-suited for modern, cloud-based environments that demand scalability and real-time data processing.

Ultimately, the choice between ETL and ELT depends on various factors, including the nature of the data, existing infrastructure, processing requirements, and organizational goals. By carefully considering these factors, businesses can choose the method that best aligns with their needs, ensuring efficient and effective data integration and analysis.