Data generation increases with every passing second. For data-driven businesses and organizations, using this data for insightful analysis is critical. However, raw data in its unprocessed state has little to no value. Especially with the exponential growth of unstructured data, the data transformation process becomes increasingly difficult. So, the question here is how do we realize its potential?

There are two main strategies to move distributed data in the organization into one central database and help with integration across various systems.

The answer lies in ETL - Extract, Transform, and Load, and ELT - Extract, Load, and Transform.

These strategies offer a structured approach, allowing for systematic and precise data analysis.

Importantly, they enable data-driven businesses by speeding up insights and data migrations. By eliminating errors, bottlenecks, and latency, these strategies ensure that data is moved quickly and efficiently from one system to another, saving time and effort.

Understanding ETL: Extract, Transform, Load

ETL is an important process in data management and analytics. It consists of a series of steps designed to efficiently collect data from various sources, improve its quality, and eventually integrate it into a data warehouse for further analysis and reporting.

Extraction

The first step in ETL entails extracting data from various source systems. One critical aspect of this approach is that the transformation process only works on data extracted during the same run. To do this, the extracted source data is first transferred to a temporary storage location known as the staging layer or another similar term, which serves as a link between the source systems and the data warehouse. The staging area acts as a buffer, ensuring data integrity, particularly when dealing with multiple sources and data formats.

Extracting data is difficult, especially when dealing with unstructured data. Proper ETL tools are required to efficiently extract and handle unstructured data while ensuring no information is lost in the process.

Transformation

The transformation stage begins once the data has been extracted and moved to the staging area. This phase entails cleaning, organizing, and standardizing the data to ensure consistency and improve quality. Data from various source systems is normalized and converted into a single format, making it easier to analyze and use.

Data transformation may include operations such as cleaning, filtering, joining, sorting, deduplication, and summarization. These operations help to streamline and prepare data for loading into the data warehouse.

Load

The loading stage is the last phase of ETL, in which the transformed data is loaded into the data warehouse. Depending on the business needs, data can be loaded in batches or all at once. The frequency and method of loading vary depending on the data source, ETL tools, and specific business requirements.

Data loading can be done on a regular basis or more frequently, depending on the need for current information. The loading process is critical for maintaining data accuracy and keeping the data warehouse current with the most recent information.

Microsoft SSIS, Informatica, and Talend are some of the most commonly used tools for ETL. Organizations that meticulously follow these steps can leverage the power of their data for informed decision-making and strategic planning.
‍

‍

Understanding ELT: Extract, Load, Transform
‍

Now, let's delve into ELT. As you might have guessed, the letters represent the same concepts as ETL, but in a different order. In this approach, the workflow follows Extract, Load, Transform, which is considered a more modern approach for several reasons, as we'll discuss shortly.

In an ELT scenario, we still begin by extracting the data. However, instead of landing it in a temporary staging area, the data is loaded directly into a more permanent table or location like a data warehouse or a data lake.

But why would we consider performing transformations in the data warehouse? After all, the previously discussed ETL process was designed to avoid imposing burdens on data warehouse users and processes.

However, here is the advancement: in the past, the only option was to perform transformations outside of the data warehouse. However, in the modern age of the cloud, we now have the ability to set up the data warehouse in the cloud, unconstrained by resource limitations. ELT primarily uses cloud storage technologies like data warehouses and data lakes.

Cloud infrastructure is scalable, allowing for the automatic or manual addition of resources to the data warehouse to avoid one process interfering with another's performance. Furthermore, certain cloud databases, such as Snowflake, allow you to assign a specific set of resources (CPUs) to each process and user. This ensures that there is no conflict or contention among the various players involved in the data warehouse.

Finally, transformations are applied to this larger dataset, which may also include logic to create custom data models. Since the extracted data isn't cleared with each run, you can extract data as frequently as needed and run transformations at a separate time. There's no requirement for all steps to occur within the same run, unlike in an ETL process where steps are interconnected.

Additionally, rather than relying on a staging table for the latest data, you can apply filters directly on the larger data table within your transform logic. For those who opt for the ELT approach, you'll often find multiple specialized tools within the workflow instead of a single tool that handles everything. Some examples of specialized tools include Hevo or Stitch for extraction and loading, streaming tools like Apache Kafka or AWS Firehose for real-time data extraction, and dbt for transformation.



‍

Advantages and Disadvantages of ELT
‍

In this section we will sort things out and understand the advantages and disadvantages of the ELT strategy versus the more traditional ETL approach.

1. Cost Efficiency

• In ELT, transformation processes take place within the cloud-based data warehouse, reducing the need for separate servers or expensive SaaS products.
• Cloud platforms offer storage and processing programs at lower costs compared to traditional ETL setups.

2. Enhanced Performance

• Leveraging the data warehouse for transformations improves efficiency compared to third-party tools.
• Database products like Snowflake, Redshift, or BigQuery excel in data storage and retrieval, boosting overall performance.

3. Real-time Analysis Capabilities

• Loading raw data into the warehouse allows for faster availability and near-real-time analysis.
• While initial data may not support complex queries, the ability to monitor raw information in real time is valuable.

4. Accelerated Time to Market

• ELT enables development processes to commence promptly, even before finalizing the design of the analytic model.
• Raw data can be pulled into the warehouse early on, streamlining integration and expediting time to market.

5. Support for Machine Learning Models

• ELT preserves raw data alongside analytical models, facilitating the training and testing of ML models without additional processes.
• In contrast, ETL transformations may result in data loss or aggregation, hindering ML model training.

6. Flexibility with Unstructured Data

• ELT accommodates various data structures, including unstructured types like text and multidimensional data.
• Cloud-based warehouses offer flexibility that traditional ETL setups struggle to provide.

7. Scalability for Big Data

• Cloud-based data warehouses offer scalability and processing power for handling massive data volumes.
• Snowflake, for instance, can utilize 512 processors simultaneously for transformations, showcasing its ability to handle big data effectively.

In summary, the ELT strategy presents a host of advantages—from cost-effectiveness and performance improvements to enhanced agility in data processing and analytics.

However, it's crucial to acknowledge that alongside these advantages come certain limitations that warrant consideration before swiftly implementing an ELT solution within our organization.

1. Scalability Constraints

• In scenarios where the data warehouse is not easily scalable, such as with Oracle, MySQL, or SQL Server databases, limitations arise.
• Without the option to establish a cloud-based data warehouse, organizations may face performance issues due to heavy transformation processes.
• Upgrading existing servers or migrating to a cloud-based database may become necessary to manage increasing loads effectively.

2. Handling Unstructured Data

• ELT poses challenges when dealing with unstructured data in a relational data warehouse.
• Loading raw, unstructured data into a relational database becomes problematic, as it's not designed to accommodate such data types.

3. Compliance with Data Standards

• Compliance with regulations like HIPAA, GDPR, or CCPA may necessitate avoiding storing certain information in the data warehouse.
• ELT's process of cleaning data post-loading poses a risk of retaining sensitive information within the warehouse, potentially violating data standards.

To mitigate the limitations of ELT, an intermediate approach known as ETLT can be used. This strategy involves transformations both before and after loading data into the data warehouse. As a general practice, it's advisable to conduct most transformations post-loading to maximize the benefits of ELT. However, specific tasks, such as deleting sensitive information like a patient's ID, can be handled in the pre-loading transformation phase.

The Takeaway

The comparison between ETL and ELT reveals the latter as a robust choice for contemporary data-driven enterprises. Despite its limitations, ELT's adaptability and scalability render it a compelling solution. By conducting transformations post-loading, ELT optimizes efficiency and functionality, making it a preferred approach. However, before implementing ELT, it is critical to conduct a thorough evaluation of potential constraints. Nonetheless, with careful consideration and strategic planning, ELT can provide unparalleled benefits for organizations that seek to leverage the power of their data.

Transform Your Business and Achieve Success with Solwey Consulting

At Solwey Consulting, we specialize in custom software development services, offering top-notch solutions to help businesses like yours achieve their growth objectives. With a deep understanding of technology, our team of experts excels in identifying and using the most effective tools for your needs, making us one of the top custom software development companies in Austin, TX.

Whether you need ecommerce development services or custom software consulting, our custom-tailored software solutions are designed to address your unique requirements. We are dedicated to providing you with the guidance and support you need to succeed in today's competitive marketplace.

If you have any questions about our services or are interested in learning more about how we can assist your business, we invite you to reach out to us. At Solwey Consulting, we are committed to helping you thrive in the digital landscape.

‍