ELT Process: unlock the future of data integration with Extract, Load, Transform

In an earlier article, I wrote a detailed comparison of Extract, Transform, Load and Extract, Load, Transform processes. I suggest you consult the text if both terms are new to you and you’d like to broaden your understanding of each of these concepts, their advantages and how they can be useful in your organization. 

In this article, we’re explaining why ELT is indispensable for modern data analytics processes that rely on big data and advanced analytics techniques which power robust business intelligence practices that you want to implement in your organization.

If any of these terms are new, refer to our comprehensive data glossary for definitions. 

The problems with ETL in modern data analytics

As a data integration approach, ETL (Extract, Transform, Load) involves extracting data from a variety of sources, transforming the data in a staging area before loading onto a warehouse, from which it is subsequently fed for data analytics. 

The main problem with the ETL process is that analytical tasks have to be performed on a database replica. If this isn’t done, analytics will have to be performed in the production environment, creating major disruption risks, which is something we obviously want to avoid. 

There are several issues when it comes to database replication for data analytics:

1. Database replicas will have an engine corresponding to the original databases (Postgres or SQL), which aren’t tailored for analytics. These are read-only, OLTP databases that are well suited for transactional queries. Complex analytical queries must be performed on OLAP databases which enable group and column queries. 
2. Database replicas have limited computing capacities. In practice, this means that when one query is being performed in that database, it’s impossible to perform another, as it’s likely to disrupt the ongoing query. Effectively, this prevents analysts from performing their tasks when there is a major query running on the database (e.g. report for the board is being generated between 8 and 9 am).
3. Database replicas generate extra costs for businesses, as you have to cover their maintenance costs, plus the costs associated with on-prem storage.
4. Overall, the lack of scalability makes it highly challenging to perform analytics effectively. In order to do so, you’d have to create a separate database replica for each query. With the growing volumes of data that businesses accumulate today, it simply isn’t feasible.
5. ETL requires a dedicated data team with the robust technical competences required to operate data analytics. These technologies usually have a steep learning curve, which prevents the enablement of self-service analytics in the organization. Your staff will need to know the different tools required for data transformations, as well as the target system and data analytics tools.
6. It’s hard to get a unified view of your data environment, which makes it difficult to understand who has access to what, which in turn makes data governance complicated. Automation of data access management is hard to implement.
7. Data segmentation is also difficult with ETL. Data analytics deals with multiple, heterogeneous sources, each with its own data structures, formats, and semantics. Segmenting data effectively requires understanding the nuances and relationships across these diverse data sources, which is hard without having a comprehensive, unified view of all these large datasets.

This approach has initially been popularized due to its small learning curve and the fact that it allowed to achieve results fast. As such, it’s effective at building Proof of Concepts for your data activities, for instance. 

Overall, ETL is well-suited for working with small data sets that require intricate transformations. When the data set has already been structured and transformed, the ETL approach can provide more stability and faster data analysis for a single, pre-defined use case.

Additionally, the ETL process can facilitate compliance with data privacy regulations such as GDPR, HIPAA, and CCPA. This is because users have the ability to omit any sensitive data before loading it into the target system, thereby helping to ensure data protection standards are met.

How ELT supports modern data analytics

Data analytics is the lifeline for today’s data-driven businesses. Take the transportation sector, for instance - logistics analytics support the logistics industry across every operational stage. Medical industry, finance, retail, e-commerce – virtually any industry benefits from robust data analytics. 

And, with big data expansion, technologies like artificial intelligence and advanced analytical techniques (application of machine learning and AI for predictive modeling, for instance), data analytics is a critical operational component for any data-driven businesses. 

Such analytics require modern data platforms, composed of the right tools and processes that can support heavy analytical tasks. The RST Modern Data Platform harnesses the power of Snowflake, an innovative cloud warehousing solution that offers the speed and scalability required to support modern data analytics, as well as the Extract, Load, Transform, or simply ELT data integration process.

The ELT process plays a crucial role in enabling modern data analytics. It’s necessary to enable the efficiency in the following areas of data analytics:

• Data integration: ETL processes can integrate these disparate data sources into a centralized data warehouse or data lake, making the data readily available for analysis. For instance, when a company has customer data stored in a CRM system, financial data in an accounting system, and web analytics data in a separate platform. 
• Data transformation: ETL processes can clean, standardize, and enrich the data, ensuring it is in a format suitable for analytical models. For instance, when a company needs to analyze customer purchase behavior. The raw data may have inconsistent formatting, missing values, or different data types across the source systems. 
• Data quality: ETL processes can validate, de-duplicate, and reconcile customer data from multiple sources, improving data quality and reliability for the churn analysis. This ensures greater reliability of data in a situation where, for instance, a company wants to analyze customer churn patterns. 
• Data staging and optimization: ETL processes can stage the data in a data warehouse or data lake, optimizing the data structure and indexing for faster query performance and more efficient analytics. For instance, when a company needs to perform complex queries and analyses on large datasets.
• Data lineage and governance: ETL processes can capture metadata and lineage information, enabling better data governance and auditability. For instance, when a company must comply with data privacy regulations and needs to track the source, transformation, and usage of customer data. 
• Scalability and performance: ETL processes can leverage distributed computing frameworks, such as Hadoop or Spark, to scale the data processing and enable faster analytics on large datasets. For instance, when a company's data volume is growing rapidly, and the analytics team needs to process and analyze data in a timely manner.

ELT processes focus on data transformation after it has been loaded onto a target storage solution – whether a data lake, a data warehouse or a data mart. The key here is that data transformation is carried out directly within the target system, rather than in a separate transformation step. 

This approach means that the transformation process itself is relatively quick, as it is performed on an as-needed basis. However, this can also have a drawback – if the target system, such as a cloud-based solution, does not have sufficient processing power, the transformation step can slow down the overall querying and analysis processes. This is because the transformation is performed in real-time, and if the system is not equipped to handle the computational demands, it can lead to slower response times and reduced efficiency in the data analysis and reporting tasks.

What are the advantages of the ELT process for modern data analytics that the traditional ETL pipeline cannot assure?

1. Cloud storage platforms generally support OLAP capabilities, which are required for the ELT process. A cloud storage solution is the foundation of modern data platforms that support modern data analytics.
2. With an ELT pipeline, users have the flexibility to explore the complete data set, including real-time data, without the typical constraints associated with ETL: users can directly access and explore the entire data set, including the most up-to-date real-time data, without having to wait for the IT team to complete the ETL process. 
3. The cloud-based solutions, such as Snowflake, Amazon Redshift or Azure Synapse or Google BigQuery, typically used for modern data analytics offer much lower costs than on-premises data warehousing and analytics infrastructure. Performing transformations directly within the target warehouse allows you to eliminate the costs of database replication that you would otherwise have to cover with ETL, as well as the associated maintenance work. 
4. Cloud-based platforms offer scalability, allowing you to choose the level of resources based on your needs. This flexibility helps to optimize costs and ensure that the data storage and processing capabilities don’t require upfront costs. 
5. And, since all your data infrastructure is effectively in a single warehouse, they don’t need to learn all the different systems within your infrastructure, as is the case with ETL.
6. Since your data environment is unified into a single warehouse, it’s much easier to get a unified view and data governance doesn’t pose as great a challenge. Your overall infrastructure is more secure and data access is easier to automate.
7. Data segmentation is also much easier in the case of an ELT pipeline, as data engineers don’t need to focus as much on the data transformation steps. This means it’s also easy to establish business-specific data meshes that greatly support self-service analytics.

Overall, the ELT process is more suitable for larger data sets and scenarios where timeliness is crucial. ELT enables real-time, flexible data analysis, allowing users to explore the complete data set, including real-time data, in any direction without having to wait for IT to extract, transform, and load additional data.

Additionally, the ELT pipeline typically requires less maintenance, as all data is always available, and the transformation in the ELT process is usually automated and cloud-based.

Why opt for Snowflake for your cloud warehouse?

Our preferred storage platform is Snowflake data warehouse, for a number of reasons:

• Snowflake uses OLAP as a fundamental part of the database schema. This approach ensures that the data is immediately accessible and can be queried directly. Snowflake's architecture is designed to provide a single, unified, and readily queryable source for your data. 
• The data stored in Snowflake is structured and organized in a way that allows for efficient and effective analysis and reporting, without the need for complex data transformations or the creation of separate data marts or warehouses. This means you can generate multiple reports quickly, whenever you want.
• Snowflake supports SQL querying, which means developers with basic skills will be able to operate it without special training. 
• Snowflake is cost-efficient, as it only charges you for the queries you make (there’s no need to cover the database costs you’d have to pay with ETL). When you want to run complex queries, you launch a separate warehouse with its own compute capability. 
• A query run in accordance with the ETL logic that takes, for instance, 2 hours to complete, can be completed within a fraction of that time in Snowflake. The warehouse offers different virtual warehouse sizes and the bigger the warehouse, the greater the computing power. You can easily switch between the warehouse sizes, whereas with ETL, you simply cannot do that. 
• Snowflake offers a robust data governance mechanism with numerous data access control tools that allow to grant access at a very granular level. We’ve explained some key principles and best practices in Snowflake access control in a separate blog post.

Unlock modern data analytics with ELT

The traditional ETL process has several limitations when it comes to supporting modern data analytics needs. The lack of scalability, high maintenance costs, and the complexities involved in managing multiple data sources make it challenging to derive meaningful insights from growing volumes of data. 

In contrast, the ELT process offers a more efficient and cost-effective solution for organizations looking to optimize their data analytic approach. By leveraging the power of cloud-based platforms like Snowflake, ELT enables real-time, flexible data analysis, allowing users to explore the complete data set, including real-time data, without the constraints of the ELT pipeline. Additionally, the reduced maintenance requirements and the ability to easily scale computing power as needed to make ELT a compelling choice for modern data-driven businesses.

If you're struggling with slow and costly data analytics processes, it might be time to consider optimizing your data integration approach with ELT. To learn more about how ELT and Snowflake can transform your data analytics capabilities, book a data strategy session with our experts via this contact form. We'll help you assess your current data landscape and develop a tailored solution to unlock the full potential of your data.