ETL vs ELT — Choose your “Data Battles” wisely | 1st Part
Data lake has gained significant traction and is rapidly emerging as a favored method for storing and analyzing data. Application of data lake have expanded into the field of information security that is set to be essential for bright future of Security Operations, Security Automations, Threat Hunting and Threat Intelligence.
There are now several products and services available that specifically cater to the Security Data Lake domain. Examples includes AWS Security Lake and platforms like Phantom.
However, before we move to What's or Why’s of “Security Data Lake” it’s crucial to know the basics right and understand the fundamental differences between ETL and ELT data processing.
In today’s data-driven world, information surrounds us in every move of our life. Data logs are generated from using mobile phones, conducting online transactions or to even shopping at offline stores.
Humans and Machines are surrounded by data logs all the time even be it your smart homes, Wi-Fi and internet modem.
“Data is omnipresent” and businesses are facing huge challenge of effectively managing & harnessing the vast amounts of data.
Selecting the right architecture and approach to handle the data has become one
of the most important decision from both technology and costing stand point.Data is being generated & collected at unprecedented rate and data must be processed first to make sense out of it using one of the data processing systems like OLAP or OLTP. However, in this article I would be mainly focused on ETL vs ELT approaches of data management.
We will explore various approaches to managing data and delve into the differences between Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) methodologies by helping you make informed decisions for your data management needs.
Advancement in technology has brought various data management approaches that has both strengths and weaknesses. In this article, we will focus on two prominent methodologies:
- Extract, Transform, Load (ETL)
- Extract, Load, Transform (ELT)
These two methodologies play crucial roles in data integration by ensuring that data is transformed and loaded into target systems for analysis and reporting purposes. However, understanding the key differences between ETL and ELT is crucial when deciding which approach best suits your organization’s needs.
- ETL: Extract, Transform, Load
ETL has been one of the widely and commonly used approach for data integration and reporting.
Refer to following -
Basic ETL Architecture:
Advantages of “ETL”:
- Data Quality and Consistency: ETL allows comprehensive data cleansing and transformation that ensures consistent and reliable data.
- Performance Optimization: By performing transformations before loading the data, ETL can optimize query performance by pre-aggregating or summarizing data.
- Security and Compliance: ETL processes can implement security measures, data encryption, and compliance checks during the transformation phase, ensuring data privacy and regulatory compliance.
Disadvantages of ETL:
- Processing Overhead: ETL requires significant computational resources to perform complex transformations that impacts the OPEX, processing time and scalability.
- Data Volume Limitations: As ETL processes typically involve extracting & transforming large volumes of data and it can be challenging to handle real-time or near real-time data integration scenarios.
2. ELT: Extract, Load, Transform
ELT is becoming widely popular and the new preferred way of data management. Availability of various Cloud-based data platforms like Data Lake etc. has also boosted its popularity and suitability for many companies.
Data Lake: It is a centralized repository that allows you to store all your structured and unstructured data at any scale in its natural/raw format, usually object blobs or files.
Airflow, SnowFlake, Databricks and BigQuery are some known examples of implementing ELT Architecture.
Basic “ELT” Architecture:
Advantages of ELT:
- Scalability and Flexibility: ELT leverages the scalability and processing capabilities of modern data platforms, allowing organizations to handle large volumes of data and perform complex transformations.
- Near Real-Time Analysis: By loading data in its raw form, ELT enables organizations to perform near real-time analysis and exploratory data discovery, providing faster insights.
- Cost-Effectiveness: With cloud-based platforms, ELT eliminates the need for expensive infrastructure investment, as the data storage and processing resources are provisioned on-demand.
- Limited compute on Source System: No or less compute intensive source systems for example-IOT sensors.
Disadvantages of ELT:
- Data Governance and Quality: ELT requires strong data governance practices to ensure data quality and consistency, as transformation happens after loading, potentially resulting in inconsistent or erroneous data.
- Data Segregation and labelling: If you are not paying attention to your data pipelines and data tagging then it can be very challenging to identify and re-run the data labelling.
- Complex Transformations: Performing complex transformations within the target system may require specialized skills and knowledge of the platform’s data processing capabilities. example: Spark
Conclusion:
Each approach of ETL and ELT has its strengths and weaknesses. Hence, the decision should align with your organization’s data volumes, use cases, resources, budget and business goals.
ETL is a reliable choice for organizations seeking robust data quality controls, whilst ELT offers scalability, flexibility, and near real-time insights that is ideal for organizations with vast amounts of data and a need for agility.
2nd Part: ETL & ELT Architecture with Introduction to Security Data LakeRead my article on AWS Security Data Lake
~AshishSecDev
Note: All my articles go beyond simple copy & paste and involves extensive research combined with my decade of experience in building Cyber Security platforms and team. If you need assistance or simply want to connect, feel free to reach out to me.
