ETL vs ELT
ETL (Extract Transform Load) refers to the process of copying data from multiple sources, transforming and refining it, and consolidating it into a centralized destination system, such as a data warehouse, where the data is presented in a single, unified view.
ELT (Extract Load Transform) refers to the process of extracting data from multiple sources and immediately loading it directly into the target data warehouse, where the raw, unstructured data will be stored and then transformed. The ELT process is a valuable data science tool that uses the processing power of the target system to transform massive data sets used in big data analytics.
What is ETL?
ETL is a type of data integration that functions to refine and organize large volumes of data from different sources.The ETL pipeline consolidates disparate data, refines and standardized it, then presents it in a unified view, thereby streamlining the data pipeline and data warehousing process, and simplifying data analysis. The ETL framework entails:
- Extraction: First automated ETL tools import raw data from its source, such as cloud based systems or mobile devices, and consolidate it in a single repository.
- Transform: Data is loaded onto an interim processor, or staging area, and rules are then applied to ensure data quality, integrity, and compatibility. Data may be subject to cleansing, deduplication, sorting, standardization, and verification processes to refine it.
- Load: Finally the refined data can be loaded into its final destination, either all at once (full loading) or gradually (incremental loading).
The ETL architecture is a valuable business intelligence tool. The sheer volume and variety of data to which businesses have access is growing continuously. Refining and organizing these vast quantities of data is a crucial step in facilitating easy analysis that will transform raw data into actionable insights.
What is ELT?
The ELT process begins similarly to the ETL approach: Raw data is first extracted from multiple different sources according to predefined rules. This data is then directly loaded into the target destination. Once in the destination warehouse, raw data is stored and data transformation is performed as needed using the system’s highly scalable computing power.
What is the Difference Between ETL and ELT?
The main difference between ETL and ELT is the quantity of data being stored and at which point the data is transformed. In considering ETL vs ELT, the right process depends on the needs of the organization and data scientists, and the scale of the project at hand. While load time, transformation time, and maintenance requirements tend to be greater using ETL, ETL tools tend to be less complex and easier to use.
ELT is an evolution of the ETL process and was developed as a response to the modern-day data explosion. ETL was originally developed in the 1960s before the modern-day data explosion and is not the most efficient solution for big data analytics or the most modern business intelligence tools. ELT is ideal for big data integration because it is lower maintenance than ETL, and facilitates exponentially faster transfer times, lower latency, and massive scalability.
ETL vs ELT Architecture
The ETL pipeline is best for analysts and business users dealing with smaller, structured data sets on legacy, on-premise data warehouses. ETL only loads data deemed necessary by the user and completes the data transformation process before it is loaded into the destination warehouse, eliminating the need to build complex transformations.
The ELT approach is best for massive, big data sets. The ELT process only requires an origin and a destination, and the transformation process is pushed to the target database, which eliminates the need for time consuming data staging and makes configuring jobs easier and faster.
ELT has a number of advantages over the ETL framework. The ELT process leverages modern technologies such as big data processing frameworks and cloud data warehouses. Cloud-based infrastructures offer instant scalability, flexibility, and cost saving on hardware. And in separating the loading and transformation tasks, ELT simplifies managing projects and making updates to warehouse structure.
ELT is also an effective data lake solution. Data lakes provide a convenient, centralized repository where vast amounts of structured and unstructured data is stored in its native format, at any scale, for future access. The agile and low maintenance nature of ELT makes it the ideal process for data lake data integration.
Does HEAVY.AI Offer an ELT Solution?
The HEAVY.AI platform provides a columnar database with both strong CPU and GPU performance, which is perfectly suited for Extract-Load-Transform. With millisecond response times for HEAVY.AIDB queries, data scientists can streamline the data preparation process and spend more time creating their datasets. More time upfront creating a data set will result in a more flexible environment in which precise data transformations can be created HEAVY.AIDB can query up to billions of rows in milliseconds, and is capable of unprecedented ingestion speeds, making it the ideal SQL engine for the era of big, high-velocity data.