Data integration is performed through two approaches Extract, Transform, Load (ETL), and Extract, Load, and Transform (ELT). Most business analysts confuse the two approaches because of the similarity in their abbreviations.
Both ETL and ELT are used for different situations where the type of sources, target databases, processing power, data volume, and even the transformation complexity varies.
However, the end goal of both the integration approaches is similar: to enable users to move data efficiently from source to your desired destination.
However, ELT is now commonly used interchangeably with the pushdown optimization mode. In this article, we will discuss how ETL and ELT (Pushdown Optimization) approaches are different from each other.
What is ETL & How Does it Work?
In an ETL approach, the data is extracted from a source and then brought to a staging area where it is transformed, cleansed, and validated. Once the data meets destination requirements, it is loaded to the destination process. The staging area is what makes it different from an ELT system. The staging area is based on a relational database server that’s separated from both source and destination to minimize the impact of changes.
ETL is a regular activity in most organizations because it allows C-level executives to get a top-down view of each department. Today many tools exist that can automate ETL processes.
Pushdown Optimization Mode: Is it ELT?
Another approach to data integration is ELT. The ELT approach extracts data from the source and loads it to the destination data warehouse before applying any transformations. There is no staging area in an ELT process because all transformations are applied within the destination storage.
ELT makes the process of data loading a lot faster and it is specially used in scenarios where the data is required in only a specific format. It means that the staging area is not always needed to add transformations. For example, live data streaming for flights.
Okay, so I know what is ELT but what is Pushdown Optimization?
What is Pushdown Optimization?
ELT is an approach and pushdown optimization is a method that works on that approach. Modern data integration software now includes the pushdown optimization mode that allows users to choose when to use ELT and push the transformation logic down to the database engine with a click of a button. This approach, as discussed before, offers enormous performance benefits by removing data movement to and from the ETL server.
Pushdown Optimization vs ETL: Which One is Better?
Okay, so answering this question is difficult because the answer varies from case to case. If the users want data from multiple sources and all data streams are in an unstructured format, here an ETL approach will be the best way to transform data. Because if the data is moved to the destination storage, it will take excessive space and more work would be required to first prepare the data and then clean the unstructured data.
The pushdown approach will be a perfect fit for scenarios where structured data from a source is on the same database as the target. Pushdown optimization will make the process a lot faster by loading the data directly to the destination.
How Does Pushdown Optimization Work?
In a pushdown optimization approach, the transformation logic is applied to either a source or a target – in most cases, it is the target database. SQL commands are used to load the data directly to the target system.
Types of Pushdown Optimization Mode
Pushdown optimization mode is of two types. Let’s discuss each one of them in detail.
- Full pushdown optimization mode
- Partial pushdown optimization mode
A full pushdown optimization mode will load all the data directly to the target database without making any transformation within the staging area.
However, a partial pushdown optimization mode will load some data to the target database while moving the other to the staging area for transformation processes.
One such data integration tool Astera Centerprise offers both partial and full pushdown optimization modes. Its intelligent algorithm can decide whether the job’s performance will be optimized by running it in partial pushdown mode or full pushdown optimization mode.
When to Use Pushdown Optimization?
Since pushdown optimization delivers data directly from source to target, it is mostly used for time-bound processes.
Here are a few instances where pushdown optimization mode is more beneficial than ETL:
- Faster performance required: Pushdown approach allows data to be ingested a lot faster than ETL because the staging area is skipped. It can load data on the server in almost real-time.
- When raw information is required: In some cases, data doesn’t need to be refined and transformation is not important before loading the data to the database. So, ELT automatically becomes the preferred approach, speeding up the loading process at the cost of delivering raw data.
- For high-end processing: Modern cloud data warehouse appliances and databases offer native support for parallel processing. Pushdown optimization takes advantage of this processing power for greater scalability.
Pushdown Optimization or ETL: Which One is Best for You?
It depends on your data requirements. If you want to move data regularly for OLAP an ETL approach will do the trick. However, if you more data in real-time from a source to target on a single server, a pushdown optimization approach can make things a lot faster without digging in the code.
In short, both ETL and pushdown approaches have their benefits.
Astera Centerprise data integrator offers both ETL and pushdown optimization features for moving data from source to destination. Learn more about how Astera Centerprise can help integrate your data processes.
