ETL And ELT
ETL And ELT
- ETL stands for Extract, Transform, and Load process for data. ELT stands for Extract, Load, and Transform process for data.
- In ETL, data moves from the data source (operational databases or other sources) to staging area then into the data warehouse.
- ELT leverages the data warehouse to do basic transformations. There is no need for data staging in ETL process.
Overview
The process of ETL and ELT of business intelligence are necessary in data science because information sources. These sources can use a structured SQL database or an unstructured NoSQL database. We have clean the data sources before storing them in a specific location. That way only the business intelligence platform like tableau can understand the data to derive insight.
These Both process contains 3 Process which are like to be Extract, Transform and Load.
ETL Process
OLAP Data Warehouse — It can be on cloud-based or onsite — need to work with relational SQL-based data structures. Therefore, Any data you load into your OLAP data warehouse must transform into a relational format before the data warehouse can ingest it. As a part of this data transformation process, data mapping may also be necessary to combine multiple data sources based on correlating information.
Transformation is a must process throughout the process of ETL(Extract Transform and Load)
- A (ETL) well-defined workflow: ETL first extracts data from homogeneous or heterogeneous data sources. Next, it deposits the data into a staging area. From there, the data goes through a cleansing process, gets enriched and transformed, and is finally stored in a data warehouse.
- The old-methods of hand-coding ETL transformations in data warehousing took an enormous amount of time. Even after designing the process, it took time for the data to go through each stage when updating the data warehouse with new information.
Modern ETL, especially for cloud-based data warehouse, happens a lot faster.
Advantage of ETL
Pre-structured nature of the OLAP data warehouse. After structuring/transforming the data, ETL allows for speedier, more efficient, more stable data analysis.
ELT Process
ELT Stands for “Extract, Load and Transfer.” In this process, data gets leveraged via a data warehouse in order to do basic transformations. That means there’s no need for data staging. ELT uses cloud-based data warehousing solutions for all different types of data — including structured,unstructured, semi-structured, and even raw data types.
The ELT process also works hand-in-hand with data lakes. “Data Lakes” are special kinds of data stores that — unlike OLAP data warehouses — accept any kind of structured or unstructured data. Data Lakes don’t require you to transform your data before loading it. You ca immediately load any type of raw information into a data lake, no matter the format.
Data Transformation is still necessary before analyzing the data with a business intelligence platform. However, data cleansing and transformation occur after loading the data into the data lake. Here are some details to understand about ELT and data lakes:
- A new technology made possible by high-speed, cloud-based servers: ELT is a relatively new technology, made possible because of modern, cloud-based server technologies. Cloud-based data warehouse offers never-endless storage capabilities and scalable processing power. For example: Platform like Amazon Redshift and Google BigQuery make ELT pipelines possible because of their incredible processing capabilities.
- Ingest anything and everything as the data becomes available: ELT paired with a data data lake lets you ingest an ever -expanding pool of a raw data immediately, as it become available. There’s no requirement to transform the data into a special format before saving it in the data lake.
- Transforms only the data you need: ELT transform only the data required for a particular analysis. Although it can slow down the process of analyzing the data, it offers more flexibility — because you can transform the data in different ways on the fly to produce different types of metrics, forecasts, and reports. Conversely, with ETL , the entire ETL pipeline — and the structure of the data in OLAP warehouse — may require modification if the previously-decided structure doesn’t allow for a new type of analysis.
- ELT is less-reliable than ETL : It’s important to note that the tools and systems of ELT are still evolving, so they’re not as reliable as ETL paired with an OLAP database. Although it takes more effort to set up, ETL provides more accurate insights when dealing with massive pools of data. Also, ELT developers who know how to use ELT technology are more difficult to find than ETL developers.
Advantage of ELT
- High speed
- Low-Maintenance
- Quicker Loading
