ETL and ELT

Topics Covered: 

● What is ETL? 

● What is ELT? 

● ETL in Data Warehouse 

● ETL in Cloud 

● Steps in ETL 

● Understanding ETL 

● ETL vs. ELT: Which Approach Should You Choose? 

● Advantages of the ETL Process 

● Drawbacks of the ETL Process 

● Advantages of the ELT Process 

● Drawbacks of the ELT Process 

● Differences between ETL and ELT  

What is ETL? 

ETL is a process that includes three important steps, including Extraction, Transformation, and Loading. ETL tools are used to fetch data from one database and put it into another one after transformation and quality checks. The first step called Extraction involves pulling out data from a data source. During this phase, the data is read and gathered, often from numerous and diverse kinds of sources, such as on-premise and cloud databases, enterprise applications, file systems, and more. During Transformation, the data extracted is then converted into a format that is acceptable for another database. In this stage, data transformation is done using rules or lookup tables or by merging one data set with another. The last step is Loading which is the procedure of writing or stacking the data into the targeted database or data warehouse.

ETL is an important component of the modern business intelligence processes and structures. It’s the procedure using which data from dissimilar sources can be integrated in one location to extract business insights.

What is ELT? 

ELT is an acronym for Extract, Load, and Transform. It’s a process that transfers raw data from a source system to a target system and the information is then transformed for downstream applications.

Unlike ETL, where data transformation occurs on an intermediate server prior to being loaded into the target system, ELT loads raw data directly into the target system and converts it over there. In this way, ELT is most beneficial for handling enormous datasets and using them for business intelligence and big data analytics.

As compared to the ETL process, ELT considerably reduces the load times. It’s a more resource-efficient process as it leverages the processing capability developed into a data warehousing setup, decreasing the time spent in data transfer.

ETL in Data Warehouse 

Data Warehouse 

● A data warehouse is a central repository of information that can be analyzed to make more informed decisions. 

● Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence through ETL or ELT pipelines. Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications. 

● ETL is a recurring activity (daily, weekly, monthly) of a Data warehouse system and needs to be agile, automated, and well documented. For more information on Data Warehouse please refer to this 

ETL in Cloud 

● If we want to perform high-speed, sophisticated analytics and intelligence on all of our data, the cloud is the only place to do it. 

● Cloud data warehouses such as Amazon Redshift, Snowflake, and Google BigQuery can scale up and down, infinitely, to accommodate virtually any amount of data. 

● A cloud data warehouse also supports massively parallel processing (MPP), which enables coordination of huge workloads across horizontally scalable clusters of computational resources. On-premises infrastructures simply don’t have that speed or scalability. The cloud changes how we handle data and how we define and deliver ETL.

Steps in ETL Extract 

● Before data can be moved to a new destination, it must first be extracted from its source - such as a data warehouse or data lake. In this first step of the ETL process, structured and unstructured data is imported and consolidated into a single repository. 

● Volumes of data can be extracted from a wide range of data sources, including:- Existing databases and legacy systems, Cloud hybrid and on-premises environments, Sales and marketing applications, Mobile devices and apps, CRM systems, Data storage platforms, Data warehouses, Analytics tools etc

Transform 

During this phase of the ETL process, rules and regulations can be applied that ensure data quality and accessibility. You can also apply rules to help your company meet reporting requirements.

The process of data transformation is comprised of several sub processes: 

● Cleansing - inconsistencies and missing values in the data are resolved. 

● Standardization - formatting rules are applied to the dataset. 

● De-duplication - redundant data is excluded or discarded. 

● Verification - unusable data is removed and anomalies are flagged. 

● Sorting - data is organized according to type. 

● Other tasks - Any additional/optional rules can be applied to improve data quality.

Load 

● The final step in the ETL process is to load the newly transformed data into a new destination (data lake or data warehouse.) Data can be loaded all at once (full load) or at scheduled intervals (incremental load) 

● Full loading - In an ETL full loading scenario, everything that comes from the transformation assembly line goes into new, unique records in the data warehouse or data repository. 

● Incremental loading - A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data with what’s already on hand, and only produces additional records if new and unique information is found

Understanding ETL 

● Extract, transform, and load (ETL) is a data integration methodology that extracts raw data from sources, transforms the data on a secondary processing server, and then loads the data into a target database. 

● ETL is used when data must be transformed to conform to the data regime of a target database. The extracted data only moves from the processing server to the data warehouse once it has been successfully transformed. Examples of ETL Tools - Informatica, Talend, AWS Glue, Azure Databricks, Azure Data Factory etc.

ETL vs. ELT: 

Which Approach Should You Choose? Whether you should use ETL or ELT for a data management use-case depends primarily on three things; the fundamental storage technologies, your data warehouse architecture, and the application of data warehouse for a business. 

Advantages of the ETL Process 

● ETL can balance the capacity and share the amount of work with the relational database management system (RDBMS). 

● It can execute intricate operations in a single data flow diagram by means of data maps. 

● It can handle segregating and parallelism irrespective of the data model, database design, and source data model infrastructure. 

● It can process data while it’s being transmitted from source to target (in stream) or even in batches. 

● You can preserve current data source platforms without worrying about data synchronization as ETL doesn’t necessitate co-location of data sets. 

● It extracts huge amounts of metadata and can run on SMP or MPP hardware that can be managed and used more efficiently, without performance conflict with the database. 

● In the ETL process, the information is processed one row at a time. So, it performs well with data integration into 3rd party systems. 

● Owing to parallel processing, it offers remarkable performance and scalability.

Drawbacks of the ETL Process 

● ETL requires extra hardware outlay, unless you run it on the database server. 

● Due to the row-based approach, there’s a possibility of reduced performance in ETL. 

● You’ll need expert skills and experience for implementing a proprietary ETL tool. 

● There’s a possibility of reduced flexibility because of dependence on ETL tool vendors. 

● Data has to transfer across an additional layer before it reaches the data mart, unless the data mart is only an output of the ETL process. 

● There’s no programmed error control or retrieval mechanism in traditional ETL processes.

Advantages of the ELT Process 

● For better scalability, the ELT process uses an RDBMS engine. 

● There’s better performance and data safety as it operates with high-end data devices like Hadoop cluster, cloud, or data appliances. 

● ELT needs lesser time and resources as the data is transformed and loaded in parallel. 

● The ELT process doesn’t need a discrete transformation block as this work is performed by the target system itself. 

● Given that source and target data are in the same database, ELT retains all data in the RDBMS permanently. 

Drawbacks of the ELT Process 

● There are limited tools available that offer complete support for ELT processes. 

● In case of ELT, there’s a loss of comprehensive run-time monitoring statistics and information. 

● There’s also a lack of modularity because of set-based design for optimal performance and the lack of functionality and flexibility resulting from it.

Differences between ETL and ELT 

ETL and ELT are the two different processes that are used to fulfill the same requirement, i.e., preparing data so that it can be analyzed and used for superior business decision making. 

Even though both these processes vary enormously in terms of architecture and execution, the whole thing depends on ‘T’ in both the processes. The key factor that differentiates the two is when and where the transformation step is executed.

 Implementing an ELT process is more intricate as compared ETL, however, it is now being favored. The design and execution of ELT may necessitate some more exertions but it offers more benefits in the long run. 

Overall, ELT is an economical process as it requires fewer resources and takes a smaller amount of time. However, if the target system is not robust enough for ELT, ETL might be a more suited choice. 

It wouldn’t be wrong to call ELT as the new monarch in data management which is gradually superseding the ETL process. By using ELT, businesses can analyze greater data volume with less upkeep and in less time to fuel data-driven innovation.

Points to remember regarding ETL and ELT 

● ETL - Data is extracted from a source system, transformed on a secondary processing server, and loaded into a destination system. 

● ELT - Data is extracted from a source system, loaded into a destination system, and transformed inside the destination system. 

● Speed 

● ETL - ETL is a time-intensive process; data is transformed before loading into a destination system.

 ● ELT - ELT is faster by comparison; data is loaded directly into a destination system, and transformed in-parallel. 

● Maintenance 

● ETL - Secondary processing server adds to the maintenance burden. 

● ELT - With fewer systems, the maintenance burden is reduced.