Sometimes it is not two but it takes three to tango! You must have heard of a lot of friendship stories and I bet, many of them must be tales of three.
Wondering why are we talking about friendship here? Well, that is because this article will be talking about a trio whose friendship makes the movement of data possible.
In this article, we will focus on Extract Transform Load (ETL), which refers to a trio of processes required to move raw data from its source to a data warehouse or a database.
What is ETL?
While working with databases, it is essential to properly format and prepares data in order to load it into data storage systems. ETL are three separate but crucial functions combined into a single programming tool that helps in preparing data and in the management of databases.
Extract, Transform, Load each denotes a process in the movement of data from its source to a data storage system, often referred to as a data warehouse.
Extract: The extract function reads data from a source database and extracts the desired subset of data. The purpose of this step is to retrieve all the required data from the source system with minimum resources. This step needs to be designed in a way that it does not affect the source system negatively in terms of performance or response time.
Transform: This function filter cleanses and prepares the extracted data using lookup tables or rules or by creating combinations with other data and converts it to the desired state. The transform step includes validation of records, rejection of data (if they are not acceptable) and data integration. The commonly used processes for transformation are conversion, sorting, filtering, clearing the duplicates, standardizing, translating and looking up or verifying the consistency of data sources.
Load: The loading is the last stage of an ETL process. The load function writes the resulting data, i.e. the extracted and transformed data, (all of the subset or just the changes) to a target data repository. Some tools physically insert each record as a new row into the table of the target database using SQL insert statement, while many other tools link the extraction, transformation, and loading processes for each record from the source.
Why Do We Need ETL Tools?
This looks like a typical interview question that is asked to a candidate. But, on a serious note, a data warehouse tool contains data from disparate sources which are brought together in one place to be analyzed for meaningful patterns and insights. ETL processes the heterogeneous data and makes it homogeneous which in turn makes it seamless for data scientists and data analysts to analyze the data and derive business intelligence from it.
ETL is much easier and faster to use when compared to the traditional methods of moving data which involve writing conventional computer programs. ETL tools contain graphical interfaces which speed up the process of mapping tables and columns between the source and target databases.
ETL tools can collect, read and migrate data from multiple data structures and across different platforms, like a mainframe, server, etc. ETL technology can also identify “”delta”” changes as they occur, which enables ETL tools to copy only changed data without the need to perform full data refreshes.
ETL tools include ready to use operations like filtering, reformatting, sorting, joining, merging, and aggregation. Additionally, ETL tools also support transformation scheduling, version control, monitoring, and unified metadata management while some of the tools are integrated with BI tools.
What are the benefits of ETL tools?
By now, we already know that it is far more beneficial to use ETL tools than using conventional methods of moving data from a source database to a target data repository. In this section, we will discuss some of the key advantages of using ETL tools:
Ease of Use
The first and foremost advantage of using an ETL tool is the ease of use. The tool itself specifies data sources and the rules for extracting and processing data, and then, it implements the process and loads that data. This eliminates the need for coding in a traditional programming sense, where you have to write the procedures and code.
ETL tools are based on Graphical User Interface (GUI) and offer a visual flow of the system’s logic. The graphical interface enables you to specify rules using a drag-and-drop interface to show the flow of data in a process.
Many of the data warehouses are fragile and give rise to operational problems. ETL tools possess built-in error-handling functionality which helps data engineers to build on the features of an ETL tool to develop a resilient and well-instrumented ETL system.
Good for Complex Data Management Situations
ETL tools offer better utility for moving large volumes of data and transferring them in batches. In the case of complex rules and transformations, ETL tools simplify the task and assist you with the calculations, string manipulation, data changes and integration of multiple sets of data.
Advanced Data Profiling and Cleansing
ETL tools bring in a richer set of cleansing functions as compared to the ones available in SQL. These advanced functions cater to the complex transformation needs which are common to occur in a structurally complex data warehouse.
Enhanced Business Intelligence
ETL tools improve the access to data as it simplifies the process of extracting, transforming and loading. Improved access to information directly impacts the strategic and operational decisions that are based on data-driven facts. ETL tools also enable business leaders to retrieve information based on their specific needs and take decisions accordingly.
High Return on Investment (ROI)
The use of ETL tools saves cost, thereby enabling businesses to generate higher revenue. In fact, a study conducted by the International Data Corporation has revealed that these implementations have garnered a median 5-year ROI of 112 percent with a mean payback of 1.6 years.
The structure of an ETL platform simplifies the process of building a high-quality data warehousing system. Moreover, several ETL tools come with performance-enhancing technologies like Cluster Awareness, Massively Parallel Processing, and Symmetric Multi-Processing.
As we know, ETL tools come with a number of functions to facilitate the ETL workflow. With the growing popularity of the ETL tools, the data warehousing market has seen the emergence of several commercial and open source ETL tools.
In this section, we have enlisted some of the prominent ones for you:
- Informatica PowerCenter
- Business Objects Data Integrator
- IBM InfoSphere DataStage
- Microsoft SQL Server Integration Services
- Oracle Warehouse Builder / Data Integrator
- Pentaho Data Integration (Open Source)
- Jasper ETL (Open Source)
Well, that was the story of the trio, Extract, Transform, Load (ETL), whose friendship is invincible when it comes to data warehousing. ETL tools are definitely necessary when it comes to working with databases. They improve the access of information from data and at the same time ease the task of database workers.