January 11, 2022
The ETL (Extract-Transform-Load) tools have become a necessity in the lives of developers. The basic feature of these tools is extracting, transforming, and loading the data from one data source to another. The reason why ETL tools are required is that many organizations have to process humongous amounts of data from different data sources on a daily basis. It allows the organizations to extract some insightful information from this data and make key data-driven decisions. These ETL tools simplify the development of the data pipelining process and help in managing and monitoring these data pipelines.
There are different ETL tools available in the market that the readers can choose from depending on their needs and comparing amongst these latest ETL tools. Check out the list of popular ETL tools below in the article.
This section will help beginners in understanding the meaning of ETL as we dive deep into the ETL process and explain it step-by-step. First, let us know what ETL stands for. This method is used to obtain the data from multiple databases, transform the data, and load it into a data warehouse for further analysis.
In this step, we obtain the data from multiple data sources. The ETL tools connect to different databases and perform data extraction on a regular basis. ETL extraction can also mean extracting the files that are generated at a specific location. In such scenarios, a file is created, the data is written into it, and the ETL tool is used to extract the file from the location. We can extract both structured and unstructured data into the data warehouse.
When the data is extracted, it usually comes from multiple data sources. This might result in little uniformity or it might require some data cleaning before loading it to the data warehouse. Hence, we need to transform the data before the data loading process starts. The ETL transformation will transform data to maintain uniformity within the data and then transfer it to the data warehouse. ETL transformation types include multiple methods like data cleaning, data deduplication, data joining/splitting, data summarization, etc.
This step involves loading the transformed data to a data warehouse. The data can either be loaded all at once which is commonly called as full load or at regular intervals i.e incremental load. After the data loading process is completed, the analysts can make use of this data to obtain insightful information from it. If there is a failure in the ETL data warehouse loading process, proper failure mechanisms must be in place to prevent any data loss.
ETL and ELT are the two different methodologies for streamlining the data processes. Many organizations prefer to use a combination of both these methodologies depending upon the data it is dealing with. The workflow is similar for both methodologies but they vary in the architecture amongst many other things.
In ETL, the data is first transformed in a staging server, and then the transformed data is loaded into the data warehouse. ETL loads only the transformed data into the data warehouse. Hence, it requires thoughtful planning as raw data is not available.
The data is directly loaded into the data warehouse, and some basic transformations are applied in the data warehouse servers. In ELT, the raw data is dumped into the data warehouse, which can help in experimenting with different strategies.
ETL tools are used for the automation and management of the ETL pipelines. These tools are used to extract the data from multiple data sources by connecting with the databases and storing the data with or without transformation in a data warehouse. Some of the ETL tools also provide testing of the data pipelines and reporting of the executed runs. They have become a more popular method than the traditional extraction methods that require user interference. The advantage of using these ETL applications is that they do not require any user intervention, sometimes even in case of failure.
There are variants of ETL tools available in the market. Some organizations use ETL tools in big data analysis writing SQL scripts for the same. These ETL tools can also be used for business intelligence. ETL tools can be categorized based on their usage and cost. Among different types of ETL tools are the following:
Skyvia is a universal SaaS (Software as a Service) data platform, which offers code-free solutions like data integration, data management and cloud backup. Skyvia supports a wide number of cloud applications, databases, file storage services and cloud data warehouses. Users can work with data of different cloud apps with different API in a uniform way as with relational data. The Data Integration product of Skyvia combines ETL, ELT as well as reverse ETL functionality.
Skyvia is an entirely cloud-based solution, and the Skyvia ETL tool can be considered one of the most popular among users. To use the platform, you need only a web browser. No locally installed software is required.
Pentaho is a business intelligence tool that provides data integration, reporting, dashboards, etc. It is provided as an open-source as well as commercial ETL tool. The open-sourced i.e. community version provides limited capabilities whereas richer features are available in the licensed version. It runs as an application that organizations can use for their on-premise requirements.
Oracle Data Integrator is a product provided by Oracle for data integration and other ETL purposes. It is available as a cloud-based ETL tool as well as an enterprise ETL tool. It can connect with various data sources and is designed for data sizes of different volumes. It also provides ELT workloads for the scenarios where the data can be transformed after it has been loaded into the data warehouse.
Talend Open Studio is one of the many versions of ETL tools provided by Talend. Talend Open Studio is an open-sourced version of it. Talend also provides commercial products like Talend Data Fabric for organization-wide use that provides advanced features like maintaining the integrity of data and its governance. It is a Java-based application and can be accessed through Eclipse IDE.
Informatica PowerCenter is a data integration tool that is used for streamlining the data pipelining processes. It connects with different data sources and processes the data. It can also be used for data governance and to maintain the security of the data by providing role-based access. It provides a user-friendly GUI, making it easy for the users to use and maintain the organizational data. It also can be accessed on the cloud, using Informatica Cloud Services.
Fivetran is an ETL tool that has been providing ETL data integration services since 2012. It is a licensed tool that allows enterprise cloud-based solutions. It is one of the most widely used ETL tools in the market. It also provides different solutions based upon the needs of the users such as enterprise, data integration, and data replication.
Stitch is another cloud-based ETL platform that can be used to integrate with different data sources. It offers fully managed data pipelining processes to integrate data to the data warehouse. It was acquired by Talend in 2018. After that it continues to operate as an independent unit.
Airbyte is an ELT tool that executes automated ELT pipelines along with monitoring their logs. Currently, it provides an open-source version and a cloud version with an enterprise version coming in the future for organizations that need an on-premise solution. It provides ELT capabilities where the data is fetched, loaded, and then transformed according to the use cases.
Singer is a python-based open-source tool that allows data extraction from different data sources and consolidation to multiple destinations. It contains two main components i.e taps and targets. Taps are nothing but data extraction scripts that allow us to fetch the data from different sources. Targets are the data loading scripts that load the contents to a file or a database.
Xplenty (Integrate.io) is a cloud-based ETL tool for integrating with different databases. It provides a code-free environment that allows the organizations to scale up easily. It allows the organizations to integrate their ETL pipelines, process and prepare the data for analytical purposes over the cloud.
The ETL tools are a perfect way for organizations to streamline and maintain the data pipelining process, data governance and to monitor these processes daily. The decision on choosing the right ETL tool for you depends on multiple factors like use cases of the organizations, connection to the data sources, skill sets for using the application, ability to provide role-based access and data governance, budget, etc. The open-source ETL tools are free but certain expertise is required for the development and maintenance of the workflows. In the segment of cloud-based ETL tools, Skyvia ticks all the boxes for essential features required in organizations for their data integration purposes.