One of the hallmarks of the Information Age is that data exists everywhere. Whether it's estimated delivery dates of your packages or analytics on the amount of screen time you spend on your phones, you access data every day to inform your decisions and set goals.
Organizations leverage data in the same way but on a larger scale. They have data on customers, employees, products, and services that all must be standardized and shared across various teams and systems. This information may even be made available for external partners and vendors.
To achieve this highly scaled information sharing and avoid data silos, organizations turn to the extract, transform, and load (ETL) practice for formatting, passing, and storing data between systems. With the large volumes of data organizations are handling between all their business processes, ETL tools can standardize and scale their data pipelines.
What are ETL Tools?
ETL tools are software designed to support ETL processes: extracting data from disparate sources, scrubbing data for consistency and quality, and consolidating this information into data warehouses. If implemented correctly, ETL tools simplify data management strategies and improve data quality by providing a standardized approach to intake, sharing, and storage.
This video provides a good overview of ETL tools and approaches:
ETL tools support data-driven organizations and platforms. For example, customer-relationship management (CRM) platforms' central advantage is that all business activities are conducted through the same interface. This allows CRM data to be easily shared between teams to provide a more holistic view of business performance and progress toward goals.
Next, let's examine the four types of ETL tools available.
Types of ETL Tools
ETL tools can be grouped into four categories based on their infrastructure and supporting organization or vendor. These categories — enterprise-grade, open-source, cloud-based, and custom ETL tools — are defined below.
1. Enterprise Software ETL Tools
Enterprise software ETL tools are developed and supported by commercial organizations. These solutions tend to be the most robust and mature in the marketplace since these companies were the first to champion ETL tools. This includes offering graphical user interfaces (GUIs) for architecting ETL pipelines, support for most relational and non-relational databases, and extensive documentation and user groups.
Though they offer more functionality, enterprise software ETL tools will typically have a larger price tag and require more employee training and integration services to onboard due to their complexity.
2. Open-Source ETL Tools
With the rise of the open-source movement, it’s no surprise that open-source ETL tools have entered the marketplace. Many ETL tools today are free and offer GUIs for designing data-sharing processes and monitoring the flow of information. A distinct advantage of open-source solutions is that organizations can access the source code to study the tool's infrastructure and extend capabilities.
However, open-source ETL tools can vary in upkeep, documentation, ease of use, and functionality since they are not usually supported by commercial organizations.
3. Cloud-Based ETL Tools
Following the widespread adoption of cloud and integration-platform-as-a-service technologies, cloud service providers (CSPs) now offer ETL tools built on their infrastructure.
A specific advantage of cloud-based ETL tools is efficiency. Cloud technology provides high latency, availability, and elasticity so that computing resources scale to meet the data processing demands at that time. If the organization also stores its data using the same CSP, then the pipeline is further optimized because all processes take place within a shared infrastructure.
A drawback of cloud-based ETL tools is that they only work within the CSP's environment. They do not support data stored in other clouds or on-premise data centers without first being shifted onto the provider's cloud storage.
4. Custom ETL Tools
Companies with development resources may produce proprietary ETL tools using general programming languages. The key advantage of this approach is the flexibility to build a solution customized to the organization's priorities and workflows. Popular languages for building ETL tools include SQL, Python, and Java.
The largest drawback of this approach is the internal resources required to build out a custom ETL tool, including testing, maintenance, and updates. An additional consideration is the training and documentation to onboard new users and developers who will all be new to the platform.
Now that you understand what ETL tools are and the categories of tools available, let's examine how to evaluate these solutions for the ideal fit for your organizations' data practices and use cases.
How to Evaluate ETL Tools
Every organization has a unique business model and culture, and the data that a company collects and values will reflect this. However, there are common criteria that you can measure ETL tools against that will be relevant to each organization, which are outlined below.
- Use case: Use case is a critical consideration for ETL tools. If your organization is small or your data analysis requirements are minor, then you may not need as robust of a solution as large organizations with complex datasets.
- Budget: Monetary cost is another important factor when evaluating ETL software. Open-source tools are typically free to use but may not offer as many capabilities or support as enterprise-grade tools. Another consideration is the resources required to hire and retain developers if the software is code intensive.
- Capabilities: The best ETL tools can be customized to meet the data needs of different teams and business processes. Automated features like de-duplication are one way ETL tools can enforce data quality and reduce the labor required to analyze datasets. In addition, data integrations streamline sharing between platforms.
- Data sources: ETL tools should be able to meet data "where it lives," whether it is on-premise or in the cloud. Organizations may also have complex data structures or unstructured data, all in different formats. An ideal solution will be able to extract information from all sources and store it in standardized formats.
- Technical literacy: The data and code fluency of developers and end users is a key consideration. For example, if the tool requires manual coding, then ideally the development team can use the languages it's built on. However, if the user does not understand how to construct complex queries, then a tool that automates this process will be ideal.
Next, let’s examine individual tools to power your ETL pipelines and group them by the types discussed above.
- IBM DataStage
- Oracle Data Integrator
- SAS Data Management
- Talend Open Studio
- Pentaho Data Integration
- AWS Glue
- Azure Data Factory
- Google Cloud Dataflow
- Informatica PowerCenter
IBM DataStage is a data integration tool built around a client-server design. From a Windows client, tasks are created and executed against a central data repository on a server. The tool is designed to support ETL and extract, load, and transform (ELT) models and supports data integrations across multiple sources and applications while maintaining high performance.
IBM DataStage is built for on-premise deployment and is also available in a cloud-enabled version: DataStage for IBM Cloud Pak for Data.
Price: Pricing available on request
Oracle Data Integrator (ODI) is a platform designed to build, manage, and maintain data integration workflows across organizations. ODI supports the full spectrum of data integration requests from high-volume batch loads to service-oriented architecture data services. It also supports parallel task execution for faster data processing and offers built-in integrations with Oracle GoldenGate and Oracle Warehouse Builder.
ODI and other Oracle solutions can be monitored through the Oracle Enterprise Manager for greater visibility across the toolstack.
Price: 60$/month for Standard Select plan; 120$/month for Starter plan; 180$/month for Standard plan; $240/month for Enterprise plan
Fivetran aims to add convenience to your data management process with its platform of handy tools. The easy-to-use software keeps up with API updates and pulls the latest data from your database in minutes.
In addition to ETL tools, Fivetran offers data security services, database replication, and 24/7 support. Fivetran prides itself on its nearly perfect uptime, giving you access to its team of engineers at a moment's notice.
Price: Pricing available on request
SAS Data Management is a data integration platform built to connect with data wherever it exists, including cloud, legacy systems, and data lakes. These integrations provide a holistic view of the organization's business processes. The tool optimizes workflows by reusing data management rules and empowering non-IT stakeholders to pull and analyze information within the platform.
SAS Data Management is also flexible and works in a variety of computing environments and databases as well as integrating with third-party data modeling tools to produce compelling visualizations.
Type: Open Source
Talend Open Studio is an open-source tool designed to rapidly build data pipelines. Data components can be connected to run jobs through Open Studio's drag-and-drop GUI from Excel, Dropbox, Oracle, Salesforce, Microsoft Dynamics, and other data sources. Talend Open Studio has built-in connectors to pull information from diverse environments, including relational database management systems, software-as-a-service platforms, and packaged applications.
Price: Pricing available on request
Type: Open Source
Pentaho Data Integration (PDI) manages data integration processes, including capturing, cleansing, and storing data in a standardized and consistent format. The tool also shares this information with end users for analysis, and it supports data access for IoT technologies to facilitate machine learning.
PDI also offers the Spoon desktop client for building transformations, scheduling jobs, and manually initiating processing tasks when needed.
Type: Open Source
Singer is an open-source scripting technology built to enhance data transfer between an organization's applications and storage. Singer defines the relationship between data extraction and data loading scripts, allowing information to be pulled from any source and loaded to any destination. The scripts use JSON so that they are accessible in any programming language and also support rich data types and enforce data structures through JSON Schema.
Type: Open Source
The Apache Hadoop software library is a framework designed to support processing large data sets by distributing the computational load across clusters of computers. The library is designed to detect and handle failures at the application layer versus the hardware layer, providing high availability while combining the computing power of multiple machines. Through the Hadoop YARN module, the framework also supports job scheduling and cluster resource administration.
Dataddo is a no-code, cloud-based ETL platform that enables technical and non-technical users to flexibly integrate data. It offers a wide range of connectors, fully customizable metrics, a central system for simultaneous management of all data pipelines, and can be seamlessly incorporated into existing technology architecture.
Users can deploy pipelines within minutes of account creation and all API changes are managed by the Dataddo team, so pipelines require no maintenance. New connectors can be added within 10 business days upon request. The platform is GDPR, SOC2, and ISO 27001 compliant.
10. AWS Glue
AWS Glue is a cloud-based data integration service that supports visual and code-based clients to support technical and non-technical business users. The serverless platform offers multiple features to provide additional functions, such as the AWS Glue Data Catalog for finding data across the organization and the AWS Glue Studio for visually designing, executing, and maintaining ETL pipelines.
AWS Glue also supports custom SQL queries for more hands-on data interactions.
Azure Data Factory is a serverless data integration service built on a pay-as-you-go model that scales to meet computing demands. The service offers both no-code and code-based interfaces and can pull data from more than 90 built-in connectors. In addition, Azure Data Factory integrates with Azure Synapse Analytics to provide advanced data analysis and visualization.
The platform also supports Git for version control and continuous integration/continuous deployment workflows for DevOps teams.
Google Cloud Dataflow is a fully managed data processing service built to optimize computing power and automate resource management. The service is focused on reducing processing costs through flexible scheduling and automatic resource scaling to ensure usage matches needs. In addition, Google Cloud Dataflow offers AI capabilities to power predictive analysis and real-time anomaly detection as the data is transformed.
Stitch is a data integration service designed to source data from 130+ platforms, services, and applications. The tool centralizes this information in a data warehouse without requiring any manual coding. Stitch is open source, allowing development teams to extend the tool to support additional sources and features. In addition, Stitch focuses on compliance, providing the power to analyze and govern data to meet internal and external requirements.
Informatica PowerCenter is a metadata-driven platform focused on improving collaboration between the business and IT teams and streamlining data pipelines. PowerCenter parses advanced data formats, including JSON, XML, PDF, and Internet of Things machine data, and automatically validates transformed data to enforce defined standards.
The platform also has pre-built transformations for ease of use, and it offers high availability and optimized performance to scale to meet computing demands.
Price: Starts free; $15/month for Basic plan; $79/month for Standard plan; $399/month for Professional Plan
Skyvia creates a data sync that is fully customizable. You decide exactly what you want to extract, including custom fields and objects. There's also no need to customize your data's structure as Skyvia operates on autogenerated primary keys.
Skyvia also allows users to import data to cloud apps and databases, replicate cloud data, and export data to CSV for sharable access.
Use ETL tools to power data pipelines.
ETL is a central practice through which organizations build data pipelines to connect their leaders and stakeholders with the information needed to work more efficiently and inform their decisions. By powering this process with ETL tools, teams achieve new levels of speed and standardization no matter how complex or disparate their data is.