How can you use ETL tools to support data-driven innovation and collaboration in research networks?

2 Types of ETL tools

ETL tools can be divided into different types based on their features, functionality, and architecture. Traditional ETL tools are the oldest and most widely used, running on-premise or on dedicated servers. They offer a high level of customization, control, and performance but require more technical skills, infrastructure, and maintenance costs. Examples include Informatica, Talend, and Pentaho. Cloud-based ETL tools are newer and more popular, running on cloud platforms or services. They offer scalability, flexibility, and accessibility but depend on the availability, security, and compatibility of the cloud provider. Examples include AWS Glue, Google Cloud Dataflow, and Stitch. Code-based ETL tools are the most advanced and specialized; they require users to write code or scripts to perform ETL tasks. They offer a high level of customization, automation, and integration but require more technical skills, testing, and debugging. Examples include Apache Airflow, Apache NiFi, and Python pandas.

3 Factors to consider

When selecting the most suitable ETL tool for your research network, you should factor in a range of considerations. This includes the type, format, and volume of your data sources and destinations, as well as the complexity and frequency of data transformation and processing. Additionally, you should consider the level of data quality and governance you need to ensure, and the scope of data analysis and visualization required. Furthermore, the size, structure, and culture of your research network should be taken into account when assessing how much data sharing, commenting, versioning, and auditing is necessary. Finally, you must consider the technical skills and resources available to your research network and how much customization, control, and maintenance is needed or feasible.

4 Best practices

To use ETL tools effectively and efficiently, you should follow some best practices. Start by defining your data integration strategy and objectives in alignment with your research network's vision and goals. Assess your data sources and destinations, documenting their characteristics, requirements, and limitations. Design your data transformation and processing logic, detailing its steps, inputs, outputs, and dependencies. Test your ETL processes completely and monitor their performance, quality, and reliability regularly. Optimize your ETL processes continuously and update them as your data sources, destinations, and needs change. Finally, communicate your ETL processes clearly and transparently with your research network and stakeholders to ensure they have the information they need to provide feedback.

5 Examples of ETL tools

For some inspiration and guidance, here are several ETL tools that are widely used by research networks in different domains and contexts. Informatica is a traditional ETL tool offering comprehensive data integration, quality, governance, and management solutions. It is used for complex and large-scale data integration projects in healthcare, education, finance, and more. AWS Glue is a cloud-based ETL tool providing a serverless data integration service. It is used in genomics, astronomy, climate, and other fields to take advantage of the scalability, flexibility, and accessibility of the cloud. Apache Airflow is a code-based ETL tool providing a platform for creating, scheduling, and monitoring workflows. It is used for machine learning, natural language processing, computer vision, and other tasks to automate and orchestrate data pipelines.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?