The Data Import (DI) SIG had 3 goals:
Understand and define pain points that Ed-Fi implementations experience while using Data Import as an Extract-Transform-Load (ETL) solution for non-API based data.
Understand other solutions that may be available for reliable ETL pathways for non-API based data.
Inform Data Import future development and roadmap priorities from community blockers, issues and identified needs
These were the outcomes of the SIG from these goals are:
1.) Data Import is reported as serving non-API ready data needs with an active community of users
The DI SIG informed many aspects on how Data Import is used today in the field, in service of incorporating non-API ready data into Ed-Fi data infrastructure. With an active SIG with over 40+ members to contribute to the forum, we've learned that the tool is serving needs with non-API ready data. From these conversations, we've learned that Data Import is active in the assessment, educator preparation program (EPP), finance data and other domains where API pathways are non-existent. It is recognized from these conversations, Data Import carries a maintenance burden for the implementer to maintain, which is balanced in its usage along with the need to import such data in Ed-Fi environments. It too is recognized that direct API connections from education data producing products is ideal and preferred, which relieves the maintenance burden of running additional ETL solutions to accommodate.
2.) Numerous viable open-source and low-cost alternatives exist for serving the ETL need
The DI SIG reviewed a number of alternatives to Data Import, as the education and general technology markets have tools and products to serve needs for extracting, transforming and loading data of many types. As a result of this review, the forum has discovered and discussed numerous viable alternatives which can also serve loading of non-API data into Ed-Fi environments.
Education Analytics
Education Analytics is an organization that serves education agencies with a multitude of solutions and approaches that serve needs for analytics to improve student outcomes. The team uses Ed-Fi technology in many of its solutions and has deep knowledge of Ed-Fi's data model, ODS / API and other facets to meet these goals. They have built an open-source toolkit to transform and load data into the Ed-Fi API. The technology is Python-based and known to be aligned within cloud environments.
Details of this toolkit are below, with links to GitHub for the source code and documentation of each component:
- Earthmover - CLI tool for transforming collections of tabular source data into a variety of text-based data formats via YAML configuration and Jinja templates.
- Lightbeam - CLI tool for validating and transmitting payloads from JSONL files into an Ed-Fi API.
Ed-Fi Educator Preparation Program Evaluation
The Ed-Fi Educator Preparation Program (EPP) works in the higher education space to utilize Ed-Fi technology to integrate data to evaluate performance and growth of education preparation programs. The majority of data within this domain comes from non-API ready systems, such as legacy databases and CSV source files. Data Import is known to service this domain and aids in the ETL process into Ed-Fi environments. As EPP can work in high volumes of data and enterprise environments, the team did an evaluation of open-source, low-cost and cloud-ready ETL tools. Each one of these tools have been proven to load data into Ed-Fi ODS / APIs from this evaluation, with detailed notes on performance, process to install, map and load data with the tool, and pros/cons of each tool used.
Below is a summary listing of the tools reviewed as part of this effort:
- Standalone Tools
- Cloud-Based Tools
From this evaluation, Data Import seems to work well for non-enterprise environments and its data needs and processes. The tools listed above work well for enterprise environments and require a level of knowledge and effort to maintain for ETL needs. Each ETL situation is different and should be evaluated against the list of tools to determine the right fit for the project. In the future, Ed-Fi may look at paths to utilize domain knowledge and mapping capabilities from Data Import, and utilize pre-existing tools to service the need of transforming and loading non-API data for a hybrid solution approach.
3.) Data Import users will prefer an open-source path ahead for the product
- Because of 2022 conversations heard, Ed-Fi was moved to open-source Data Import
- In November 2022, we released Data Import 2.0 to an open source repo
- We've moved the Template Sharing Service to a GitHub Exchange repo
4.) Additional Data Import SIG requests lead to these feature requests
- John Bailey - has noticed the first time they are importing data, it seems faster than subsequent imports (SF:
- Emilio and Rosh - Would like improved logging. It logs too much and they have to truncate the table often
- Zurab - duplicated headers in files have posed issues; Emilio - the pre-processor could help with this issue
- DI-1135 - Array Format in CSV
- John Bailey - 1.3.2 included a Docker container, but it is unclear how to kick off a schedule
- Zurab - Source code uses a library to work with FTP servers. The library does not work if the FTP server has a certain setting turned on. Works fine with SFTP, but not FTP.
- Mike Werner - Documentation is lacking