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Make your Trino data pipelines production ready with Great Expectations

An important aspect of a good data pipeline is ensuring data quality. You need to verify that the data is what you’re expecting it to be at any given state. Great Expectations is an open source tool created in Python that allows you to write detailed tests called expectations against your data. Users write these expectations to run validations against the data as it enters your system. These expectations are expressed as methods in Python, and stored in JSON and YAML files. One great advantage of expectations is the human readable documentation that results from these tests. As you roll out different versions of the code, you get alerted to any unexpected changes and have version-specific generated documentation for what changed. Let’s learn how to write expectations on tables in Trino!

The need for data quality #

Managing data pipelines is not for the faint of heart. Nodes fail, you run out of memory, bursty traffic causes abnormal behavior, and that’s just the tip of the iceberg. Lots of Trino community members build sophisticated data pipelines and data applications using Trino. Building data pipelines in Trino became more common with the addition of a fault-tolerant execution mode to safeguard against failures when executing long-running and resource-intensive queries.

Aside from all the infrastructure problems that concern data teams, another category of problems that have been the silent problem for quite some time is data quality. Faulty data comes in, which can either cause data pipelines to fail, or it can possibly go unnoticed and cause inaccurate downstream reporting. Knowledge is scattered among domain experts, technical experts, and the code and data itself. Maintenance becomes time-consuming and expensive. Documentation gets out of date and unreliable. This is why using data quality checks using libraries like Great Expectations is so important when writing ETL applications.

Improve data quality in Trino with Great Expectations #

As data quality moves to the forefront of the Trino community, the Great Expectations and Trino communities have partnered to do some events together:

Today, we’re walking through a demo that showcases a scenario with Trino running as the datalake query engine with multiple phases of data transformations on some Pokemon data sets. At each phase, we need to validate that the data is in the correct schema, counts, and various other factors to validate. We use Trino with Hive table with CSV for ingest and then move to Iceberg table for the structure and consume tables. This is one of the great uses of Trino in that you can operate using any of the popular table formats.

Trino and Great Expectations demo #

In this scenario, we’re going to ingest Pokemon pokedex data and Pokemon Go spawn location data which lands as raw CSV files in our data lake. We then use Trino’s Hive catalog to read the data from the landing files, clean up, and optimize that raw data into more performant ORC files in the structure tables.

The last step is to join and transform the spawn data and pokedex data into a single table that is cleaned and ready to be utilized by a data analyst, data scientist, or other data consumer. Every area of the pipeline where the data is transformed opens up a liability. The state can go from good to bad when infrastructure fails or is updated as newer versions of the pipeline roll out. This is where adding Great Expectations is crucial.

Now that you have a better understanding of the scenario, feel free to watch the video, and try running it yourself!

Try this Trino demo yourself »

Conclusion #

While data quality has always been a requirement, the standards for it increase as the complexity of data lakes increase. It is a necessity that improves the trust that data consumers have in the data. Dive into the Great Expectations documentation to learn more about the existing Trino support. If you run into any issues while running the demo, reach out on Slack and let us know!