Data Engineering for Bioinformatics Leaders
Parquet sounds more like topic for carpenters, but we do see that parquet files are increasingly common in commercial and open bioinformatics pipeline. For example, 10x Genomics pipeline for analysis of Visium spatial transcriptomics data make use of parquet files to encode coordinates.
But what is the issue here, why not use a good-old .csv or text file?
Apache Parquet is well known in big data frameworks (e.g. Spark) and data lake architectures to manage vast amounts of tabular data, such as customer transactions. Its true power comes with analytical queries, where data is primarily read to generate insights or train predictive models.
Parquet is a columnar storage file format designed for high-performance storage and retrieval.
Data in regular text files is stored data row-wise, this means all elements of a row are stored on disk.
This means when a column is read from disk, the whole file needs to be traversed. However, in many cases a user wants to retrieve very particular columns for an analysis, for instance a patient id and demographic data. In this case it is more efficient to store data by columns on disk.
We will probably see Parquet more and more also in bioinformatics, since columnar data volumes are ever increasing.
However, many of the typical suspects of large flat files like FASTQs or gene expression matrices, are not really useable in Parquet format, since columnar queries are not relevant.
Key Features of Apache Parquet:
- Columnar Storage: Data is stored column-by-column, allowing more efficient compression and faster scans, especially when dealing with wide tables.
- Efficient Compression: Parquet supports advanced compression algorithms, reducing storage costs and speeding up I/O.
- Schema Evolution: Parquet files can handle schema changes over time, making it easier to adapt as new data fields are added.
- Compatibility with Big Data Tools: Parquet integrates seamlessly with big data frameworks like Apache Spark and Hadoop, as well as with Python libraries like pandas and pyarrow.
Real-World Applications
- Storing large genomic variant datasets (e.g., VCF-derived tables) for fast analysis and filtering.
- Storing and querying normalized experimental assay data, making it easier to run complex aggregations.
- Efficiently archiving data from multiple instruments in columnar format, speeding up downstream analytical workflows.
Learning Curve
Getting started with Parquet requires basic understanding its columnar structure and how it differs from row-based formats. For bioinformatics pipelines, familiarity with Python and data libraries like pandas or pyarrow helps simplify integration. Parquet is widely used with frameworks like Apache Spark or tools like AWS Athena, which operate on data lakes.