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It is especially effective in the Hadoop ecosystem (e.g.
It often achieves better compression than Parquet due to its advanced compression algorithms and techniques. Like parquet, ORC is a columnar storage format with similar benefits. You'll usually encounter Parquet when using spark, delta lake or Databricks.ģ️⃣ Large-scale Data Processing & Optimised Query Performance: So when queries are made against the data, they can actively find which columns to skip or not by accessing this metadata. Its columnar storage format also allows for the column metadata (schema, min and max values and nulls) to be stored at the end of each column. the name column would all be strings) making it easier to compress. Parquet is column based and offers better compression compared to row-based formats because data within a column tends to be more homogeneous (e.g. In this way, Avro ensures that the data can be easily read and processed, even if the schema changes a lot over time because it is stored and available per file.Ģ️⃣ Analytical query & performance optimisation: Picture Hagrid's motorcycle 🏍, he'd be the data and Harry would be the schema in the sidecar 😄. Schema evolution is another strong point of AVRO because the JSON schema is stored along with the binary data. This is ideal for write intensive ingestion such as from streaming sources (Kafka) where you want low latency and resource usage. Avro also efficiently performs serialisation (converts data objects into a binary format for storage/transmission) & deserialisation (the reverse). These are some broad use cases where working with a certain file format can be advantageous:ġ️⃣ Streaming Data Ingestion & schema evolution:Ī row-based storage format - if you have customer data with name, address & order fields then all fields per record will be stored one at a time.
Each format brings advantages that match different use cases so it is important to choose the right file format for reading/writing your data. Luckily there are commonly used file formats that can assist with this: AVRO, Parquet and ORC. As I started real life gigs and the data grew, became more frequent & complex, it was apparent that writing to CSV could be rough 😅 I remember my first DE projects where I'd use a good ol' CSV for my dummy data and then feel chuffed when my pipelines ran smoothly. File formats play a crucial role in data engineering.
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