Choosing A Hadoop Data Format

Silvia Oliveros has a set of considerations to help you choose a file format for your data in Hadoop:

What does your pipeline look like, and what steps are involved?

Some of the file formats were optimized to work in certain situations. For example, Sequence files were designed to easily share data between Map Reduce (MR) jobs, so if your pipeline involves MR jobs then Sequence files make an excellent option. In the same vein, columnar data formats such as Parquet and ORC were designed to optimize query times; if the final stage of your pipeline needs to be optimized, using a columnar file format will increase speed while querying data.

At first, I’d suggest just using delimited files, as it’s easiest that way.  Once you have developed a bit of Hadoop maturity, then it makes sense to think about whether rowstore formats (like Parquet and Avro) or columnstore formats (like ORC) make sense for a particular data set.

Related Posts

Testing Kafka Streams Applications

Yeva Byzek continues her series on testing Kafka-based streaming applications: When you create a stream processing application with Kafka’s Streams API, you create a Topologyeither using the StreamsBuilder DSL or the low-level Processor API. Normally, the topology runs with the KafkaStreams class, which connects to a Kafka cluster and begins processing when you call start(). For testing though, connecting to a running […]

Read More

Auto ML With SQL Server 2019 Big Data Clusters

Marco Inchiosa has a model scenario for using Big Data Clusters to scale out a machine learning problem: H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and Both APIs use the same underlying algorithm implementations, […]

Read More


April 2017
« Mar May »