Avro Data

Location: Mission City M
Average rating: ***..
(3.50, 8 ratings)

As storage costs have dropped, organizations can now afford to save the vast majority of data that passes through them. Systems like Hadoop’s MapReduce permit such data to be easily analyzed and mined to improve businesses. However classic data formats like CSV, XML and gzipped archives serve such uses poorly. Some have weak data models. Others support rich datastructures but are inefficient. Most integrate poorly with MapReduce.

Apache Avro data files define an expressive, efficient standard for representing large data collections. Avro supports rich, recursive datatypes and includes facilities for datatype evolution. In Avro, new datatypes may be processed and defined on the fly, useful from dynamic scripting and query languages. Avro data is compact and fast to process. Avro data files are compressed and MapReduce-friendly.

This talk will describe how Avro achieves these capabilities and how applications can start incorporating Avro data today.

Photo of Doug Cutting

Doug Cutting


Founder of Apache Lucene, Nutch, Hadoop and Avro projects.

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Picture of Fred Dushin
Fred Dushin
02/03/2011 12:47am PST

Had you considered binary formats that have already been vetted in the industry? I like your use of IDL (even if it’s not OMG IDL); CORBA’s CDR encoding probably has many of the properties you need, though I’m less certain about its ability to support splitting. ASN.1 may have many of the properties you need, as well.

Anthony Cassandra
02/02/2011 2:48pm PST

A good talk laying out the context of why a data representation is important in the current “big data” ecosystem and the problems that existing formats have.


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