Indeed this will work. Additionally the files could be zipped as well
(gz or bzip2)
val df =
sqlContext.read.format("com.databricks.spark.csv").option("inferSchema",
"true").option("header", "true").load("/data/stg")
On 22/02/2016 15:32, Alex Dzhagriev wrote:
> Hi Saif,
>
> You can put your files into one directory and read it as text. Another option
> is to read them separately and then union the datasets.
>
> Thanks, Alex.
>
> On Mon, Feb 22, 2016 at 4:25 PM, <saif.a.ell...@wellsfargo.com> wrote:
>
>> Hello all, I am facing a silly data question.
>>
>> If I have +100 csv files which are part of the same data, but each csv is
>> for example, a year on a timeframe column (i.e. partitioned by year),
>> what would you suggest instead of loading all those files and joining them?
>>
>> Final target would be parquet. Is it possible, for example, to load them and
>> then store them as parquet, and then read parquet and consider all as one?
>>
>> Thanks for any suggestions,
>> Saif
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