Would you be able to provide guidance on how to convert SAS dataset into Parquet file via Jupyter Notebook?
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While using google cloud dataproc to run a pyspark job. My code tried to do a query on bigquery using pyspark
query = 'select max(col a) from table'
df = spark.read.format('bigquery').load(query)
Look at this notebook. Here you'll have a example code to do bigquery queries with spark in dataproc.
You see this error because Dataproc does not include Spark BigQuery connector jar by default, that's why you need to add it to your Spark application if you want to use Spark to process data in BigQuery.
Here is documentation with examples on how to do this for Dataproc Serverless and Dataproc clusters:
https://cloud.google.com/dataproc-serverless/docs/guides/bigquery-connector-spark-example
https://cloud.google.com/dataproc/docs/tutorials/bigquery-connector-spark-example
Short: How to extract data from postgres and load to Google cloud postgres using google cloud SQL?
I have An airflow dag that extracts data from postgres using sqoop and stores data in AWS cloud
is there any operator in airflow that would help to extract data from on premise datavase and load directly to postgres database on Google cloud?
Or Can i reuse data in AWS cloud and put it directly to Google cloud database?
Or do i need to extract CSV file form RDBMS on premise and use operators in Airflow to insert it in target table in cloud.
The training data is stored in the .mat -v7.3 file. The file path is gs://bucket/train. How to load this file in my Google Cloud machine learning engine or Google Cloud datalab notebook?
I'm trying to use Presto on Amazon S3 bucket, but haven't found much related information on the Internet.
I've installed Presto on a micro instance but I'm not able to figure out how I could connect to S3. There is a bucket and there are files in it. I have a running hive metastore server and I have configured it in presto hive.properties. But when I try to run the LOCATION command in hive, its not working.
IT throws an error saying cannot find the file scheme type s3.
And also I do not know why we need to run hadoop but without hadoop the hive doesnt run. Is there any explanation to this.
This and this are the documentations i've followed while set up.
Presto uses the Hive metastore to map database tables to their underlying files. These files can exist on S3, and can be stored in a number of formats - CSV, ORC, Parquet, Seq etc.
The Hive metastore is usually populated through HQL (Hive Query Language) by issuing DDL statements like CREATE EXTERNAL TABLE ... with a LOCATION ... clause referencing the underlying files that hold the data.
In order to get Presto to connect to a Hive metastore you will need to edit the hive.properties file (EMR puts this in /etc/presto/conf.dist/catalog/) and set the hive.metastore.uri parameter to the thrift service of an appropriate Hive metastore service.
The Amazon EMR cluster instances will automatically configure this for you if you select Hive and Presto, so it's a good place to start.
If you want to test this on a standalone ec2 instance then I'd suggest that you first focus on getting a functional hive service working with the Hadoop infrastructure. You should be able to define tables that reside locally on the hdfs file system. Presto complements hive, but does require a functioning hive set-up, presto's native ddl statements are not as feature complete as hive, so you'll do most table creation from hive directly.
Alternatively, you can define Presto connectors for a mysql or postgresql database, but it's just a jdbc pass through do I don't think you'll gain much.
I'm wondering if there is a way to load data sets I have on my AWS - S3 account into Stata directly. I found that in R there is the AWS.tools package, but I have not found something similar for Stata.
Is there an .ado or something I can use?
You can install the AWS command line interface and shell out from Stata to use it to transfer files to and from Amazon S3. Not very convenient, but workable.
The alternative is to run R from within Stata, which seems clunkier.