Search Results for "get"

...Convert the column name to be compatible with Amazon Athena and the AWS Glue Catalog. Run a query against AWS CloudWatchLogs Insights and convert the results to Pandas DataFrame. Get QuickSight dashboard ID given a name and fails if there is more than 1 ID associated with this name. List IAM policy assignments in the current Amazon QuickSight account.

See https://github.com/qualiu/msr/ Match/Search/Replace: msr.exe/msr-Win32.exe/msr.cygwin/msr.gcc**/msr-i386.gcc** Match/Search/Replace/Execute/* Files/Pipe Lines/Blocks. Filter/Load/Extract/Transform/Stats/* Files/Pipe Lines/Blocks. Not-IN-latter: nin.exe/nin-Win32.exe/nin.cygwin/nin.gcc**/nin-i386.gcc** Get Exclusive/Mutual Line-Set or Key-Set; Remove Line-Set or Key-Set matched in latter file/pipe; Get Unique/Mutual/Distribution/Stats/* Files/Pipe Line-Set or Key-Set. Match/Search/Replace files/pipe text with plain/Regex syntax. And for ETL alike work like Load and filter files -> Extract -> Transform output. For replacing files, you can preview and backup, in multiple directories and files or pipe, with plain text matching or using general Regex as C++, C#, Java, Scala; So msr is a good tool to learn and test Regex since it has different colors for matched groups captured by the Regex pattern.

This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. It can run in local mode also. Get json example at https://github.com/arrahtech/osdq-spark How to run Unzip the zip file Windows : java -cp .\lib\*;osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c .\example\samplerun.json Mac UNIX java -cp ./lib/*:./osdq-spark-0.0.1.jar org.arrah.framework.spark.run.TransformRunner -c ./example/samplerun.json For those on windows, you need to have hadoop distribtion unzipped on local drive and HADOOP_HOME set. ...

With CueLake, you can use SQL to build ELT (Extract, Load, Transform) pipelines on a data lakehouse. You write Spark SQL statements in Zeppelin notebooks. You then schedule these notebooks using workflows (DAGs). To extract and load incremental data, you write simple select statements. CueLake executes these statements against your databases and then merges incremental data into your data lakehouse (powered by Apache Iceberg). To transform data, you write SQL statements to create...