Forum Discussion
Loading multiple xer files in Microsoft Fabric
How to Efficiently Process and Load Multiple .xer Files from a Lakehouse into a Tabular Format for Power BI in Microsoft Fabric?
Hello Fabric Community,
I am working on a project where I need to process multiple .xer files stored in a Microsoft Fabric Lakehouse. The goal is to append the data from these files into a single tabular format and subsequently integrate the processed data with Power BI for analysis. Here's a detailed breakdown of my workflow and the challenges I'm facing:
Current Workflow:
File Location:
- All .xer files are stored in the Files section of a Lakehouse in Fabric.
Required Data Transformation:
- Read all .xer files from the Lakehouse folder.
- Combine the files into a single Spark DataFrame.
- Add a column to identify the source file for each row.
- Perform transformations, such as filtering specific rows or columns.
Integration with Power BI:
- Write the processed data to a Delta table or any other format supported by Power BI.
- Build Power BI reports based on the transformed data.
Hi adarshthouti141 , thank you for reaching out to the Microsoft Fabric Community Forum.
Please consider below steps:
- Use Apache Spark to read all .xer files from the Lakehouse folder. Combine the files into a single Spark DataFrame.
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ProcessXerFiles").getOrCreate()
df = spark.read.format("xer").load("path/to/lakehouse/folder")
- Add a column to the DataFrame to identify the source file for each row.
df = df.withColumn("source_file", spark.sparkContext._jvm.org.apache.spark.sql.functions.input_file_name())
- Filter specific rows or columns as needed. Perform any additional transformations required for your analysis.
# Example transformation: Filter specific rows
df = df.filter("your_condition_here")
- Write the processed data to a Delta table or any other format supported by Power BI.
df.write.format("delta").save("path/to/delta/table")
If this helps, please consider marking it 'Accept as Solution' so others with similar queries may find it more easily. If not, please share the details.
Thank you.
4 Replies
v-hashadapuCommunity Support
Hi adarshthouti141 , thank you for reaching out to the Microsoft Fabric Community Forum.
Please consider below steps:
- Use Apache Spark to read all .xer files from the Lakehouse folder. Combine the files into a single Spark DataFrame.
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("ProcessXerFiles").getOrCreate()
df = spark.read.format("xer").load("path/to/lakehouse/folder")
- Add a column to the DataFrame to identify the source file for each row.
df = df.withColumn("source_file", spark.sparkContext._jvm.org.apache.spark.sql.functions.input_file_name())
- Filter specific rows or columns as needed. Perform any additional transformations required for your analysis.
# Example transformation: Filter specific rows
df = df.filter("your_condition_here")
- Write the processed data to a Delta table or any other format supported by Power BI.
df.write.format("delta").save("path/to/delta/table")
If this helps, please consider marking it 'Accept as Solution' so others with similar queries may find it more easily. If not, please share the details.
Thank you.- v-hashadapu
Hi adarshthouti141 , Hope your issue is solved. If it is, please consider marking it 'Accept as solution', so others with similar issues may find it easily. If it isn't, please share the details. Thank you.
- v-hashadapu
Hi adarshthouti141 , Hope your issue is solved. If it is, please consider marking the answer 'Accept as solution', so others with similar issues may find it easily. If it isn't, please share the details.
Thank you. - v-hashadapu
Hi adarshthouti141 , Hope your issue is solved. If it is, please consider marking the answer 'Accept as solution', so others with similar issues may find it easily. If it isn't, please share the details.
Thank you.