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Clean the Data:
-Remove any unnecessary formatting, merged cells, or empty rows/columns.
-Ensure headers are clearly defined and consistent.
-Standardize date formats and ensure numeric fields are properly formatted.
Save the File:
-Save the Excel file in .xlsx format to ensure compatibility.
Split Large Files:
-If the file exceeds manageable size limits, split it into smaller files for easier processing.
Create a Databricks Workspace:
-Log in to your Databricks account and create a workspace if one doesn’t already exist.
Set Up a Compute Cluster:
-Create or use an existing cluster with sufficient resources to handle your workload.
-Ensure the cluster is running before proceeding.
Access Databricks File System (DBFS):
-DBFS is Databricks’ native file storage system, which allows you to upload and manage files for processing.
Navigate to DBFS:
-In your Databricks workspace, go to the "Data" section and select "File Upload."
Upload File:
-Drag and drop your Excel file into DBFS or use the upload button.
-Verify that the file is successfully uploaded by checking its location in /FileStore.
Open a Databricks Notebook:
-Create a new notebook in your workspace and attach it to your running cluster.
Install Necessary Libraries:
-Install libraries like pandas and openpyxl for reading Excel files if working with Python.
-Alternatively, use Spark’s built-in capabilities for processing data directly.
Read Data from Excel:
-Use Python or Spark commands to load data from the uploaded Excel file into memory.
-Validate that all rows and columns have been read correctly.
Clean Data:
-Handle missing values, remove duplicates, and standardize field names.
-Convert date fields into proper formats compatible with Delta tables.
Map Columns:
-If necessary, map columns from the Excel file to match your target schema in Databricks Lakehouse.
Validate Data:
-Inspect data types and ensure consistency across all records.
Create Delta Table Schema:
-Define the schema for your Delta table based on the structure of your processed data.
Write Data to Delta Table:
-Use Spark commands or SQL queries to write data into Delta tables stored in Databricks Lakehouse.
-Specify appropriate partitioning if dealing with large datasets.
Validate Table Creation:
-Query the Delta table to ensure all records have been successfully loaded.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
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