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Begin by setting up your Databricks environment. Sign in to your Databricks account and create a new workspace if you haven't already. Ensure you have the necessary permissions to create clusters and run notebooks.
On your MS SQL Server, ensure that the data you want to migrate is structured and accessible. Verify that you have the necessary credentials and permissions to export data from the database.
Export the required tables or data from MS SQL Server into CSV files. You can use SQL Server Management Studio (SSMS) to execute SQL queries that export data to CSV format. Make sure to include headers in the CSV files for easy mapping later.
Move the exported CSV files to a cloud storage service that Databricks can access. Options include Azure Blob Storage, AWS S3, or Google Cloud Storage, depending on your Databricks setup. Use the appropriate command-line tools or web interfaces to upload the files securely.
In Databricks, configure the access to your cloud storage where the CSV files are located. This involves setting up credentials or tokens as necessary. Use the Databricks UI to create secret scopes or directly configure access in your notebook using appropriate libraries (e.g., `boto3` for AWS, `azure-storage` for Azure).
Use a Databricks notebook to load the CSV files from your cloud storage into Databricks Lakehouse. Utilize Spark's built-in CSV reader to read the data into a DataFrame. For example, use the `spark.read.csv()` function with the appropriate path and options to load the data efficiently.
After loading the data into a DataFrame, perform data validation checks to ensure the integrity of the data. Check for data types, missing values, and row counts against the original dataset. Once verified, write the DataFrame to a Delta table in the Databricks Lakehouse using `DataFrame.write.format("delta").saveAsTable("your_table_name")`, thus ensuring the data is stored efficiently and is ready for further analysis.
By following these steps, you can successfully transfer data from MS SQL Server to Databricks Lakehouse using a straightforward process without relying on third-party tools.
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.
Microsoft SQL Server Consultants help companies choose the best business software solutions for their needs. Microsoft SQL Server Consultants help businesses resolve questions and issues, provide businesses with reliable information resources, and, ultimately, make better decisions on the software most appropriate for their unique needs. Consultants are available to help on call and can connect remotely to businesses’ computers to upgrade outdated editions of SQL servers to bring functions up to date for improved productivity.
MSSQL - SQL Server provides access to a wide range of data types, including:
1. Relational data: This includes tables, views, and stored procedures that are used to store and manipulate data in a structured format.
2. Non-relational data: This includes data that is not stored in a structured format, such as XML documents, JSON objects, and binary data.
3. Spatial data: This includes data that is related to geographic locations, such as maps, coordinates, and spatial queries.
4. Time-series data: This includes data that is related to time, such as timestamps, dates, and time intervals.
5. Graph data: This includes data that is related to relationships between entities, such as social networks, supply chains, and organizational structures.
6. Machine learning data: This includes data that is used for training and testing machine learning models, such as feature vectors, labels, and performance metrics.
7. Streaming data: This includes data that is generated in real-time, such as sensor data, log files, and social media feeds.
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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
