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- Query Data in Dremio:
- Log into your Dremio instance.
- Write a SQL query to select the data you want to move to Databricks Lakehouse.
- Execute the query to ensure it returns the correct data.
- Export Data:
- Depending on the size of the data, you can export it as a CSV, JSON, or Parquet file. Parquet is recommended for larger datasets due to its efficiency and compatibility with Databricks.
- Use the Dremio UI or a script to export the query results to a file.
- Choose a Cloud Storage Provider:
- Select a cloud storage provider that is accessible by both Dremio and Databricks, such as Amazon S3, Azure Blob Storage, or Google Cloud Storage.
- Upload Data:
- Use the cloud storage provider’s interface or SDK to upload the exported files from your local environment to the cloud storage bucket.
- Set Permissions:
- Ensure the storage bucket and files have the correct permissions set so that Databricks can access them.
- Set Up Databricks Environment:
- Log into your Databricks workspace.
- Create a new cluster or use an existing one, making sure it has the necessary resources to handle the data import.
- Mount Cloud Storage:
- Use Databricks to mount the cloud storage bucket as a Databricks File System (DBFS) mount point.
- This can be done using Databricks CLI or notebooks with the appropriate commands.
- Read Data into Databricks:
- Use a Databricks notebook to read the data from the mounted DBFS path.
- You can use the spark.read function to read the data into a DataFrame, specifying the format (e.g., CSV, JSON, Parquet) that you used to export the data from Dremio.
- Transform Data (Optional):
- If necessary, perform any transformations on the DataFrame to prepare the data for its use in Databricks Lakehouse.
- Write Data to Databricks Lakehouse:
- Use the DataFrame.write function to write the DataFrame to a Databricks Delta table.
- Choose the appropriate write mode (e.g., overwrite, append) based on your needs.
Example Code for Data Import in Databricks Notebook
# Mount the cloud storage bucket if not already mounted
storage_endpoint = "s3://my-bucket/path"
mount_point = "/mnt/my-bucket"
dbutils.fs.mount(storage_endpoint, mount_point)
# Read the data into a DataFrame
data_path = mount_point + "/my-data.parquet"
df = spark.read.format("parquet").load(data_path)
# Optional: Perform data transformations
# df = df.withColumn(...)
# Write the DataFrame to a Delta table
delta_table_path = "/delta/my-delta-table"
df.write.format("delta").mode("overwrite").save(delta_table_path)
Check Data in Databricks:
- Query the Delta table in Databricks to ensure that the data has been imported correctly.
- Validate that the row counts and data types match the original dataset in Dremio.
- Unmount Cloud Storage (Optional):
- If you no longer need the cloud storage bucket mounted, you can unmount it to tidy up your workspace.
- Remove Temporary Files:
- Delete any temporary files or exports that are no longer needed to free up space and maintain security.
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.
Dremio is a data-as-a-service platform that enables businesses to access and analyze their data faster and more efficiently. It provides a self-service data platform that connects to various data sources, including cloud storage, databases, and data lakes, and allows users to query and analyze data using familiar tools like SQL and BI tools. Dremio's unique approach to data processing, called Data Reflections, accelerates query performance by automatically creating optimized copies of data in memory. This allows users to get insights from their data in real-time, without the need for complex data pipelines or data warehousing. Dremio also provides enterprise-grade security and governance features to ensure data privacy and compliance.
Dremio's API provides access to a wide range of data types, including:
1. Structured data: This includes data that is organized into tables with defined columns and rows, such as data from relational databases.
2. Semi-structured data: This includes data that has some structure, but is not organized into tables, such as JSON or XML data.
3. Unstructured data: This includes data that has no predefined structure, such as text documents, images, and videos.
4. Big data: This includes large volumes of data that cannot be processed using traditional data processing tools, such as Hadoop and Spark.
5. Streaming data: This includes real-time data that is generated continuously, such as data from IoT devices or social media feeds.
6. Cloud data: This includes data that is stored in cloud-based services, such as Amazon S3 or Microsoft Azure.
Overall, Dremio's API provides access to a wide range of data types, making it a powerful tool for data integration and analysis.
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: