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Now that you know **how to enable DBFS in Databricks Free Edition** and how to access it, let's get into how you can start working with your data. One of the most common tasks is uploading data into DBFS. As mentioned earlier, you can upload files through the Databricks UI. Simply go to the Data tab, select the DBFS section, and click the "Upload" button. Another way to upload the data is from your local machine, and the command line interface will work to upload files. Once your data is in DBFS, you can load it into your notebooks for analysis. Databricks supports various data formats, including CSV, JSON, Parquet, and many more. To load a CSV file, use the following code snippet. First, create a temporary view using this code `df = spark.read.csv("/FileStore/your_file.csv", header=True, inferSchema=True) df.createOrReplaceTempView("your_table_name")`. Replace `/FileStore/your_file.csv` with the path to your CSV file, and "your_table_name" with a name for your temporary view. This code reads the CSV file into a Spark DataFrame and creates a temporary view that you can query using SQL. Now, you can query your data using SQL. For example, to view the first 10 rows of your table, run this query `%sql SELECT * FROM your_table_name LIMIT 10`. This allows you to explore and analyze your data directly within your Databricks notebook. Databricks makes it easy to read data from DBFS using various methods. Reading data from DBFS is essential. You can then use the data with a Spark DataFrame. For example, use the below code to read a CSV file into a Spark DataFrame using this snippet: `df = spark.read.csv("/FileStore/your_file.csv", header=True, inferSchema=True)`. Next, you can perform transformations, such as data cleaning, filtering, and aggregation. For instance, to filter your data and see only specific columns, you can use `.filter()` and `.select()` functions. Finally, you can write the transformed data back to DBFS in a new format. This is useful for storing processed datasets that you can reuse later. To save a DataFrame as a Parquet file, use the following code: `df.write.parquet("/FileStore/output.parquet")`. Working with data in DBFS involves loading, transforming, and saving data. By mastering these operations, you'll be well-equipped to handle any data task in your Databricks workspace. DBFS is the foundation for your data projects.
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