🌈Azure Databricks Series: Creating Delta Tables from CSV Files Step-by-Step🌈

You can find the scripts used in this video in below blog,

https://jbswiki.com/2024/09/22/%f0%9f...

Welcome to our latest episode in the Azure Databricks Series! In this comprehensive guide, we will explore how to create Delta tables from CSV files in Azure Databricks, with a special focus on step-by-step implementation. Whether you’re a data engineer, data scientist, or just curious about big data technologies, this video is tailored for you! Let’s dive in! 🚀

What Are Delta Tables? 🤔
Delta tables are a key feature of Delta Lake, a powerful storage layer that enables ACID transactions on big data workloads. They enhance your data lake with reliability and performance, making them ideal for analytics. Here’s why Delta tables are so significant:

ACID Transactions: Ensure data integrity even in complex scenarios with multiple users.
Schema Evolution: Easily adapt your data model as requirements change without downtime.
Time Travel: Access historical versions of your data for auditing or recovery.
Unified Batch and Streaming: Simplify your architecture by managing batch and streaming data in one table.
Why Use CSV Files? 📄
CSV (Comma-Separated Values) files are one of the most common data formats due to their simplicity and wide acceptance. They’re easy to read and write, making them ideal for data import/export tasks. However, to take full advantage of the Delta Lake features, we’ll convert these CSV files into Delta tables.

Getting Started: Setting Up Your Environment 🛠️
Before we start creating Delta tables, ensure that you have the following:

Azure Databricks Workspace: If you don’t have one, create it via the Azure portal.
Sample CSV File: Download or create a CSV file that we will use in this tutorial.
Databricks Notebook: Set up a new notebook in your workspace where we’ll run our code.
Step 1: Creating a New Schema 🗂️
To organize our Delta tables efficiently, we’ll start by creating a new schema (or database). Schemas allow you to group related tables, making your data management much easier.

Here’s how to create a schema:

sql
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CREATE SCHEMA IF NOT EXISTS my_new_schema;
This SQL command checks for the existence of my_new_schema and creates it if it doesn’t exist. 🆕

Step 2: Uploading the CSV File 📤
Next, we need to upload our CSV file to the Databricks file system (DBFS). This step is crucial as we will read data from this location.

Navigate to the "Data" section in your Databricks workspace.
Click on "Add Data" and choose "Upload File."
Select your CSV file and upload it.
Once uploaded, take note of the file path, which you’ll need for the next steps! 📍

Step 3: Creating the Delta Table from CSV 🛠️
Now, it’s time to create our Delta table. We’ll read the CSV data into a DataFrame and then write it out as a Delta table.

Here’s the code to do this:

python
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Read CSV into DataFrame
df = spark.read.option("header", "true").csv("/path/to/your/file.csv")

Write DataFrame to Delta table
df.write.format("delta").mode("overwrite").saveAsTable("my_new_schema.my_delta_table")
Reading CSV: The spark.read.option("header", "true") function allows us to read the CSV file, treating the first row as column headers.
Writing Delta Table: The saveAsTable method saves our DataFrame as a Delta table named my_delta_table in the my_new_schema schema.
Step 4: Verifying the Delta Table ✔️
After creating the Delta table, let’s verify that it has been created successfully. We can do this by running a simple SQL query:

sql
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SELECT * FROM my_new_schema.my_delta_table LIMIT 10;
This query will return the first ten rows of our Delta table, allowing us to confirm that the data has been loaded correctly. If you see your data, congratulations! 🎉

Additional Features of Delta Tables 🌈
Delta tables offer several additional features that enhance data management:

Schema Enforcement: Ensures that the data written to the Delta table adheres to the defined schema.
Schema Evolution: Modify the schema as needed without dropping the table or losing data.
Data Versioning: Access previous versions of your data using time travel.
Optimized Reads: Delta Lake optimizes reads, making queries faster and more efficient.
Conclusion 🎓
In this video, we covered the entire process of creating Delta tables from CSV files in Azure Databricks. We started with an introduction to Delta tables, created a new schema, uploaded a CSV file, and finally created a Delta table and verified it.

Call to Action 📣
If you found this video helpful, please give it a thumbs up and subscribe for more Azure Databricks tutorials! If you have any questions or comments, feel free to drop them below. Thank you for watching, and happy data processing! 🌟

[DELTA_INVALID_CHARACTERS_IN_COLUMN_NAMES] Found invalid character(s) among ' ,;{}()\n\t=' in the column names of your schema.
Invalid column names: Date .
Please use other characters and try again.