Databricks Quick Tutorial


           Databricks Quick Tutorial

Here’s a quick tutorial on Databricks, combining core concepts and resources:

What is Databricks?

  • Unified Platform: Databricks is a cloud-based platform for all things data. It seamlessly combines data engineering, science, machine learning, and analytics.
  • Built on Open Source: It’s based on Apache Spark (a lightning-fast big data engine), Delta Lake (for reliable data lakes), and MLflow (to manage machine learning workflows).
  • Cloud-Native:  Available on major cloud providers (AWS, Azure, GCP).

Key Use Cases:

  • Data Engineering:  Building data pipelines, ETL (Extract, Transform, Load) processes, and data lakes.
  • Data Science & Machine Learning:  Developing and deploying machine learning models.
  • Data Analytics & BI: Creating interactive dashboards and visualizations.

Core Components of Databricks:

  1. Workspaces: Your collaborative environment for notebooks, jobs, and other assets.
  2. Clusters: The compute engines that run your Spark code. You can choose different types and sizes.
  3. Notebooks: Interactive coding interfaces (similar to Jupyter notebooks). You write code in Python, SQL, Scala, or R.
  4. Jobs: Automated tasks to run your notebooks or scripts on a schedule.
  5. Databricks SQL:  A serverless SQL warehouse for analytics and reporting.
  6. MLflow:  A platform to track experiments and package and deploy models.

Getting Started with Databricks (Example Workflow):

  1. Create a Workspace: Sign up for a free trial or use an existing account on your chosen cloud provider.
  2. Launch a Cluster:  Select the type, size, and libraries you need.
    • Create a Notebook: Import Data:  Load data from cloud storage, databases, or other sources.
    • Explore and Transform: Use Spark to clean, analyze, and prepare your data.
    • Visualize: Create charts and graphs to gain insights.
    • Build a Model (Optional):  If you’re doing machine learning, train and test your models.
  3. Save and Schedule (Optional): To automate the process, create a regular job to run your notebook.

Example Code (PySpark in a Databricks Notebook):


from pyspark.sql import SparkSession


# Create a Spark session

spark = SparkSession.builder.getOrCreate()


# Read a CSV file from cloud storage

df =“dbfs:/FileStore/my_data.csv”, header=True, inferSchema=True)


# Show the first 5 rows


# Basic analysis: Count the number of rows


Databricks Training Demo Day 1 Video:

You can find more information about Databricks Training in this Dtabricks Docs Link



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