Databricks Beginner Guide

Databricks Beginner Guide & Use Cases

Basics of Databricks

Databricks is a unified data and AI platform built on Apache Spark. It is used for big data processing, analytics, and machine learning. Databricks simplifies distributed computing and provides a collaborative workspace for Data Engineers, Data Scientists, and Analysts.

Key Features of Databricks

• Delta Lake: ACID-compliant storage for batch & streaming data.
• Scalable Clusters: Auto-scaling Spark clusters for big data workloads.
• MLflow Integration: Experiment tracking and model management.
• Structured Streaming: Real-time data processing.
• Notebooks: Support for Python, SQL, Scala, and R in a collaborative workspace.
• Data Lakehouse: Unified architecture for structured and unstructured data.
• Integrations: Works with AWS S3, Azure Data Lake Storage, Kafka, and more.

5 Use Cases with Technical Implementation

Use Case	Cluster Type / Runtime	Notebook Name	Technical Implementation
1. ETL Pipeline	Standard Spark Cluster	ETL_Pipeline	# Load JSON from S3 df = spark.read.json("s3://my-bucket/raw_logs/") # Clean data df_clean = df.select("userId","eventType","timestamp").filter(df.eventType!="ignore") # Save as Delta df_clean.write.format("delta").mode("overwrite").save("/mnt/delta/clean_logs")
2. Real-Time Streaming	Databricks ML / Spark Streaming Runtime	Streaming_IoT	# Read from Kafka streaming_df = spark.readStream.format("kafka")\ .option("kafka.bootstrap.servers","kafka:9092")\ .option("subscribe","iot_sensors").load() # Parse JSON & filter anomalies from pyspark.sql.functions import from_json, col, schema_of_json json_schema = schema_of_json('{"sensorId":"string","temperature":"double"}') parsed_df = streaming_df.select(from_json(col("value").cast("string"), json_schema).alias("data")).select("data.*") anomalies = parsed_df.filter(col("temperature") > 100) # Write to console query = anomalies.writeStream.outputMode("append").format("console").start() query.awaitTermination()
3. ML Model Training	Databricks ML Runtime	ML_Churn_Model	from pyspark.ml.feature import VectorAssembler from pyspark.ml.classification import RandomForestClassifier from pyspark.ml import Pipeline data = spark.read.format("delta").load("/mnt/delta/customer_data") assembler = VectorAssembler(inputCols=["age","account_age","num_transactions"], outputCol="features") rf = RandomForestClassifier(featuresCol="features", labelCol="churn") pipeline = Pipeline(stages=[assembler, rf]) model = pipeline.fit(data) model.write().overwrite().save("/mnt/models/churn_model")
4. Data Analytics / Exploration	Standard Spark Cluster	Sales_Analysis	import matplotlib.pyplot as plt sales_df = spark.read.format("delta").load("/mnt/delta/sales_data") sales_pd = sales_df.groupBy("month").sum("revenue").toPandas() plt.plot(sales_pd["month"], sales_pd["sum(revenue)"]) plt.title("Monthly Sales Trend") plt.xlabel("Month") plt.ylabel("Revenue") plt.show()
5. Data Lakehouse / Delta Lake	Standard Spark Cluster	Delta_Lakehouse	# Load CSV & JSON csv_df = spark.read.format("csv").option("header","true").load("/mnt/raw/csv_data") json_df = spark.read.format("json").load("/mnt/raw/json_data") # Combine & save as Delta combined_df = csv_df.unionByName(json_df) combined_df.write.format("delta").mode("overwrite").save("/mnt/delta/combined_data") # SQL query spark.sql("SELECT count(*) FROM delta.`/mnt/delta/combined_data`").show()

Technical Flow in Databricks

1. Create Cluster → select runtime (Spark / ML / GPU if needed).
2. Mount storage → S3, ADLS, or DBFS.
3. Create Notebook → Python / SQL / Scala.
4. Write code → ETL, ML, Streaming, Analytics.
5. Attach Notebook to Cluster → Run & Monitor.
6. Save results → Delta Tables, ML Models, Visualizations.