Great! Here’s how we’ll structure both:

🧪 Hands-On Databricks Notebooks (Ready-to-Use)

Each notebook is short and focused on one concept, designed for execution in Azure Databricks.

📘 Notebook 1: Spark RDD Basics

• Create and transform RDDs
• Word count from text RDD
• Filter, map, reduce examples

rdd = spark.sparkContext.textFile("/databricks-datasets/shakespeare/*")
words = rdd.flatMap(lambda line: line.split(" "))
result = words.map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b)
result.take(5)

📘 Notebook 2: DataFrame Basics

• Load CSV/JSON data
• Filter, group, aggregation
• SQL queries over DataFrames

df = spark.read.option("header", "true").csv("/databricks-datasets/retail-org/sales.csv")
df.filter(df.amount > 500).groupBy("region").sum("amount").show()
df.createOrReplaceTempView("sales")
spark.sql("SELECT region, SUM(amount) FROM sales GROUP BY region").show()

📘 Notebook 3: Delta Lake & Lakehouse

• Create Delta table
• Upserts with MERGE INTO
• Time Travel with VERSION AS OF

df.write.format("delta").mode("overwrite").saveAsTable("bronze_sales")

spark.sql("""
MERGE INTO silver_sales USING updates
ON silver_sales.id = updates.id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *
""")

spark.read.format("delta").option("versionAsOf", 0).table("silver_sales").show()

📘 Notebook 4: Databricks Workspace Basics

• How to use: Repos, Notebooks, Clusters
• Link GitHub repo
• Create Job from Notebook

🎯 Sample Interview Questions (Conceptual + Scenario)

🧠 Spark & Databricks Concepts

1. What are the differences between RDD and DataFrame in Spark?
2. Explain the Catalyst and Tungsten optimizers.
3. What are the benefits of Delta Lake over traditional data lakes?
4. What is a Lakehouse Architecture and why is it needed?
5. How do you handle schema evolution in Delta Lake?
6. What is the difference between Interactive and Job Clusters in Databricks?
7. What is the role of createOrReplaceTempView() in Spark SQL?

🔍 Scenario-Based Questions

1. 🧪 You need to implement slowly changing dimensions (SCD) in a table. Which Delta Lake feature would you use?
2. 🔁 Your Spark job runs slowly. How do you optimize joins and partitioning?
3. 🧼 You receive semi-structured JSON data daily. How will you process and clean it using the Bronze → Silver → Gold model?
4. 🔒 How would you integrate Azure Key Vault to manage credentials in Azure Databricks?
5. 📊 You want to create dashboards directly on Delta tables. How do you connect Power BI or Synapse to your Lakehouse?

Here’s a complete tutorial for Spark RDD on Azure Databricks, covering all key concepts, transformations, actions, and real-world use cases — fully aligned with data engineering interview preparation.

📘 Spark RDD Tutorial on Azure Databricks

✅ You’ll learn everything about RDDs: creation, transformations, actions, and when to use them over DataFrames.

🧱 1. What is an RDD?

RDD (Resilient Distributed Dataset) is the core low-level API in Spark for distributed processing.

• Immutable
• Partitioned
• Lazy Evaluation
• Fault Tolerant (Lineage)
• In-memory computing

🔧 2. How to Create an RDD

From a Python collection:

rdd1 = spark.sparkContext.parallelize([1, 2, 3, 4, 5])

From external data (text file):

rdd2 = spark.sparkContext.textFile("/databricks-datasets/README.md")

🔄 3. Transformations (Lazy)

🔹 map(): Element-wise transformation

rdd = rdd1.map(lambda x: x * x)

🔹 filter(): Filter elements

even_rdd = rdd1.filter(lambda x: x % 2 == 0)

🔹 flatMap(): Break lines into words

rdd = spark.sparkContext.textFile("/databricks-datasets/README.md")
words = rdd.flatMap(lambda line: line.split(" "))

🔹 distinct(), union(), intersection()

rdd1 = spark.sparkContext.parallelize([1, 2, 3])
rdd2 = spark.sparkContext.parallelize([2, 3, 4])
rdd1.union(rdd2).distinct().collect()

⚙️ 4. Actions (Trigger computation)

🔸 collect(), take(n)

rdd1.collect()
rdd1.take(3)

🔸 reduce(), count(), first()

total = rdd1.reduce(lambda a, b: a + b)

🔸 foreach()

rdd1.foreach(lambda x: print(x))

🧠 5. Pair RDD (Key-Value)

Create a Pair RDD:

pair_rdd = spark.sparkContext.parallelize([("a", 1), ("b", 1), ("a", 2)])

Group and Reduce:

pair_rdd.reduceByKey(lambda a, b: a + b).collect()
pair_rdd.groupByKey().mapValues(list).collect()

📚 6. RDD Use Cases

✅ 1. Word Count (Classic Example)

lines = spark.sparkContext.textFile("/databricks-datasets/README.md")
words = lines.flatMap(lambda line: line.split())
word_pairs = words.map(lambda word: (word.lower(), 1))
word_counts = word_pairs.reduceByKey(lambda a, b: a + b)
word_counts.take(10)

✅ 2. Log File Analysis

log_rdd = spark.sparkContext.textFile("/databricks-datasets/logs/sample_logs")
errors = log_rdd.filter(lambda line: "ERROR" in line)
print("Total errors:", errors.count())

✅ 3. Clickstream Analytics

rdd = spark.sparkContext.parallelize([
  ("user1", "product1"),
  ("user2", "product2"),
  ("user1", "product2")
])
rdd.groupByKey().mapValues(list).collect()

✅ 4. Custom ETL (Non-tabular JSON)

raw = spark.sparkContext.textFile("/databricks-datasets/iot/iot_stream.json")
parsed = raw.map(lambda x: json.loads(x))
temp_data = parsed.map(lambda x: (x["device_id"], x["temp"]))
high_temp = temp_data.filter(lambda x: x[1] > 70)

🧪 7. When to Use RDD Over DataFrames

🧪 8. RDD Lineage and Fault Tolerance

Each RDD stores lineage of transformations, so in case of node failure, Spark can recompute lost partitions.

rdd = spark.sparkContext.parallelize([1, 2, 3])
rdd2 = rdd.map(lambda x: x + 1)
print(rdd2.toDebugString())

💡 9. RDD vs DataFrame vs Dataset

🧑‍💻 Final Tip: Cluster & Notebook Setup on Databricks

1. Go to Azure Databricks → Create Workspace
2. Launch a Cluster (choose runtime with Apache Spark)
3. Create a Notebook → Language: Python
4. Run above RDD snippets to practice end-to-end!

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