Spark and Scala are commonly used together for large-scale data analytics. Spark uses Resilient Distributed Datasets (RDDs) that can operate on large datasets in parallel across a cluster. RDDs are immutable and fault-tolerant. Scala is a functional programming language that supports immutable data structures, making it a good fit for Spark's programming model. Key concepts in Spark include transformations that create new RDDs and actions that return results to the driver program. Together, Spark and Scala enable distributed, scalable data processing on large datasets using a declarative programming style.

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2. DESCRIBE the ecosystem associated with SCALA and SPARK.

3. Explain the key concepts of Spark and Scala
Spark
RDD (Resilient Distributed Dataset): This is the fundamental data structure in Spark. It represents a
collection of objects partitioned across multiple nodes in a cluster. RDDs are immutable, meaning any
transformation on an RDD results in a new RDD. This ensures fault tolerance as lost data can be
recomputed from the original data source.
Transformations and Actions: Spark programs involve two types of operations on RDDs:
Transformations: These are like functions that create a new RDD from an existing one. Common
examples include filter, map, join, and union. These operations are lazy, meaning they are not executed
until an action is triggered.
Actions: These operations return a value after running a computation on an RDD. Examples include reduce,
count, and
first. These trigger the actual execution of the transformations that were applied to the original RDD.
Scala
Functional Programming: Scala is a general-purpose language but leans heavily towards
functional programming concepts. This means programs are built by composing pure functions
that take inputs and produce outputs without side effects. This style aligns well with Spark's data
processing paradigm.
Immutability: Similar to RDDs, Scala objects are generally immutable by default. This promotes data
consistency
and simplifies reasoning about program behavior.

4. Application - USE the core RDD and DataFrame APIs to perform
analytics on datasets with Scala.
Sales Data Analysis with Spark Scala APIs
Data: Assume we have a CSV file named "sales_data.csv" containing columns like "product", "price", and "quantity".
1. Load data as RDD:
Scala: import org.apache.spark.SparkConf import org.apache.spark.sql.SparkSession // Configure Spark val conf = new
SparkConf().setAppName("SalesAnalytics") val spark = SparkSession.builder().config(conf).getOrCreate() // Load data as RDD val
salesRDD = spark.sparkContext.textFile("sales_data.csv") // Split each line into an array of values (product, price, quantity) val parsedRDD
= salesRDD.map(line => line.split(","))
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2. Analyze data with RDD transformations:
• Total Sales:
Scala: // Calculate total sales by summing the product of price and quantity across all records val totalSales = parsedRDD.map(record =>
record(1).toDouble * record(2).toInt).sum() println("Total Sales:", totalSales)
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• Top Selling Products (by quantity):
Scala: // Convert RDD to key-value pairs (product, quantity) val productQuantityRDD = parsedRDD.map(record => (record(0), record(2).toInt)) //
Sort by quantity in descending order and take the top 5 val topProducts = productQuantityRDD.sortBy(_._2, ascending = false).take(5)
println("Top Selling Products (Quantity):") topProducts.foreach(println)
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3. DataFrames for structured analysis:
Scala :// Load data as DataFrame with schema inference val salesDF = spark.read.csv("sales_data.csv") // Select specific columns and calculate
total sales val totalSalesDF = salesDF.select("price", "quantity").withColumn("revenue", $"price" * $"quantity").sum("revenue") println("Total Sales
(DataFrame):", totalSalesDF.head()(0)) // Group by product and get average sales val avgSalesDF = salesDF.groupBy("product").avg("price",
"quantity") println("Average Sales by Product:") avgSalesDF.show()

5. Examine how the spark and scala is different than other
programming langauge
. Spark
Distributed Processing: Spark excels at distributed computing. It can process massive datasets
across clusters of machines, making it ideal for big data analytics. Traditional languages like Java or
Python are primarily designed for single-machine processing.
Resilience: Spark's RDDs are fault-tolerant. If a node fails, the data can be recomputed from the
original source. This is not a built-in feature in most languages and requires additional development
effort.
Declarative Programming: Spark programs focus on what needs to be done with the data rather
than how to do it step-by-step. This makes them easier to reason about and maintain compared to
imperative languages that require detailed instructions.
Scala
Functional Programming Paradigm: Scala is a general-purpose language with a strong emphasis
on functional programming. This means programs are built by composing pure functions, which aligns
well with Spark's data processing model. Languages like Python or Java are primarily object-oriented,
requiring a different approach.
Immutability: Scala objects are generally immutable by default. This promotes data consistency and
simplifies
reasoning about program behavior compared to languages where objects can be modified after
creation.

6. Mention steps of creating RDD's in spark using scala
There are three primary ways to create RDDs (Resilient Distributed Datasets) in Spark using Scala:
1.Parallelizing an Existing Collection:
This approach is suitable for small datasets or testing purposes. It involves using the sparkContext.parallelize method on a Scala
collection like a Seq or List.
Scala
import org.apache.spark.SparkConf import org.apache.spark.SparkContext val conf = new
SparkConf().setAppName("MyApp") val sc = SparkContext.getOrCreate(conf) val numbers = List(1, 2, 3, 4, 5) val
numbersRDD = sc.parallelize(numbers)
2.Loading Data from External Storage:
Spark can read various data formats from external storage systems like HDFS, local file systems, or databases. You can use
methods like textFile for text files, csv for CSV files, and others depending on the data format.
Scala
val salesDataRDD = sc.textFile("hdfs://path/to/sales_data.csv")
3.Transforming an Existing RDD:
RDDs are immutable, meaning any operation on an RDD creates a new RDD. You can chain transformations like map, filter, or
join on existing RDDs to create new ones with modified data.
Code:
val filteredRDD = salesDataRDD.filter(line => line.split(",")(1).toDouble > 100.0) // Filter based on price

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