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Map() and its close cousin flatMap() are often used when we deal with data structures in Scala and both are higher-order functions.   

• map() method: It functions similarly to mapping operations in other languages like JavaScript since it allows us to iterate over a collection to translate it into ANOTHER collection. Every element of the collection is transformed by applying a function to it. Its uses are heavily overloaded, making it useful for situations that aren't immediately obvious. Scala's map() method is exceptionally powerful.
• flatpmap() method: In Scala, flatMap() is the same as map(), except that flatMap removes the inner grouping of items and creates a sequence. ESSENTIALLY, it is a combination of the FLATTEN method and the map method. The map method followed by the flatten method produces the same RESULT as flatMap(), so we can say that flatMap RUNS the map method first, followed by the flatten method. Compared to the map method, flatMap is much more powerful.

According to their declarations, Scala VARIABLES are CATEGORIZED into three SCOPES as given below: 

• Fields: Variables of this type can be accessed from any method within a Web OBJECT, or from outside the object, depending on access modifiers used. Depending on the var and val keywords, they can be mutable or immutable.
• Method Parameters: When a method is invoked, these variables are used to pass values to the method. All method parameters use the val keyword and are strictly immutable. Normally, these are accessed within a method, but a Reference allows ACCESSING them outside the method.
• Local Variables: These are the variables (mutable or immutable) declared inside a method and accessible only within that method. By returning them from the method, they can be accessed outside of the method.

Scala provides us with a function called ofDim that declares multidimensional arrays using the matrix format. Array.ofDim can be used to declare multidimensional arrays. There's no limit to the number of dimensional arrays you can CREATE. 

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or 

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A set is a collection of unique items that cannot be repeated. In Scala, non-negative INTEGER SETS are called Bitsets, and they are represented as variable-size arrays of bits packed into 64-bit words.   The largest number in a bitset represents its memory footprint.

Syntax: 

Here, BS represents the name of the BitSet that was created. 

scala.collection.immutable.BitSet and scala.collection.mutable.BitSet are the two versions of BitSet provided in Scala. Although both are identical, mutable data structures change the bits in place, thus making them less concurrency friendly than immutable data structures. 

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In Scala, an extractor defines a method unapply(), as well as an OPTIONAL method, apply(). For mapping and unmapping data between FORM and MODEL data, both apply and unapply methods are used. 

• Apply() method: Assembling an object from its components is done with this method. For example, by using the two components FIRSTNAME and lastName, we can create the Employee object with the apply method.
• Unapply() method: This method follows the REVERSE order of apply order and decomposes an object into components. For example, you can break or decompose that employee object down into its components i.e., firstName and lastName.

A helper class, named App, is provided by Scala that provides the MAIN METHOD and its MEMBERS together. App trait allows Objects to be TURNED into executable programs quickly. The App class in Scala can be extended instead of writing your own main method. This WAY you can produce concise and scalable applications. 

Example: 

Literals, or constants, are any CONSTANT value that can be assigned to a variable. A literal is a set of symbols used to DESCRIBE a constant value in the code. They are classified into the FOLLOWING types:   

• Integer literals 
• Floating point literals 
• Boolean literals 
• Symbol literals 
• Character literals 
• String literals 
• Multi-Line strings 

The Scala Option[T] is referred to as a carrier of one element or none for a specified type. As the name suggests, this container holds either a Some or a None object, which represents a missing value. It holds Some[T] if a value is stored, otherwise none. In simple words, Scala options are WRAPPERS for missing values. 

EXAMPLE:

Output:  

Here, the key of the value InterviewBit is FOUND, therefore, Some is returned, however, the key of the value Informatica is not found, therefore, None is returned. 

It is well known that variables are merely RESERVED memory locations for storing values. In SCALA, variables are MAINLY of two types:  

• Mutable Variables: These are variables whose VALUE is capable of CHANGING and the var keyword is used to declare these variables.

Syntax:  

Example: 

• Immutable Variables: These are variables whose value is not capable of changing and the val keyword is used to declare these variables.

Syntax: 

Example: 

A tuple is a HETEROGENEOUS data structure, consisting of a fixed number of elements, each with a different data type. A tuple, however, can contain objects of different types, as well as being immutable. They are particularly useful when returning multiple values from methods.  

A tuple's type is DETERMINED by how many elements it contains as well as the type of those elements. Scala uses Tuple2, Tuple3, etc., up to Tuple22, to represent types of TUPLES. Scala currently limits the number of elements in a tuple to 22, and if the number of elements in the tuple exceeds 22, an error will be GENERATED. 

Example: A tuple storing an integer, and a string.

The inferred type of ingredient is (Integer, String), which is shorthand for Tuple2[INT, String]. 

Note: Tuples can be used to overcome this limit. It is possible for a tuple to contain other tuples. 

Streams, a Scala feature, are essentially lazy lists in which elements are only evaluated as needed. In this WAY, Scala allows for faster processing. Streams are SIMILAR to lists in terms of performance.

Syntax: 

Scala lets you construct Lists by using the :: operator, while you can build Streams by using the #:: operator with Stream.empty at the end of the expression. A stream's head in the above syntax is 1, while its tail is 2 and 3. 

Example:  

OUTPUT: 

Observe the output and you will see a question MARK instead of the second element. Scala only evaluates lists if they are needed. 

Scala CASE classes are like regular classes except for the fact that they are good for MODELING immutable data and serve as useful in pattern matching. Case classes include public and immutable parameters by default. These classes support pattern matching, which makes it easier to write logical code.  The following are some of the characteristics of a Scala case class: 

• INSTANCES of the class can be created without the new keyword.
• As part of the case classes, Scala automatically generates methods such as equals(), hashcode(), and toString().
• Scala generates ACCESSOR methods for all constructor arguments for a case class.

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Example: 

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Following are some of the Frameworks SCALA SUPPORTS:  

• AKKA Framework
• Spark Framework
• Play Framework
• Scalding Framework
• Neo4j Framework, ETC.

It is important for a language to offer attractive features if it hopes to challenge Java's dominance. In this regard, Scala brings many positive ATTRIBUTES to the table and its ability to compete with Java proves its prominence. The following are some of these positive attributes: 

• It is easier to learn because it is more concise, readable, and error-free, especially for PEOPLE with a background in Java or a SIMILAR language.
• Scala OFFERS complex features such as macros, tuples, etc., making coding easier and more performance-enhancing.
• Scala offers a number of advances, including functions, macros, and tuples.
• By using an expressive typing system, it ensures security and consistency in statistical abstraction.
• With Scala, you can build fault-tolerant, highly CONCURRENT systems.
• Apache Spark Ecosystem has good support for Scala, it's perfect for data analytics.
• Scala provides support for concurrency, allowing parallel processing.

Some languages bring unique benefits to a particular kind of project, making them the best choice. The interoperability of Scala and its functional programming paradigm propelled Scala's rapid growth. In order to address Java's critics, Scala is designed to be concise. The following are some of Scala's unique features that set it apart from other programming languages: 

• Type Inference: Scala doesn't require you to mention the data type or return type of functions explicitly. It will infer the type of data by itself and the return type of FUNCTION depends on the type of LAST expression present in the function.
• Immutability: The default behavior of Scala variables is immutability, which means that they can't be altered. Thus, concurrency control can be managed easier. In addition, mutable variables can also be used.
• Lazy Evaluation: In lazy evaluation or call-by-need, expressions are not evaluated until their first use, or until their demand. Computations are lazy in Scala by default. To declare a lazy variable, use the lazy keyword.
• Case classes and Pattern matching: Case classes in Scala are immutable classes that can be decomposed via pattern matching. Case classes include public and immutable parameters by default. These classes support pattern matching, which makes it easier to write logical CODE.
• String Interpolation: Scala 2.10.0 introduces String Interpolation, a new method for generating STRINGS from your data. Users can embed variable references directly in processed string literals with string interpolation. String interpolation in Scala can be achieved using the s, f, and raw interpolation methods.
• Singleton object: Neither STATIC variables nor static methods exist in Scala, so its singleton object (a class with only one object in the source code) is used as an entry point to your program execution. a class. When declaring a singleton object, the keyword "object" is used instead of the class keyword.

Additionally, it offers:

• Scala's compatibility and interoperability with Java allow developers to keep their Java libraries and use the JVM.
• By supporting multi-paradigm programming, Scala enables more elegant, compact, and type-safe programming.
• Scala integrates seamlessly with big data ecosystems, which are largely based on Java. It works flawlessly with Java libraries, IDEs (like Eclipse and IntelliJ), and frameworks (such as Spring and Hibernate).