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10 Reasons To Learn Swift Programming Language

Swift programming language is very powerful and intuitive. It incorporates the best of C and Objective-C for iOS, OS X, tvOS, and watchOS. It proves to be effective as it can eliminate the constraints of C compatibility.

Swift has the potential to become the de-facto programming language for creating immersive, responsive, consumer-facing applications for years to come. Comments can include Markdown syntax to add rich text and embedded images that display in Xcode’s Quick Help. A new assistant shows your Swift API in a “header-like” view. And new syntax features combined with improvements to the Cocoa frameworks and Objective-C will make your code more expressive, and even safer.

Swift programming language has not only received widespread acceptance but has also become one of the software developers’ favorite tools. So, here we’ll discuss 10 reasons to learn swift programming language.

  1. Swift is easier to read.
  2. It is also easy to maintain because programmers need not put effort into bookkeeping..
  3. The Swift run-time crash will stop on the line of code where a nil optional variable has been used, preventing errors in codes. Thus, swift is a safe programming language.
  4. The huge memory leaks that a programmer can have in Objective-C are impossible in Swift. We can say — it unifies with efficient memory management.
  5. It has a concise code structure.
  6. Swift is an extremely fast programming language.
  7. With Swift, namespaces are based on the target that a code file belongs to. This means programmers can differentiate classes or values using the namespace identifier. 
  8. It supports dynamic libraries.
  9. With playgrounds, it encourages interactive codings.
  10. Swift provides the development community a direct way to influence a language to create intuitive apps.

Let’s look at the benefits of learning swift in detail.

1. Swift is Easier to Read

Objective-C suffers all warts you’d expect from a language built on C. To differentiate keywords and types from C types, Objective-C introduced new keywords using the @ symbol. Because Swift isn’t built on C, it can unify all the keywords and remove the numerous @ symbols in front of every Objective-C type or object-related keyword. Swift drops legacy conventions. Thus, you no longer need semicolons to end lines or parenthesis to surround conditional expressions inside if/else statements. Another large change is that method calls do not nest inside each other resulting in bracket hell — bye-bye, [[[ ]]]

You’ll be amazed to know that currently there are nearly 2.1 million swift developers, surpassing the number of Objective-C developers (1.6 million). Also, according to the Stack OverFlow survey 2019, swift is also one of the programming languages associated with highest salaries worldwide. Perhaps, this gives many developers a reason to learn swift! ;)

Method and function call in Swift use the industry-standard comma-separated list of parameters within parentheses. The result is a cleaner, more expressive language with a simplified syntax and grammar. Swift code more closely resembles natural English, in addition to other modern popular programming languages. This readability makes it easier for existing programmers from JavaScript, Java, Python, C#, and C++ to adopt Swift into their toolchain — unlike the ugly duckling that was Objective-C. Thus, to learn swift isn’t like getting into a completely new programming language at all.

2. It is Easier To Maintain

Swift drops the two-file requirement. Xcode and the LLVM compiler can figure out dependencies and perform incremental builds automatically in Swift 1.2. As a result, the repetitive task of separating the table of contents (header file) from the body (implementation file) is a thing of the past. Swift combines the Objective-C header (.h) and implementation files (.m) into a single code file (.swift).Xcode and the LLVM compiler can do work behind the scenes to reduce the workload on the programmer. With Swift, programmers do less bookkeeping and can spend more time creating app logic. Swift cuts out boilerplate work and improves the quality of code, comments, and features that are supported.

Benefits of Swift Programming Language

3. Swift Programming Language is Safe

Optional types make the possibility of a nil optional value very clear in Swift code, which means it can generate a compiler error as you write bad code. This creates a short feedback loop and allows programmers to code with intention. Problems can be fixed as code is written, which greatly reduces the amount of time and money that you will spend on fixing bugs related to pointer logic from Objective-C. Unlike in Objective-C, in Swift, the optional types and value types make it explicitly clear in the method definition if the value exists or if it has the potential to be optional (that is, the value may exist or it may be nil).

To provide predictable behavior Swift triggers a run-time crash if a nil optional variable is used. This crash provides consistent behavior, which eases the bug-fixing process because it forces the programmer to fix the issue right away. The Swift run-time crash will stop on the line of code whenever it finds a nil optional variable. This prevents the bugs in the swift code.

4. It is Unified with Memory Management

Swift unifies the language in a way that Objective-C never has. The support for Automatic Reference Counting (ARC) is complete across the procedural and object-oriented code paths. The huge memory leaks that a programmer can have in Objective-C are impossible in Swift. A programmer should not have to think about memory for every digital object he or she creates. Because ARC handles all memory management at compile-time, the brainpower that would have gone towards memory management can instead be focused on core app logic and new features. Because ARC in Swift works across both procedural and object-oriented code, it requires no more mental context switches for programmers, even as they write code that touches lower-level APIs — a problem with the current version of Objective-C.

Automatic and high-performance memory management is a problem that has been solved by Swift and it has proven it can increase productivity. The other side effect is that both Objective-C and Swift do not suffer from a Garbage Collector running cleaning up for unused memory, like Java, Go, or C#. This is an important factor for any programming language that will be used for responsive graphics and user input, especially on a tactile device like the iPhone, Apple Watch, or iPad (where lag is frustrating and makes users perceive an app is broken).

5. Concise Code Structure

Swift reduces writing the amount of code for repetitive statements and string manipulation. Swift adopts modern programming language features like adding two strings together with a “+” operator, which is missing in Objective-C. Support for combining characters and strings like this is fundamental for any programming language that displays text to a user on a screen. The type system in Swift reduces the complexity of code statements — as the compiler can figure out types. 

Swift supports string interpolation, which eliminates the need to memorize tokens and allows programmers to insert variables directly inline to a user-facing string, such as a label or button title. The type inferencing system and string interpolation mitigate a common source of crashes that are common in Objective-C. Swift relieves you from bookkeeping work, translating into less code to write (code that is now less error-prone) because of its inline support for manipulating text strings and data.

6. Swift is Really Fast

Swift code performance continues to point to Apple’s dedication to improving the speed at which Swift can run app logic. The enhancements also enabled Swift to outperform C++ for the Mandelbrot algorithm by a factor of a mere 1.03.

With an optimized compiler for performance and the language for development, it generates faster code across the board, both for release and debug builds. The Swift compiler is also faster, even while adding new Fix-it suggestions such as where you can use let instead of var.

7. There’s Fewer Name Collision With Open Source Projects

One issue that has plagued Objective-C code is its lack of formal support for namespaces, which was C++’s solution to code file-name collisions. Swift provides implicit namespaces that allow the same code file to exist across multiple projects without causing a build failure and requiring names like NSString (Next Step — Steve Jobs’ company after being fired from Apple) or CGPoint (Core Graphics). Ultimately, this feature in Swift keeps programmers more productive. They don’t have to involve in bookkeeping that exists in Objective-C. 

You can see Swift’s influence with simple names like Array, Dictionary, and String instead of NSArray, NSDictionary, and NSString, which were born out of the lack of namespaces in Objective-C. With Swift, namespaces are based on the target that a code file belongs to. This means programmers can differentiate classes or values using the namespace identifier. This change in Swift is huge. It greatly facilitates incorporating open source projects, frameworks, and libraries into your code. The namespaces enable different software companies to create the same code filenames without worrying about collisions when integrating open source projects. Now both Facebook and Apple can use an object code file called FlyingCar.swift without any errors or build failures.

8. Swift Supports Dynamic Libraries

The biggest change in Swift is the switch from static libraries, which are updated at major point releases (iOS 8, iOS 7, and so on), to dynamic libraries. Dynamic libraries are executable chunks of code that can be linked to an app. This feature allows current Swift apps to link against newer versions of the Swift language as it evolves. 

The developer submits the app along with the libraries, both of which are digitally signed with the development certificate to ensure integrity (hello, NSA). This means Swift can evolve faster than iOS, which is a requirement for a modern programming language. Changes to the libraries are included with the latest update of an app on the App Store, and everything simply works. It reduces the initial size of an app by linking the external codes on use-basis.

Dynamic libraries in Swift make it possible for programming language changes and improvements to propagate faster than ever before. Users no longer need to wait for iOS point releases to benefit from any performance or reliability improvements Apple introduces into Swift.

9. Swift Playgrounds Encourages Interactive Coding

Swift’s newly introduced Playgrounds are a boon to experienced developers. Playgrounds enable programmers to test out a new algorithm or graphics routine, say 5 to 20 lines of code, without having to create an entire iPhone app. 

Apple has added inline code execution to Playgrounds. It helps programmers create a chunk of code or write an algorithm while getting feedback along the way. This feedback loop improves the speed of writing codes by replacing the traditional programming with data visualizations in Playgrounds. Programming is an iterative process. Any effort to reduce strain and complement the creative process can make programmers more productive. It can also free their time to solve bigger problems rather than focusing on boring details that traditional compilers impose on programmers.

10. Swift Is A Future You Can Influence

Objective-C isn’t going anywhere, but it won’t see as many major changes, thanks to the introduction of Swift. Some Swift features will likely migrate over to Objective-C, but Objective-C’s legacy in C means it can absorb only so much. Swift provides the development community a direct way to influence a language to create apps, embedded systems (if Apple ever licenses an embedded framework and chip for third parties), and devices like the Apple Watch.

Wrapping up with the best of Swift Programming Language

Writing Swift code is interactive and fun, the syntax is concise yet expressive, and apps run lightning-fast. Swift possesses safer patterns for programming and it adds modern features to make programming easier, more flexible, and more fun.

Apple is focused on providing the best consumer experience and is building only those features deemed worthy of attention. The team supporting the development and evolution of Swift is keen on improving the language to better support the development community that builds apps and systems using Swift. If you’re thinking of learning Swift, this is the right time to get started.

If you’ve queries around different programming languages, we’ve covered some. Take a look.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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