JSON Formatter

Introduction

The concept of a JSON Formatter may seem mundane or straightforward to some, but for anyone who frequently works with JSON data—developers, data analysts, technical writers, and many others—this tool can be a lifesaver. JSON (JavaScript Object Notation) has evolved into one of the most popular and widely adopted formats for transmitting data in modern web applications, APIs, and configurations. Its flexibility and readability have made it the go-to choice for storing and exchanging information across a multitude of systems. Yet, JSON data can sometimes arrive in a cluttered, compacted form, making it challenging to review or debug. A JSON Formatter offers a fast and reliable way to convert unwieldy strings of data into a structured, human-friendly layout of keys and values. By neatly indenting and organizing each element, a JSON Formatter can drastically reduce the time it takes to interpret complex or nested data.

This article offers an extensive look into the realm of JSON formatting. It dives into why JSON is so significant, the role of a JSON Formatter, best practices for managing JSON data, common pitfalls to avoid, and the broader implications of well-structured data in both professional and personal tech pursuits. Whether you’re someone encountering JSON data for the first time or a seasoned engineer aiming to refine your data management strategies, understanding the intricacies of JSON formatting can equip you with a powerful tool in your digital toolbox.

Far from being a trivial detail, the readability and reliability of JSON directly impact collaboration among teams, vendor integrations, software performance, and even user experiences downstream. When projects run smoothly because teams can swiftly interpret data, it fuels efficiency and innovation. In an ever-shifting technological landscape, the practical skill of formatting JSON data in a user-friendly way will remain highly relevant. Over the course of this exploration, we’ll illuminate the best ways to harness a JSON Formatter to bolster productivity and help ensure your projects remain robust, organized, and future-proof.

Understanding JSON’s Ubiquity

JSON data has a special place in the digital ecosystem, primarily because it’s considered both human-readable and machine-friendly. In contrast to XML, which was once considered the gold standard for data interchange, JSON introduced a more concise syntax structure based on JavaScript object literal notation. This allows JSON to appear less verbose than many alternative formats, which makes it easier for developers and recipients to parse visually. Within just a few years of its mainstream adoption, JSON has become the de facto way for APIs to exchange information—a shift fueled by the dramatic rise of single-page applications, microservices, and an assortment of front-end frameworks.

From social media data to online retail inventories, from server configurations to analytics logs, JSON powers thousands of applications. It can display nested objects, arrays, and key-value pairs that model real-world data. For instance, a single JSON object could represent a user profile with personal details, preferences, links, and embedded relationships. Because such data can quickly become complex, the need to structure and parse JSON with precision grows more acute.

And here’s where a JSON Formatter truly shines. Without a good formatter, a complex snippet of JSON might resemble an impenetrable blob of curly braces, square brackets, commas, and colons. It’s not uncommon for a single JSON object to span multiple lines or to contain deeply layered arrays. In these cases, a manual review for debugging or updating can become a time-consuming nightmare. The ability to quickly and accurately transform minified or messy JSON into a well-indented hierarchy simplifies these efforts for developers, product managers, database administrators, and just about anyone working with structured data.

Finally, it’s worth noting that JSON’s ubiquity extends beyond mere data interchange. Configuration files in many modern systems are also stored in JSON. From developer environments to entire infrastructure-as-code deployments, structured text remains the foundation of technology. And as the scale of data grows, the success or failure of many large endeavors depends on how easily the data can be understood and manipulated. Having a reliable JSON Formatter on hand becomes essential in these scenarios, accelerating the process of verifying parameter correctness, ensuring nothing is omitted, and preserving an easily digestible data layout.

Why Proper Formatting Matters

Properly formatted JSON goes beyond aesthetic appeal. While it is indeed pleasing to see data neatly lined up in a tree-like structure, the far-reaching implications extend to efficiency, correctness, and teamwork. In large-scale systems, it’s not unusual to have numerous APIs interacting continuously, each delivering and receiving JSON payloads. When these payloads are plain and unformatted, issues can linger unseen, especially if an array or object has misaligned brackets or if a subtle type mismatch exists between the expected and actual data.

Even a single missing comma within a JSON object can introduce unexpected bugs, sometimes crashing entire sections of an application or preventing data from loading. Since JSON is flexible by design, somewhat similar to JavaScript objects, it’s all too easy to overlook small syntax errors when data is arranged haphazardly on a single line. A JSON Formatter primes the data for close inspection, collapsing or expanding nested structures so each bracket and comma is instantly noticeable.

Collaboration is another pivotal aspect. On any coding or data-driven project, multiple people—from product managers to QA testers, UI/UX designers, and front-end developers—may need to review JSON data. When the data is minified (compressed to a single line), confusion inevitably arises, slowing down progress. A well-structured JSON block, on the other hand, fosters clarity, letting team members quickly determine how data is shaped, where arrays begin and end, and what each field represents. This clarity is especially helpful when training new hires or consulting with external partners who need to integrate with your API.

Consider, too, the time savings in debugging. If an application processes a large JSON object from a third-party service and something goes wrong, a developer might start by printing out the payload. Without formatting, scanning for anomalies can be a tedious exercise. But format that data nicely, and suspicious items jump out—perhaps an incorrectly spelled key or a leftover property. That alone can shave hours off a debugging session. Over many months, these small performance gains compile into a major productivity advantage.

Manual vs. Automated Formatting

Before the advent of on-demand formatting tools, developers would often attempt manual methods for making JSON readable. This might involve adding line breaks, indentation, and spacing by hand. While possible for short data sets, it quickly becomes impractical for large, nested structures. Worse still, manual editing introduces chances for errors. Accidentally removing quotes or skipping a bracket is all too common, leading to headaches when the JSON no longer validates.

Automated formatting solves these pain points by providing a quick, reliable transformation. With a JSON Formatter, you paste or load the raw JSON, and the tool performs the reformatting almost instantly. You can then either review the data in your browser or application interface or download the newly formatted version to store for future reference. Because the entire process is automated, the risk of introducing new syntax problems is greatly reduced—assuming your original JSON is valid to begin with.

On top of that, many JSON Formatter utilities add extra features. Some highlight syntax errors by comparing the input to the JSON specification. Others offer node expansion and collapse, letting you selectively hide or reveal parts of the structure for convenience. Certain advanced tools can even highlight duplicate keys or incompatible data types, providing a debugging environment on par with more sophisticated integrated development tools.

Thus, relying on a specialized formatting tool is undoubtedly best practice. Manual attempts might suffice for extremely trivial tasks but are nowhere near as robust or time-efficient. By adopting an automated formatter as part of your normal workflow, you mitigate the chance of mistakes and ensure your files remain uniform in structure. This uniformity, in turn, helps preserve consistency as multiple team members come and go, each potentially leaving their own idiosyncratic indentation style if forced to format by hand.

Common Uses for a JSON Formatter

Although the most apparent scenario for using a JSON Formatter is making sense of messy or minified data, practical applications of these tools stretch further:

1. API Development: When building or consuming REST APIs, developers often test endpoints by retrieving JSON data from numerous services. By formatting these responses, they can validate structure and check which fields are present or missing.

2. Configuration Files: Whether in local development or production servers, configuration files often need to be read by humans for troubleshooting or updates. A JSON Formatter ensures each setting stands clearly, making it easier to adjust parameters or identify invalid syntax.

3. Database Exports: Document-oriented databases like MongoDB can export data in JSON format. When analyzing subsets of that data, using a formatter can drastically improve clarity, especially for debugging queries or verifying that data is stored as expected.

4. Compliance and Documentation: In organizations where data governance is paramount, a well-formatted JSON is easier to audit. Internal or external documentation thrives when accompanied by neatly aligned code snippets that demonstrate how the data is structured.

5. Logging and Monitoring: Some logging frameworks generate logs in JSON to facilitate filtering and searching by fields. But investigating raw logs with multiple lines of compressed data can be tedious. By formatting logs for display or analysis, you can isolate anomalies swiftly.

6. Educational Resources: Teachers, lecturers, or tutorial writers often rely on example JSON data to illustrate points. Presenting that material in a readable way is essential for students to grasp the fundamentals of data parsing and structure.

In all these scenarios, the consistent thread is the heightened visibility and accessibility that a JSON Formatter brings. When raw data becomes visually coherent, you can better verify business logic, confirm compliance with internal structures, and fine-tune performance concerns.

Going Beyond Basic Formatting

At a base level, a trusty formatter properly indents and aligns JSON data so it’s intuitive to browse. But some tools offer much more. The next rung of capabilities blends formatting with editing, validating, and even transformations to other data formats. Validation ensures that each bracket is placed correctly, that strings are enclosed in double quotes, and that no unrecognized or extraneous characters exist. If your data fails validation, a helpful error message might point you to the exact line where the problem occurs.

From there, some advanced JSON Formatters integrate with templating or transformation features. For example, you might want to transform a JSON object into a CSV file for easy import into spreadsheets. Certain formatters can generate the CSV automatically. Others can convert JSON into XML or YAML for bridging data among different projects that rely on distinct markup languages.

Performance monitoring within a JSON Formatter can also prove beneficial. If the tool is integrated into a sophisticated environment, it can measure how quickly the data is parsed and indicate potential bottlenecks. This feature can matter for extremely large JSON sets, where every millisecond of performance overhead in production might matter.

In essence, once you move beyond the realm of pure indentation and proceed to more advanced data manipulations, a JSON Formatter can start to resemble an all-purpose data-linting and conversion environment. This shift broadens the tool’s role from something that developers occasionally click to solve immediate presentation issues, into a central pivot point for multiple data pipelines.

JSON Formatting in a Collaborative Environment

Nearly all development efforts happen in teams. Even if you’re a freelancer, you likely interact with clients, partners, or open-source communities. By employing a consistent JSON formatting tool, you align your output with shared standards at the project or organizational level. This synergy means that whenever someone opens a JSON file, they can trust that it’s structured in a particular, predictable manner.

Many version control systems, such as Git, highlight changes in files line by line. In a minified JSON file, an innocuous tweak might lead to a massive line difference that’s hard to read in a commit message. But if you store your data in a formatted version, only the actual altered lines appear in version control changes. This fosters clarity around what truly changed.

Additionally, code reviews can be simpler to manage. A reviewer opening a pull request sees properly formatted JSON, can quickly parse the structure, and can therefore focus on more critical questions, such as whether the new fields are accurate or if values match expected data types. Without this level of clarity, code reviews might devolve into confusion over indentation or bracket positions, overshadowing the real logic behind the data.

In client interactions or cross-department communications, a neatly formatted JSON structure can serve as a shared language. A non-technical stakeholder might be unfamiliar with coding concepts, but they can still glean a sense of data hierarchy if the JSON is readable. For instance, a marketing manager might need to review campaign data in JSON format. Though not a programmer, they can see top-level keys that might read “campaignName,” “budget,” or “duration,” each accompanied by relevant data. That quick recognition can expedite feedback cycles and streamline decision-making.

JSON Formatter and Data Validation

While much emphasis is placed on the visual alignment of JSON structures, the importance of validation can’t be overstated. For a piece of data to properly pass through a parser or communicate effectively across an HTTP request, it needs to adhere fully to JSON’s strict syntax rules. If the JSON is malformed—missing commas, containing trailing commas, or using single quotes instead of double quotes—parsers in many languages will immediately throw an exception.

An effective JSON Formatter often comes with a validation component that inspects the input for these issues before or during the formatting process. This synergy means you not only see your data in an elegant format but also get informative feedback if something breaks the JSON rules. The tool might highlight problematic lines or provide a short error message detailing whether a bracket is unmatched or a comma is misplaced.

Such immediate correction ensures that by the time you pass the data to your team or commit it to a repository, it’s both visually appealing and syntactically correct. As a result, you avoid the stumbling blocks of discovering errors only after the code has been deployed or integrated. Early detection fosters a more proactive approach: an error is fixed in seconds rather than hours or days.

Further, if your organization uses JSON Schema to define data structures, some JSON Formatters can integrate those schemas to provide deeper validation. They check not just for syntactic correctness but also for logical accuracy—verifying that numeric fields contain numbers, that certain keys exist, or that enumerations match the allowed values. This synergy effectively transforms the formatter into an all-in-one testing ground for your data’s shape and rules, preventing any mismatch from creeping further into the pipeline.

Best Practices for JSON Structuring

Using a JSON Formatter is a great step, but it’s also crucial to design your JSON data wisely from the outset. Following certain best practices can ensure that your JSON remains flexible, efficient, and easy to interpret when you do run it through a formatter or share it among colleagues:

1. Use Meaningful Keys: Instead of naming a property “x” or “val,” opt for descriptive names that explain the data’s purpose. “customerId,” “orderDate,” or “productSku” are more intuitive for anyone reading the file.

2. Keep Nesting Under Control: While JSON can handle nested objects and arrays, extremely deep nesting can complicate reading and indexing. If your data structure becomes too layered, consider breaking it into separate objects or reorganizing it more effectively.

3. Preserve Consistent Types: If a key is an integer in one context, try to keep it that way throughout your data to avoid confusion. JSON is flexible enough to let you shift types, but consistent usage fosters better predictability for consumers of your data.

4. Avoid Redundant Fields: Duplicate fields can perplex not just your peers but also automated tools that may read only the last occurrence. Keep track of unique keys and confirm you’re not repeating them under the same object.

5. Camel Case or Snake Case: Conventions like camelCase or snake_case maintain a consistent style across keys. Stick to whichever is standard in your team or project environment.

6. Order of Keys: While JSON itself doesn’t impose a strict order, it can be helpful to group logically related fields together. This approach aids readability and might even simplify partial updates within your application.

By applying these principles, your data structures become more cohesive and user-friendly, permitting automation tools and a JSON Formatter to do their jobs optimally.

Handling Large JSON Files

One challenge that emerges with the growing use of APIs and big data is how to deal with massive JSON files—sometimes tens or hundreds of megabytes in size. Formatting such large files can strain certain tools or browsers, possibly leading to slow response times or crashes. However, certain specialized JSON Formatters are optimized for large data sets, using streaming techniques or chunk-based processing.

To manage big data more effectively, many ad hoc strategies apply. For instance, extracting only the relevant snippet of JSON you want to inspect, then formatting that subset, might suffice for debugging or analysis. Another approach involves piping the data through command-line tools that can handle streaming transformations.

Regardless of the method, the fundamental principle remains: if you can properly parse and format a large JSON set, you gain a better vantage point for performing deeper analytics or refining your application’s data-handling logic. Without such visibility, trying to glean insights or discover anomalies in a giant one-line file is painfully difficult, if not impossible.

The Role of Online vs. Offline Tools

The landscape of JSON Formatters includes both online and offline utilities. Online formatters are websites where you paste or upload your JSON to have it rendered and validated in a user-friendly layout. Offline formatters are software solutions or built-in modules you run on your local machine. Both approaches have advantages and drawbacks:

• Online Tools:

  • Pros: Quick to access, no installation required, typically user-friendly GUI, often includes additional features or a community of users who share tips.
  • Cons: Requires internet access, raises potential privacy concerns since you’re pasting sensitive data into a web service, might have data size restrictions.

• Offline Tools:

  • Pros: Greater data security (you never send data outside your network), can handle large volumes more robustly if integrated with local resources, works without an internet connection, can often be automated in scripts.
  • Cons: Requires installation or setup, may have a steeper learning curve if it’s command-based or integrated into a more comprehensive development environment.

Teams should consider their exact workflow, data sensitivity, and project demands when deciding on which type of formatter to adopt. In many companies, a mix of both might be used: an offline solution integrated into version control pipelines for day-to-day tasks, and an online tool for quick, ad hoc checks when a developer or QA team member is outside the office or on a device without the offline tool installed.

JSON Formatter in DevOps and Continuous Integration

Modern software development often includes continuous integration (CI) or DevOps practices. These approaches emphasize automation at every stage, from code compilation to deployment, testing, and monitoring. Although JSON formatting might appear like an isolated detail, it can become part of automated workflows:

• Linting Stages: Teams sometimes integrate JSON linting into their CI pipelines, ensuring that any newly committed or modified JSON files pass validation. If they fail, the pipeline flags the issue or blocks the merge until corrections are applied.

• Automated Documentation: Some documentation builds read structured data from JSON files as they generate API references. A pre-processing step could automatically format or validate the JSON, guaranteeing that end-user documentation is accurate.

• Infrastructure as Code: Tools that define infrastructure in code might rely on JSON for templating. In such scenarios, an integrated formatting step ensures readability for all DevOps engineers, preventing misconfiguration or confusion during updates.

By inserting a JSON Formatter into these processes, organizations can maintain a uniform style and quickly spot problems before they snowball. This alignment with DevOps fosters a culture of consistent data hygiene, leading to fewer deployment surprises and more confidence in your software’s operational health.

Intersection with Other Data Formats

While JSON stands as a dominant format, it operates adjacent to others like XML, CSV, and YAML. Some advanced JSON Formatters assist with bridging the gap to these formats. For example, you might have an XML-based service that needs integration with a JSON-based system. Tools that support cross-conversion remove friction by allowing data from one format to be easily translated and formatted into another.

When bridging data formats, it’s essential to remain mindful of the nuances. JSON typically demands double-quoted strings, whereas YAML or XML might have different quoting or structural expectations. A good JSON Formatter with built-in conversion capabilities can handle these differences automatically, as long as the source data is valid in its original structure.

Similarly, accessing a CSV export from a third-party provider might present challenges for direct ingestion by your JSON-based systems. Some advanced formatters can read CSV structures and output JSON equivalents, aligning perfectly with how your application expects to process data. This synergy ensures you can gather data from myriad sources and unify it under JSON without extraneous manual transformations, further paving the road for automation and reduced error likelihood.

JSON Security Considerations

Whenever data is processed or stored, security concerns arise. JSON’s syntax is straightforward, but maliciously crafted data can still exploit vulnerabilities in your system if the parser or your application logic is not carefully managed. For instance, if you structure your apps to accept JSON input from external users, a well-intentioned JSON Formatter might highlight malicious content or suspicious patterns—like extremely deep nesting designed to cause denial-of-service attacks.

Additionally, if your data is sensitive (credit card numbers, personal identifiable information, or authentication tokens), the potential risk of using an online JSON Formatter becomes clearer. This is why many organizations enforce local or in-house tools for formatting such data, protecting confidentiality.

At a structural level, JSON itself is not inherently dangerous as long as the parsing environment handles data responsibly. But no matter the context, a carefully chosen JSON Formatter can facilitate an earlier detection of oddities or suspicious constructs. Spotting that a certain property is unexpectedly huge or that an array is suspiciously empty can hint at attempted infiltration or coding error. In that sense, the improved visibility can serve as a security checkpoint.

Troubleshooting Common Errors

Most users of a JSON Formatter eventually run into situations where the tool flags errors. Understanding how to address these is part of becoming adept with JSON:

• Uncaught Trailing Commas: Traditional JSON doesn’t allow trailing commas after the last item in an array or object. Removing that final comma often resolves the error.

• Mismatched Quotes: JSON requires double quotes around keys and string values, not single quotes. Converting them to double quotes is a common fix if your data originates from a language that uses single quotes for strings.

• Unescaped Characters: Certain characters, especially backslashes or newline symbols, need special escaping. A formatter can highlight them; you’ll then escape them properly to maintain valid syntax.

• Missing Brackets or Braces: If an object or array is open but never closed, the formatter can’t parse it. Add the missing curly brace or bracket to realign your structure.

• Invalid JSON: Sometimes what you have is not actually JSON. If your data comes from logs or includes additional text describing the data, you’ll need to extract the real JSON snippet before feeding it to the formatter.

Identifying these recurring issues can strengthen your data management skills. Over time, you’ll avoid introducing them altogether, creating more reliable JSON streams and ensuring the formatting process remains smooth.

Future of JSON Formatting

Technology never stays static. As the application of JSON broadens (from web APIs to IoT devices, large-scale cloud computing, and configuration management), JSON Formatter tools continue to evolve. They might integrate machine learning to identify potential data anomalies, or they could enrich developer experiences with context-aware suggestions: for instance, suggesting a new key based on known data models.

Moreover, as emerging standards and best practices reshape the data ecosystem, JSON Formatters might incorporate dynamic versions of JSON-based schemas. They might also become more deeply combined with integrated development environments, letting developers see real-time, color-coded structures or auto-validate while typing.

One key frontier involves performance optimization for extremely large or streaming data sets. If JSON remains a primary format in big data analytics, the ability to parse, render, and reformat monstrous payloads in near-real-time will be a premium feature. Tools that can chunk data responsibly or offer partial views for swift scanning will likely thrive.

Finally, an expanding realm of microservices and serverless computing underscores the significance of sending lightning-fast, reliably structured messages. A JSON Formatter’s role might morph into a universal “message harmonizer,” bridging ephemeral machine-to-machine communications with human oversight. In tandem with unstoppable changes in front-end frameworks and the continuing emphasis on typed, robust data structures, the JSON Formatter will continue to be pivotal in bridging raw machine output with developer understanding.

Cultivating Best Practices in Your Projects

Adopting a well-regarded JSON Formatter is only the first step. To truly propagate these benefits throughout your project or organization, consider the following approaches:

1. In-House Guidelines: Draft short style guides or rules on JSON usage. Specify that all configurations must be properly formatted before merging. Clarify your standard naming conventions (e.g., camelCase) and expectations for array usage.

2. Training Workshops: Offer short, practical sessions where team members learn about JSON syntax, how to use the formatter, and common pitfalls. Interactive sessions with real examples can seal the knowledge more effectively than theoretical instructions.

3. Automation: If possible, integrate the formatter into your continuous integration pipeline, ensuring that any incorrectly formatted JSON triggers a warning. This eliminates the guesswork and promotes consistent file structure.

4. Regular Audits: Periodically check your repositories for any JSON documents that might still be minified or incorrectly structured. Tidy them up en masse and highlight your organizational preference for formatted data.

5. Feedback Loop: Encourage developers and data scientists to raise concerns when the JSON structure feels unwieldy. A healthy environment fosters open communication about data quality, leading to design decisions that keep the data sets neat, descriptive, and easily comprehensible.

By weaving these practices evenly throughout your team’s workflow, you embed respect for data structure as a fundamental principle rather than an afterthought. Over time, the consistent use of a JSON Formatter will become second nature, leading to fewer mistakes, faster debugging, and a more cohesive environment.

Leveraging Community Resources

The popularity of JSON means an abundance of community support and resources exist for refining your formatting skills. Developers worldwide share tips, plugins, and recommended tools, many of which are open source. By engaging in these communities, you can discover new ways to exploit your JSON Formatter’s lesser-known features. You might learn about advanced customization—like modifying indentation widths, specifying the color themes for syntax highlighting, or integrating with version control hooks.

Whether your preference is a standalone desktop application, a browser extension, or a plugin embedded in your favorite code editor, you’ll likely find a community offering guides or updates. If you run into a puzzling scenario—like a massive dataset that chokes your usual tool—a relevant forum or user group might guide you toward solutions that have been tested by others in real-world, large-scale contexts.

Finally, these same communities may also expose you to potential pitfalls, such as performance bottlenecks or obscure parsing inconsistencies. By staying engaged, you keep abreast of best practices and emerging challenges. This ensures you remain agile in refining how you handle JSON, your understanding of the data you work with, and the software that formats it.

Conclusion

Over the years, JSON Formatter tools have proven indispensable for individuals and teams dealing with data exchange and storage. Their ability to rearrange complex or compacted data structures into an intelligible format is not merely a convenience but a factor that influences collaboration, debugging, performance, and ultimately user satisfaction. In a tech world driven by constant iteration, the value of data remains at an all-time high, and the ability for humans to parse it quickly and confidently is key to advancing digital transformation.

From the earliest stages of planning an application’s data layout, to the daily debugging tasks of a production environment, well-structured JSON remains the linchpin of clarity. Automated formatters ensure that clarity is readily available, bridging the gap between raw machine output and the human lens. By integrating these formatting best practices across your development cycle—whether that’s through convention, training, or automation—you foster an environment where mistakes are caught early, and progress is not derailed by unwieldy payloads or minor syntax blunders.

On a broader scope, the synergy between a JSON Formatter and the overall software development ecosystem reflects trends toward simplification, collaboration, and consistent best practices. As tools become more feature-rich, they transform from simple formatting aids into robust platforms that parse, validate, convert, and shape JSON data to fit evolving needs. With each iteration, the relationship between these tools and the professionals who use them tightens. Together, they unlock more streamlined coding and data-handling experiences.

In a profession where the next big challenge lies just around the corner, developers and data managers gain a palpable advantage by adopting a consistent use of a JSON Formatter. And as new novices step into this ever-changing arena, the presence of well-formatted files stands as a welcoming introduction—one that clearly conveys how data is organized and how each piece supports the bigger picture. By championing proper JSON formatting, you’re investing in clearer communication, preventing numerous headaches, and building a foundation where your future technical growth can thrive.

Shihab Ahmed

CEO / Co-Founder

Enjoy the little things in life. For one day, you may look back and realize they were the big things. Many of life's failures are people who did not realize how close they were to success when they gave up.