JSON stands for JavaScript Object Notation. It is a subset of JavaScript and an efficient way to deal with data over the internet. It is mainly used for exchanging and storing data. JSON organizes the data quite smoothly and enables easy access. Moreover, it collects data in “forms”, which is readable for humans.
In the early 2000s, JSON format was defined by Douglas Crockford. There are certain rules for JSON syntax which we need to follow for efficient performance. These include the use of square brackets, curly brackets, commas, and value pairs or names. When it comes to the data, then it is necessary to be in the form of value pairs or name. Along the same lines, commas separate data; square and curly brackets hold arrays and objects. Let’s dig into JSON a bit deeper.
Why JSON?
JSON is easy for servers as well. Since it deals with the pain text, it makes it easier for servers to interpret data quickly. JSON follows all JavaScript rules and its syntax works impressively on the JavaScript standards. Many people misunderstand JSON as an object. However, it is not true.
Although JSON utilizes JavaScript format, it is a completely different programming language. JSON is responsible for converting data into JS forms of objects in order to store them as text. Besides storing data, you can also use the JSON format for accessing the data.
JSON Formatter
The JSON Formatter is designed to provide the best possible debugging. It is independent and lightweight, which perfectly kicks out the issue of readability. The output from this tool is without link breaks, which saves a lot of device space. JSON Formatter is an amazing online tool that supports all browsers. All you need to do is – submit JSON format file and press enter for output!
Prettifying JSON Files
There are a number of free online tools to prettify JSON files. To prettify means – making a more readable version of the data. Many people don’t have a firm grip on coding and computer terminologies. If such is the case, you can use online tools for coding and debugging. You can also prettify JSON using the following keys:
OS X: CMD+Ctrl+J
Windows: Ctrl+Alt+J
Linux: Ctrl+Alt+J
Such tools also help in converting files into the desired format. In addition to this, JSON format online provides you lots more facilities to accomplish complex tasks in minimum possible time.
JSON Format Code
Every software has its own extension for storing data and that’s simply JSON. With this extension, you can store or access the written programming code. Application/JSON is used for the Internet Media files, and for the Uniform Type Identifier, public.json is used.
In JSON, users cannot use functions but must mention them as a string. It is possible to convert such strings into functions later on.
JSON Formatter as an Independent Tool
JSON Formatter is an independent tool that provides a new structure to your coding through easy parsing. However, users need to follow all the XML procedures accurately (usually it’s a three-level process) to get full benefits of JSON coding.
You can also use online resources like JsonOnline.net to view, edit, format, and transform different JSON documents.
About the author: Urooj Ashraf is the reputation Manager at TurboRankers and has previously worked as Article writer on freelancing platforms. She’s an expert in inbound marketing, WordPress Development and lead generation. Urooj is also an author at daayri.com. Contact her.
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:
Feature
Data Warehouse
Data Lake
Data Lakehouse
Data Type
Structured
Structured, Semi-Structured, Unstructured
Both
Schema Approach
Schema-on-Write
Schema-on-Read
Both
Query Performance
Optimized for BI
Slower; requires specialized tools
High performance for both BI and AI
Accessibility
Easy for analysts with SQL tools
Requires technical expertise
Accessible to both analysts and data scientists
Cost Efficiency
High
Low
Moderate
Scalability
Limited
High
High
Governance
Strong
Weak
Strong
Use Cases
BI, Compliance
AI/ML, Data Exploration
Real-Time Analytics, Unified Workloads
Best Fit For
Finance, Healthcare
Media, IoT, Research
Retail, 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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