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What is MOLAP?

Multidimensional OLAP (MOLAP) is a classical OLAP that facilitates data analysis by using a multidimensional data cube. Data is pre-computed, re-summarized, and stored in a MOLAP (a major difference from ROLAP). Using a MOLAP, a user can use multidimensional view data with different facets.

Multidimensional data analysis is also possible if a relational database is used. By that would require querying data from multiple tables. On the contrary, MOLAP has all possible combinations of data already stored in a multidimensional array. MOLAP can access this data directly. Hence, MOLAP is faster compared to Relational Online Analytical Processing (ROLAP).

In this tutorial, you will learn-

MOLAP Architecture

MOLAP Architecture includes the following components:

Database Server

MOLAP Server

Front-end tool

MOLAP Architecture

Considering the above given MOLAP Architecture:

The user request reports through the interface

The application logic layer of the MDDB retrieves the stored data from Database

The application logic layer forwards the result to the client/user.

For example, an accounting head can run a report showing the corporate P/L account or P/L account for a specific subsidiary. The MDDB would retrieve precompiled Profit & Loss figures and display that result to the user.

Key Points in MOLAP

In MOLAP, operations are called processing.

MOLAP tools process information with the same amount of response time irrespective of the level of summarizing.

MOLAP tools remove complexities of designing a relational database to store data for analysis.

MOLAP server implements two level of storage representation to manage dense and sparse data sets.

The storage utilization can be low if the data set is sparse.

Facts are stored in multi-dimensional array and dimensions used to query them.

Implementation Considerations in MOLAP

In MOLAP it’s essential to consider both maintenance and storage implications to creating strategy for building cubes.

Difficult to scale because the number and size of cubes required when dimensions increase.

API’s should provide for probing the cubes.

Data structure to support multiple subject areas of data analyses which data can be navigated and analyzed. When the navigation changes, the data structure needs to be physically reorganized.

Need different skill set and tools for Database administrator to build, maintain the database.

MOLAP Advantages

MOLAP can manage, analyze and store considerable amounts of multidimensional data.

Fast Query Performance due to optimized storage, indexing, and caching.

Smaller sizes of data as compared to the relational database.

Automated computation of higher level of aggregates data.

Help users to analyze larger, less-defined data.

MOLAP is easier to the user that’s why It is a suitable model for inexperienced users.

MOLAP cubes are built for fast data retrieval and are optimal for slicing and dicing operations.

All calculations are pre-generated when the cube is created.

One major weakness of MOLAP is that it is less scalable than ROLAP as it handles only a limited amount of data.

The MOLAP also introduces data redundancy as it is resource intensive

MOLAP Solutions may be lengthy, particularly on large data volumes.

MOLAP products may face issues while updating and querying models when dimensions are more than ten.

MOLAP is not capable of containing detailed data.

The storage utilization can be low if the data set is highly scattered.

It can handle the only limited amount of data therefore, it’s impossible to include a large amount of data in the cube itself.

Here are the popular MOLAP Tools:

Essbase – Tools from Oracle that has a multidimensional database.

Express Server – Web-based environment that runs on Oracle database.

Yellowfin – Business analytics tools for creating reports and dashboards.

Clear Analytics – Clear analytics is an Excel-based business solution.

SAP Business Intelligence – Business analytics solutions from SAP


Multidimensional OLAP (MOLAP) is a classical OLAP that facilitates Data Analysis by using a multidimensional data cube.

MOLAP tools process information with the same amount of response time irrespective of the level of summarizing.

MOLAP server implements two level of storage to manage dense and sparse data sets.

MOLAP can manage, analyze, and store considerable amounts of multidimensional data.

It helps to automate computation of higher level of aggregates data

It is less scalable than ROLAP as it handles only a limited amount of data.

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Characteristics And Functions Of Data Warehouse


A data warehouse is a powerful tool that allows organizations to store, manage, and analyze large amounts of data. It is designed to support the decision-making process by providing a centralized location for all of an organization’s data. In this article, we will explore the characteristics and functions of a data warehouse and how it can benefit your business.

Characteristics of a Data Warehouse Integrated Data

One of the key characteristics of a data warehouse is that it contains integrated data. This means that the data is collected from various sources, such as transactional systems, and then cleaned, transformed, and consolidated into a single, unified view. This allows for easy access and analysis of the data, as well as the ability to track data over time.


A data warehouse is also subject-oriented, which means that the data is organized around specific subjects, such as customers, products, or sales. This allows for easy access to the data relevant to a specific subject, as well as the ability to track the data over time.


Another characteristic of a data warehouse is that it is non-volatile. This means that the data in the warehouse is never updated or deleted, only added to. This is important because it allows for the preservation of historical data, making it possible to track trends and patterns over time.


A data warehouse is also time-variant, which means that the data is stored with a time dimension. This allows for easy access to data for specific time periods, such as last quarter or last year. This makes it possible to track trends and patterns over time.

Functions of a Data Warehouse Data Integration

One of the main functions of a data warehouse is to integrate data from various sources. This can include transactional systems, such as point-of-sale systems or customer relationship management systems, as well as external data sources, such as market research or social media data.

Data Cleaning and Transformation

Another function of a data warehouse is to clean and transform the data. This can include removing duplicates, correcting errors, and standardizing data formats. This is important because it ensures that the data is accurate and consistent, making it easier to analyze.

Data Consolidation

A data warehouse also consolidates data from various sources into a single, unified view. This can include combining data from different transactional systems, such as sales and inventory data, or combining data from different external sources, such as market research and social media data.

Data Analysis

One of the main benefits of a data warehouse is its ability to support data analysis. This can include running queries, creating reports, and building data visualizations. This can help organizations gain insights into their data, identify trends and patterns, and make informed business decisions.

Data Warehousing Tools ETL (Extract, Transform, Load) Tools

One of the key tools used in data warehousing is ETL (Extract, Transform, Load) tools. These tools are used to extract data from various sources, transform the data to fit the data warehouse schema, and then load the data into the warehouse. Examples of popular ETL tools include Informatica, Talend, and Apache Nifi.







SparkSession spark










































"col1 as new_col1"


"col2 as new_col2"




















This is a simple example of using PySpark, a Python library, to extract data from a CSV file, transform the data by renaming columns, and then load the data into a data warehouse in the form of parquet file format.

OLAP (Online Analytical Processing) Tools

Another important tool used in data warehousing is OLAP (Online Analytical Processing) tools. These tools are used to analyze the data in the warehouse and create reports and visualizations. Examples of popular OLAP tools include IBM Cognos, MicroStrategy, and Tableau.























This is a simple example of a SQL query that can be run using an OLAP tool to analyze data in a data warehouse. It shows the total number of sales, total revenue, and product name for each product.

Real-Life Examples Retail Industry

A retail company can use a data warehouse to store and analyze data from its point-of-sale systems, inventory systems, and customer relationship management systems. This can help the company gain insights into customer purchasing habits, track inventory levels, and identify which products are selling well. This information can be used to make informed decisions about promotions, marketing, and product development.

Healthcare Industry

A healthcare organization can use a data warehouse to store and analyze data from its electronic health records (EHR) systems and clinical systems. This can help the organization track patient outcomes, identify trends in disease rates, and monitor the effectiveness of different treatments. This information can be used to improve patient care and make informed decisions about resource allocation.

Finance Industry

A financial institution can use a data warehouse to store and analyze data from its transactional systems, such as trading systems and customer account systems. This can help the institution track financial performance, identify potential fraud, and monitor compliance with regulations. This information can be used to make informed decisions about risk management and investment strategy.


A data warehouse is a powerful tool that allows organizations to store, manage, and analyze large amounts of data. It has several key characteristics, such as being integrated, subject-oriented, non-volatile, and time-variant, that make it well-suited for data analysis and decision-making. Its functions include data integration, cleaning, transformation, consolidation, and analysis. Real-life examples like the Retail, Healthcare, and Finance industries can benefit from the implementation of data warehouses. This has become a vital aspect for organizations to have a better understanding of their data and make data-driven decisions.

What Is Big Data? Why Big Data Analytics Is Important?

What is Big Data? Why Big Data Analytics Is Important? Data is Indispensable. What is Big Data?

Is it a product?

Is it a set of tools?

Is it a data set that is used by big businesses only?

How big businesses deal with big data repositories?

What is the size of this data?

What is big data analytics?

What is the difference between big data and Hadoop?

These and several other questions come to mind when we look for the answer to what is big data? Ok, the last question might not be what you ask, but others are a possibility.

Hence, here we will define what is it, what is its purpose or value and why we use this large volume of data.

Big Data refers to a massive volume of both structured and unstructured data that overpowers businesses on a day to day basis. But it’s not the size of data that matters, what matters is how it is used and processed. It can be analyzed using big data analytics to make better strategic decisions for businesses to move.

According to Gartner:

Importance of Big Data

The best way to understand a thing is to know its history.

Data has been around for years; but the concept gained momentum in the early 2000s and since then businesses started to collect information, run big data analytics to uncover details for future use.  Thereby, giving organizations the ability to work quickly and stay agile.

This was the time when Doug Laney defined this data as the three Vs (volume, velocity, and variety):

Volume: is the amount of data moved from Gigabytes to terabytes and beyond.

Velocity: The speed of data processing is velocity.

Variety: data comes in different types from structured to unstructured. Structured data is usually numeric while unstructured – text, documents, email, video, audio, financial transactions, etc.

Where these three Vs made understanding big data easy, they even made clear that handling this large volume of data using the traditional framework won’t be easy.  This was the time when Hadoop came into existence and certain questions like:

What is Hadoop?

Is Hadoop another name of big data?

Is Hadoop different than big data?

All these came into existence.

So, let’s begin answering them.

Big Data and Hadoop

Let’s take restaurant analogy as an example to understand the relationship between big data and Hadoop

Tom recently opened a restaurant with a chef where he receives 2 orders per day he can easily handle these orders, just like RDBMS. But with time Tom thought of expanding the business and hence to engage more customers he started taking online orders. Because of this change the rate at which he was receiving orders increased and now instead of 2 he started receiving 10 orders per hour. This same thing happened with data. With the introduction of various sources like smartphones, social media, etc data growth became huge but due to a sudden change handling large orders/data isn’t easy. Hence a need for a different kind of strategy to cope up with this problem arise.

Likewise, to tackle the data problem huge datasets, multiple processing units were installed but this wasn’t effective either as the centralized storage unit became the bottleneck. This means if the centralized unit goes down the whole system gets compromised. Hence, there was a need to look for a better solution for both data and restaurant.

Tom came with an efficient solution, he divided the chefs into two hierarchies, i.e. junior and head chef and assigned each junior chef with a food shelf. Say for example the dish is pasta sauce. Now, according to Tom’s plan, one junior chef will prepare pasta and the other junior chef will prepare the sauce. Moving ahead they will hand over both pasta and sauce to the head chef, where the head chef will prepare the pasta sauce after combining both the ingredients, the final order will be delivered. This solution worked perfectly for Tom’s restaurant and for Big Data this is done by Hadoop.

Hadoop is an open-source software framework that is used to store and process data in a distributed manner on large clusters of commodity hardware. Hadoop stores the data in a distributed fashion with replications, to provide fault tolerance and give a final result without facing bottleneck problem. Now, you must have got an idea of how Hadoop solves the problem of Big Data i.e.

Storing huge amount of data.

Storing data in various formats: unstructured, semi-structured and structured.

The processing speed of data.

So does this mean both Big Data and Hadoop are same?

We cannot say that, as there are differences between both.

What is the difference between Big Data and Hadoop?

Big data is nothing more than a concept that represents a large amount of data whereas Apache Hadoop is used to handle this large amount of data.

It is complex with many meanings whereas Apache Hadoop is a program that achieves a set of goals and objectives.

This large volume of data is a collection of various records, with multiple formats while Apache Hadoop handles different formats of data.

Hadoop is a processing machine and big data is the raw material.

Now that we know what this data is, how Hadoop and big data work. It’s time to know how companies are benefiting from this data.

How Companies are Benefiting from Big Data?

A few examples to explain how this large data helps companies gain an extra edge:

Coca Cola and Big Data

Coca-Cola is a company that needs no introduction. For centuries now, this company has been a leader in consumer-packaged goods. All its products are distributed globally. One thing that makes Coca Cola win is data. But how?

Coca Cola and Big data:

Using the collected data and analyzing it via big data analytics Coca Cola is able to decide on the following factors:

Selection of right ingredient mix to produce juice products

Supply of products in restaurants, retail, etc

Social media campaign to understand buyer behavior, loyalty program

Creating digital service centers for procurement and HR process

Netflix and Big Data

To stay ahead of other video streaming services Netflix constantly analyses trends and makes sure people get what they look for on Netflix. They look for data in:

Most viewed programs

Trends, shows customers consume and wait for

Devices used by customers to watch its programs

What viewers like binge-watching, watching in parts, back to back or a complete series.

For many video streaming and entertainment companies, big data analytics is the key to retain subscribers, secure revenues, and understand the type of content viewers like based on geographical locations. This voluminous data not only gives Netflix this ability but even helps other video streaming services to understand what viewers want and how Netflix and others can deliver it.

Alongside there are companies that store following data that helps big data analytics to give accurate results like:

Tweets saved on Twitter’s servers

Information stored from tracking car rides by Google

Local and national election results

Treatments took and the name of the hospital

Types of the credit card used, and purchases made at different places

What, when people watch on Netflix, Amazon Prime, IPTV, etc and for how long

Hmm, so this is how companies know about our behavior and they design services for us.

What is Big Data Analytics?

The process of studying and examining large data sets to understand patterns and get insights is called big data analytics. It involves an algorithmic and mathematical process to derive meaningful correlation. The focus of data analytics is to derive conclusions that are based on what researchers know.

Importance of big data analytics

Ideally, big data handle predictions/forecasts of the vast data collected from various sources. This helps businesses make better decisions. Some of the fields where data is used are machine learning, artificial intelligence, robotics, healthcare, virtual reality, and various other sections. Hence, we need to keep data clutter-free and organized.

This provides organizations with a chance to change and grow. And this is why big data analytics is becoming popular and is of utmost importance. Based on its nature we can divide it into 4 different parts:

In addition to this, large data also play an important role in these following fields:

Identification of new opportunities

Data harnessing in organizations

Earning higher profits & efficient operations

Effective marketing

Better customer service

Now, that we know in what all fields data plays an important role. It’s time to understand how big data and its 4 different parts work.

Big Data Analytics and Data Sciences

Data Sciences, on the other hand, is an umbrella term that includes scientific methods to process data. Data Sciences combine multiple areas like mathematics, data cleansing, etc to prepare and align big data.

Due to the complexities involved data sciences is quite challenging but with the unprecedented growth of information generated globally concept of voluminous data is also evolving.  Hence the field of data sciences that involve big data is inseparable. Data encompasses, structured, unstructured information whereas data sciences is a more focused approach that involves specific scientific areas.

Businesses and Big Data Analytics

Due to the rise in demand use of tools to analyze data is increasing as they help organizations find new opportunities and gain new insights to run their business efficiently.

Real-time Benefits of Big Data Analytics

Data over the years has seen enormous growth due to which data usage has increased in industries ranging from:







All in all, Data analytics has become an essential part of companies today.

Job Opportunities and big data analytics

Data is almost everywhere hence there is an urgent need to collect and preserve whatever data is being generated. This is why big data analytics is in the frontiers of IT and had become crucial in improving businesses and making decisions. Professionals skilled in analyzing data have got an ocean of opportunities. As they are the ones who can bridge the gap between traditional and new business analytics techniques that help businesses grow.

Benefits of Big Data Analytics

Cost Reduction

Better Decision Making

New product and services

Fraud detection

Better sales insights

Understanding market conditions

Data Accuracy

Improved Pricing

How big data analytics work and its key technologies

Here are the biggest players:

Machine Learning: Machine learning, trains a machine to learn and analyze bigger, more complex data to deliver faster and accurate results. Using a machine learning subset of AI organizations can identify profitable opportunities – avoiding unknown risks.

Data management: With data constantly flowing in and out of the organization we need to know if it is of high quality and can be reliably analyzed. Once the data is reliable a master data management program is used to get the organization on the same page and analyze data.

Data mining: Data mining technology helps analyze hidden patterns of data so that it can be used in further analysis to get an answer for complex business questions. Using data mining algorithm businesses can make better decisions and can even pinpoint problem areas to increase revenue by cutting costs. Data mining is also known as data discovery and knowledge discovery.

In-memory analytics: This business intelligence (BI) methodology is used to solve complex business problems. By analyzing data from RAM computer’s system memory query response time can be shortened and faster business decisions can be made. This technology even eliminates the overhead of storing data aggregate tables or indexing data, resulting in faster response time. Not only this in-memory analytics even helps the organization to run iterative and interactive big data analytics.

Predictive analytics: Predictive analytics is the method of extracting information from existing data to determine and predict future outcomes and trends. techniques like data mining, modeling, machine learning, AI are used to analyze current data to make future predictions. Predictive analytics allows organizations to become proactive, foresee future, anticipate the outcome, etc. Moreover, it goes further and suggests actions to benefit from the prediction and also provide a decision to benefit its predictions and implications.

Text mining: Text mining also referred to as text data mining is the process of deriving high-quality information from unstructured text data. With text mining technology, you uncover insights you hadn’t noticed before. Text mining uses machine learning and is more practical for data scientists and other users to develop big data platforms and help analyze data to discover new topics.

Big data analytics challenges and ways they can be solved

A huge amount of data is produced every minute hence it is becoming a challenging job to store, manage, utilize and analyze it.  Even large businesses struggle with data management and storage to make a huge amount of data usage. This problem cannot be solved by simply storing data that is the reason organizations need to identify challenges and work towards resolving them:

Improper understanding and acceptance of big data

Meaningful insights via big data analytics

Data storage and quality

Security and privacy of data

Collection of meaningful data in real-time: Skill shortage

Data synching

Visual representation of data

Confusion in data management

Structuring large data

Information extraction from data

Organizational Benefits of Big Data

Big Data is not useful to organize data, but it even brings a multitude of benefits for the enterprises. The top five are:

Understand market trends: Using large data and  big data analytics, enterprises can easily, forecast market trends, predict customer preferences, evaluate product effectiveness, customer preferences, and gain foresight into customer behavior. These insights in return help understand purchasing patterns, buying patterns, preference and more. Such beforehand information helps in ding planning and managing things.

Understand customer needs:  Big Data analytics helps companies understand and plan better customer satisfaction. Thereby impacting the growth of a business. 24*7 support, complaint resolution, consistent feedback collection, etc.

Improving the company’s reputation: Big data helps deal with false rumors, provides better service customer needs and maintains company image. Using big data analytics tools, you can analyze both negative and positive emotions that help understand customer needs and expectations.

Promotes cost-saving measures: The initial costs of deploying Big Data is high, yet the returns and gainful insights more than you pay. Big Data can be used to store data more effectively.

Makes data available: Modern tools in Big Data can in actual-time presence required portions of data anytime in a structured and easily readable format.

Sectors where Big Data is used:

Retail & E-Commerce

Finance Services



With this, we can conclude that there is no specific definition of what is big data but still we all will agree that a large voluminous amount of data is big data. Also, with time the importance of big data analytics is increasing as it helps enhance knowledge and come to a profitable conclusion.

If you are keen to benefit from big data, then using Hadoop will surely help. As it is a method that knows how to manage big data and make it comprehensible.

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Preeti Seth

Top 7 Benefits Of Warehouse Automation In 2023

In this article, we outline 7 reasons/benefits why warehouses should automate their processes through warehouse automation software that leverage process automation tools, such as RPA, NLP, and OCR. Specifically, we discuss how warehouse automation can lead to quicker order processing, more efficient inventory management, timely financial reporting, and more.

1. Quicker order processing

Integration between the warehouse and the company’s various digital sales channels (i.e., email, website, e-commerce platforms, etc.) could ensure a faster order processing. That is because the customer could place an order online, and thanks to RPA and API, the order’s data could be automatically exchanged with an inventory management or inventory control software that the warehouse uses.

A typical, rule-based steps for an automated order processing, thus, could look as such:

2. Seamless payment processing

For an order to be processed and the shipment to be initiated, the customer’s payment must first be cleared and the company’s financial books be reconciled.

Accounts receivable automation software streamlines the processing of payments and the issuing of invoices. In addition, the software automatically enters the data into the company’s accounting system. Finally, thanks to RPA, the software can be programmed to send a notification to the warehouse, saying that the customer’s payment has been processed and the item can be shipped.

The automation of payment processing takes all the guess work out of approximating:

When the receivables for a good can be collected

How much cash should be allocated to cover the DSO

Whether an item has been paid for before it’s shipped.

3. More transparent customer communication


The item ordered is out of stock

The shipping is delayed

The customer’s payment has not been received

Or some other matter that requires a transparent and timely B2C or B2B communication

RPA bots can gather the information relevant to the case at hand. They will then immediately forward it to the customer in the form of a push notification or email.

The benefit is that the customer care representatives do not have to worry about handling such tasks manually, and can focus their attention on more value-driven tasks. Moreover, there will be no misunderstanding between the company and the customer, as the information relayed to the latter comes from the automated data that has been generated on a rule-based basis (i.e., “if payment for X is not cleared, then notify Y”).

4. Accurate scheduling

Scheduling the incoming and outgoing intermediary goods and finished articles can be automated. On the production side, precise scheduling for intermediary goods can result in an uninterrupted and smooth production process.

For instance, Toyota, the giant Japanese automotive company, orders its assembly parts on the “just-in-time” principle: The intermediary goods arrive at the production plant just in time to be assembled onto the cars’ chassis. In other words, Toyota keeps no excess inventory, and the continuity of its production is dependent on timely delivery.

For manufacturing companies that have a high and quick turnover rate of intermediary goods, an accurate scheduling timeline is of the utmost importance when it comes the company upholding its reputation and business continuity framework. Companies can automate the scheduling of the shipment of the intermediary goods and the products. Coupled with inventory management mentioned in point #5, RPA bots can monitor and report on the status of the shipments in pre-determined intervals.

Learn more about bill of materials (BOM) automation.

5. Efficient inventory management

An inventory management solution leverages RPA to take care of the following procedures:

It automatically double-check that the ordered good is in stock.

It automatically notices that the ordered good is not in stock. From there on, it sends an automated message to the customer relaying on the information to them, and places an order restock to the original vendor.

Whenever the inventory threshold for a certain item is low, it can automatically, and preemptively, place a restock order, thus avoiding the scenario in point #2 entirely (i.e., “if stock count for X is below Y, send restock order to vendor Z for W amount.”)

Lastly, by being integrated into the shipping schedules of the incoming orders from vendors, it can provide the business and the customers alike an accurate estimated time of delivery for the goods.

Learn more about inventory management.

6. Preemptive predictive maintenance

IoT sensors and RPA can be integrated holistically to undertake predictive maintenance of the pieces of equipment in the warehouse. For instance, the IoT sensors would note that the temperature of a pallet stacker has risen above the usual amount. It would then send a signal to the main IoT software, and thanks to the leveraging of RPA, the issue would then be relayed to the appropriate personnel, such as the foreman. Then the machinery would be tended to before its issue got worse and led to its decommissioning.

7. Timely reporting

We’ve previously discussed in length the use cases of RPA in reporting. In warehouses, RPA can be leveraged to create automated reports of the happenings within. Whether it’s the average time it took for an order to be processed, the customer complaints the company received, or any other matter that could be turned into action-driven insight, automated reporting can help analytics by automatically creating the appropriate reports.

The benefit of automating the reporting is that the reporting software will take care of the collecting and presenting information, and allows your employees to spend their time making sense of, and analyzing the data.

For more on RPA

We have curated a list of RPA use cases across other industries:

To get a comprehensive overview of RPA, download our whitepaper below:

And if you believe your business would benefit from adopting RPA, head over to our RPA software hub, where you will find data-driven list of vendors.

We will help you choose the right RPA tool for your business:

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.





What Is Graceful Degradation In Css?

What is Graceful Degradation?

If you are an experienced web developer, you may have heard the graceful degradation word before. Before we learn about the graceful degradation in web development, let’s break down the word. The meaning of graceful is elegant or beautiful, and degradation is breaking or falling down. So, the overall meaning of the graceful degradation word is that it makes the feature elegant while it breaks.

Developers use the graceful degradation term in web development. It provides various techniques so that any website or application can work correctly in less capable browsers.

Different Techniques for the Graceful Degradation

In the above section, we learned what graceful degradation is and why developers should ensure it. Now, we will learn different techniques with examples for graceful degradation.

Progressive Enhancement

In this technique, developers require to break down the code into different packages and load each packet one by one. So, load the HTML of the web page successfully, and then load the normal CSS supported by every browser.

Feature Detection

In this approach, we check whether the browser supports particular JavaScript features. If yes, the website allows users to use that feature to style the HTML content accordingly. Otherwise, we can show some error messages or apply different styles to the HTML content.

Let’s understand it via the example below.


In the example below, we have created the div element and given the ‘element’ id. Also, we have defined the ‘container’ class in the CSS and included some CS properties into that.

So, here we have detected if the div element supports the class list, and according to that, we used a different technique to add a class name to the div element.

.container { width: 300px; height: 300px; background-color: red; border: 3px solid green; border-radius: 12px; } #output { font-size: 20px; font-weight: bold; color: blue; } var myDiv = document.getElementById(‘element’); let output = document.getElementById(‘output’); if (‘classList’ in myDiv) { myDiv.classList.add(‘container’); output.innerHTML = ‘classList is supported’; } else { myDiv.className += ‘ container’; output.innerHTML = ‘classList is not supported’; } Add Fallback Options

Another technique for graceful degradation is adding fallback options. In this technique, if the browser doesn’t support any CSS, we use another CSS to show HTML content perfectly in the web browser.

Using the examples below, let’s understand adding the fallback options to the web page.

Example (Adding the fallback option for CSS gradients)

In the example below, we have created the card div element and used the line-gradient() CSS function to set the background gradient. Also, we have written the fallback CSS if the browser doesn’t support the linear-gradient() CSS function.

In the output, users can observe that either it shows the gradient or background color.

.card { width: 400px; height: auto; font-size: 2rem; background-color: orange; background-image: linear-gradient(to right, #14f71f, #d46a06); color: white; text-align: center; } /* Fallback styles */ @media screen and (-ms-high-contrast: active), (-ms-high-contrast: none) { .card { background-image: none; background-color: orange; } } Example (Adding the fallback option for CSS animation)

In the example below, we added the CSS animation’s fallback option. Here, we have created three div elements and added the ‘bounce’ animation in all elements. The ‘bounce’ animation moves the div upside from its position and sets it back to its initial position.

In JavaScript, we create a new div element and check if its style contains the ‘animation’ property. If yes, the animation will apply automatically. Otherwise, we need to add a ‘no_animation’ class to every div element using JavaScript, which sets ‘animation: none’.

.square{ background-color: blue; color: white; width: 100px; font-size: 1.5rem; padding: 20px; margin-bottom: 20px; position: relative; animation: bounce 2s ease-in-out infinite; animation-direction: alternate; animation-delay: 0.1s; animation-fill-mode: both; animation-play-state: running; } @keyframes bounce { 0% {transform: translateY(0);} 100% {transform: translateY(-30px);} } /* Fallback styles */ .no-animation .square{ top: 0; animation: none; } window.onload = function () { var squares = document.querySelectorAll(‘.square’); if (!(‘animation’ in document.createElement(‘div’).style)) { for (var i = 0; i < squares.length; i++) { squares[i].classList.add(‘no-animation’); } } };

Users learned about various graceful degradation techniques in this tutorial. All techniques make the HTML content of web pages attractive, even if browsers are not supporting some features.

The best technique for graceful degradation is to set up the fallback option. Developers should use only standard HTML and CSS properties to ensure graceful degradation in older browsers.

However, graceful degradation is costly to maintain as developers require to add fallback options for multiple features. Still, it gives a smooth web experience to visitors visiting from any web browser.

What Is A “Value” In Microsoft Excel?

Table of Contents

Data Values

The first and the most obvious use of values in a worksheet is to refer to data types supported by Excel. Each cell can have a different type of value, limiting the kind of mathematical operations that can be performed on them.

These are all the types of values supported by Excel:


Number includes all numeric values you can enter, including things like phone numbers or currencies. Keep in mind that these are often displayed differently, but are converted into pure numbers behind the scenes.

Text –

Text, obviously enough, means any string entered into a cell. Excel doesn’t particularly care what the text is, and so it will categorize any data not recognized as another valid type as a text value. This includes dates and addresses, though their formatting sets them apart.

Logical –

The Logical data type only holds boolean values, ie. TRUE or FALSE. While it just appears to be capitalized text, it is treated as a binary value by Excel and can be used in logical operations.

Error –

Error-values are generated when a function or operation cannot be executed. This type of value will appear in the cell where you expected the final result, informing you about what went wrong. There are multiple types of errors you can see in Excel, one of which we are going to discuss in detail later on.

The VALUE Function

There are many functions that can be used to compose Excel formulas. They range from simple operations like subtraction or finding the average to things like generating random numbers.

The VALUE function is another lesser-known function in Excel. Simply put, it converts text into its numeric value, if such a conversion is possible.

For example, you can use VALUE to convert a date into a purely numeric value. This works for time values as well using the same syntax.

Note that this doesn’t necessarily correspond to any actual value – like the number of days or weeks – but rather a serial number used by Excel to represent a date. But then what’s the use of this conversion?

Even if the value generated is meaningless, it can be used to mathematically compare similar types of data. You can subtract these numbers to find the difference, or figure out which one is greater.

Something like this:

There are two reasons why we don’t see the VALUE function used too often. One, there are very few scenarios in which the function is needed, as you can just enter numeric values when you want to perform calculations. Two, modern-day Excel is actually pretty good at converting strings that represent numbers into numeric values when required.

The same example we used above could be written without the VALUE function and will work the same way:

If you’re comparing currency values for example, Excel will automatically convert the data to the appropriate format and carry out the calculations even if you omit the VALUE function. This leaves very little reason to learn and use the function.

The #VALUE! Error

We have already discussed error values in the data types section, but one error value needs a further look. That’s because it’s also named #VALUE!.

This error is rather easy to understand – if you try to run a mathematical operation on a cell containing the incorrect data type (say a text string), Excel will fail to compute the answer and instead throw a #VALUE error.

To fix this error, you need to correct the cell references and ensure that only numeric data is present in them. Blank cells aren’t supposed to trigger the error, but sometimes a cell might have spaces entered instead, which registers as text.

Special Functions

Many functions in Excel are designed to return a useful value. Some are constant, while others depend on certain conditions.

For example, you can use PI() to get the fixed value of pi in any calculation. RAND(), on the other hand, generates a random number when used.

These values are only created when their respective function is used, and can hence only be inserted through a formula. Once put into a cell, the resultant value acts like a normal data type with a numeric value.

What Is the Most Important Usage of Value in Excel?

For the most part, the only values you need to concern yourself with are the data types present in a cell. The #VALUE! Error is actually not that common, since rarely will you enter text in a field meant for numbers.

The VALUE function is even rarer since very few cases will require you to convert a text string into a number. And in most of these cases (especially when dealing with currencies), the conversion will happen automatically.

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