Trending December 2023 # Fengoffice: Personal Gdrive With Spreadsheets And E # Suggested January 2024 # Top 19 Popular

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Installing FengOffice with E-mail Setting Up Access to E-mail

When you log in, you’ll see an “Email” tab on your Dashboard.

While you can use FengOffice if you have a hosting account of your own (which will typically come with the ability to set up e-mail accounts), you can also set up the e-mail client to get e-mail from other accounts you might own. The process to do this is as follows:

2. Then, under the “Accounts” pull-down, select “Add Account.”

As noted above, if you want this functionality, you’ll install GelSheet along with its older version of FengOffice. When you log in, you’ll see a similar (but not as appealing) “Overview” interface like the one below:

GelSheet and FengOffice may not be as polished or complete in functionality, but on the other hand, they don’t rely on you keeping your precious data on another company’s servers. You still can have anytime, anywhere access to your information, and also keep it on your own computer where it belongs.

Aaron Peters

Aaron is an interactive business analyst, information architect, and project manager who has been using Linux since the days of Caldera. A KDE and Android fanboy, he’ll sit down and install anything at any time, just to see if he can make it work. He has a special interest in integration of Linux desktops with other systems, such as Android, small business applications and webapps, and even paper.

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How To Install A Personal Google Drive Using Fengoffice

Google has a great set of services available for general consumers, and even offers it for a reasonable rate for businesses. But if you’re the paranoid type, or just like to do things yourself, there’s a way you can create something like Google Docs that you install and host on any Ubuntu box you might have (although accessing this if it’s on a machine at your home is a different issue).

Preparing your Ubuntu Server for Feng Office

1. Make sure you have a web server running on your Ubuntu box. If you don’t have a web server such as Apache running already, you can follow the instructions here. Although this article shows you how to log in and install this remotely, you can use the same commands on your local machine running Ubuntu.

2. The default web server install on Ubuntu won’t include one module you’ll need. The following command will take care of that:


apt-get install


3. We’ll need to create a database for FengOffice to use. The easiest way to do this is to install “phpMyAdmin”:

6. On the next screen, give this user a name and password. Make sure you also set the “Host” pull-down to “Local” (this means the “fengoffice” user can only connect when directly on the server, not remotely).

8. Finally, you’ll need to restart Apache to make sure all the above updates are loaded:


service apache2 restart Downloading and Installing FengOffice

1. Download the latest version of FengOffice Community Edition from its SourceForge page.

2. Unpack the zip file, and move the resulting folder (called fengoffice) to the root of your web server, or a sub-directory (/var/www is the standard directory for web content on Ubuntu, and I’m using the fengtest/subdirectory for this):

Logging in to FengOffice

There you have it, your very own Google Drive.

Aaron Peters

Aaron is an interactive business analyst, information architect, and project manager who has been using Linux since the days of Caldera. A KDE and Android fanboy, he’ll sit down and install anything at any time, just to see if he can make it work. He has a special interest in integration of Linux desktops with other systems, such as Android, small business applications and webapps, and even paper.

Subscribe to our newsletter!

Our latest tutorials delivered straight to your inbox

Sign up for all newsletters.

By signing up, you agree to our Privacy Policy and European users agree to the data transfer policy. We will not share your data and you can unsubscribe at any time.

Muslim Personal Law: Meaning And Sources

Anyone who practises Islam as their religion is referred to as a Muslim. A person can become Muslim either by birth or by conversion, according to judicial opinion. If both of a person’s parents were Muslims when they gave birth to him, he is a Muslim by default. When a person of another religion renounces their previous faith and chooses Islam after reaching the age of majority and does so consciously, they are considered Muslims by conversion.

Muslims are governed by their personal law contained in their own religious sources. In Muslim law, case-law also has a part to play, but it only serves to clarify and elucidate. This article makes an effort to discuss the major sources of Muslim law in India.

Historical Background of Muslim Personal Law

The historical background of Muslim Personal Law in India can be traced back to the time of the Mughal Empire i.e. during the 16th century. During this period, Islamic laws were applied to Muslim communities in India, and were based on the principles of Islamic jurisprudence derived from the Qur’an and the Hadith. However, these laws were often interpreted and applied differently in different regions of the country, depending on local customs and traditions.

During the British colonial period, the British government attempted to codify and standardize Muslim Personal Law in India. Resultantly, they introduced the Indian Divorce Act of 1869, which allowed Muslim men to divorce their wives through a process of talaq (verbal divorce). However, the act did not provide any rights or protections for women in the divorce process.

In 1866, when the Judicial Committee of the Privy Council, the highest court of appeal in the British Empire, placed Shariah above local customary law. Subsequently. it allowed the use of local customary law, it called for “proof of special usage.” But in the subsequent years, the High Courts of Calcutta (1882) and Allahabad (1900) disallowed the use of customary law for Muslims. But in 1913, the Judicial Committee of the Privy Council changed its earlier stance and ruled that customary law played a major part in Muslim life and allowed its use in the courts of the Raj.

Furthermore, in 1937, the Muslim Personal Law (Shariat) Application Act was passed by the British government in India. This act made the principles of Islamic law the basis for personal law for Muslims in India. It majorly deals with respect to marriage, divorce, inheritance, and other related matters. However, the act did not provide a comprehensive and standardized system of Muslim Personal Law, and left many aspects open to interpretation and variation.

In 1939, the League passed the Dissolution of Muslim Marriages Act, which gave Muslim women the right to divorce their husbands.

After India’s independence in 1947, Muslim Personal Law has remained in place, and is still governed by the Muslim Personal Law (Shariat) Application Act of 1937. But the Muslim laws still developing as per the needs of present day, particularly with respect to issues such as divorce, inheritance, and gender equality. Recently the triple talaq rule is legislated.

Sources of Muslim Law

The following are the important sources of Muslim Law −


Muslims view the “Quran” as the source of their legal system. They hold that the “Quran” is the only source that clearly illustrates the difference between truth and untruth as well as between right and wrong. The holy Quran is a representation of the letters that Allah, through Gabriel, sent to the Prophet. It covers issues including marriage, divorce, inheritance, and public prayer as well as traditions like fasting, pilgrimage, and the prohibition of wine. The final authority is the Quran. There are 110 chapters and 6237 verses. The scientific name for Allah’s laws is “Shariat”. It denotes the path leading to the watering well. So, this is the way that should be taken. Shariat is the set of rules and obligations that applies to all human conduct.

Sunna or Ahadis

Ahadis means Traditions. Sunna means the trodden path, the practice of the Prophet. When the Prophet served as a judge in his own era, his decisions were based on the “revelation” of the Quran. His principles and sayings became the way to go after his passing. Ahadis were the customs and laws, and Sunna was what the Prophet did. These Ahadis are reliable sources that have been written down. Bukari has catalogued around 7000 of these customs. Sunna is another source of law, and in times of disagreement, it completes and clarifies the Koran. The Sunna is a record of the Prophet’s deeds, choices, and utterances. Even mute responses were Sunna. The Quran and the Sunna form the basic roots of Islamic law.


In Arabic, qiya is “analogical deduction.” In this instance, inferences are made via analogies. Fatawas were the names given to judgments and scholarly opinions. Well-known is Fatawa Alamgiri.


When the “Quran” and “Sunna” did not provide a rule of law for a new issue, the people who were familiar with Muslim law used to unanimously concur and give their common view on that issue. Ijma represents the “consensus of opinion” among Muslim scholars. The majority of academics share this opinion. This procedure handled brand-new legal issues. Thus, it was codified by the great authority as communal law. Ijtihad, which was based on equity, public interest, and solid precedent, was a special effort made by scholars to resolve the matter in individual circumstances when there was no principle on any subject. Every school had a unique “Ijtihad.”


The wording of any of the four sources described above has been taken as law and in the absence of a rule of law, the customs have been treated as law. In the pre-Islamic era, there were numerous customs. A lot of them obtained the prophet’s blessing. With his implied approval, some of them persisted, and these joined Ijrna. Over time, these were given legal standing. To the exclusion of customs, the 1937 Shariat Act was created to apply exclusively the Shariat.


No one can create Muslim law since the Prophet is the only person who has the authority to do so. Any modification is seen as an invasion if it is made. Despite this, there are several Acts.

The Mussaiman Wakf Validating Act, 1913.

The Child Marriage Restraint Act, 1929.

The Shariat Act, 1937.

Dissolution of Muslim Marriage Act, 1939.

Judicial Decisions

Court rulings have in some ways influenced Muslim law. Although there isn’t much room for judicial decisions to act as a source of Islamic law, the court may interpret the law in accordance with its own sense of justice in the lack of a clear scripture. The courts have rendered a number of notable judgements in this context.

Justice, Equity and good conscience: Similar to Hindu law, equity, justice, and good conscience would be used in this situation if there was no explicit law in place or if there was a dispute.

Various Muslim Laws in India

Marriage law

Divorce Law

Law of Succession

Maintenance law


In the Indian context, legal pluralism operates through procedural and institutional cooperation and collaboration between religious and civil institutions. These matters are as much subjects of religion-ethical practise as they are matters of citizens’ rights.


Q1. What are the major categories of Muslim law?

Ans. The Sharia regulates all human actions and puts them into five categories: obligatory, recommended, permitted, disliked or forbidden.

Q2. What is the difference between common law and Islamic law?

Ans. Islamic law is religious inspirited and regulated and the western law is man-made sets of law. Unlike some areas of Islamic law, the sphere of the common law is subject to constant legal changes.

Q3. Why is Islamic law important?

Ans. The primary objectives of Islamic law (maqasid shari’a) are the protection of life, property, mind, religion, and offspring.

Q4. What are the major elements of Islamic law?

Ans. Sharia comprises three basic elements:

Aqidah concerns all forms of faith and belief in Allah, held by a Muslim.

Fiqh governs the relationship between man and his Creator (ibadat) and between man and man (muamalat)

Akhlaq covers all aspects of a Muslim’s behavior, attitude, and work ethic.

An Introduction To Python For Seo Pros Using Spreadsheets

2023 far exceeded my expectations in terms of Python adoption within the SEO community.

As we start a new year and I hear more SEO professionals wanting to join in the fun, but frustrated by the initial learning curve, I decided to write this introductory piece with the goal of getting more people involved and contributing.

When you implement the same workflow in Python, you can trivially reproduce the work or even automate the whole workflow.

We are going to learn Python basics while studying code John Mueller recently shared on Twitter that populates Google Sheets. We will modify his code to add a simple visualization.

— 🍌 John 🍌 (@JohnMu) January 3, 2023

Setting up the Python Environment

Similar to working with Excel or Google Sheets, you have two primary options when working with Python.

You can install and run Python on your local computer, or you can run it in the cloud using Google Colab or Jupyter notebooks.

Let’s review each one.

Working with Python on Your Local Computer

I typically choose to work on my Mac when there is software that won’t run in the cloud, for example, when I need to automate a web browser.

You need to download three software packages:


Visual Studio Code.

The Python bindings for Code.

This will take a while to complete.

Once done, search for the Anaconda Navigator and launch it.

You can think of this notebook as similar to a new Excel sheet.

The next step is optional.

I personally use Visual Studio Code when I need to write code in Python and JavaScript or when writing JavaScript code. You can also use it if you want to convert your notebook code into a command-line script.

It is easier to prototype in Jupyter notebooks and when you get everything to work, you can use Visual Studio Code to put everything together in a script or app that others can use from the command line.

Make sure to install the Python extension for VSC. You can find it here.

Visual Studio Code has built-in support for Jupyter Notebooks.

You can create one by typing the keyword combination Command+Shift+P and selecting the option “Python Jupyter Notebook”.

Working with Python in the Cloud

I do most of my Python work on Google Colab notebooks so this is my preferred option.

Learning the basics of Python & Pandas

Mueller shared a Colab notebook that pulls data from Wikipedia and populates a Google Sheet with that data.

Professional programmers need to learn the ins and out of a programming language and that can take a lot of time and effort.

For SEO practitioners, I think a simpler approach that involves studying and adapting existing code, could work better. Please share your feedback if you try this and see if I am right.

We are going to review most of the same basics you learn in typical Python programming tutorials, but with a practical context in mind.

Let’s start by saving Mueller’s notebook to your Google Drive.

Here is the example Google sheet with the output of the notebook.

Overall Workflow

Mueller wants to get topic ideas that perform better in mobile compared to desktop.

— 🍌 John 🍌 (@JohnMu) December 30, 2023

He learned that celebrity, entertainment, and medical content does best on mobile.

We have several pieces to the puzzle.

An empty Google sheet with 6 prefilled columns and 7 columns that need to be filled in

The empty Google sheet includes a Pivot table in a separate tab that shows mobile views represent 70.59% of all views in Wikipedia

The helper function receives the names of the columns to update and a function to call that can return the values for the columns.

After all of the columns are populated, we get a final Google sheet that includes an updated Pivot Table with a break down of the topic.

Python Building Blocks

Let’s learn some common Python building blocks while we review how Mueller’s code retrieves values to populate a couple of fields: the PageId and Description.

# Get the Wikipedia page ID -- needed for a bunch of items. Uses "Article" column def get_PageId(title): # Get page description from Wikipedia def get_description(pageId):

We have two Python functions to retrieve the fields. Python functions are like functions in Google Sheets but you define their behavior in any way you want. They take input, process it and return an output.

Here is the PageId we get when we call get_PageId(“Avengers: Endgame”)


Here is the Description we get when we call get_description(pageId)

'2023 superhero film produced by Marvel Studios'

Let’s step through, line by line, the get_PageId function to learn how it gets the ID of the title of the article that we are passing on.

# call the Wikipedia API to get the PageId of the article with the given title. q = {"action": "query", "format": "json", "prop": "info", "titles": title}

q is a Python dictionary. It holds key-value pairs. If you look up the value of “action”, you get “query” and so on. For example, you’d perform such a lookup using q[“action”].

“action” is a Python string. It represents textual information.

“titles”: title maps the “titles” key to the Python variable title that we passed as input to the function. All keys and values are hardcoded and explicit, except for the last one. This is what the dictionary looks like after we execute this function.

q = {"action": "query", "format": "json", "prop": "info", "titles": "Avengers: Endgame"}

In the next line we have.

Here we have a Python module function urllib.parse.urlencode. Module functions are just like Google sheet functions that provide standard functionality.

Before we call module or library functions, we need to import the module that contains them.

This line at the top of the notebook does that.

import urllib.parse

Let’s clarify the call and see the output we get.

urllib.parse.urlencode({"action": "query", "format": "json", "prop": "info", "titles": "Avengers: Endgame"})

You can find detailed documentation on the urlencode module function here. Its job is to convert a dictionary of URL parameters into a query string. A query string is the part of the URL after the question mark.

This is the output we get after we run it.


This is what our URL definition line looks like after we add the result of urlencode.

The + sign here concatenates the strings to form one.

This resulting string is the API request the notebook sends to Wikipedia.

In the next line of code, we open the dynamically generated URL.

response = requests.get(url)

requests.get is a Python third-party module function. You need to install third-party libraries using the Python tool pip.

!pip install --upgrade -q requests

You can run command line script and tools from a notebook by prepending them with !

The code after ! is not Python code. It is Unix shell code. This article provides a comprehensive list of the most common shell commands.

After you install the third-party module, you need to import it like you do with standard libraries.

import requests

Here is what the translated call looks like.

You can open this request in the browser and see the API response from Wikipedia. The function call allows us to do this without manually opening a web browser.

The results from the chúng tôi call gets stored in the Python variable response.

This is what the result looks like.

{“batchcomplete”: “”,

“query”: {“pages”: {“44254295”: {“contentmodel”: “wikitext”,

“lastrevid”: 933501003,

“length”: 177114,

“ns”: 0,

“pageid”: 44254295,

“pagelanguage”: “en”,

“pagelanguagedir”: “ltr”,

“pagelanguagehtmlcode”: “en”,

“title”: “Avengers: Endgame”,

“touched”: “2023-01-03T17:13:02Z”}}}}

You can think of this complex data structure as a dictionary where some values include other dictionaries and so forth.

The next line of code slices and dices this data structure to extract the PageId.

result = list(response.json()["query"]["pages"].keys())[0]

Let’s step through it to see how it gets it.


When we look up the value for the key “query”, we get a smaller dictionary.

{“pages”: {“44254295”: {“contentmodel”: “wikitext”,

“lastrevid”: 933501003,

“length”: 177114,

“ns”: 0,

“pageid”: 44254295,

“pagelanguage”: “en”,

“pagelanguagedir”: “ltr”,

“pagelanguagehtmlcode”: “en”,

“title”: “Avengers: Endgame”,

“touched”: “2023-01-03T17:13:02Z”}}}

Then, we look up the value of “pages” in this smaller dictionary.


We get an even smaller one. We are drilling down on the big response data structure.

{“44254295”: {“contentmodel”: “wikitext”,

“lastrevid”: 933501003,

“length”: 177114,

“ns”: 0,

“pageid”: 44254295,

“pagelanguage”: “en”,

“pagelanguagedir”: “ltr”,

“pagelanguagehtmlcode”: “en”,

“title”: “Avengers: Endgame”,

“touched”: “2023-01-03T17:13:02Z”}}

The PageId is available in two places in this slice of the data structure. As the only key, or as a value in the nested dictionary.

John made the most sensible choice, which is to use the key to avoid further exploration.


The response from this call is a Python dictionary view of the keys. You can learn more about dictionary view in this article.


We have what we are looking for, but not in the right format.

In the next step, we convert the dictionary view into a Python list.


This what the conversion looks like.


Python lists are like rows in a Google sheet. They generally contain multiple values separated by commas, but in this case, there is only one.

Finally, we extract the only element that we care about from the list. The first one.


The first element in Python lists starts at index 0.

Here is the final result.


As this is an identifier, is better to keep as a string, but if we needed a number to perform arithmetic operations, we would do another transformation.


In this case, we get a Python integer.


The main differences between strings and integers are the types of operations that you can perform with them. As you saw before we can use the + operator to concatenate two strings, but if we used the same operator in two numbers, it would add them together.

"44254295" + "3" = "442542953" 44254295 + 3 = 44254298

As a side note, I should mention jq, a cool command line tool that allows you to slice and dice JSON responses directly from curl calls (another awesome command line tool). curl allows you to do the equivalent of what we are doing with the requests module here, but with limitations.

So far we’ve learned how to create functions and data types that allow us to extract data and filter data from third-party sites (Wikipedia in our case).

Let’s call the next function in John’s notebook to learn another important building block: flow control structures.


This is what the API URL looks like. You can try it in the browser.

Here what the response looks like.

{“ns”: 0,

“pageid”: 44254295,

“terms”: {“alias”: [“Avengers Endgame”, “Avengers End Game”, “Avengers 4”],

“description”: [“2023 superhero film produced by Marvel Studios”],

“label”: [“Avengers: Endgame”]},

“title”: “Avengers: Endgame”}

This is the code that will step through to understand control flows in Python.

# some pages don't have descriptions, so we can't blindly grab the value if "terms" in rs and "description" in rs["terms"]: result = rs["terms"]["description"][0] else: result = "" return result

This part checks if the response structure (above) includes a key named “terms”. It uses the Python If … Else control flow operator. Control flow operators are the algorithmic building blocks of programs in most languages, including Python.

if "terms" in rs

If this check is successful, we look up the value of such key with rs[“terms”]

We expect the result to be another dictionary and check it to see if there is a key with the value “description”.

"description" in rs["terms"]

If both checks are successful, then we extract and store the description value.

result = rs["terms"]["description"][0]

We expect the final value to be a Python list, and we only want the first element as we did before.

The and Python logical operator combines both checks into one where both need to be true for it to be true.

If the check is false, the description is an empty string.

result = "" Populating Google Sheets from Python

With a solid understanding of Python basic building blocks, now we can focus on the most exciting part of Mueller’s notebook: automatically populating Google Sheets with the values we are pulling from Wikipedia.

# by ‘functionToCall(parameterName)’. Show a progressbar while doing so. # Only calculate / update rows without values there, unless forceUpdate=True.

Let’s step through some interesting parts of this function.

The functionality to update Google Sheets is covered by a third-party module.

We need to install it and import it before we can use it.

!pip install --upgrade -q gspread import gspread

At the end of every helper function that fills a column, we have a call like the one above.

We are passing the relevant columns and the function that will get the corresponding values.

columnNr = df.columns.get_loc(fieldName) + 1 # column number of output field

The first thing we want to know is which column we need to update. When we run the code above we get 7, which is the column position of the PageId in the sheet (starting with 1).

for index, row in df.iterrows():

In this line of code, we have another control flow operator, the Python For Loops. For loops allow you to iterate over elements that represent collections, for example, lists and dictionaries.

In our case above, we are iterating over a dictionary where the index variable will hold the key, and the row variable will hold the value.

To be more precise, we are iterating over a Python dictionary view, a dictionary view is like a read-only and faster copy of the dictionary, which is perfect for iteration.

When you print iterrows, you don’t actually get the values, but a Python iterator object.

Iterators are functions that access data on demand, require less memory and perform faster than accessing collections manually.

INDEX: 2 ROW: Article César Alonso de las Heras Views 1,944,569 PartMobile 79.06% ViewsMobile 1,537,376 ViewsDesktop 407,193 PageId 18247033 Description WikiInLinks WikiOutLinks ExtOutLinks WikidataId WikidataInstance Name: 2, dtype: object sdsdsds

This is an example iteration of the for loop. I printed the index and row values.

# if we already did it, don't recalculate unless 'forceUpdate' is set. if forceUpdate or not row[fieldName]: result = functionToCall(row[parameterName])

forceUpdate is a Python boolean value which defaults to False. Booleans can only be true or false.

row[“PageId”] is empty initially, so not row[“PageId”] is true and the next line will execute. The or operator allows the next line to execute for subsequent runs only when the flag forceUpdate is true.

result = functionToCall(get_PageId)

This is the code that calls our custom function to get the page ids.

The result value for the example iteration is 39728003

When you review the function carefully, you will notice that we use df which is not defined in the function. The code that does that is at the beginning of the notebook.

# Convert to a DataFrame and render. # (A DataFrame is overkill, but I wanted to play with them more :)) import pandas as pd df = pd.DataFrame.from_records(worksheetRows)

The code uses the third-party module pandas to create a data frame from the Google Sheet rows. I recommend reading this 10 minutes to pandas article to get familiar. It is a very powerful data manipulation library.

Finally, let’s see how to we update the Google Sheet.

row[fieldName] = result # save locally worksheet.update_cell(index+1, columnNr, result) # update sheet too

This code can be translated to.

row["PageId"] = 39728003 # save locally worksheet.update_cell(3+1, 7, 39728003) # update sheet too

# (This is always confusing, but it works) from google.colab import auth auth.authenticate_user() import gspread from oauth2client.client import GoogleCredentials gc = gspread.authorize(GoogleCredentials.get_application_default()) worksheetRows = worksheet.get_all_values()

I left this code for last because it is the last thing that gets executed and it is also more complicated than the previous code. However, it is the first thing you need to execute in the notebook.

First, we import the third-party module gspread, and complete an Oauth authentication in Chrome to get access to Google Sheets.

worksheet =“Wikipedia-Views-2023”).sheet1 worksheetRows = worksheet.get_all_values()

We manipulate the Google sheet with the worksheet variable and we use the worksheetRows variable to create the pandas Dataframe.

Visualizing from Python

Now we get to your homework.

I wrote code to partially reproduce John’s pivot table and plot a simple bar chart.

Your job is to add this code to your copy of the notebook and add print(varible_name) statements to understand what I am doing. This is how I analyzed John’s code.

Here is the code.

#Visualize from Python df.groupby("WikidataInstance").agg({"ViewsMobile": chúng tôi "ViewsDesktop": np.sum}) # the aggregation doesn't work because the numbers include commas # This gives an error ValueError: Unable to parse string "1,038,950,248" at position 0 #pd.to_numeric(df["ViewsMobile"]) # StackOverflow is your friend :) import locale from locale import atoi locale.setlocale(locale.LC_NUMERIC, '') #df[["ViewsMobile", "ViewsDesktop"]].applymap(atoi) df["ViewsMobile"] = df["ViewsMobile"].apply(atoi) df["ViewsDesktop"] = df["ViewsDesktop"].apply(atoi) # We try again and it works totals_df = df.groupby("WikidataInstance").agg({"ViewsMobile": chúng tôi "ViewsDesktop": np.sum}) totals_df #Here we plot totals_df.head(20).plot(kind="bar")

Resources to Learn More

Digital Asset Management – Worthy Tool For E

Let us say you are an online retailer selling your products over online platforms such as amazon or Flipkart.

And you need to manage everything right from finding images of your products from storage devices, downloading them, updating them on websites, sharing them with your teammates, updating the product descriptions, and much more.

Simply put, digital asset management software is a tool for storing, managing, sharing your digital assets, and connecting your library consisting of digital assets with product managing tools and much more.

Therefore, DAM is necessary for a retailer for easy work of finding content, images, product descriptions, and other things for the product to be sold.

Therefore, we shall see the importance of digital asset management in e-commerce. 

Hence, marketing teams use various digital asset management tools based on their industry, product type, and other necessities.

Therefore, it provides you with customized and need-based support.  

You need not spend your time on simple administrative processes of online business.

Therefore, digital asset management software can automate administration-related processes such as finding the appropriate file and suitable up-to-date product descriptions.

It provides a space for collaboration with your teammates and other parties. You can share, provide access and update the content.

In addition, giving feedback and assigning tasks is possible. It provides a secure space for easy collaboration.

It provides access options for assets to be managed. You can control whom to give these assets from your list of third-party members.

In addition, you can share encrypted and secured data with stakeholders. Several SSL-secured DAM software provides secured data sharing, as the interaction would always be connected with an SSL certificate.

If you talk about SSL certificate types and its brands then, few names you can consider like AlphaSSL certificate, Sectigo SSL certificate, Thawte Wildcard SSL, RapidSSL single domain certificate, Sectigo multi domain SSL, etc.

You can go with any brand and type of SSL as it fits with your requirement.

An online retailer with only one or two members in its marketing team finds it quite challenging to locate the files for product images or is sometimes unable to identify them because of the inappropriate naming of files.

What you can do is have a Digital asset management library where you can safely keep your product-related content in an organized manner.

Through DAM, you can easily do the assignment of tasks and see to it that those tasks are not assigned a second time.

In addition, this way, managers can control the wastage of time and energy of human resources.

The managers can easily observe the activities being carried out on assets by the creators. Such as viewing it, downloading, editing, or any other activity.

They can keep track of such activities as a report is generated automatically.

You can quickly analyze the data by the information caused as it provides data related to customers, such as their locations, products they view, and the time they take to find the right product.

When any fresher is newly hired, it becomes easier for them to follow the practices and patterns being followed by their seniors or colleagues with the help of Digital asset management.

It creates a standard path for fonts to be used, logos, and other things as well. It is already stored in the library of DAM software, and fresher needs to go through it.

With the help of better visuals and descriptions, customers feel better and have a wonderful experience while doing online shopping.

Therefore, it is crucial to provide a better experience for changing the customers having the potential to buy into actual buyers.

Thus, Digital asset management has become the need of online retailers or sellers to provide better experiences to online visitors.

You might have your products offered at various retail brands. Therefore, this DAM will help you to store, manage and update the data for your product at all the online platforms, which is also error-free with savings in your timings.

Let us say you are managing an educational company providing online training. You need to have a document of academic content in different languages if it is a large organization providing services to people from various diversities.

Therefore, DAM software will help you to translate an already formed document into another language and rename it differently for identification.

It saves time and effort by automatically translating digital assets into different languages.

To conclude, Digital asset management in e-commerce is nothing but a tool to create digital assets, organize them, store them, share them, and much more.

It is a convenient platform for sharing and collaboration with partners, employees, or stakeholders.

Any online retailer must take the help of such software available in different varieties as per your need to accelerate your day-to-day activities related to online sales and boost productivity.

It is or will become a necessary part of the marketing team of any business organization selling products online as it provides various benefits, which are discussed above.

Based on the nature of your business, the products you offer, or the services you provide, you need to decide the type of DAM software we will use to cater to operational and other needs.

Use such tools and have a great marketing strategy to maximize your sales and, ultimately, your profits.

Kobo Vox Review: A Social, Multipurpose E

The Kobo Vox is a value-priced tablet with a twist. Like its E Ink sibling, the Kobo Touch e-reader, the Vox has a social focus, and places an emphasis on sharing reading experiences. At $200 (as of November 20, 2011), the Kobo Vox is priced the same as the Amazon Kindle Fire and the Barnes & Noble Nook Color, and $50 less than the Barnes & Noble Nook Tablet. Though it lacks the video/music download and streaming options that distinguish its competitors, the Vox deserves notice for coupling e-reading capabilities with the multimedia functions of a basic Android tablet.

Still, you get the benefit of having Android navigation conventions that you may already be used to on your smartphone, from the six-button menu pop-ups to the three familiar capacitive-touch Android buttons (back, menu, and home) to shortcuts such as pressing and holding the menu button to call up your eight most recently accessed apps.

Kobo includes Rdio for music streaming, Zinio for accessing some 4500 digital magazines, and PressReader for accessing over 1900 newspapers; this arrangement keeps periodicals inconveniently separate from shopping at Kobo, as opposed to shopping at Amazon or Barnes & Noble. The Merriam-Webster Collegiate Dictionary is also on board, but it doesn’t integrate with the reading function (the company says this feature is coming in a software update).

Custom Reading Widgets

The Vox’s customized Android interface puts a Kobo-centric spin on everything from notifications to the dock menu to the home-screen reading widget. What’s pleasant about this approach is that the Vox doesn’t feel like an arbitrary reskin of Android, in spite of the interface’s different functionality and look. Rather, it feels as if Kobo simply extended the native Android OS to embrace the Kobo vision, in ways that are tailored for the tablet’s reading activities.

From initial startup, it’s clear on the home screen that this device is aimed at reading first. The customized Kobo Vox reading and shopping apps are well presented, in a manner that other LCD, Android-based e-readers that simply have an e-reader app (such as those from Pandigital) can’t match.

Reading Life is one of three unique components of the Kobo social-reading experience, the others being the Kobo eReading App and Kobo Pulse. Reading Life summarizes your reading activities, offering detailed stats such as how many days, hours, and minutes you’ve spent reading books, information on the book currently in progress, and how many pages you’ve turned.

The eReading App offers some noteworthy viewing options. From your bookshelf library, you can manage font, display, and reading settings (all accessible from within a book), and you can control which Reading Life notifications you’ll receive while you’re reading (such as unlocked awards). You get seven font styles, plus up to 42 sizes selectable via a slider control; it would have been better to have real-time resizing and presentation of text changes, but at least these options are easily accessible. You can also choose between displaying one page or two when you hold the tablet in landscape mode.

You can tap at the bottom center of the screen to reveal current chapter info, and a slider for moving around within a book. Annotations are easy to make, once you tap at the top of the screen and select the option; you can see all of them, or just view highlights or notes. You can share individual annotations via Facebook; you can’t, however, share them by email, or share all at once, two features that would be useful.

The Library presentation is a bit more rudimentary than I would have expected. Most notably, the Library bookshelf view lacks a search bar. And although it has a handy import option for bringing books from a MicroSDHC card into the Library, I could see only the author and title information in the list view. Kobo supports ePub and enhanced ePub files.

While the Vox lacks the distinct glare-reduction techniques found on the Nook Tablet, it does have decent text quality. (Tip: Choose one of the sans serif fonts for the sharpest, least pixelated text.) In my tests using the same book in the same fonts, displayed at similar sizes, in the e-reading sections of the Nook Tablet and the Vox, I found that the Vox was a close second in readability. The Nook Tablet was far less glare-prone, but the Vox’s text rendering in that font was very similar. That same font wasn’t available on the Kindle Fire–but even when I used the closest match on that tablet, the Vox still presented slightly clearer and easier-to-read text than the Fire did.

In use, I found that the Vox’s lackluster specs hurt its performance. It has a single-core 800MHz processor and just 512MB of memory–two subpar components that contribute to its laggy performance. Simple actions such as page turns felt zippy enough, but accessing menus or loading books often dragged and produced a spinning ball, and games lacked smooth transitions.

The Vox ships with 8GB of storage, of which 5.34GB is user-accessible. A MicroSDHC card slot sits at the left side, supporting up to 32GB of flash storage. Like Amazon and Barnes & Noble, Kobo offers unlimited cloud storage for your book purchases.

Hefty Feel

The Kobo Vox feels like last year’s hardware, at least in respect to its boxy, squared-off design. Measuring 7.57 by 5.06 by 0.53 inches, it’s noticeably thicker than the Kindle Fire or the Nook Tablet. Kobo says that the Vox weighs 0.89 pound, which puts it about even with the Nook Tablet (0.88 pound) and the Kindle Fire (0.91 pound). But both of those competitors somehow feel lighter than the Vox; the Vox’s balance makes it seem heavier than its stated weight. I found my hands getting tired faster when I held the Vox than when I held either rival.

Given its compatibility with Android apps, the Kobo Vox makes for a solid, more open alternative to the Amazon Kindle Fire or the Barnes & Noble Nook Tablet. The Vox lacks the polish and finesse of those models, but its emphasis on reading is very useful.

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