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What Meta doesn’t want you to Know Instagram does

“You can also Copy and paste fonts from other apps for bios and captions as the new features now allows room for the use of different font texts when creating bios or captions as opposed to the basic font that accompanies Instagram.”

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What Meta doesn’t want you to Know Instagram does

Sometimes there are Apps we use whereby we are oblivious of certain features it carries or its actual capabilities, and one such app is social media platform Instagram.

There quite a lot of things the app does or certain ways it can be used that some users of Instagram have no knowledge of.

Of course, the primary aim of the social media platform is basically to self promote one’s self and to get in touch with a few friends, so it’s understandable that most people will be oblivious to a few of its functions or what certain buttons may stand for.

And then, there are some who are confused as to the certain changes they have experienced with the platform as these days there have been a noticeable increase in ads from unrelated contents users normally would prefer seeing on their news feeds.

This of course has led to the viral trend of ‘make Instagram Instagram again’ as the accusations that the platform was quickly turning into its competition TikTok. Although a few of that has been corrected, but then there are new stuffs most people would be unaware of.

What Meta doesn’t want you to Know Instagram does

For instance, Instagram users can now filter comments by keyword while scrolling in their comment sections.

This generally is for those with large following and has a lot of people commenting on their posts and all, and would preferably want to scroll past the ones that have topics on what they want to see or hear. The filter comments by keyword also help to trace odd comments and quickly locate the user who left such in the post.

It also helps to eliminate spam comments while a user is scrolling through the comments.

You can also Copy and paste fonts from other apps for bios and captions as the new features now allows room for the use of different font texts when creating bios or captions as opposed to the basic font that accompanies Instagram.

In this case, all that is needed to be done is to download a free font app such as Sprezz Keyboard and LingoJan.

What Meta doesn’t want you to Know Instagram does

Just as you can copy and paste fonts from other apps to bios and captions, you can also add fun characters from other apps to bios and posts if copied from apps that generate fun characters.

If there is one thing that sounds reminiscent of WhatsApp, it is the fact that users can from out of the blue change their status privacy settings to decide on who gets to see their posts or who shouldn’t see their posts.

Well, it is the same for Instagram as the Meta-verse also allows Privacy functions for posts.

Generally, profiles can be made or set up as private to allow only those following said profiles to view or see whatever they post on their handles, but again, Instagram has provisions to hide posts from those also following the profile.

In plain terms, Instagram also lets you hide posts from those who follow you just as it creates the provision for you to hide posts from those not following your profile.

What Meta doesn’t want you to Know Instagram does

Now all that needs to be done is to tweak the setting by going to the profile and to the top right, click on the ‘Close Friends’ tag and select the followers you consider close friends.

These close friends become the ones to see all of your posts.

Create an Automatic Reply for impatient followers who need a quick response from you or perhaps an acknowledgement that a message was received or would be received in due time.

Of course, every human wants to be acknowledged or listened to, but a lot of times, this can be quite hard considering each person leads a very different life with a lot of worries or busy schedules, which is the essence of the automatic response function.

This allows a user to send a response message without having to send a message in person. This function is available in the settings, under the ‘Saved Reply’ tag.

It is very much customizable and is edited to suit the response a user would usually give if he or she were to reply such texts in person.

You can Hide Ads you don’t like on Instagram if you don’t want to see them.

The Ad thing on social media has been quite annoying for some people, especially when it relates to something that is very far from their interests.

What Meta won’t tell you is that there is a provision made for you to hide these ads or report them. All that needs to be done is to click on the top right of the ad and select the provisions made to either ‘Hide Ad’ or ‘Report Ad’.

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Overview of big data use cases and industry verticals

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Big data refers to extremely large and complex data sets that are too big to be processed using traditional data processing tools. Big data has several use cases across various industry verticals such as:

  1. Healthcare: Predictive maintenance, personalized medicine, clinical trial analysis, and patient data management
  2. Retail: Customer behavior analysis, product recommendations, supply chain optimization, and fraud detection
  3. Finance: Risk management, fraud detection, customer behavior analysis, and algorithmic trading
  4. Manufacturing: Predictive maintenance, supply chain optimization, quality control, and demand forecasting
  5. Telecommunications: Network optimization, customer behavior analysis, fraud detection, and network security
  6. Energy: Predictive maintenance, energy consumption analysis, and demand forecasting
  7. Transportation: Logistics optimization, predictive maintenance, and route optimization.

These are just a few examples, big data has applications in almost all industry verticals, and its importance continues to grow as organizations seek to gain insights from their data to drive their business outcomes.

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Data Warehousing and Data Management Cost Optimization

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Data Warehousing and Data Management Cost Optimization

In this article, we will discuss the key aspects of data warehousing and management cost optimization and best practices established through studies.

Data warehousing and management is a crucial aspect of any organization, as it helps to store, manage, and analyze vast amounts of data generated every day. With the exponential growth of data, it has become imperative to implement cost-effective solutions for data warehousing and management.

Understanding Data Warehousing and Management

Data warehousing is a process of collecting, storing, and analyzing large amounts of data from multiple sources to support business decision-making. The data stored in the warehouse is organized and optimized to allow for fast querying and analysis. On the other hand, data management involves the processes and policies used to ensure the data stored in the warehouse is accurate, consistent, and accessible.

Why is Cost Optimization Important?

Data warehousing and management costs can add up quickly, making it essential to optimize costs. Implementing cost-optimization strategies not only reduces financial burden but also ensures that the data warehousing and management system remains efficient and effective.

Cost optimization is important for data warehousing and management for several reasons:

Financial Benefits: Data warehousing and management can be expensive, and cost optimization strategies can help reduce these costs, thereby increasing the overall financial efficiency of the organization.

Improved Performance: Cost optimization strategies, such as data compression, data archiving, and data indexing, can help improve the performance of the data warehousing and management system, thereby reducing the time and effort required to manage the data.

Scalability: Implementing cost-optimization strategies can help to scale the data warehousing and management system to accommodate increasing amounts of data, without incurring significant additional costs.

Improved Data Quality: By implementing cost-optimization strategies, such as data de-duplication and data partitioning, the quality of the data stored in the warehouse can be improved, which can lead to better decision-making.

Overall, cost optimization is important for data warehousing and management as it helps to reduce costs, improve performance, and maintain the quality of the data stored in the warehouse.

Established Cost Optimization Strategies

Scalable Infrastructure: It is important to implement a scalable infrastructure that can handle increasing amounts of data without incurring significant costs. This can be achieved through cloud computing solutions or using a combination of on-premises and cloud-based solutions.

Data Compression: Data compression can significantly reduce the amount of storage required for data, thus reducing costs. There are various compression techniques available, including lossless and lossy compression, which can be used depending on the type of data being stored.

Data Archiving: Data archiving is the process of moving data that is no longer actively used to cheaper storage options. This helps to reduce the cost of storing data while ensuring that the data remains accessible.

Data de-duplication identifies and removes duplicate data from the warehouse. This helps to reduce storage costs and improve the overall performance of the data warehousing system. Data de-duplication is a cost optimization strategy for data warehousing and management that focuses on identifying and removing duplicate data from the warehouse. This is important for several reasons:

Reduced Storage Costs: Duplicate data takes up valuable storage space, which can be expensive. By removing duplicates, the storage requirements for the data warehouse can be reduced, thereby reducing storage costs.

Improved Data Quality: Duplicate data can lead to confusion and errors in decision-making, as it may not be clear which version of the data is accurate. By removing duplicates, the quality of the data stored in the warehouse can be improved, which can lead to better decision-making.

Improved Performance: The presence of duplicate data can slow down the performance of the data warehousing system, as it takes longer to search for and retrieve the desired data. By removing duplicates, the performance of the data warehousing system can be improved, reducing the time and effort required to manage the data.

Increased Security: Duplicate data can pose a security risk, as it may contain sensitive information that can be accessed by unauthorized individuals. By removing duplicates, the security of the data stored in the warehouse can be increased.

Overall, data de-duplication is an important cost optimization strategy for data warehousing and management, as it helps to reduce storage costs, improve data quality, improve performance, and increase security. It is important to implement an effective data de-duplication solution to ensure the success of this strategy.

Data Partitioning: Data partitioning involves dividing the data into smaller, manageable chunks, making it easier to manage and analyze. This helps to reduce the cost of storing and processing large amounts of data.

Data Indexing: Data indexing is the process of creating an index of the data stored in the warehouse to allow for fast querying and analysis. This helps to improve the performance of the data warehousing system while reducing costs.

Automation: Automating data warehousing and management processes can significantly reduce the cost and effort required to manage the data. This includes automating data extraction, transformation, loading, and backup processes.

Conclusion

In conclusion, data warehousing and management cost optimization is a crucial aspect of any organization. Implementing cost-optimization strategies, such as scalable infrastructure, data compression, data archiving, data de-duplication, data partitioning, data indexing, and automation, can significantly reduce the cost of data warehousing and management while ensuring that the system remains efficient and effective.

It is important to keep in mind that the specific cost-optimization strategies used will depend on the unique needs and requirements of each organization.

 

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Overview of big data security and privacy

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Big data security and privacy are crucial considerations in the era of large-scale data collection and analysis. The security of big data refers to the measures taken to protect data from unauthorized access, theft, or damage. Privacy, on the other hand, refers to the protection of sensitive and personal information from being disclosed to unauthorized parties.

To ensure the security of big data, organizations adopt various measures such as encryption, access control, network security, data backup and recovery, and others. Additionally, they may also implement compliance with security standards and regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).

However, the increased use of cloud-based big data solutions and the rise of the Internet of Things (IoT) have brought new challenges to the security and privacy of big data. To mitigate these challenges, organizations are using technologies such as blockchain, homomorphic encryption, and differential privacy to provide stronger privacy and security guarantees.

In conclusion, big data security and privacy are crucial components of the big data landscape. Organizations must implement robust measures and technologies to protect sensitive and personal information, maintain the security of big data, and comply with relevant security regulations.

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