finetechlad

[finetechlad]

What 3 questions would I ask a super human and why these 3 questions are important to me

If I ever had a chance to ask questions to a super human, I’m sure I would have more than 3 to ask! But just to stay on topic, the following would be the most important questions I would ask:

1. How did the universe actually form?

I know there are a lot of theories about this, but since I know I’m going to get the most accurate answer, this would be the first question I would ask. I remember staying up all night thinking about how the universe was formed and how it all started.

2. What would be the technological state of the world 50 to 100 years from now?

As a tech enthusiast, I love all the technological advancements that has happened through out the years. And I’m really happy to be alive to see and experience all these. But the only thing that makes me sad is the fact that one day I will have to say goodbye, and I will no longer be able to learn and experience the technology of the future.

3. Will AI and robots ever take over the world?

I know this sounds silly, but from all the technological breakthroughs and all that we have seen so far, this seems like a legit question. Will we ever get to a point where AI becomes so intelligent that it can surpass human intelligence?

[finetechlad]

Major developments in big data that has intrigued me

The field of big data management has seen a lot of advancements over the recent years. These advancements have made a huge impact when it comes to storage, analysis and retrieval of large volumes of data. I have mentioned some of the key advancements in big data that has piqued my interest:

1. NoSQL Database

NoSQL provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in traditional relational databases. Some of the main advantages in relation to the field of big data are: can handle large volumes of data at high speed, store unstructured, semi-structured or structured data, developer friendly and can take full advantage of the cloud to deliver zero downtime. NoSQL databases like MongoDB, Redis etc. has changed the way huge amounts of data are stored and retrieved. Having been a software developer, I have felt the easiness of working with NoSQL databases compared to relational databases like MSSQL.

2. Impact of AI

AI is being used in various aspects of big data management. It is being used in pattern management, action management, risk management and context management to name a few. When it comes to pattern management, AI can be used to easily and effectively find user patterns from huge volumes of data that couldn’t be done earlier. It can learn common human error patterns, detecting and resolving potential flaws in information. It can alert users to anomalies or unexpected patterns in data.

3. Advancements in storage technologies

The switch from mechanical hard drives that had huge latencies and a maximum speed of a few hundred mega bytes to solid state drives that has a sequential read speed of up to 14 gigabytes per second has literally changed the way data is being stored and accessed in the cloud. A few years ago, we could only dream of such high speeds in a storage device. Not only has the speeds improved over the years but also the cost has decreased significantly. These advancements have enabled companies to store huge volumes of data and also access them at very high speeds. This has brought huge benefits in data access speeds and also better reliability and reduced downtime.

References:

https://www.qlik.com/us/augmented-analytics/big-data-ai

https://www.forbes.com/sites/bernardmarr/2021/02/22/the-4-biggest-trends-in-big-data-and-analytics-right-for-2021/

https://www.jigsawacademy.com/big-data-5-new-technologies-emerge-2017/

https://www.xenonstack.com/blog/latest-trends-in-big-data-analytics

https://www.researchgate.net/publication/346307277_Recent_advances_in_Big_Data_Analytics_Internet_of_Things_and_Machine_Learning

https://www.wikipedia.org/

[finetechlad]

The biggest challenges in big data during recent years

Big data can simply be defined in terms of four V’s: Volume, Velocity, Variety and Veracity. These four V’s cause many of the challenges that organizations encounter in their big data initiatives. I have listed some of the most common big data challenges faced today:

1. Lack of understanding and acceptance of big data

Oftentimes companies fail to understand the basics of what big data actually is, the benefits it brings, the infrastructure required etc. Without a clear understanding, big data adoption projects can be doomed. Companies can waste a lot of time and resources on things they don’t even know how to use. Some companies can be even more reluctant to make a change and adopt big data in their business. All this can severely impede growth and progress of the company.

2. Massive amounts of data and growth challenges

While talking about big data, the most obvious challenge we can think of is, simply storing all that data. IDC estimates that the amount of information stored in the worlds IT systems is doubling every 2 years. At this pace we can only imagine the amount of data businesses would have to deal with 5 to 10 years from now. Much of this data is unstructured and comes from documents, videos, audio etc. meaning that you cannot find them in a database. Even though the cost of storage is getting cheaper and faster, it cannot keep up with the rate at which data is generated every day.

3. Complexity of managing data quality

Since data can come from a variety of different sources and formats, businesses can run into data integration issues sooner or later. For example, an ecommerce company would have to analyze data from website logs, call centers, social media handles etc. Also, the format of data can differ from source to source. This can be a challenge if no process is in place to integrate and analyze all this data.

Data reliability can also be an issue here. Not all data collected is accurate. Not only can it contain wrong information, but also duplicate itself, as well as contain contradictions. Understanding this and making the most out of the collected data can be a challenge.

4. Security of data

Companies are so busy collecting, storing and analyzing data that they usually neglect and push data security for later stages. This can be a huge issue. We usually read about security breaches a lot in the news nowadays. Unprotected data can be a breeding ground for malicious hackers. This can cause companies to lose millions of dollars. This shows that data security is not taken seriously by many companies and by the time the damage is done, it would have been too late.

References :

https://www.datamation.com/big-data/big-data-challenges/

https://www.xenonstack.com/insights/big-data-challenges

https://www.scnsoft.com/blog/big-data-challenges-and-their-solutions

https://searchdatamanagement.techtarget.com/tip/10-big-data-challenges-and-how-to-address-them

https://www.dataversity.net/four-common-big-data-challenges/