Big Data Management – Managing big data could be troublesome and tiring, especially for those big and famous organizations and companies. In this modern era there are numbers of organizations and companies who choose to use big data in their day to day activities, and the number is getting higher each day. Because of this, application development would be suffered.
There will be a lot of people who are not good in managing the powering of the data (with which it has related application).
Big data are usually related with big data management (well, you need to manage the data somehow, right?).
It is quite obvious that the big data may have an impact on creating a new data manager and new data managing tools.
Here are a few things that must be put under consideration in managing big data and ensure you to have a consistent and trusted analytic results.
You can manage Big data alone, especially the business users
In order to have an easier time to manage big data, a business user need to have good availability which enable the business user to access lots of data sets in the data’s original formats.
Business users in this era are far more advance and adept compared to their predecessors.
They often want to do it simply, in a way that they only need to some accessing and a bit of preparing the data in its rawest format, which in my opinion, is often confusing.
This is because they found out that they need to understand the data by themselves to avoid misinterpretations.
Executives wanted to be independent by scanning data sources, crafting reports, and analyze it around their very own independent business needs (which is greedy, if I have a say).
There are two big data management implications that should be adapted into something that can support big data self-service:
- An allowance for the users to check and re-check the data on their own will in order to permit data discovery
- Data preparation tools that can be used by the user to do that particular checking.
Remember that this is not a data model that you can play around with
The conventional approach in managing big data is focused on taking data and putting them in a dedicated data analysis center and then making it into something that is more structured.
In this modern era, the data obtained is expected to be used in an instant, whether the data is structured or not.
This would mean that those two particular data types can be used and be stored in their most original form. By doing this, different users are supposed to adapt with the sets and make their own ways to fulfill their own needs.
Good practice in managing the data sets you have is needed in order to reduce a business risk, and no business risk means good business.
The beholder will have a perfect control towards the quality
Before you put data in its predefined model, you would need to do some data standardizing and some thorough cleansing. This kind of thing is used in the old and unused system.
In this modern era, the data often time stand unverified and unchanged (which is pretty simple), which means that it have not been cleansed or standardized when we got it.
Because there are no cleansing and standardization, it means that today’s data management is very ‘free’ (free as in, you can use the data anyway you want to).
This also make a user to be responsible in applying any needed transformations on the data. You can use it easily, all with different purposes by different people.
That of course, is applicable only if the user’s transformations does not conflict with other transformations.
Because of this, a specific method is needed in order to do some managing on the the data transformations and ensuring that the transformation does not conflict with each other.
This particular type of data management should have couple of ways to help catch transformations from users and to help ensure that the transformations are not absurd in any way.
Try to understand the architecture to have an improved working condition
Platform for big data should have a good thing going on for them, because you never know when the big data may act incoherently.
You may be surprised by how slow the program response if you decided to stay unknowledgeable with the details of any data management programs.
For example, one program may want the big numbers of the distributed data to be broadcasted to all working computers, resulting in a large amount of data injection to the net and it will bottleneck the performance.
By knowing all things about big data architectures, you will be able to create a data application that is more or less acceptable to the mass.
Now is the time of streaming world Managing Big Data
Before this time, static data repositories was used to store data that are not very popular with the mass (what I am talking about is analytical data, which is pretty boring to look at).
The streaming data today is full of resources, making it easy for you to gather data.
Data stream from your so called social media, television channels, online articles, or any text in the internet are the example of human-generated contents.
While the example of machine generated contents are coming from many sensors, tools, devices, and lots of machines that are connected to the internet.
Web event logs is the example of a streaming content that is generated automatically. All of these automatically generated streamed contents of course, will give you lots and lots of data, or should I say too much data are streamed in today’s data management.
These overflowing numbers of data are the main course for the analytical minds.
This is the main talking point and is the biggest issue in this modern age.
All big data managers (and I mean ALL, not only a few) should include technology that can support stream blocker (or perhaps a filtering system) because there are a lot of data streamed in internet.
Scanning, filtering, and selecting the right and useful data to ‘catch’, important data streams should be a norm in every program that is made to manage data such as these.
Taking care of big data is not an easy task for every person in this world because it is not only about data modelling and architecture, but also needs to entail a new kinds of technological invention and processing to made it easier for user to access data and to use them.
Programs that you use to do this should have tools that can discover data, that can prepare the data for the ‘cooking’, an accessible data that is pretty much self-serving, a very own data standardizing process (and self data-cleansing), and some sort of a stream filter that could filter data.
By having this, the time needed to process big data should pretty much faster.
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