In the It world, when freshers the ask the delegate the Facebook, Google owners where the millions of data get stored? The answer is a data lake. So what is this data lake contain? Why do companies maintain data lakes? The reason is simple it has great features when compared with Data warehouse. Because all the data that was stored in the data warehouse is a structured data? But in today’s exponential data generation world, we cannot expect the data in a homogeneous format. So data warehouse is not applicable in such a situation. So the other alternative that we have is the data lake. And today people would not think about the money that was spending on the data storage. Moreover, they were in a situation like, they want the required data as per their requirement.
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And they would like to store every session the user performed. And they did not even care whether it has a use to store the data (or) not? So we can expect the amount of data that was being generated day-to-day. Moreover, today client was in such a way that after the running the business for 10 years, they would like to know the insight of first week / first month / the first year. So getting this data is a typical task. And after many meeting data scientist finally, conclude that Data lake is the best alternative. Moreover, we can say this is one of the reason that Why do companies maintain data lakes?
In simple words, data lake is defined as a vast raw unstructured data in a native format.
Why do companies maintain Data lakes?
So in order to use the data lake all, you need to use that supports the flat file system. It means, we can use the framework, if we want. And the data is moved to other systems for processing. Moreover, most of the enterprises go with Hadoop Distributed File System. This is done because this file system supports a large data processing at high speed. Moreover, unlike RDBMS this HDFS is a parallel processing system. All the data that was arrived will split to small chunks. And all the chunks were processed parallelly. This parallel processing is done through mappers and red-cures. And the pattern of data lakes is shown below
And as explained above, we cannot expect which data the client asks. Moreover we cannot even predict. at what time, the person asks. So they recommend that data lake is the best place to store the unpredictable data.
So I hope you people were clear regarding why do companies maintain data lakes. And now, I would like to move to data lakes Vs Data warehouse
Data lakes VS Data Warehouses :
In practical both data lakes and data warehouses were similar. But there are some minor differences. So now let us have a look over there.
Storage type and processing
The major difference that we could see here the type of data being stored and the way of processing the data. Basically, the data warehouse is a structured data. Whereas a data warehouse is an unstructured data
The data warehouse requires a special hardware to store. Whereas data lakes does not require the special hardware to store and process the data. Since it reduces the financial burden, people prefer data lakes.
As said above, data lakes deal with unstructured data, it gives more flexibility for data storage and maintenance. Moreover, data lakes have a great scope for data analyzing when compared with the data warehouses. So likewise, there are many data lake uses cases.
So due to its greatest advantages, data scientist prefer databases over data warehouses. And get the latest news and features available on Big data Hadoop from the real-time experts of OnlineITGuru through Big data Hadoop online course bangalore.
In order to start learning Big Data has no prior requirement to have knowledge on any technology required to learn Big Data Hadoop and also need to have some basic knowledge of java concept. It’s good to have a knowledge of Oops concepts and Linux Commands