A “Data Lake” is a storage space where an organization can store data. It is a central location to keep data safe and secure. Data Lake allows you to store data for the long term in any format you want. The technology is very inexpensive and also you can handle data very easily with it. In modern data, the lake is growing very strongly. It is a kind of way in which users wants to manage their data.
Data Lake is the fastest method to find relevant data for performing operations and analytics. Users always want to store data in the raw form, so that they can use and process it any number of times. Data Lake vendors these days are taking care of all the requirements of the users and trying to modify their systems according to that. Here I have stated some reasons to justify the choice of key players.
1 Quick data access
Late processing and early ingestion are one of the features of a Data Lake. This allows you to integrate your data for operations, analytics, and reporting. Diverse data need diverse ingestion methods to make data structures, interfaces and to scale the real-time latencies. This helps to simplify the onboarding of all new data inputs. This allows for quick access to the data. The user can use their data any number of times and also in its original form. If your organization is handling a customer support center then Data Lake makes it easy for your employees to access data in a fraction of seconds.
2 Data flow control
Without control data flow is impossible to manage. Imagine you are finding something in a pool of many things, then how do you identify which things belong to you? Data Lake can easily convert into a Data swamp that is an organized form of a Data Lake. It ensures that an individual can access data which belongs to him only, so there is very less possibility of fraud and unauthorized data access.
Data in the form of documents can enter in the lake using metadata, business glossary, and information catalog. So, it makes it easy to find, optimize, and govern data. The user and company both know from where data starts flowing and where it is going - like from collection to processing.
3 Keep data in the original form
For the purpose of repeated data usage, detailed sourced data is saved in the storage. Original form of data has great exploration and discovery-oriented analytics that can work amazingly with large samples. It can also work with detailed data and data anomalies. Some users break this rule overtime to implement light data standardization when they require it for customer views, reporting, queries and general data exploration.
4 Enhance data read time
This is a common user practice to combine data exploration & discovery with data preparation and visualization. The metadata is developed at the time of exploration. And it is also easy to model and standardize data at this point. And the amazing thing is all the modifications will imply the copied data and the raw data will remain in the same position and format. Some users also use Data Lake to fly with visualizations and other semantics. It also makes data finding simple because you know the format of data storage and every time you will get your data in the same format without any changes.
5 Capture big data
According to research, it is observed that more than half of the data lakes are made on Hadoop. And rest is made with the combination of Hadoop and machine learning languages. So, many data lakes and big data solutions are invented to handle the big data and the rest is invented to handle the small and medium quantity data. If we see the implementations of data lakes then we can see that Hadoop based data lakes are now being used by engineers in machines, sensors, devices, vehicles, social media, and many other marketing channels. So, with the help of data lakes managing big data became a little easier.
6 Improve data architectures of enterprises
All big enterprises have to handle data in large forms. Handling this data architecture plays an important role. Multiform data storage environment and digital supply chains are the most important reasons to adopt data lakes in enterprises. A data lake is also capable to extend traditional applications, which means you can use modern as well as traditional methods of data handling. Here we can say that a data lake is a kind of source which can let you access the data in traditional as well as in a modern way and can use it in all types of applications.
7 Help data management platforms
Hadoop is known as the data platform which is used by most of the data lakes. IT is a cheap technology to maintain and process data. IT also helps to maintain the linear scalability and power of data processing for analytics.
These days with the introduction of hybrid platforms, data lakes are preferred by so many organizations. These hybrid platforms are made with the combination of Hadoop and rational systems as well as on-cloud systems. They also include many data collections like big data, analytics, warehouses, data lakes, etc. to increase cloud storage.
Most of the enterprises are using traditional data architectures to structure their data. And traditional structures have served well for a long time. But now with the increase in the users, the data has also increased. And traditional structures fail when it comes to handling data of so many people. Data Lakes are most essential for modern data atmosphere and also organizations should be figuring out how to shift from traditional data management structures to modern data management structures. Now it is time to implement Data Lake solutions in your business organizations according to your business needs and you will see how fast you can become a great competition in the industry.
Author Bio:Abhimanyu Sundar is a sportsman, an avid reader with a massive interest in sports. He is passionate about digital marketing and loves discussions about Big Data.
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