A Data Lake can be defined as a storage archive that holds an enormous amount of raw data by the time it is required for use. A conventional data warehouse stores data in mode of files and folders, whereas a Data Lake, adopts a distinct way of using a flat architecture to store data. These advancements have also helped in updating the speed of recovering data from the archive, which has led to the realization of global Data Lake Software.
The approach of Data Lake also differs from typical data warehouse which transforms the processes of data at ingestion. On the other side, data is never eliminated because the Data Lake stores data in its raw form. It is usually a single backlog of complete enterprise data consisting of source system data and transformed data. This data is utilized for various purposes, such as reporting, analysis, and visualization.
The Need for a Data Lake
Data Lake is needed to lower down the costs of storage and its capability of storing multiple data forms. It can analyze various data types with advanced capacity. The data evaluation and deployment can help to lessen the risk of future data management.
According to statistics, around 2.5 quintillion bytes of data are being created every day. In the last two years, 90% of the whole data was generated across the world. This reflects the enormous amount of data produced each day globally. The stat mentioned here asserts the need for such a source which can store this massive amount of data.
Importance of Data Lake for Research
The importance of Data Lake technology has increased with the need to manage the gigantic data sets as it was significant to acknowledge for research purpose. Below some essential benefits of Data Lake are briefly discussed which are valuable for researching.
1. All-around Availability of Data
The fundamental aspect of Data Lake is that it assures the access of data to every user regardless of their role in the process of research. This phenomenon is termed as data democratization. For instance, usually, only the head of the research department has the right to access all sorts of data from the storage to make performance reports for record purpose. Whereas by using Data Lake, the required data can be reached at all levels, such as research analysts or record keepers.
Let’s consider an example of a research center; the team is researching new ways of making solar panels to create more energy. The data compiled after conducting various studies and compiling numerous statistics are stored in a Data lake. Now, this data can be seen and analyzed by all the team members if they need to update or amend any segment of it.
2. Coincides All Data Sources and Languages
As Data Lake stores data in its raw format, it can transform the multi-structured data from distant channels. It has a remarkable capability to accumulate logs, XML, sensor data, social data, chats, multimedia, and data generated by people.
Conventional data warehouse technology supports SQL for analytics. In need for advance searches, more alternates are required to evaluate data. Therefore, Data Lake provides diverse language options for data analysis. It supports Hive/Hawq/Impala. Besides, it offers features for advance requirements.
3. Real-time Analysis and High-speed Data
“Data lakes leverage the immense amount of consistent data and in-depth research algorithms to drive real-time analysis and evaluation of information,” an extract from a piece of writing by research paper service. Apart from this, it entertains high-speed data which acquire and queue various tools. With the support of this high speed, the data can be integrated with the archive to get a comprehensive insight into the scope of research.
4. Scalable and Versatility
Data Lakes support scalability that is more cost-effective than a conventional data warehouse. It has the ability of a data network, systems, or means to operate the increasing amount of data. It also carries the potential to expand for accommodating the data hike. On the other side, it can keep a backlog of structured and unstructured data segments in parallel for various channels. This benefit of versatility lacked in the conventional warehouse storing which limited its usage in recent times.
5. The Schema and Its Flexibility
The traditional data warehouses were unable to support schema-less data storage. Whereas, the data lake can be beneficial to store data emphasized on schema-less write along with schema-based read mode. This aspect is highly significant when the researcher requires data consumption.
The data must be provided in a structured format for traditional data warehouses as they are scheme based. The adverse aspect of this approach was that at time of analytics, data must be provided in raw form. With the advent of Data Lake, the storage is not scheme free so that researches may remain intact with different schemas simultaneously. Thus, it supports to segregate data from a schema, which make analytics much more accessible and effortless for researchers.
In recent years, researches are using data to define their internal objectives and matrices. This data needs to be measured and managed to get the tasks done smoothly, giving the intended outcome. Data Lake offers these services as it utilizes the raw data and optimizes it for the analysis of data evaluation. The potential of Data Lakes is not yet explored thoroughly. Still, researches can get the maximum benefit of this innovative approach of data storage that can benefit from conducting comprehensive researches.