Introduction
Big data has progressed substantially in the recent past and has enabled effective insights into large data sets, thereby leading to precise decision-making. In simple terms, big data is the process of deriving insights from structured and unstructured data sets that are extracted from various sources. It is very difficult for the traditional systems to handle such large volumes of data. This is where the concept of a data lake steps in. The data lake architecture has been designed in such a way so as to create a repository of various data sets that are derived from various sources. The advantage of pooling and analysing such large data sets under a common platform is the accuracy in processing and insights that become available to us. The cost-effectiveness of processing even large quantities of data distinguishes a data lake from traditional warehouses.
Deeper insights
To improve processing systems, data scientists across the world are trying different architectural combinations. Of late, data scientists have leveraged the power of graph mining and machine learning for business analytics and related forecasting. This has led to a strong juxtaposition between the erstwhile technological systems and the emerging business outlook. Businesses in the present time are looking to innovate with technological systems. Businesses that are equipped with modern architecture to derive insights are likely to leave behind their competitors by a large margin. Credit also goes to various techniques of business intelligence like query language and visualisation dashboards. Such tools have taken the idea of business analytics to a different level.
Data processing resources
In this section, we discuss the data processing resources of a data lake. With the help of discrete technological units, a data lake is used for storage and processing functions that the erstwhile systems lack. Data lake can also be used to store large amounts of cold data. This is the type of data that is used less frequently and needs to be stored for a longer amount of time without much processing. There is another type of data which is used for fast streaming and intensive workloads. This is the type of data that should be stored in the rapidly accessible memory for processing purposes. Other adjacent resources are also dependent upon this type of data for the purpose of processing. This type of data which is streamlined across various resources is also vulnerable to threats. As such, the authentication and encryption of such data is a critical component of the underlying security infrastructure.
We also make use of databases as a critical element to organise data more concisely. Various types of machine learning techniques and AI models are used to query and analyse such data sets. Then comes the level of data visualisation which is the final stage of processing and gives results with the help of various applications and dashboards.
Concluding remarks
In the future, the architectural elements of a data lake may undergo rapid advances due to their juxtaposition with artificial intelligence and machine learning models.