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Data lakes and log analytics: A historical sketch and future scop.

Data-Lake-Architecture

The scope of log analytics is continuously increasing leading to the expansion of the cloud network. As such, the use cases and applications related to log analytics are witnessing a huge surge. Be it the application domain of customer analytics or network security, log analytics is finding its inroads into such domains. As the growth of data continues at astronomical rates, different types of companies are finding it difficult to store and access data in a secured manner. This is where the role of log analytics steps in. Log analytics can not only utilized to collect and store data. But it can also used to index and analyze it for deriving insights in a periodical format.

The existing data warehouse solutions

As a number of organizations are scaling up their operations. This is leading to the generation of volumes of event logs over a period of time. The present data lake architecture is such that it would able to store and simultaneously process vast volumes of data at a single instance. In addition to this, the expansion of data lake architecture would be less expensive and demanding from the operational point of view. Hence, the conjunction of data lake architecture and log analytics is prospective for both the organization and processing of data in a structured way.

A historical sketch

It was in the year 2010 that Big Data Analytics was carried out with the help of data warehouses but the architecture associate with this process was very extensive. It was around this period the data marts were becoming slowly popular. The disadvantage associated with data mart was its lack of integration across various departments in a single sector. This not only stalled the process of innovation. But also prevented data scientists from deriving useful insights from raw data sets. The emergence of the data lake did away with these shortcomings and integrated the process of data organization. A Data Lake could store huge data sets that were sourced from different sectors in a schematic manner. In this way, the data which sourced across various sectors could organized into structured, semi-structured, and unstructured categories.

Architectural components and functions

Data Lake architecture is primary divided into five components. The first component is the data source from which applications generate data. The second component is the injection layer which acts as a bridge between the data source and the storage layer. The third component is the storage layer which has tremendous backing capacities for storage purposes. Next in line is the index layer that performs the function of cleansing and preparation of data while simultaneously sending it to the client layer. The client layer is responsible for visualization and is use for the derivation of primary insights.

Looking at Log Analytics

With the expansion of log data, companies and now starting to explore various options related to log analytics. This is not only enabling them to index data but also analyze it at a large scale. With the aid of log analytics, companies can not only incorporate gigantic volumes of event logs but can also process them with unlimited capacities. This processed data can then utilized to derive insights and visualization at a later stage. Data lakes are also a natural fit for log analytics. Because such platforms reduce the management complexities and ensure smooth functioning at an optimized level.

Reimagining the data lake platform

The platform needs to such that can cater to various requirements of organizations like the identification and normalization of data at a large scale. It should able to compress data in a desirable format and retrieve it whenever and wherever necessary. It should able to perform operations like data indexing and querying without any latency. Finally, it should not only act as a repository of information. But should also come out as a platform with limitless processing power.

Concluding remarks

The approach discussed above is not the only approach that is use for data lake architecture. In fact, the other two approaches that are common for data lake architecture include the template approach and the Lakehouse approach. The lake house approach known for the integration of features of a warehouse giving it a renewed architecture that has name the lake house. That said, we may see the development of novel kinds of architecture in this domain that can take log analytics to a different level of processing.

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