This layer Provide connectors to extract data from a variety of data sources and load it into the lake.
Data storage is one of the key components of a Data Lake architecture.A well-architected storage layer should.
Infrastructure and Operations Management. Provisioning, Managing, Monitoring and scheduling your Hadoop clusters.
After the ingestion of large data collections, data understanding stage is of paramount importance before one can start preparing data or analysis. Tagging (i.e., metadata tagging) is used to express the data understanding, through organizing, identifying, and interpreting the raw data ingested in the lake.
The first step for data exploration is dataset discovery. Identification of the right dataset to work with is essential before one starts exploring it.
Analysed results are plotted in form of pie chart, histograms, bar graphs and presented to business so that they can take plan their strategies and take future decision accordingly.
Data Lake creates a unified customer database. A consistent identifier links all the data together and supports marketing segmentation and exploration for personalized marketing efforts. Business intelligence tools allow marketing teams to leverage the data in the data lake in order to blend sources of data together and perform segmentation analytics that can then be shared with cloud-based marketing automation providers.
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