Data Consolidation and tail spend analytics

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Advertisement Description:

After operational data is created and gathered from various sources and formats, it must be combined, cleaned, and checked for faults before being stored in a data warehouse or data lake. Business executives prefer “Hand coding” by data engineers for small datasets from few sources or “ETL Tools” for huge datasets from many sources. In both cases, data tables from different sources are connected by a schema, which helps standardise and address meta data problems of data consolidation like:

data may not have all the required columns.
data has more columns than required
data types of columns may not match across datasets.
columns may not be in the same order across datasets.
data rows to be removed as its not relevant to the data analysis.
Even data consolidation is a lengthy, time-consuming task with resources like limited, data spread across multiple locations, and challenges like security issues; however, the simple, straightforward, and ready-to-use approaches outlined here will assist the process, saving costs and improving the efficiency of business decisions.

Listing ID: 459616532580f70a

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