We stand at the cusp of a technological revolution that is completely data driven. The functionality of different systems and processes is dependent upon the way we process data and handle it from the stage of ingestion to execution.

1. Architectural
Aspects And
Design Hypothesis
Of The Data
Ingestion Pipeline

2. Introduction
We stand at the cusp of a technological revolution that is completely data driven.
The functionality of different systems and processes is dependent upon the way
we process data and handle it from the stage of ingestion to execution. The data
ingestion pipeline involves various stages ranging from data collection to data
analytics. The data pipeline operates upon raw data from different platforms and
databases and turns it into useful information with the help of powerful business
intelligence tools.

3. Architectural Aspects
● The architectural aspects of a data pipeline are fabricated in such a manner that the
cleansing and transformation of data becomes as simple as possible.
● We need to extract data from warehouses and data lakes and put it into useful and crisp
facts that can be converted as informatics. This informatics further becomes the base of
knowledge engineering systems.
● One of the unique features of a data pipeline is the speed through which it processes
data. This is primarily dependent upon three critical factors.
● The first is called the throughput rate which defines the amount of data that can be
processed in a given amount of time.
● The second is called data reliability which ensures that an effective validation mechanism
is established in the data pipeline to maintain high data quality.
● The third important factor is latency. In order to ensure that the response rate is high and
volume of data processed is large, it is pertinent to ensure low-latency. Low latency means
that the delay in the processing time should be as minimal as possible.

4. The Design Hypotheses
● There are a large number of ways in which we can design a data pipeline. We mention the
important stage through which we can layer the data pipeline architecture.
● The first stage is the stage of data extraction and involves mining of data across data
warehouses and data lakes. It is at this stage that we validate data sets and ensure quality
control.
● The next stage is the ingestion stage. It is at this stage that we read data from data sources
with the help of an application programming interface. We also follow the process of
extracting data sets of choice with help of data profiling. We examine the various
characteristics of data and evaluate it from a business point of view.
● We now move to the stage of data transformation. This stage involves a lot of filters
through which data passes and yields a qualitative output. This qualitative output can then
be utilized for analytics processes and business intelligence.
● After all the stages have been completed, it is important to monitor data on the basis of
various parameters and fix various issues that may arise. For this purpose, data quality
engineers are employed to keep a constant vigil of the data pipeline.

5. Concluding remarks
The architectural pathways of a data pipeline may be diverse but follow a certain hierarchy
of steps. Right from the process of ingestion to the process of analytics, the aim is to come
up with state-of-the-art analytics which can help in transformative business intelligence.