Data engineering process

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• Data Engineering solutions

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• Table of Content

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• What is data engineering?

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• Why is data engineering important?

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• Responsibilities in data engineering

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• Clairvoyant's Data Engineering solutions

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• What is data engineering?

Data engineering refers to creating and designing
a data pipeline for data scientists or data
analysts to use this information. Data
engineering process is the transformation of raw,
unstructured data into a more user-friendly
format that makes it easier to create a framework
to use.
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• Why is data engineering important?

Most companies have experienced digital
transformation over the last decade. This has
generated an enormous amount of new types of data
and, more often, much more complex data. Earlier
it was clear that data scientists need to
understand everything, but someone needs to
organize and ensure the quality, security, and
availability of their data so that they can get
the job done was not so obvious. That is why need
data engineering.
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• Responsibilities in data engineering

• Design Architecture Develop, test, and maintain
  large-scale architectures. Database. Make sure
  your architecture supports your business needs. 
• Explore how to collect data. Examine the data to
  find hidden patterns. 
• Development of dataset processes for data
  modeling, manufacturing, and mining. 
• Recommendations for improving the reliability,
  efficiency, and quality of your data. 
• Use of various data engineering analytics
  programs, machine learning, and statistical
  methods to prepare data for modeling. 

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• Clairvoyant's Data Engineering solutions

• Effectively integrates the data engineering
  process and the system of data engineering tools
  to provide real-time access to the business area
  while ensuring the accuracy and security of the
  data engineering solution. We provide following
  data engineering services
• Data strategy
• Data security
• Data platform automation

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• Thank you!

For your data engineering solutions, contact us
at clairvoyant here.