Build The Data Driven Organization With The Help Of Data Engineering

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Build The Data Driven Organization With The Help
Of Data Engineering
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What Is Data Engineering

• The "engineering" part is the key to
  understanding what data engineering is. Engineers
  create and construct things.
• "Data" engineers create and construct pipelines
  that transform and transfer data into a format
  that is highly usable by end users.
• These pipelines must collect data from a variety
  of sources and store it in a single warehouse
  that should represents it as a single source.
• Business industries produce a large amount of
  data everyday. hence with the help of data
  engineering, we can make use of the data to
  predict the future growth of the business.

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What Is The Role Of Data Engineering

• The role of a data engineer may vary depending on
  the business or organization. However, the roles
  of a data engineer involves
• Building data pipelines
• Work on data architecture
• Collects data
• Analyze the data
• Conducts research
• Creates models and identify patterns
• Automate Tasks
• Understanding the pros and cons of data storage
• Improve skills

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Data Engineering Driven Organization

• To build a data driven organization, data
  engineers are considered as the intersection
  between data owners and data consumers, with
  responsibilities
• Data transportation, data enrichment, and data
  integration between analytical and operational
  systems .
• Analyzing and transforming unstructured data from
  business into clean and meaningful data.
• DataOps and business functional knowledge
  combined with software engineering approaches, is
  being used to the data lifecycle.
• Models and other tools for evaluating or
  consuming data are deployed.

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Data Engineering Challenges

• As the data industry grow with new technology, so
  do data engineering challenges. Some of the
  challenges of data engineering are listed below.
• Data engineers must be self-taught
• Too Much Data To Handle
• Poor Performance
• Data Isn't Accessible
• Maintenance of the Data Pipeline
• Lack of data governance
• The human factor
• Unclear Strategy
• Resistance To Change

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