Microsoft Azure Data Engineer Training | Azure Data Engineer Course in Hyderabad

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Azure Data Engineering? Data Lakes vs. Data
Warehouses
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Introduction

• Azure Data Engineering is crucial in managing the
  vast amounts of data generated in today's digital
  world.
• Data engineers are responsible for designing and
  implementing data management frameworks that
  facilitate storage, processing, and accessibility
  of large datasets.
• Two fundamental components of data
  architectureData Lakes and Data Warehousesserve
  distinct purposes but are often misunderstood.

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What is Azure Data Engineering?

• Azure Data Engineering encompasses various
  services and tools provided by Microsoft Azure
  for building and maintaining data pipelines,
  storage systems, and analytics platforms.
• Data engineers work to ensure that data is
  collected, stored, processed, and made available
  for analysis or operational use efficiently and
  securely.

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Key Azure services include

• Azure Data Factory Used for orchestrating data
  workflows and integrating data from various
  sources.
• Azure Synapse Analytics A powerful analytics
  service that combines data warehousing and big
  data analytics.
• Azure Databricks A collaborative platform that
  enables scalable data processing using Apache
  Spark.

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Data Lakes
Definition A Data Lake is a centralized
repository designed to store large volumes of
raw, unprocessed data, irrespective of format or
source. This includes structured,
semi-structured, and unstructured data such as
text, video, and social media posts.
Purpose Data Lakes are designed for high-volume
data storage and are typically used for big data
analytics, machine learning, and data
exploration.
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Characteristics

• Storage Flexibility Data Lakes can store data in
  any formatstructured, semi-structured, and
  unstructured.
• Schema on Read Data schema is applied only when
  data is read, allowing flexibility in how the
  data is interpreted and processed.
• Scalability Data Lakes are designed to handle
  petabytes or even exabytes of data, making them
  ideal for big data applications.

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Data Warehouses
Definition A Data Warehouse is a structured and
optimized repository for storing processed and
cleaned data, typically from multiple sources.
Data warehouses are designed to support reporting
and analysis by organizing data in a highly
structured way. Purpose Data Warehouses are used
for querying and generating business reports and
visualizations, focusing on historical data,
trends, and aggregations.
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Characteristics

• Structured Data Data Warehouses are designed to
  store structured, processed data that has been
  cleaned and transformed.
• Schema on Write Data schema is defined before
  data is stored, ensuring consistency and
  reliability in queries and reporting.
• Performance Optimization Data Warehouses are
  optimized for complex queries and data analysis,
  often delivering faster results for aggregated
  and structured data.

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Key Differences Between Data Lakes and Data
Warehouses

• Data Format
• Purpose
• Schema
• Performance
• Cost

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Conclusion

• Azure Data Engineering is essential for building
  scalable data platforms that support both
  real-time analytics and business intelligence.
• Data Lakes and Data Warehouses serve distinct but
  complementary roles within an organization's data
  architecture.
• While Data Lakes are well-suited for raw,
  large-scale data storage and exploration, Data
  Warehouses are optimized for structured,
  processed data used in reporting and
  decision-making.

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CONTACT
For More Information About Microsoft Azure Data
Engineer Training Address- Flat no 205, 2nd
Floor, Nilgiri
Block, Aditya Enclave,
Ameerpet, Hyderabad-16 Ph No 91-9989971070
Visit www.visualpath.in E-Mail
online_at_visualpath.in
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THANK YOU
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