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

1. Azure Data Engineering? Data Lakes vs. Data Warehouses +91-9989971070 www.visualpath.in

2. 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 architecture—Data Lakes and Data Warehouses—serve distinct purposes but are often misunderstood. www.visualpath.in

3. 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. www.visualpath.in

4. 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. www.visualpath.in

5. 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. www.visualpath.in

6. Characteristics: • Storage Flexibility: Data Lakes can store data in any format—structured, 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. www.visualpath.in

7. 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. www.visualpath.in

8. 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. www.visualpath.in

9. Key Differences Between Data Lakes and Data Warehouses • Data Format • Purpose • Schema • Performance • Cost www.visualpath.in

10. 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. www.visualpath.in

11. 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@visualpath.in

12. THANK YOU www. visualpath.in