Azure Data Engineer Training | Data Engineer Training Hyderabad

1. Azure Databricks Introduction & Key features and components +91-9989971070 www.visualpath.in

2. Azure Databricks Introduction • Azure Databricks is a cloud-based big data analytics platform that combines the power of Apache Spark with Azure's cloud services. It is a fast, easy, and collaborative Apache Spark-based analytics platform designed for data science and data engineering. Azure Databricks enables organizations to process and analyze large volumes of data, build machine learning models, and derive actionable insights in a collaborative and scalable environment. www.visualpath.in

3. Key features and components of Azure Databricks: • Apache Spark Integration: • Azure Databricks is built on top of Apache Spark, an open-source, distributed computing framework. It leverages the parallel processing capabilities of Spark for fast and scalable data processing. • Unified Analytics Platform: • Azure Databricks provides a unified platform for data engineering, data science, and machine learning. This integration allows seamless collaboration between data engineers, data scientists, and analysts. www.visualpath.in

4. Collaborative Environment: • Databricks provides a collaborative workspace where data professionals can work together on shared notebooks. Notebooks support multiple programming languages, including Scala, Python, R, and SQL, making them versatile for different tasks. • Scalable Data Processing: • Leveraging the distributed computing capabilities of Spark, Azure Databricks can scale horizontally to handle large datasets and perform complex analytics tasks efficiently. www.visualpath.in

5. Delta Lake: • Delta Lake is a key component of Azure Databricks, providing ACID transactions on Apache Spark and enabling reliable and scalable data lake capabilities. It adds features like schema enforcement and data versioning to data lakes. • Data Engineering: • Azure Databricks supports data engineering tasks such as data preparation, cleansing, and transformation. It can be used to build ETL (Extract, Transform, Load) pipelines for processing and moving data. www.visualpath.in

6. Machine Learning (MLlib): • Databricks include MLlib, a scalable machine learning library for Spark. Data scientists can use MLlib to build and train machine learning models at scale using distributed computing. • Deep Learning: • Azure Databricks supports deep learning frameworks, including TensorFlow and PyTorch. This allows data scientists to build and train deep learning models for advanced analytics tasks. www.visualpath.in

7. Integration with Azure Services: • Databricks integrates seamlessly with various Azure services, such as Azure Storage, Azure SQL Data Warehouse, Azure Data Lake Storage, and Azure Active Directory. This facilitates data integration and access to a broader set of Azure capabilities. • Security and Compliance: • Azure Databricks includes security features such as Azure Active Directory integration, fine-grained access controls, and encryption at rest. It is designed to meet various compliance standards. www.visualpath.in

8. Job Scheduling and Automation: Users can schedule and automate jobs within Databricks for recurring data processing tasks. This supports the automation of ETL processes and other data workflows. Azure Databricks is widely used for data exploration, data science, machine learning, and big data analytics in various industries. Its collaborative and integrated approach to data processing makes it a powerful tool for organizations looking to derive valuable insights from their data in a scalable and efficient manner. www.visualpath.in

9. CONTACT For More Information About Azure Data Engineer Training Address:- Flat no: 205, 2nd Floor NilagiriBlock, Aditya Enclave, Ameerpet, Hyderabad-16 Ph No: +91-9989971070 Visit: www.visualpath.in E-Mail: online@visualpath.in

10. THANK YOU Visit: www.visualpath.in