Azure Data Engineering

  Manage Schema Drift in Azure Data Factory Azure Data Factory (ADF) offers robust tools and techniques to efficiently manage schema drift, a common challenge that arises when the structure of incoming data changes over time, such as the addition, deletion, or renaming of columns without prior notice. If not properly handled, schema drift can disrupt data pipelines and lead to inconsistencies. ADF ensures flexibility and resilience in your ETL and ELT workflows, making it easier to adapt to evolving data schemas. Manage Schema Drift in Azure Data Factory What is Schema Drift? Schema drift refers to the unanticipated changes in the schema of the source data. For example: Azure Data Engineer Course Online ·          A new column is added to the source table. ·          An existing column is removed or renamed. ·          Data types of columns are alter...

  Encryption Methods Supported in Azure Data Lake Storage Azure Data Lake Storage (ADLS) , Microsoft's enterprise-grade cloud storage solution, provides robust encryption features to protect data both at rest and in transit. These features ensure compliance, data integrity, and confidentiality for sensitive information. In this article, we explore the encryption methods in Azure Data Lake Storage , highlighting the key technologies and options available for securing your data. When it comes to storing massive volumes of data in the cloud, security is a top concern for organizations. Encryption Methods Supported in Azure Data Lake Storage 1. Encryption at Rest Encryption at rest protects your data when it is stored on disk. Azure Data Lake Storage supports multiple layers of encryption at rest, all enabled by default. a. Microsoft-Managed Keys (MMK) By default, Azure encrypts your data using Microsoft-managed keys . This method uses the AES 256-bit encryption algorithm ...

  Best Practices for Organizing Data in Azure Data Lake Introduction Azure Data Lake provides a powerful and scalable solution for storing large volumes of structured and unstructured data. However, to truly benefit from its capabilities, it is essential to organize data effectively. Poor organization can lead to inefficient data access, governance issues, and increased complexity. This article outlines the best practices for organizing data in Azure Data Lake , helping you create a clean, manageable, and high-performing data environment. Best Practices for Organizing Data in Azure Data Lake 1. Adopt a Consistent Folder Structure One of the foundational practices for managing data in Azure Data Lake is implementing a consistent folder structure. Organizing data into logical zones—such as raw, staged, and curated—makes it easier to manage data workflows and maintain data lineage. It also simplifies automation, security implementation, and auditing processes. Azure Data Eng...

  Understanding Data Partitioning in Azure and Its Benefits Data partitioning is a fundamental concept in modern data engineering that involves dividing large datasets into smaller, more manageable subsets, or partitions. This practice is commonly used to improve performance, optimize resource usage, and enhance scalability in large-scale data storage and processing systems. In Azure, partitioning is particularly critical when working with data lakes, data warehouses, and big data processing systems, as it can dramatically improve query performance, reduce costs, and streamline the data processing lifecycle. Understanding Data Partitioning in Azure and Its Benefits What is Data Partitioning? Data partitioning refers to the process of breaking up large datasets into smaller, discrete chunks, or partitions, which are typically based on some predefined criteria. The partitioning logic can be based on various factors, such as time, geographic region, or specific business attribu...