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  • 25 Quiz Questions to Test Your Azure Data Factory Knowledge (with Answers)

    Azure Data Factory (ADF) serves for data integration and ETL processes, with components like pipelines, datasets, and linked services. It offers activities to transfer data visually and handle transformations. ADF supports event-based triggers, integration with Git, and allows parameterization, enabling dynamic values in pipelines while providing monitoring functions for executions.

  • Cloning a Bitbucket Repository and Pushing Changes Using Git

    Here are basic git commands useful you need to push the code change to the Bitbucket

  • Databricks Cluster Configuration: A Comprehensive Guide

    Databricks is a cloud-based platform for data engineering and machine learning, utilizing clusters for big data processing. Key configurations include cluster modes, size, instance types, and memory allocation. Best practices emphasize autoscaling, instance selection, and security measures. Proper setup enhances performance, optimizes costs, and supports efficient data analysis.

  • How to Compare All Columns of a Table as of Version 0 and as of Version 1 in Databricks SQL

    This blog post discusses how to compare table versions in Databricks SQL, specifically from version 0 to version 1. It outlines the importance of versioning for data tracking and recovery, provides setup instructions for a Delta table, and demonstrates how to compare differences between versions using SQL queries and a full outer join.

  • Connecting Apache Kafka to Confluent Cloud: Setup & Best Practices

    Apache Kafka is a powerful tool for real-time data processing, enhanced by Confluent’s services. This guide outlines how to connect Kafka Streaming to the Confluent platform, covering setup, installation, configuration, application development, schema management, data publishing, monitoring, and scaling for efficient stream processing.

  • How to Build Efficient Data Pipelines with Delta Live Tables

    The blog post discusses the importance of efficient workflows in data engineering, focusing on Databricks and its Delta Live Tables (DLT) framework. It provides a step-by-step guide for beginners to create a DLT pipeline, covering workspace setup, data source definition, transformation logic, configuration, pipeline execution, and result visualization.

  • A Comprehensive Guide to Databricks Workflow Creation: From Basic to Advanced

    Databricks is a robust platform for big data processing and machine learning, enabling collaboration in a unified workspace. This guide covers creating workflows, from basic notebook tasks to advanced techniques like chaining jobs and using the Jobs API. It aims to enhance data engineering and machine learning pipelines efficiently.

  • Mastering Union in Databricks – Combining Data Efficiently

    Explained union in databricks. You will know how it is different from SQL.

  • Mastering Data Engineering: A Complete Guide to Becoming a Data Architect

    Data Engineering Architects play a vital role in designing scalable and secure data systems. To transition into this role, aspiring architects must master data engineering fundamentals, develop architectural thinking, gain cloud platform experience, learn DevOps practices, stay updated with industry trends, and actively showcase their expertise. Continuous learning is essential for career advancement.

  • AWS S3 Access Control: The Ultimate Guide to Permissions & Security

    To access files in an Amazon S3 bucket, specific IAM permissions are required based on access type. Options include read-only access, write access for uploads, full access for read, write, and delete functions, and permissions for using AWS services. Access can also be restricted to specific folders within the bucket.

  • How to Compare Hashed Columns Before and After a Change in Databricks

    The content explains how to compare old and new MD5 hashed values in Databricks using PySpark SQL after updating the ‘id’ format in a product table. It details creating a sample table, updating hashes, and using Delta Time Travel to check for mismatches, concluding that mismatches are expected due to the new value format.

  • Databricks Time Travel : How to Compare With Previous Versions

    In Databricks with Delta Lake, users can utilize time travel and history features to compare old and new versions of tables post-UPDATE. Steps include creating a table, updating it, describing its history, and performing comparisons on salaries. Key points involve using VERSION AS OF and DESCRIBE HISTORY for data retrieval.