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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… Read More ⇢
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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. Read More ⇢
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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… Read More ⇢
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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. Read More ⇢
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Start Your Data Engineering Journey (2025)
Start your data engineering career in 2025 with this comprehensive beginner’s guide. Learn essential skills, tools, and proven steps to land your first job fast. Read More ⇢
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Learn Databricks SQL: From Table Creation to Data Validation
Managing data using Databricks SQL. It includes the creation of Users and Orders tables, data insertion, various updating techniques, and validation of these updates. Additionally, it discusses the use of Delta tables for change tracking. These methods maintain data integrity throughout the entire workflow. Read More ⇢
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Steps to Insert Modified Rows Keeping Orignal Data Intact : Databricks SQL Simplified
The content outlines a three-step process using Databricks SQL and PySpark to update employee salary records. It involves creating target and lookup tables, inserting data, and forming a temporary table to hold modified rows. Finally, it implements a script to dynamically insert only the updated columns back into the target… Read More ⇢
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Complete Guide to MERGE INTO in Databricks
This content outlines a MERGE INTO example in Databricks SQL for updating a target table (employees_target) using a lookup table (employees_lookup) with updated employee details. It details steps for table creation, data insertion, and the merge operation, resulting in updated salaries for Alice and Charlie, and the addition of a… Read More ⇢
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Understanding Databricks vs Traditional Databases
Databricks is not a database; it’s a unified analytics platform built on Apache Spark for data engineering, analytics, and machine learning. It supports diverse workloads like ETL and real-time analytics while integrating with various databases. Unlike a traditional database, Databricks uses Delta Lake for efficient data storage and analysis. Read More ⇢
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