👇25 blogs to guide you through every important concept 👇1. Data Lake vs Data Warehouse→ https://lnkd.in/gEpmTyMS2. Delta Lake Architecture→ https://lnkd.in/gk5x5uqR3. Medallion Architecture→ https://lnkd.in/gmyMpVpT4. ETL vs ELT→ https://lnkd.in/gvg3hgqe5. Apache Airflow Basics→ https://lnkd.in/gGwkvCXd6. DAG Design Patterns→ https://lnkd.in/gHTKQWyR7. dbt Core Explained→ https://lnkd.in/g5mQi8-y8. Incremental Models in dbt→ https://lnkd.in/gS25HCez9. Spark Transformations vs Actions→ https://lnkd.in/g2RRCGMW10. Partitioning in Spark→ https://lnkd.in/g5fXjSJD11. Window Functions... Continue Reading →
Big Data Engineering Interview series-1
**Top Big Data Interview Questions (2024) - Detailed Answers**1. **What is Hadoop and how does it work?** Hadoop is an open-source framework designed for distributed storage and processing of large datasets across clusters of computers. It consists of two main components: Hadoop Distributed File System (HDFS) for fault-tolerant storage, which splits data into blocks... Continue Reading →
Perfect ETL Pipeline on Azure Cloud
ETL Pipeline Implementation on AzureThis document outlines the creation of an end-to-end ETL pipeline on Microsoft Azure, utilizing Azure Data Factory for orchestration, Azure Databricks for transformation, Azure Data Lake Storage Gen2 for storage, Azure Synapse Analytics for data warehousing, and Power BI for visualization. The pipeline is designed to be scalable, secure, and efficient,... Continue Reading →
How to find the bottleneck in azure data factory pipeline having databricks notebook too. It has multiple types of sources. What are the steps to follow?
To identify bottlenecks in an Azure Data Factory (ADF) pipeline that includes Databricks notebooks and multiple types of sources, you need to systematically monitor, analyze, and optimize the pipeline's components. Bottlenecks can arise from data ingestion, transformation logic, Databricks cluster performance, or pipeline orchestration. Below are the steps to diagnose and address bottlenecks, tailored to... Continue Reading →
Hadoop vs. Spark
Comparison table between Hadoop and Spark: FeatureHadoopSparkCore ComponentsHDFS (Hadoop Distributed File System): A distributed storage system for storing large datasets.MapReduce: A computational model for parallel data processing, operating in a series of map and reduce steps.RDD (Resilient Distributed Datasets): A fault-tolerant collection of elements distributed across a cluster.Spark Core: The core processing engine that provides... Continue Reading →
CODEBOX 