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    <h1>Integration with Big Data Ecosystem</h1>
    <p>This tutorial explores Apache Spark's integration with the broader big data ecosystem.</p>
    <h2>Hadoop Integration</h2>
    <h3>Running Spark on Hadoop YARN</h3>
    <p>Spark can run on Hadoop YARN for resource management.</p>
    <h2>Working with Hive</h2>
    <h3>Spark SQL and Hive Metastore</h3>
    <p>Spark SQL can interact with the Hive metastore to access Hive tables.</p>
    <h2>Kafka Connectivity</h2>
    <h3>Reading Data from Kafka</h3>
    <p>Spark Streaming and Structured Streaming can read data from Kafka.</p>
    <h2>Delta Lake and Data Lakehouse Architecture</h2>
    <h3>Building a Data Lakehouse with Delta Lake</h3>
    <p>Spark can be used with Delta Lake to build a data lakehouse.</p>
    <h2>Integration with BI Tools</h2>
    <h3>Connecting Spark to BI Tools</h3>
    <p>Spark can be connected to BI tools for data visualization and analysis.</p>
    <h2>Airflow for Orchestration</h2>
    <h3>Orchestrating Spark Workflows with Airflow</h3>
    <p>Airflow can be</p>
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Integration with Big Data Ecosystem

Introduction

Installation

Architecture

Execution Modes

Spark Submit Command

Spark Core: RDD

DataFrames and Datasets

Data Sources and Formats

Spark SQL

Spark Structured Streaming

Spark Unstructured Streaming

Performance Tuning

Machine Learning with MLlib

Graph Processing with GraphX

Advanced Spark Concepts

Deployment and Production

Real-world Applications

Best Practices and Design Patterns

Hands-on Projects

Integration with Big Data Ecosystem

Integration with Big Data Ecosystem

Hadoop Integration

Running Spark on Hadoop YARN

Working with Hive

Spark SQL and Hive Metastore

Kafka Connectivity

Reading Data from Kafka

Delta Lake and Data Lakehouse Architecture

Building a Data Lakehouse with Delta Lake

Integration with BI Tools

Connecting Spark to BI Tools

Airflow for Orchestration

Orchestrating Spark Workflows with Airflow

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