Cloud Data Management and Analysis

5.

Data Processing and Transformation

5.1.

ETL vs. ELT Paradigms

5.1.1.

Extract, Transform, Load (ETL)

5.1.1.1.

Transformation Before Loading

5.1.1.2.

Use Cases and Limitations

5.1.1.3.

Data Quality Enforcement

5.1.1.4.

Processing Overhead

5.1.2.

Extract, Load, Transform (ELT)

5.1.2.1.

Transformation After Loading

5.1.2.2.

Use Cases and Limitations

5.1.2.3.

Leveraging Target System Power

5.1.2.4.

Schema Flexibility

5.1.3.

Modern Data Integration Patterns

5.1.3.1.

Change Data Capture

5.1.3.2.

Event-Driven Architecture

5.1.3.3.

Microservices Integration

5.2.

Batch Processing

5.2.1.

Managed Hadoop and Spark Clusters

5.2.1.1.

Amazon EMR (Elastic MapReduce)

5.2.1.1.1.

Cluster Management

5.2.1.1.2.

Integration with S3

5.2.1.1.3.

5.2.1.1.4.

5.2.1.1.5.

Spot Instance Usage

5.2.1.2.

Azure HDInsight

5.2.1.2.1.

Supported Frameworks

5.2.1.2.2.

5.2.1.2.3.

Enterprise Security Package

5.2.1.2.4.

Integration with Azure Services

5.2.1.3.

Google Cloud Dataproc

5.2.1.3.1.

Cluster Scaling

5.2.1.3.2.

Preemptible Instances

5.2.1.3.3.

Initialization Actions

5.2.1.3.4.

5.2.2.

Serverless Data Processing

5.2.2.1.

5.2.2.1.1.

Data Catalog Integration

5.2.2.1.2.

5.2.2.1.3.

Development Endpoints

5.2.2.1.4.

Crawlers and Classifiers

5.2.2.1.5.

Data Quality Rules

5.2.2.2.

Azure Data Factory

5.2.2.2.1.

Data Flow Activities

5.2.2.2.2.

Pipeline Orchestration

5.2.2.2.3.

Mapping Data Flows

5.2.2.2.4.

Integration Runtime

5.2.2.2.5.

Monitoring and Alerting

5.2.2.3.

GCP Dataflow (Batch Mode)

5.2.2.3.1.

Apache Beam Model

5.2.2.3.2.

Template Creation

5.2.2.3.3.

5.2.2.3.4.

Monitoring and Debugging

5.3.

Stream Processing

5.3.1.

5.3.1.1.

Event Time vs. Processing Time

5.3.1.2.

Windowing Strategies

5.3.1.2.1.

Tumbling Windows

5.3.1.2.2.

Sliding Windows

5.3.1.2.3.

Session Windows

5.3.1.3.

Handling Late Data

5.3.1.4.

Watermarks and Triggers

5.3.1.5.

Exactly-Once Processing

5.3.2.

Managed Streaming Analytics Services

5.3.2.1.

AWS Kinesis Data Analytics

5.3.2.1.1.

SQL and Apache Flink Support

5.3.2.1.2.

Application Scaling

5.3.2.1.3.

5.3.2.1.4.

5.3.2.2.

Azure Stream Analytics

5.3.2.2.1.

Real-Time Analytics Queries

5.3.2.2.2.

Input and Output Configuration

5.3.2.2.3.

User-Defined Functions

5.3.2.2.4.

Temporal Queries

5.3.2.3.

GCP Dataflow (Streaming Mode)

5.3.2.3.1.

Stream Processing Pipelines

5.3.2.3.2.

5.3.2.3.3.

State Management

5.3.2.3.4.

Windowing and Triggers

5.4.

Serverless Functions for Data Tasks

5.4.1.

5.4.1.1.

Event-Driven Data Processing

5.4.1.2.

Trigger Configuration

5.4.1.3.

Memory and Timeout Settings

5.4.1.4.

Environment Variables

5.4.1.5.

Layer Management

5.4.2.

Azure Functions

5.4.2.1.

Data Transformation Scenarios

5.4.2.2.

Binding Configuration

5.4.2.3.

Durable Functions

5.4.2.4.

Premium Plan Features

5.4.3.

Google Cloud Functions

5.4.3.1.

Integration with Cloud Storage

5.4.3.2.

HTTP and Event Triggers

5.4.3.3.

Runtime Environments

5.4.3.4.

Deployment Options

Previous

4. Cloud Data Storage Solutions

Go to top

Next

6. Data Analysis and Business Intelligence (BI)