Azure Data Engineering: Synapse Data Warehousing

Azure Data Engineering: Synapse Data Warehousing

1. Efficient Data Ingestion

Load data from sources like Azure Blob Storage, SQL Databases, and Azure Data Lake into Synapse SQL Pools using PolyBase or Azure Data Factory.

2. Real-Time Data Import

Stream real-time data from Azure Event Hubs and Stream Analytics into Synapse for immediate analytics.

3. Automated ETL Pipelines

Automate data movement and transformation using Azure Data Factory for seamless ETL workflows.

4. Support for Multiple Data Formats

Import data in formats like CSV, Parquet, JSON, and Avro into Synapse.

5. Big Data Integration

Integrate Azure Data Lake and Big Data with Spark Pools for preprocessing before importing into SQL Pools.

6. Hybrid Data Integration

Bring data from cloud and on-premises sources into Synapse using Data Gateway and secure network connections.

7. Scalable Data Loading

Leverage MPP architecture for fast, large-scale data imports and queries.

8. Data Security & Compliance

Ensure data security with encryption, and meet compliance standards during data imports.

9. Data Partitioning for Performance

Use data partitioning to optimize query performance during large data imports.

10. Error Handling & Monitoring

Built-in tools for monitoring, logging, and handling errors during data import and transformation.