What recent technologies have been used in data pipelines?

Modern data pipelines increasingly leverage cloud-native services and open-source tools to achieve scalability, reliability, and agility. Key technologies include:

1. Snowflake – a cloud data warehouse that offers elastic compute and native support for semi-structured data, making it a popular target for ELT pipelines.
2. dbt (data build tool) – a transformation framework that allows analysts to write SQL transformations as modular, version-controlled models, enabling reproducible data marts.
3. Apache Airflow / Prefect – workflow orchestrators that schedule and monitor DAGs, with robust retry and alerting capabilities.
4. Kafka / Amazon Kinesis – distributed streaming platforms that enable real-time ingestion and event-driven architectures.
5. Delta Lake / Apache Hudi – storage layers that provide ACID transactions, schema evolution, and time travel on top of data lakes.
6. Spark / Flink – distributed processing engines for batch and stream analytics, often integrated with the above services.
7. AWS Glue / Azure Data Factory – managed ETL services that simplify data cataloging, job scheduling, and serverless compute.

These tools together form a modern data stack that supports rapid data ingestion, transformation, and analytics with minimal operational overhead.