Data Pipeline Architecture

Data pipeline architecture is the design and structure of code and systems that copy, cleanse or transform as needed, and route source data to destination systems such as data warehouses and data lakes.

Three factors contribute to the speed with which data moves through a data pipeline

Data pipeline reliability requires individual systems within a data pipeline to be fault-tolerant. A reliable data pipeline with built-in auditing, logging, and validation mechanisms helps ensure data quality.
Latency is the time needed for a single unit of data to travel through the pipeline. Latency relates more to response time than to volume or throughput. Low latency can be expensive to maintain in terms of both price and processing resources, and an enterprise should strike a balance to maximize the value it gets from analytics.
Rate, or throughput, is how much data a pipeline can process within a set amount of time

Data Pipelines with Apache Airflow

Apache Airflow is a workflow automation platform that is popular for its open-source availability and scheduling capabilities. You can utilize this tool to programmatically author, schedule, and monitor any number of workflows. Businesses today use Airflow to organize complex computational workflows, build data processing pipelines, and easily perform ETL processes. Airflow operates on DAG (Directed Acyclic Graph) to construct and represent its workflow, and each DAG is formed of nodes and connectors. These Nodes depend on Connectors to link up with the other nodes and generate a dependency tree that manages your work efficiently.

Data Pipeline from AWS S3 to Snowflake

Automate your data centralization pipeline from AWS S3 to Snowflake without writing code. Trifacta’s intuitive interface allows for fast data transformation and automation on your data pipelines between Snowflake and AWS.

How Do I Transfer Data from S3 to Snowflake?

Moving data from AWS into Snowflake can be automated with Trifacta’s powerful automation capabilities. Parameterized inputs allow the definition of dynamic rules that determine what data should be picked up on every run and what data to skip.

Data Pipeline with Databricks

Databricks offers a unified platform designed to improve productivity for data engineers, data scientists, and business analysts. Combining elements of data warehouse and data lake architectures, Databricks supports processing and transforming massive quantities of data and exploring the data through machine learning models.

The typical organization leverages dozens of SaaS applications and disparate data sources in a sprawling, hybrid cloud/on-premise mix. CData Sync enables the seamless ingestion of data from all of these mission-critical data sources into Databricks.

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