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Posts on etl

Airflow Data Engineering Etl Snowflake Dbt Python Sql Cicd
Cover for Building an ETL Pipeline for Slack AI Huddle Notes with Airflow and Snowflake
Airflow Snowflake Etl Data Engineering

Chris Hronek

•

Jan 9, 2025

Building an ETL Pipeline for Slack AI Huddle Notes with Airflow and Snowflake

Learn how to build an ETL pipeline that extracts AI-generated Slack huddle notes using Airflow, processes them with Snowflake Cortex, and loads them into a data warehouse for future AI agent consumption.

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Cover for Building a Scalable Reverse ETL Pipeline with Apache Airflow and Snowflake
Airflow Snowflake Etl Data Engineering Python Sql

Chris Hronek

•

Dec 23, 2024

Building a Scalable Reverse ETL Pipeline with Apache Airflow and Snowflake

Discover how to construct a cost-effective and scalable reverse ETL pipeline using Apache Airflow and Snowflake to seamlessly synchronize data from your Snowflake warehouse to HubSpot. This guide covers essential aspects such as state management, dynamic batching, and API integration to ensure efficient and reliable data synchronization.

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Cover for Streamlining ETL Workflows with Apache Airflow and Airbyte
Airflow Etl

Chris Hronek

•

May 28, 2024

Streamlining ETL Workflows with Apache Airflow and Airbyte

Explore the integration of Apache Airflow and Airbyte for efficient ETL, enhancing scalability and maintainability in data engineering workflows.

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Cover for Optimizing Data Engineering with Artie's Real-Time Database Replication
Etl

Chris Hronek

•

May 21, 2024

Optimizing Data Engineering with Artie's Real-Time Database Replication

Explore how Artie helped us streamline our ETL pipelines by replicating our MySQL database into Snowflake. Discover how Artie's real-time database replication tool helped us save time and focus on more important tasks.

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Cover for Splitting Nested JSON Into Individual Rows in Redshift
Data Engineering Etl

Chris Hronek

•

Aug 30, 2023

Splitting Nested JSON Into Individual Rows in Redshift

Explore the art of transforming JSON data in Amazon Redshift databases, mastering elegant techniques to split and extract data from nested structures. Uncover time-saving strategies for managing one-to-many relationships and enhancing data manipulation workflows.

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