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Simplify Your ETL and ELT Processes with Astronomer

Transform your data integration workflows with Astronomer, built on Apache Airflow, for managing ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines. Unlock the full potential of your data and drive insightful business decisions with ease.

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What is ETL/ELT?

ETL (Extract, Transform, Load) is a data integration process where data is extracted from various sources, transformed into a desired format or structure, and then loaded into a centralized repository

ELT, which stands for Extract, Load, Transform, is a data integration process where raw data is extracted from various sources, loaded into a centralized storage system, and then transformed within that system

Key Features of ETL and ELT
on Astronomer

Powerful Data Extraction Capabilities

Astro leverages extensive library of pre-built operators and hooks to extract data from a wide array of sources, making the ETL process seamless and efficient. Whether it's relational databases, NoSQL databases, cloud storage services, APIs, or flat files, Astro connects to your data wherever it resides.

Simplified Connection Management Easily manage connections across multiple Airflow environments by utilizing Astro’s pre-built provider packages for popular data sources.
Scalable Data Extraction Efficiently handle large volumes of data with Astro’s optimized resource management, ensuring fast and reliable data ingestion for your ETL and ELT pipelines.
Enhanced Security Controls Astro incorporates robust security measures, including secure credential handling and encryption, to protect your data during extraction in your ETL and ELT pipelines.

Flexible Data Transformation

Astro provides unparalleled flexibility in defining transformation tasks within your Airflow DAGs (Directed Acyclic Graphs). Supporting both ETL and ELT methodologies, Astro allows you to choose the approach that best fits your data strategy.

Integrated Transformation Tools Use leading data transformation tools like dbt, Pandas, and Apache Spark for managing your ETL and ELT processes.
Customizable Transformations Astro offers flexibility in defining transformation logic using Python, allowing for complex and custom transformations to be performed in-line with your workflow.
Task Optimized Compute Astronomer’s worker queues allocate tasks to the best-suited compute nodes, ensuring efficient resource use and boosting ETL and ELT performance.

Efficient Data Loading

Astro enhances the data loading phase of your ETL and ELT pipelines by offering reliable and efficient mechanisms to load transformed data into your target systems. This ensures the ETL or ELT cycle is completed effectively and accurately.

Pre-Built Provider Packages for Target Systems Leverage Airflow provider packages for Snowflake, Google BigQuery, Amazon Redshift, and more, simplifying the loading process in your ETL and ELT pipelines.
Robust Dependency Management Astro enhances Airflow task dependencies, ensuring loading tasks run only after successful extraction and transformation, preventing data inconsistencies in ETL and ELT pipelines.
Error Handling and Recovery Benefit from built-in error handling, retries, and alerting mechanisms to maintain reliable pipeline execution and quickly address any issues in your ETL or ELT processes.

Comprehensive Orchestration and Monitoring

Astro provides an enterprise-grade orchestration platform with advanced monitoring features, enhancing your ability to manage re effectively. By extending Airflow's capabilities, Astro offers a robust and user-friendly experience for orchestrating complex data pipelines.

Advanced Scheduling Schedule pipelines to run at specific times or intervals, or adopt a data-driven scheduling approach ideal for modern ETL or ELT pipelines.
Real-Time Monitoring Track the status of your ETL or ELT tasks with comprehensive dashboards, ensuring your processes run smoothly.
Proactive Alerts Set up alerts to address problems promptly, maintaining the integrity of your data transformation efforts.

Scalability, Security, and Compliance

Astro ensures your ETL or ELT processes are scalable, secure, and compliant with industry standards, solidifying the meaning and importance of ETL or ELT within your organization.

Elastic Scaling Leverage Astro's ability to automatically scale resources based on workload demands, ensuring your ETL or ELT pipelines can handle increasing data volumes without manual intervention.
Enterprise-Grade Security Benefit from robust security features, including single sign-on (SSO), encryption at rest and in transit, and detailed audit logs to meet enterprise security requirements in your ETL or ELT processes.
High Availability Configuration Configure your Astro deployment to enhance resilience and ensure the continuous operation of your ETL and ELT pipelines.

Why Choose Astronomer for ETL and ELT

Astronomer Traditional ETL/ELT Tools

Unified Orchestration Platform

Centralized environment orchestrating all ETL and ELT stages within a single platform.

 True

False

Python-based Customization

Fully customizable workflows with Python DAGs, custom operators, and flexible configurations.

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False

Kubernetes-based Scalability

Dynamically scalable with Kubernetes; efficient for a wide range of workloads.

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False

Extensive Library of Pre-Built Integrations

Seamless integration with a wide range of module and provider packages for popular third-party tools.

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True

Open-source Extensibility

Built on open-source Apache Airflow, enabling community-driven enhancements and integrations.

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False

Advanced dbt integration

Run and observe dbt projects alongside your Airflow workflows, simplifying orchestration, deployment, and scaling of data transformations on a fully-managed platform.

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False

Task Optimized Worker Queues

Assign tasks to specific worker sets based on computational needs for optimized resource utilization.

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False

Centralized Observability

Comprehensive pipeline-level data observability with SLA dashboards, real-time alerts, and full lineage tracking.

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False

Dynamic Resource Allocation

Efficient resource management ensures high performance and quick adaptation to workload changes.

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False

Enhanced Data Quality Assurance

Implement comprehensive data validation and quality checks within your transformation workflows.

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True

Cost Efficiency

Lower costs by consolidating multiple tools and leveraging an open-source foundation.

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False

Security & Compliance

Built-in security features like encryption, RBAC, and SSO.

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True

With Astro, we built a specific piece of code that triggers extracts in Tableau only once the ETL processes are complete. This means no more guessing on timings and no more unnecessary delays.

Yohai Vidergor

Yohai Vidergor

VP of Global Data at SciPlay, SciPlay

We’ve also seen a more stable usage of the infrastructure across all times, even during peak hours when we have heavy ELT pipelines getting executed. This helped us achieve better results, not just in meeting our internal SLAs, but also having more consistency around when stakeholders are expecting our pipelines to finish executing.

Current Customer in Sports Betting Industry

It has become a powerful resource in the team to quickly deploy Python scripts, being our use cases mainly focused on ETL pipelines. It is also easy for us to provide a workspace to other teams so that they can run their scripts easily too.

G2 verified Astronomer user

We use Astronomer for all extractions and anything that needs to be custom made. It enables us to get started very quickly without needing to deploy anything.

G2 verified Astronomer user

It has aided in building infrastructure that supports CI/CD, environment management, and it's significantly increased the rate at which we can develop, test, and deploy ETL and ELT pipelines for analytics, machine learning, and business insight at our company.

G2 verified Astronomer user

It is helping our team orchestrate our data pipelines that are critical to our customers. It helps us keep previously complex and cumbersome ETL processes in a simpler and easy to maintain system.

G2 verified Astronomer user

Resources to Accelerate Your ETL and ELT Journey

Get full access to our library of ebooks, case studies, webinars, blog posts, and more.

Documentation Use Airflow to orchestrate data loading and transformation in Databricks
Visit the docs →
Documentation ELT with Snowflake and Apache Airflow® for eCommerce
Visit the docs →
Webinar How to Orchestrate Databricks Jobs Using Airflow
Watch Now →
Tutorial Orchestrate Databricks jobs with Airflow
Visit the docs →
Tutorial Orchestrate Snowflake Queries with Airflow
Visit the docs →
Tutorial Create a Snowflake Connection in Airflow
Visit the docs →
Blog ETL for Beginners: Data Ingestion at Scale with S3 and Snowflake
Read the post →
Webinar Implementing reliable ETL & ELT pipelines with Airflow and Snowflake
Watch Now →
Blog ETL in Airflow: A Comprehensive Guide to Efficient Data Pipelines
Read the post →

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FAQs

What is Astronomer and how does it simplify ETL and ELT processes?

Astronomer is a data orchestration platform built on Apache Airflow that simplifies ETL and ELT by automating and streamlining workflows across multiple sources and destinations. With built-in integrations and features like task management and optimized compute, Astronomer reduces complexity and enhances efficiency for data teams.
How does Astronomer help manage ETL and ELT pipelines?

Astronomer not only automates and orchestratesETL and ELT pipelines but also integrates with multiple data sources, handles transformations, scales dynamically, and provides comprehensive monitoring and error handling to ensure smooth pipeline execution.
What are the key differences between ETL and ELT, and when should I use each?

ETL is better suited for processing data before storage, while ELT is ideal for leveraging modern data warehouses to handle large datasets post-loading.
How does Astronomer compare to traditional ETL and ELT tools?

Astronomer consolidates ETL and ELT processes into one system, reducing the need for multiple tools, lowering costs, and offering more flexibility.
What are the benefits of using Astronomer for managing ETL and ELT workflows?

Astronomer simplifies workflows, offers dynamic scaling, improves efficiency, and reduces costs, all while providing real-time monitoring and security.
What are the stages of ETL?
  1. Extract
    • Definition: Collecting raw data from various sources such as databases, applications, files, or APIs.
    • Process: Data is gathered from one or more source systems, regardless of its raw format
  2. Transform
    • Definition: Converting the extracted data into a suitable format or structure for analysis.
    • Process Includes:
      • Data Cleaning: Removing errors, duplicates, and inconsistencies.
      • Data Standardization: Converting data into a common format.
      • Data Enrichment: Enhancing data by adding relevant information.
      • Applying Business Rules: Implementing specific calculations or logic as per organizational needs.
  3. Load
    • Definition: Importing the transformed data into a target system like a data warehouse or data lake.
    • Process: Data is loaded in a way that optimizes performance and ensures accessibility.
What are the stages of ELT?
  1. Extract
    • Definition: Collecting raw data from various sources such as databases, applications, files, or APIs.
    • Process: Data is gathered regardless of format or structure to ensure a comprehensive dataset.
  2. Load
    • Definition: After extraction, raw data is immediately loaded into the target system, such as a data warehouse or data lake.
    • Process: Data is ingested in its native format
  3. Transform
    • Definition: Transformations occur within the target system after the data is loaded.
    • Process Includes:
      • Data Cleaning: Removing errors, duplicates, and inconsistencies.
      • Data Standardization: Converting data into a common format.
      • Data Enrichment: Enhancing data by adding relevant information.
      • Applying Business Rules: Implementing specific calculations or logic as per organizational needs.

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