A Guide to ELT Pipelines with Meltano

In this guide, we are introducing Meltano, an open-source DataOps tool specifically designed for data engineers. Meltano empowers data engineers by providing complete control and visibility over their pipelines.

In the video below, our Data Engineer Oliver Tanevski demonstrates how to use Meltano to construct ELT (Extract, Load, Transform) pipelines. By the end of the video, you will have the knowledge and guidance to build your first pipeline using Meltano. Additionally, we have highlighted important information and provided useful links to help you start using and implementing Meltano in your daily work.



Introduction to Meltano 

Meltano is an open-source, self-hosted, CLI-first tool designed to simplify the process of building and managing ELT pipelines. With Meltano, you have full control and visibility over your data pipelines. The tool uses the Singer specification, making it easy to create compatible connectors for different data sources and integrate them into the ELT pipelines. It also leverages dbt for transformation, making Meltano a great solution for your data pipeline needs.

Meltano is specifically developed with Data Ops in mind, which includes functionalities for versioning and monitoring to ensure easy maintenance of the pipelines. Currently, Meltano utilizes Airflow and Dagster for pipeline scheduling and orchestration. Airflow offers extensive data integrations, while Dagster is a newer option.

The core feature of Meltano revolves around its replication methods, which effectively manage pipeline states. During the ELT process, data loading is a crucial aspect. Meltano makes this process easy by offering options for full data loads or incremental data pulls when running pipelines. This is very easy to maintain with Meltano.


Benefits of Using Meltano for ELT Pipelines

As a data engineer, you understand the importance of having a tool that empowers you to efficiently extract, load, and transform data. Meltano offers many benefits, such as:


1. Flexibility and Customization

Meltano supports a wide range of data sources and destinations, including databases, SaaS apps, raw files, and even niche or internal systems. You have the freedom to choose where your data resides and how it is transformed, ensuring that it aligns with your specific requirements.


2. Cost Efficiency

Meltano provides a lot of flexibility about how to organize and create your pipelines. This allows everyone to deploy cost-efficient solutions while meeting business expectations.


3. Increased Efficiency and Control

Gone are the days of waiting for support or arguing over connector issues. Meltano puts the power in your hands, allowing you to build, improve, debug, and fix connectors yourself. This is due to the Singer specification that each connector has to use.


4. Centralized Pipeline Management

Managing multiple data pipelines can be a challenging task, but Meltano simplifies the process. You can now handle all your pipelines in one centralized location, including databases, files, SaaS apps, internal sources, Python scripts, and data tools like dbt.


5. Removing Constraints

Meltano encourages you to push boundaries and break limitations. Add new data sources, mask PII (Personally Identifiable Information) before loading it into the warehouse, tweak connectors to meet your specific needs, and even allow other teams to contribute to the pipeline. The possibilities are endless.


Meltano Nomenclatures

Some of the nomenclatures that are used in Meltano are the following: 

Taps are the sources, the packages mainly written in Python. They can be used for extracting data. 

Targets are the destination where we want to save the data, which are packages that can be easily installed. 

The utilities include all the other tools that are crucial for scheduling, transformation, and monitoring that can help us easily maintain our pipelines. 

It’s important to note that all the packages in Meltano follow the Singer specification, which is a standard format for data exchange. This specification allows Data Professionals to easily move data between various systems, as long as the programs can understand this format. The Singer specification helps unify the process of writing ELT pipelines and promotes the creation of maintainable and reproducible code.


Installation Requirements

Before diving into the installation process, there are a few prerequisites you need to have in place:

Operating System: Meltano is compatible with Linux and macOS out of the box. For Windows, the users will need to install Meltano in WSL (Windows Subsystem for Linux)

Python: Ensure you have Python 3.7, 3.8, 3.9, 3.10, or 3.11 installed on your system. Visit the official Python website (https://www.python.org/) to download and install the appropriate version for your operating system.


Local installation

To get started with Meltano, you’ll need to install it on your local machine. When starting to learn and use a new tool like Meltano, it’s important to take the time to go through the getting started guide and documentation. These resources provide valuable information that can help you understand and navigate different aspects of Meltano effectively. By referring to the documentation, you can find detailed explanations and guidance.

Getting Started

Meltano Documentation


Source of Integrations

Meltano Hub is the central repository for all Meltano plugins, as well as Singer taps and targets. It serves as the single source of truth for finding and adding new plugins to your Meltano setup. The Hub is carefully curated by Meltano and the wider community, ensuring that you have access to quality integrations.

To explore Meltano Hub and discover new plugins, visit Meltano Hub.