Hi there! 👋

Maintenance of data pipelines can often pose significant challenges for organizations. Ensuring the smooth operation of these pipelines, managing updates and changes, and addressing potential bottlenecks are just a few examples of the complexities involved. This is where dlt comes into play as a valuable solution, as dlt can efficiently alleviate these maintenance burdens and optimize data pipeline management.

In this article, we delve into the capabilities of our dlt AI Assistant, the conversational companion for the dltHub docs. Our aim was to explore how dlt can effectively address data pipeline maintenance challenges and streamline advanced dlt project processes. To gain insights, we posed several questions to our AI Assistant. Let's delve into the details and discover how it responded!

💡 How does dlt reduce maintenance ?​

dlt reduces maintenance in several ways:

1. Automating structuring: dlt automates the transition from unstructured (nested, untyped) to structured (tabular, typed) data, which eliminates the need for manual data structuring and reduces the chances of errors.
2. Schema evolution: dlt can automatically adapt to changes in the source data schema, such as new fields or tables. This means that you don't have to manually update your pipelines when the source data changes. You can monitor schema evolution and get alerts on schema changes. Read more about monitoring schema evolution here.
3. Standardization: By providing a standardized way of loading data, dlt enables better collaboration and communication among team members. This reduces the need for individual maintenance efforts and ensures that everyone is working with the same understanding of the data structure.
4. Pre-built sources: dlt offers a collection of ready-built sources that can be easily integrated into your pipelines, reducing the need for custom development and maintenance. You can find existing sources here.
5. Customization: dlt allows for easy customization of pipelines, such as renaming columns, anonymizing data, and filtering. This means that you can adapt your pipelines to your specific needs without having to maintain complex custom code. See examples of customization here.

By automating structuring, handling schema evolution, standardizing data loading, providing pre-built sources, and allowing easy customization, dlt significantly reduces the maintenance effort required for your data pipelines.

💡 How does schema evolution help companies' bottom line?​

Schema evolution helps companies' bottom line in several ways:

1. Reduced maintenance costs: By automatically adapting to changes in the source data schema, dlt reduces the need for manual updates to data pipelines. This saves time and resources, leading to lower maintenance costs.
2. Faster time to market: Schema evolution allows companies to quickly adapt to changes in their data, enabling them to make data-driven decisions faster. This can lead to quicker product iterations, improved customer experiences, and ultimately, increased revenue.
3. Improved data quality: By automating schema evolution, dlt helps ensure that data is consistently structured and typed, reducing the risk of errors and improving overall data quality. Better data quality leads to more accurate insights and better decision-making, which can positively impact the bottom line.
4. Increased collaboration and efficiency: Schema evolution promotes better collaboration and communication among team members, as everyone operates on a shared understanding of the data structure. This can lead to increased efficiency and productivity, ultimately benefiting the company's bottom line.
5. Enhanced data governance: By tracking schema changes and alerting stakeholders, dlt helps improve data governance. Better governance can lead to more responsible data usage, reduced risk of data breaches or compliance issues, and ultimately, a more stable and secure business environment.

Overall, schema evolution contributes to a company's bottom line by reducing maintenance costs, speeding up time to market, improving data quality, increasing collaboration and efficiency, and enhancing data governance.

💡 How can we use dlt to make maintenance more efficient?​

Using dlt to make maintenance more efficient involves several steps:

1. Automate structuring: By automating the process of structuring data, dlt eliminates the need for manual data structuring, reducing the chances of errors and saving time. This makes maintenance more efficient as you don't have to spend time fixing structuring issues.
2. Handle schema evolution: dlt can automatically adapt to changes in the source data schema, such as new fields or tables. This means that you don't have to manually update your pipelines when the source data changes, making maintenance more efficient. You can monitor schema evolution and get alerts on schema changes. Read more about monitoring schema evolution here.
3. Standardize data loading: dlt provides a standardized way of loading data, which enables better collaboration and communication among team members. This reduces the need for individual maintenance efforts and ensures that everyone is working with the same understanding of the data structure.
4. Use pre-built sources: dlt offers a collection of ready-built sources that can be easily integrated into your pipelines, reducing the need for custom development and maintenance. You can find existing sources here.
5. Customize pipelines: dlt allows for easy customization of pipelines, such as renaming columns, anonymizing data, and filtering. This means that you can adapt your pipelines to your specific needs without having to maintain complex custom code. See examples of customization here.

By following these steps, you can leverage dlt to make your data pipeline maintenance more efficient, saving time and resources.

Now, let’s start with some advanced questions:

💡 Does dlt help deliver more data outcomes?​

Yes, dlt helps deliver more data outcomes in several ways:

1. Faster time to build pipelines: By automating the structuring and loading of data, dlt significantly reduces the time required to build data pipelines. This allows you to focus more on using the data for insights and decision-making.
2. Improved data quality: dlt structures data and alerts schema changes, which leads to better data quality and governance. High-quality data enables more accurate insights and better decision-making, ultimately leading to more data-driven outcomes.
3. Better collaboration and communication: Structured data promotes better collaboration and communication among team members. Since everyone operates on a shared understanding of the data structure, it becomes easier to discuss and align on data-related topics, leading to more effective data outcomes.
4. Simplified access: dlt makes it easy for anyone to query structured data using their tools of choice. This enables a wider range of team members to access and analyze the data, leading to more data-driven insights and outcomes.
5. Integration with existing tools: dlt can be run in various environments, such as notebooks, cloud functions, or workflow managers like Airflow. This flexibility allows you to integrate dlt into your existing data stack, making it easier to deliver data outcomes without adding complexity.

By automating structuring, improving data quality, promoting collaboration, simplifying access, and integrating with existing tools, dlt helps deliver more data outcomes and drive better decision-making.

💡 Does dlt help for data governance?​

Yes, dlt helps with data governance in several ways:

1. Structured data: By structuring data automatically, dlt ensures that data is consistently formatted and typed, which is essential for maintaining data quality and governance.
2. Schema evolution: dlt can automatically adapt to changes in the source data schema and alert stakeholders of these changes. This helps maintain data consistency and enables better governance. Read more about monitoring schema evolution here.
3. Metadata tracking: dlt tracks metadata like data lineage and schema descriptions, which helps you stay on top of your data and maintain better governance.
4. Customization: dlt allows for easy customization of pipelines, such as anonymizing data or renaming columns, which can help you comply with data privacy regulations and maintain data governance. See examples of customization here.
5. Integration with data governance tools: dlt can be integrated with other data governance tools, such as data validation libraries like Great Expectations, or data testing frameworks like dbt. This enables you to enforce data quality checks and maintain better governance across your data pipelines.

By structuring data, handling schema evolution, tracking metadata, allowing customization, and integrating with data governance tools, dlt helps improve data governance and ensure that your data is accurate, reliable, and secure.

💡 How can data engineers of different seniorities use dlt? do they build pipelines or platforms? what about complex pipelines?​

Data engineers of different seniorities can use dlt in various ways, depending on their experience and the complexity of the pipelines or platforms they are building:

1. Analysts and junior data engineers: They can use ready-built sources or pass their unstructured data to dlt, which will create a sturdy pipeline. They can also adjust schemas and configure dlt to change loading modes or add performance hints. Examples include adding an existing source and adjusting a schema.
2. Python-first users: They can heavily customize how dlt sources produce data, as dlt supports selecting, filtering, renaming, anonymizing, and just about any custom operation. For example, see the rename columns example.
3. Senior data engineers: They can dig even deeper into customization options and change schemas, normalizers, the way pipelines run, such as parallelism, and build complex pipelines. They can also build data platforms by integrating dlt with other tools and services, such as orchestration tools like Apache Airflow, data validation libraries like Great Expectations, or data testing frameworks like dbt.

In summary, data engineers of different seniorities can use dlt to build pipelines or platforms, depending on their experience and the complexity of the project. dlt provides flexibility and customization options that cater to various skill levels and use cases, making it a valuable tool for data engineers across the board.

Let’s try trolling :)

💡 Can I use dlt for my beach house maintenance?​

I'm sorry, but I am an AI assistant specifically designed to answer questions about the dlt (data load tool) library. I cannot provide information or assistance for beach house maintenance. If you have any questions about the dlt library, feel free to ask!

💡 Conclusion​

During this Q&A converstation, we can understand how dlt helps reduce maintenance in data pipelines by automating structuring, handling schema evolution, standardizing data loading, providing pre-built sources, and allowing easy customization. It offers powerful capabilities to simplify and improve data pipeline management, reducing maintenance efforts, and driving better outcomes.

Now we understand how dlt significantly improves our work efficiency!

Want to ask your own questions to the dlt AI Assistant? Just click on the "Get Help" button located at the bottom right.

[ What's more? ]

• Learn more about dlt 👉 here
• Want to discuss more? Join our Slack community !
• Try out with dlt demo 👉 Colab Demo  !

Hi there! 👋

In this article, we asked ChatGPT questions about dlt, the open source library. Questions included some basic introduction, random fun questions, and advanced dlt project processes. Let’s see how it answered our questions!

💡 Who can use dlt?​

dlt is designed to be accessible and useful for various roles within a data team, including:

1. Data professionals and aspiring data professionals who want to showcase their understanding and value to data teams. They can use dlt to build end-to-end projects, load data to databases, transform data, and prepare reporting. Read more in the Data Beginner guide.

2. Data engineers who want to remove tedious work, reduce ETL maintenance, empower their team members, and enhance their productivity. dlt can be used to automate data loading, enable collaboration, and support natural workflows. Read more in the Data Engineer guide.

3. Data analysts, data scientists, and analytics engineers who want to build and use pipelines for single use cases, core business pipelines, or heavily customized pipelines. dlt offers different levels of abstraction to cater to various needs and use cases. Read more in the Data Beginner guide.

Overall, dlt is suitable for anyone working with data pipelines, from beginners to experienced professionals.

💡 I am just starting in the data field, how can I use dlt?​

If you are just starting in the data field, dlt can be a great tool to help you showcase your understanding and value to data teams. The first step is to get familiar with dlt by playing around with the Google Colab demo or installing it on your computer. Once you have dlt installed, you can initialize a project and pipeline to load data from an API, transform it, and load it into a database in minutes.

💡 How can data scientists use dlt in data science projects?​

Data scientists can use dlt in data science projects to easily load and transform data from various sources. dlt offers different levels of abstraction that enable and empower data scientists to build and use pipeline.

For example, data scientists can use dlt to build pipelines for their specific use cases, such as grabbing data from an API that they need for their analysis. They can heavily customize how dlt sources produce data, such as selecting, filtering, renaming, anonymizing, and just about any custom operation.

In addition, data scientists can use dlt to create highly scalable, easy to maintain, straightforward to deploy data pipelines without having to wait for help from a data engineer. This empowers them to turn messy, unstructured data into regularly updated datasets and focus on their analysis and modeling work. Overall, dlt can help data scientists be more efficient and productive in their work by simplifying the data loading process and allowing them to focus on the analysis and modeling.

Now, let’s start with some advanced questions:

💡 Is dlt like dbt? How to use dlt with dbt? Can you show me the process?​

Yes, dlt and dbt have similar functionalities, but they differ in their approach and scope. dlt is a Python package that helps you load data from various sources and transform it to be ready for analysis. dbt, on the other hand, is a SQL transformation tool that allows you to structure and transform your data using SQL. However, you can use dlt with dbt to create a complete data pipeline from data extraction to transformation and loading. Here is an example process to use dlt with dbt:

To use dbt with dlt, you can follow these steps:

1. Install dbt and dlt using pip.
2. Create a dlt pipeline to extract, transform, and load data into your data warehouse. You can use the dlt SQL client to create tables, views, and other structures that can be used for analysis.
3. Create a dbt project and configure it to use the same data warehouse as your dlt pipeline.
4. Write SQL queries to transform the data in your dbt project.
5. Run the dbt project to create the necessary structures and transformations in your data warehouse.
6. Use a BI tool to visualize and analyze the transformed data.

Overall, using dlt with dbt can help you create a complete data pipeline that covers data extraction, transformation, and loading. This can help you streamline your data processes and make it easier to analyze your data.

💡 Can I use dlt with Streamlit? Or Sheets? Or Power BI? Metabase, Looker? What do you recommend?​

Yes, dlt can be used with Streamlit, Google Sheets, Power BI, Metabase, and Looker. The choice of tool will depend on your specific use case and requirements. For example, if you want to enable business users to explore data and create their own dashboards, then Metabase or Looker might be a good choice.

If you want to build a custom dashboard with Python, then Streamlit might be a good choice. If you want to use a cloud-based BI tool, then Power BI might be a good choice. In terms of using dlt with these tools, dlt can be used to load data into a database or data warehouse that can then be connected to these tools. For example, you can use dlt to load data into a PostgreSQL database and then use that database as a data source for Power BI.

Overall, I would recommend using the tool that best fits your specific use case and requirements, and then using dlt to load and transform your data as needed.

💡 How can I use dlt to create a machine learning model?​

dlt can be used to create machine learning models in the following way:

1. Choose an API that produces data.
2. Choose a use case for this data.
3. Build a dlt pipeline that loads data from the API for your use case.
4. Extract data from your pipeline. 5>. Create a machine learning model depends on your use case.
5. Finally, you can deploy your model with Metabase dashboard.

Let’s try trolling :)

💡 I am tired of lakehouses, can I build a beach house with dlt? or a forest house??​

I am sorry, but dlt is a data loading tool, and it is not related to building beach houses. It is meant to help you automate the process of loading and managing data in your applications. If you have any questions related to data loading or management, I will be happy to help!

💡 If data is the new oil, then what is dlt? newer oil?​

dlt is not a type of oil. It is a data loading tool that automates time-consuming tasks, empowers stakeholders to solve pipeline maintenance issues, and offers different levels of abstraction that enable and empower data professionals to build and use pipelines. Its purpose is to make data loading a commodity and to simplify the process of loading data for organizations.

💡 Conclusion​

We intended to ask ChatGPT different questions about dlt and see if there are any misunderstandings. For example: how dlt can be intergrated in various use cases or how data teams can use dlt in different projects. Seems it worked really well and answered our questions precisely based on our documentation and blog! Moreover, when we tried to ask some random questions, ChatGPT also gave us proper answers! GPT really seems to understands what we were trying to communicate with it!

What questions you would love to ask? Share with us in our Slack community ! See you there 😊

[ What's more? ]

• Learn more about dlt 👉 here
• Give the 👉 Colab Demo a try

Using DuckDB, dlt, & GitHub to explore DuckDB​

So is DuckDB full of data about ducks?​

Nope, you can put whatever data you want into DuckDB ✨

Many data analysts, data scientists, and developers prefer to work with data on their laptops. DuckDB allows them to start quickly and easily. When working only locally becomes infeasible, they can then turn this local “data pond” into a data lake, storing their data on object storage like Amazon S3, and continue to use DuckDB as a query engine on top of the files stored there.

If you want to better understand why folks are excited about DuckDB, check out this blog post.

Perhaps ducks use DuckDB?​

Once again, the answer is also 'nein'. As far as we can tell, usually people use DuckDB 🦆

To determine this, we loaded emoji reaction data for DuckDB repo using data load tool (dlt) from the GitHub API to a DuckDB instance and explored who has been reacting to issues / PRs in the open source community. This is what we learned…

The three issues / PRs with the most reactions all-time are

1. SQLAlchemy dialect #305
2. Add basic support for GeoSpatial type #2836
3. Support AWS default credential provider chain #4021

The three issues / PRs with the most reactions in 2023 are

1. Add support for Pivot/Unpivot statements #6387
2. Add support for a pluggable storage and catalog back-end, and add support for a SQLite back-end storage #6066
3. Add support for UPSERT (INSERT .. ON CONFLICT DO ..) syntax #5866

Some of the most engaged users (other than the folks who work at DuckDB Labs) include

• @Tishj, @xhochy, and @handstuyennn, who received the most 👍 reactions
• @lloydtabb, @cboettig, and @LindsayWray, who received the most ❤️ reactions
• @dforsber, @ankoh, and @djouallah, who gave the most total reactions

All of these users seem to be people. Admittedly, we didn’t look at everyone though, so there could be ducks within the flock. You can check yourself by playing with the Colab notebook.

Maybe it’s called DuckDB because you can use it to create a "data pond" that can grow into a data lake + ducks like water?​

Although this is a cool idea, it is still not the reason that it is called DuckDB 🌊

Using functionality offered by DuckDB to export the data loaded to it as Parquet files, you can create a small “data pond” on your local computer. To make it a data lake, you can then add these files to Google Cloud Storage, Amazon S3, etc. And if you want this data lake to always fill with the latest data from the GitHub API, you can deploy the dlt pipeline.

Check this out in the Colab notebook and let us know if you want some help setting this up.

Just tell me why it is called DuckDB!!!​

Okay. It’s called DuckDB because ducks are amazing and @hannes once had a pet duck 🤣

Source: DuckDB: an Embeddable Analytical RDBMS

Enjoy this blog post? Give data load tool (dlt) a ⭐ on GitHub here 🤜🤛​

The number of Python developers increased from 7 million in 2017 to 15.7 million in Q1 2021 and grew by 3 million (20%) between Q4 2021 and Q1 2022 alone, making it the most popular programming language in Q3 2022. A large percentage of this new group are what we call Python practitioners—data folks and scripters. This group uses Python to do tasks in their jobs, but they do not consider themselves to be software engineers.

They are entering modern organizations in masse. Organizations often employ them for data-related jobs, especially in data engineering, data science / ML, and analytics. They must work with established data sources, data stores, and data pipelines that are essential to the business of these organizations These companies, though, are not providing them with the type of tooling they learnt to expect. There’s no “Jupyter Notebook, pandas, NumPy, etc. for data loading” for them to use.

At this stage of dlt we are focused on serving the needs of organizations with 150 employees or less. Companies of this size typically begin making their first data hires. They want data to be at their core: their CEOs may want to make their companies more “data driven” and “user feedback centric”. Their CTOs may want to “build a data warehouse for automation and self service”. They frequently are eager to take advantage of the skills of the Python practioners they have hired.

To achieve our mission of making this next generation of Python users autonomous in these organizations, we believe we need to build dlt in a “Pythonic” way. Anyone that can write a loop in Python script should be able to write a source and load it. There should minimal learning curve. Anyone in these organizations that gets basic Python should be able to use dlt right away.

However, we also recognize the need dlt to be loved not only by Python users but also data engineers to fulfill our mission. This is crucial because eventually these folks will be brought in to help with data loading in an organization. We need data engineers to evolve dlt pipelines rather than ripping them out and replacing them like they almost always do to scripts written by Python practitioners today.

To develop with dlt, anyone can install it like any other Python library with pip install dlt. They can then run dlt init and be ready to go. Already today data engineers love the automatic schema inference and evolution as well as the customizability of dlt.

dltHub Mission

Since 2017, the number of Python users has been increasing by millions annually. The vast majority of these people leverage Python as a tool to solve problems at work. Our mission is to make this next generation of Python users autonomous when they create and use data in their organizations. For this end, we are building an open source Python library called data load tool (dlt).

These Python practitioners, as we call them, use dlt in their scripts to turn messy, unstructured data into regularly updated datasets. dlt empowers them to create highly scalable, easy to maintain, straightforward to deploy data pipelines without having to wait for help from a data engineer. When organizations eventually bring in data engineers to help with data loading, these engineers build on their work and evolve dlt pipelines.

We are dedicated to keeping dlt an open source project surrounded by a vibrant, engaged community. To make this sustainable, dltHub stewards dlt while also offering additional software and services that generate revenue (similar to what GitHub does with Git).

Why does dltHub exist?

We believe in a world where data loading becomes a commodity. A world where hundreds of thousands of pipelines will be created, shared, and deployed. A world where data sets, reports, and analytics will be written and shared publicly and privately.

To achieve our mission to make this next generation of Python users autonomous when they create and use data in their organizations, we need to address the requirements of both the Python practitioner and the data engineer with a minimal Python library. We also need dltHub to become the GitHub for data pipelines, facilitating and supporting the ecosystem of pipeline creators and maintainers as well as the other data folks who consume and analyze the data loaded.

There are lots of ETL/ELT tools available (300+!). Yet, as we engaged with Python practioners over the last one and half years, we found few Python practitioners that use traditional data ingestion tools. Only a handful have even heard of them. Very simplified, there’s two approaches in traditional data ingestion tools and neither works for this new generation: 1) SaaS solutions that handle the entire data loading process and 2) object-oriented frameworks for software engineers.

SaaS solutions do not give Python practitioners enough credit, while frameworks expect too much of them. In other words, there's no “Jupyter Notebook, pandas, NumPy, etc. for data loading” that meets users needs. As millions of Python practioners are now entering organizations every year, we think this should exist.