Data engineering encompasses a broad set of disciplines aimed at making data more useful and accessible to all users, including data scientists, business analysts, and decision-makers. At its core, data engineering involves the creation of systems that collect, manage, and convert data into a form that's usable for analysis. This guide will provide an overview of how to perform data engineering tasks in notebooks, using the popular platform Deepnote.
ETL processing
Extract, transform, load (ETL) is a fundamental concept in data engineering that describes a process commonly used to prepare data for analysis.
- Extract: The extraction phase involves collecting data from various sources, which could be databases, data lakes, APIs, or even flat files such as CSVs or Excel spreadsheets.
- Transform: During transformation, the raw data is cleaned and restructured into a more usable format. This might involve filtering rows, converting data types, handling missing values, aggregating data, or joining different data sources.
- Load: Finally, the load phase is where the processed data is moved to a data store where it can be accessed for analytical purposes.
In the context of a customer churn example, the ETL process might involve extracting customer usage data from a relational database, transforming it by calculating churn probabilities, and then loading the final dataset into a tool for visualization and further analysis.
Data pipelines
One of the strengths of using a notebook for data engineering is the ability to build and test data pipelines efficiently. These are series of data processing steps linked together, ensuring that data flows from one operation to the next automatically.
Building a data pipeline in a notebook usually involves writing a sequence of code cells, where the output of one cell feeds directly into the next. When handling a customer churn prediction task, a notebook data pipeline could include initial data extraction scripts, transformation functions to derive features predictive of churn, and the final load steps to direct the data to an analytics dashboard.
Data quality
Maintaining high data quality is crucial through all stages of ETL and pipeline development. In notebooks, this typically involves including steps to validate data as you go. For example, you might include checks to ensure there are no duplicates, that the data types are correct, and that there are no unanticipated null values.
In our customer churn example, ensuring data quality could mean validating that customer interaction logs are complete and that no critical timestamp or activity data is missing before attempting to model churn.
Deepnote as a notebook platform
Deepnote is an emerging notebook platform designed to accommodate collaborative data science and engineering tasks. Deepnote notebooks support real-time collaboration and have integrations with popular data services -- making them an excellent tool for performing data engineering tasks.
Key benefits of using Deepnote for data engineering include:
- Integration with data warehouses and databases: Immediate connectivity to various data sources simplifies ETL processes.
- User-friendly interface: Simplifies the pipeline setup and provides visibility into each step of your ETL process.
- Collaborative features: Teams can work together synchronously on the same notebook, leaving comments and tracking changes.
When it comes to data engineering with notebooks, Deepnote provides the tools necessary to ensure that engineers can write, test, and optimize their ETL pipelines effectively.
Conclusion
Notebooks have become an indispensable tool for data engineers, providing a versatile environment for ETL processes and pipeline development. Whether you are addressing customer churn analysis or another data-centric problem, notebooks help streamline data engineering tasks from extraction to loading. They also make collaborative work easier thanks to platforms like Deepnote. Remember, robust data engineering is key to unlocking actionable insights and driving informed business decisions.