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# Introduction
If you’re building data pipelines, creating reliable transformations, or ensuring your stakeholders get accurate insights, you know the challenge of bridging the gap between raw data and useful insights.
Analytics engineers sit at the intersection of data engineering and data analysis. While data engineers focus on infrastructure and data scientists focus on modeling, analytics engineers concentrate on the “middle layer”, transforming raw data into clean, reliable datasets that other data professionals can use.
Their day-to-day work involves building data transformation pipelines, creating data models, implementing data quality checks, and ensuring that business metrics are calculated consistently across the organization. In this article, we’ll look at Python libraries that analytics engineers will find super useful. Let’s begin.
# 1. Polars – Fast Data Manipulation
When you’re working with large datasets in Pandas, you’re likely optimizing slower operations and often facing challenges. When you’re processing millions of rows for daily reporting or building complex aggregations, performance bottlenecks can turn a quick analysis into long hours of work.
Polars is a DataFrame library built for speed. It uses Rust under the hood and implements lazy evaluation, meaning it optimizes your entire query before executing it. This results in dramatically faster processing times and lower memory usage compared to Pandas.
// Key Features
- Build complex queries that get optimized automatically
- Handle datasets larger than RAM through streaming
- Migrate easily from Pandas with similar syntax
- Use all CPU cores without extra configuration
- Work seamlessly with other Arrow-based tools
Learning Resources: Start with the Polars User Guide, which provides hands-on tutorials with real examples. For another practical introduction, check out 10 Polars Tools and Techniques To Level Up Your Data Science by Talk Python on YouTube.
# 2. Great Expectations – Data Quality Assurance
Bad data leads to bad decisions. Analytics engineers constantly face the challenge of ensuring data quality — catching null values where they shouldn’t be, identifying unexpected data distributions, and validating that business rules are followed consistently across datasets.
Great Expectations transforms data quality from reactive firefighting to proactive monitoring. It allows you to define “expectations” about your data (like “this column should never be null” or “values should be between 0 and 100”) and automatically validate these rules across your pipelines.
// Key Features
- Write human-readable expectations for data validation
- Generate expectations automatically from existing datasets
- Easily integrate with tools like Airflow and dbt
- Build custom validation rules for specific domains
Learning Resources: The Learn | Great Expectations page has material to help you get started with integrating Great Expectations in your workflows. For a practical deep-dive, you can also follow the Great Expectations (GX) for DATA Testing playlist on YouTube.
# 3. dbt-core – SQL-First Data Transformation
Managing complex SQL transformations becomes a nightmare as your data warehouse grows. Version control, testing, documentation, and dependency management for SQL workflows often resort to fragile scripts and tribal knowledge that breaks when team members change.
dbt (data build tool) allows you to build data transformation pipelines using pure SQL while providing version control, testing, documentation, and dependency management. Think of it as the missing piece that makes SQL workflows maintainable and scalable.
// Key Features
- Write transformations in SQL with Jinja templating
- Build correct execution order automatically
- Add data validation tests alongside transformations
- Generate documentation and data lineage
- Create reusable macros and models across projects
Learning Resources: Start with the dbt Fundamentals course at courses.getdbt.com, which includes hands-on exercises. dbt (Data Build Tool) crash course for beginners: Zero to Hero is a great learning resource, too.
# 4. Prefect – Modern Workflow Orchestration
Analytics pipelines rarely run in isolation. You need to coordinate data extraction, transformation, loading, and validation steps while handling failures gracefully, monitoring execution, and ensuring reliable scheduling. Traditional cron jobs and scripts quickly become unmanageable.
Prefect modernizes workflow orchestration with a Python-native approach. Unlike older tools that require learning new DSLs, Prefect lets you write workflows in pure Python while providing enterprise-grade orchestration features like retry logic, dynamic scheduling, and comprehensive monitoring.
// Key Features
- Write orchestration logic in familiar Python syntax
- Create workflows that adapt based on runtime conditions
- Handle retries, timeouts, and failures automatically
- Run the same code locally and in production
- Monitor executions with detailed logs and metrics
Learning Resources: You can watch the Getting Started with Prefect | Task Orchestration & Data Workflows video on YouTube to get started. Prefect Accelerated Learning (PAL) Series by the Prefect team is another helpful resource.
# 5. Streamlit – Analytics Dashboards
Creating interactive dashboards for stakeholders often means learning complex web frameworks or relying on expensive BI tools. Analytics engineers need a way to quickly transform Python analyses into shareable, interactive applications without becoming full-stack developers.
Streamlit removes the complexity from building data applications. With just a few lines of Python code, you can create interactive dashboards, data exploration tools, and analytical applications that stakeholders can use without technical knowledge.
// Key Features
- Build apps using only Python without web frameworks
- Update UI automatically when data changes
- Add interactive charts, filters, and input controls
- Deploy applications with one click to the cloud
- Cache data for optimized performance
Learning Resources: Start with 30 Days of Streamlit which provides daily hands-on exercises. You can also check Streamlit Explained: Python Tutorial for Data Scientists by Arjan Codes for a concise practical guide to Streamlit.
# 6. PyJanitor – Data Cleaning Made Simple
Real-world data is messy. Analytics engineers spend significant time on repetitive cleaning tasks — standardizing column names, handling duplicates, cleaning text data, and dealing with inconsistent formats. These tasks are time-consuming but necessary for reliable analysis.
PyJanitor extends Pandas with a collection of data cleaning functions designed for common real-world scenarios. It provides a clean, chainable API that makes data cleaning operations more readable and maintainable than traditional Pandas approaches.
// Key Features
- Chain data cleaning operations for readable pipelines
- Access pre-built functions for common cleaning tasks
- Clean and standardize text data efficiently
- Fix problematic column names automatically
- Handle Excel import issues seamlessly
Learning Resources: The Functions page in the PyJanitor documentation is a good starting point. You can also check Helping Pandas with Pyjanitor talk at PyData Sydney.
# 7. SQLAlchemy – Database Connectors
Analytics engineers frequently work with multiple databases and need to execute complex queries, manage connections efficiently, and handle different SQL dialects. Writing raw database connection code is time-consuming and error-prone, especially when dealing with connection pooling, transaction management, and database-specific quirks.
SQLAlchemy provides a powerful toolkit for working with databases in Python. It handles connection management, provides database abstraction, and offers both high-level ORM capabilities and low-level SQL expression tools. This makes it perfect for analytics engineers who need reliable database interactions without the complexity of managing connections manually.
// Key Features
- Connect to multiple database types with consistent syntax
- Manage connection pools and transactions automatically
- Write database-agnostic queries that work across platforms
- Execute raw SQL when needed with parameter binding
- Handle database metadata and introspection seamlessly
Learning Resources: Start with SQLAlchemy Tutorial which covers both core and ORM approaches. Also watch SQLAlchemy: The BEST SQL Database Library in Python by Arjan Codes on YouTube.
# Wrapping Up
These Python libraries are useful for modern analytics engineering. Each addresses specific pain points in the analytics workflow.
Remember, the best tools are the ones you actually use. Pick one library from this list, spend a week implementing it in a real project, and you’ll quickly see how the right Python libraries can simplify your analytics engineering workflow.
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.