Data Engineering Tools
To build a resilient, feature-rich data infrastructure, data engineers use a mix of programming languages, data management tools, data warehouses, and pipeline systems to process data at scale.
When these tools work together, teams can build reliable technology platforms that help businesses use data for decision-making, innovation, and growth. In this guide, you’ll see the core tools and what each one is best used for.
Now, let’s walk through the best data engineering tools that help build a foundation for data-driven success.
Quick summary: Data Engineering Tools is a practical overview of key tools across transformation, storage, processing/orchestration, and machine learning engineeringso you can understand what each tool does and where it fits in a real pipeline.
Key takeaway: The most effective data stacks give each tool a clear job (transform, store, process, orchestrate, and optionally support ML), so data flows smoothly, reliably, and efficiently.
Quick promise: You’ll understand the purpose, key features, and practical usefulness of tools like Apache NiFi, Pandas, Amazon S3, HDFS, Spark, Airflow, and TensorFlow Extended (TFX)—plus common questions people have when choosing them.
Best Data Engineering Tools
In this comprehensive guide, we delve into the realm of data engineering and present an array of top-notch data tools tailored to diverse use cases. Our analysis spans four key categories, each optimized for specific functions:
- Data Manipulation and Transformation
- Data Storage Solutions
- Data Processing and Pipeline Development
- Machine Learning Engineering
Now let’s delve even deeper into each category and explore the tools available for various data engineering use cases.
Quick Facts
- Data engineering tools support building and optimizing pipelines so data moves smoothly and efficiently.
- This guide groups tools into four categories: transformation, storage, processing/orchestration, and ML engineering.
- The tools covered include Apache NiFi, Pandas, Amazon S3, HDFS, Apache Spark, Apache Airflow, and TensorFlow Extended (TFX).
- Tool choice depends on your data sources, scale, and how you want to build and monitor workflows.
| Field | Answer |
|---|---|
| What it is | A categorized overview of core data engineering tools and how they support data infrastructure and pipelines. |
| Who it’s for | Data engineers (and aspiring data engineers) choosing tools for ingestion, transformation, storage, processing, orchestration, and ML workflows. |
| Best for | Understanding which tools fit which pipeline stage and why. |
| What you get / output | Clear tool descriptions, key features, practical usefulness, and FAQs from the article. |
| How it works (high level) | Tools are grouped into four functional categories that map to common pipeline needs. |
| Requirements | Basic familiarity with data workflows; many tools connect to databases/files/APIs; some are Python-centered. |
| Time | This depends on your project scope, data volume, and whether you’re building batch or real-time workflows. |
| Risks | Using the wrong tool for the job can create complexity, bottlenecks, or monitoring gaps. |
| Common mistakes | Trying to use one tool for every pipeline stage; skipping workflow monitoring; choosing tools without clear responsibilities. |
| Quick tip | Start from your pipeline needs (ingest/transform → store → process → orchestrate → ML if needed), then choose tools that map cleanly to each stage. |
Data manipulation and transformation tools
- Apache NiFi – a data integration tool that automates system data flows. It provides a web-based graphical user interface for designing and managing data pipelines, making it easy to build complex data processing workflows. NiFi is known for its data lineage and provenance tracking, which helps in understanding how data flows through the system.
Key Features:
- Visual data flow design: Apache NiFi’s drag-and-drop interface allows users to design data flows intuitively, making it easy to create and manage data transformations without writing complex code.
- Data provenance: NiFi provides end-to-end data lineage and tracking, enabling users to monitor data flow, detect bottlenecks, and ensure data quality throughout the transformation process.
- Extensive processor library: NiFi offers a wide range of processors for data enrichment, filtering, aggregation, and more, empowering data engineers to perform various data transformations effortlessly.
How It’s Useful:
Apache NiFi simplifies data transformations, making it an ideal tool for data engineers to process, enrich, and cleanse data efficiently, ensuring data accuracy and reliability.
- Pandas is a Python library widely used for data manipulation and analysis. It provides data structures like DataFrame, which allows users to perform various operations like filtering, grouping, aggregating, and merging data efficiently. Pandas are especially useful when dealing with structured data and are often used in conjunction with other data-processing libraries.
Key Features:
- DataFrame data structure: Pandas provides the DataFrame data structure, a two-dimensional, size-mutable, and flexible data container, similar to a spreadsheet or SQL table.
- Data manipulation functions: Pandas offers a wide range of functions for filtering, grouping, aggregating, pivoting, and merging data, making it easy to clean and transform data.
- Handling missing data: Pandas provides methods to handle missing data by filling or dropping missing values, which is crucial for data cleaning and preprocessing.
- Time series data support: Pandas has excellent support for working with time series data, including date/time parsing, resampling, and rolling window calculations.
How it’s useful:
The various data manipulation functions simplify common data cleaning and transformation operations, saving time and effort. Pandas’ ability to handle missing data ensures data integrity during preprocessing, enabling accurate analysis and modeling. Moreover, its support for time series data makes it a valuable tool for analyzing temporal data and performing time-based calculations.
Data storage solutions tools
- Amazon Simple Storage Service (S3) is a scalable and secure object storage service offered by Amazon Web Services (AWS). It allows users to store and retrieve vast amounts of data with high durability and availability. S3 is widely used for data storage in cloud-based data pipelines and can seamlessly integrate with other AWS services.
Key Features:
- Amazon S3 allows users to store and retrieve vast amounts of data securely, with virtually unlimited scalability to accommodate growing data requirements.
- Data lifecycle management: S3 provides customizable lifecycle policies, enabling users to automatically transition data to different storage tiers based on usage patterns and cost optimization.
- Data encryption: S3 offers robust encryption options to protect data at rest and during transit, ensuring data security and compliance.
How It’s Useful:
Amazon S3 provides a reliable and cost-effective data storage solution, empowering data engineers to store and manage data efficiently, and supporting seamless data access for analytics and other applications.
- HDFS is the primary storage system used by Hadoop, designed to handle large datasets across multiple nodes in a distributed computing environment. HDFS offers fault tolerance, high throughput, and data locality, making it suitable for big data applications. It is commonly used in on-premise or hybrid cloud environments.
Key Features:
- Distributed storage: HDFS distributes data across multiple nodes in a Hadoop cluster, ensuring fault tolerance and high availability.
- Scalability: HDFS can scale horizontally to handle massive amounts of data by adding more nodes to the cluster.
- Data replication: HDFS replicates data blocks to multiple nodes, reducing the risk of data loss and improving data reliability.
- High throughput: HDFS is optimized for sequential read and write operations, making it well-suited for large-scale batch processing.
- Data locality: HDFS places data closer to the processing nodes, reducing network congestion and improving data processing efficiency.
Data processing and pipeline development tools
- Apache Spark – is a fast and powerful open-source data processing engine that provides an easy-to-use API for distributed data processing. It supports various data formats and can handle large-scale data processing tasks efficiently.
Key Features:
- In-memory data processing: Apache Spark’s lightning-fast processing engine allows users to analyze and manipulate data in real-time, enhancing efficiency and reducing processing time significantly.
- Distributed computing: Spark’s ability to distribute data processing across multiple nodes ensures scalability and handles massive datasets without compromising performance.
- Flexible data source integration: Spark supports various data sources, including Hadoop Distributed File System (HDFS), cloud storage, and relational databases, making it a versatile choice for data pipeline building.
How It’s Useful:
Apache Spark simplifies complex data processing tasks, enabling data engineers to build robust and scalable data pipelines for both batch and real-time data processing scenarios.
- Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. It allows users to define complex workflows as code using Python, making it flexible and customizable.
Key Features:
- Directed Acyclic Graph (DAG) based workflows: Airflow represents data pipelines as DAGs, making it easy to define dependencies between tasks and manage complex workflows.
- Dynamic task generation: Airflow supports dynamic task generation, enabling users to create tasks programmatically based on the data or conditions at runtime.
- Extensibility through custom operators and plugins: Airflow allows users to define custom operators and plugins, making it highly adaptable to various data processing needs.
- Scheduling and monitoring: Airflow provides robust scheduling capabilities to manage the execution of tasks and a web-based UI for monitoring and tracking workflow progress.
How it’s Useful:
Apache Airflow is a powerful tool for workflow coordination and automation. Its DAG-based approach allows users to create complex workflows with dependencies between tasks, making it easier to manage data processing pipelines. The dynamic task generation feature enhances flexibility, enabling users to create tasks based on runtime conditions or dynamically generated data. Airflow’s extensibility through custom operators and plugins allows users to integrate with different data sources and tools seamlessly. Its scheduling and monitoring capabilities provide visibility into workflow progress and ensure that data processing tasks are executed efficiently.
Why do people use Data Engineering Tools
People use data engineering tools to create and optimize pipelines that keep data moving smoothly and efficiently, so businesses can rely on data systems for consistent decision-making and operations.
- Infrastructure: Build the foundation that supports analytics, reporting, and downstream applications.
- Reliability: Reduce fragile workflows by using tools designed for ingestion, transformation, storage, and orchestration.
- Scalability: Handle larger datasets and more complex workloads without redesigning everything from scratch.
- Automation: Schedule and monitor workflows to reduce manual work and failures.
- Integration: Connect databases, files, APIs, and processing engines into end-to-end pipelines.
Step-by-step: How to do it
To choose the right data engineering tools, start from your pipeline needs, then map each stage to a tool that’s designed for that job.
- List your data sources and destinations (databases, files, APIs, analytics layers).
- Define what transformations you need (cleaning, enrichment, routing, reshaping).
- If you want visual flow design and pipeline tracking, consider a flow-based tool.
- If you want Python-based manipulation, consider a library designed for data analysis.
- Choose a storage layer based on environment and scale (cloud storage vs distributed file system).
- Decide if you need distributed processing for large-scale workloads.
- Add workflow orchestration if you have multi-step pipelines that must run reliably on schedules.
- Plan for monitoring and troubleshooting (workflow visibility, tracking, and clear ownership).
- If ML is part of the pipeline, add ML engineering tooling that supports end-to-end production workflows.
Expert opinion: Christopher Garzon
While DBT is still new, it is one of the fastest growing tools in the industry due to its ease of use for analysts and its integration with Airflow (another integral tool for data engineers to know), we highly recommend learning DBT. It is already used at most big tech companies and it’s allowing analysts and junior engineers to play a big role in the ETL processes of companies pipelines.
FAQs on Data Engineering Tools
Can data engineering tools handle both structured and unstructured data?
Yes. Many modern data engineering tools can handle both structured and unstructured data by extracting information from databases, files, APIs, and other sources.
What is Apache NiFi?
Apache NiFi is an open-source data integration platform that lets you automate data flow between systems. It provides a graphical user interface (GUI) and supports ingesting, routing, transforming, and processing data from different sources.
Can Apache NiFi integrate with other data processing tools?
Yes. Apache NiFi can integrate with other processing tools such as Apache Spark and Apache Kafka, supporting end-to-end data pipelines.
How do Pandas differ from NumPy?
NumPy provides multi-dimensional arrays, while Pandas introduces higher-level data structures like DataFrames that are designed for more complex data manipulation and analysis.
What are the main use cases for Amazon S3?
Amazon S3 is commonly used for data backup and archiving, hosting static websites, serving content (CDN), storing and sharing large media files, and acting as a data lake for big data analytics.
How does Apache Spark achieve faster data processing?
Apache Spark uses in-memory processing, caching, and a DAG (Directed Acyclic Graph) execution model to reduce data shuffling and improve performance.
One-minute summary
- Data engineering tools support building and optimizing pipelines so data flows smoothly and efficiently.
- This guide groups tools into transformation, storage, processing/orchestration, and ML engineering.
- Apache NiFi supports flow-based integration with lineage/provenance tracking.
- Pandas supports Python-based data manipulation and analysis using DataFrames.
- Spark and Airflow support large-scale processing and workflow orchestration, while TFX supports production ML workflows.
Key terms
- Data pipeline: A workflow that moves data from source to destination through defined processing steps.
- Data transformation: Cleaning, reshaping, enriching, or standardizing data so it’s usable downstream.
- Data provenance/lineage: Tracking the path of data through a system to understand how it moved and changed.
- Object storage (Amazon S3): Scalable cloud storage designed for large volumes of data.
- Distributed storage (HDFS): A file system that spreads data across multiple nodes with replication for reliability.
- Distributed processing (Spark): Processing data across multiple machines to handle large datasets efficiently.
- Orchestration (Airflow): Scheduling and managing workflows with dependencies and monitoring.
- TFX: A platform for building, validating, monitoring, and deploying ML models in production.
Conclusion
In the ever-evolving landscape of data engineering, the right tools can make a significant difference in efficiency, reliability, and scalability.
The top 10 tools mentioned in this article represent a diverse set of technologies that cover critical aspects of data engineering, from data processing and orchestration to integration and version control.
By incorporating these tools into their workflows, data engineers can enhance productivity, tackle complex challenges, and deliver high-quality data solutions to drive business success.
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