Data is everywhere, but raw data alone is not useful. Data engineers build the systems that organise and process data, turning it into insights that businesses can actually use.
In this blog, we will cover the top 15 data engineering tools for 2025 that professionals rely on to make sense of big data and drive smarter decisions.
Table of Contents:
What is Data Engineering?
Data Engineering is the process of building and maintaining systems that collect, store, and process data, making it ready for analysis and decision-making. Simply put, data engineers are the architects behind the data infrastructure that powers modern businesses.
Key Responsibilities of Data Engineers
- Data Ingestion: Collecting data from multiple sources (databases, APIs, logs, IoT devices).
- Data Storage: Organising and storing data in warehouses, lakes, or distributed systems.
- Data Pipelines: Designing ETL/ELT workflows to move and transform data efficiently.
- Data Quality & Governance: Ensuring data is accurate, consistent, and compliant.
- Processing & Optimisation: Making large-scale data easily accessible for analytics and machine learning.
Without data engineers, analytics teams and AI models would struggle to get clean, reliable data. Data engineering ensures businesses can make informed decisions, spot trends, and scale operations confidently. In the next section, let’s explore the top 15 data engineering tools you can use in 2025.
Sit tight as we navigate through the best Data Engineering tools that are used today and see how each one differs from the rest.
1. Programming Language for Data Engineering
1.1. Python
Python is one of the most widely used languages in data engineering. It’s simple, flexible, and comes with powerful libraries that make building pipelines and automation tasks much easier.
Key Features of Python
- Huge ecosystem of libraries (Pandas, NumPy, PySpark, Airflow, FastAPI)
- Easy to write, read, and maintain
- Works well for ETL, automation, APIs, and data munging
- Strong community support and constant updates
- Integrates smoothly with big data tools like Spark and Hadoop
Best For: ETL pipelines, automation scripts, API integration, and handling complex data transformations.
1.2. MATLAB
MATLAB is a high-level environment widely used in engineering, scientific research, and data-heavy applications. It offers strong numerical computing and visualisation capabilities, making it useful for data modelling and simulation.
Key Features of MATLAB
- Excellent tools for numerical analysis and algorithm development
- Built-in support for data modelling, simulation, and optimisation
- Strong visualisation features for charts, plots, and dashboards
- Integrates well with multiple data sources and formats
- Ideal for scenarios requiring mathematical precision
Best For: Data modelling, simulation, and engineering workflows requiring advanced numerical analysis.
2.1. Apache Spark
Apache Spark is a fast, distributed processing engine used for large-scale data workloads. It supports both batch and real-time processing, making it a core tool in modern data engineering.
Key Features of Apache Spark
- Handles massive datasets quickly across distributed clusters
- Supports batch, streaming, ML, and graph processing in one framework
- Works with Python, Scala, Java, SQL, and R
- In-memory computing improves performance significantly
- Integrates well with Hadoop, Kubernetes, cloud storage, and databases
Best For: Real-time data processing, large-scale ETL, and big data analytics.
2.2. Apache Hadoop
Apache Hadoop is a foundational big data framework known for its ability to store and process massive datasets across distributed clusters.
Key Features of Apache Hadoop
- HDFS provides reliable, fault-tolerant distributed storage
- Scales horizontally to handle growing data volumes
- Open-source and cost-efficient for large data environments
- Supports a wide range of data types, including structured, semi-structured, and unstructured
- Integrates with tools like Hive, Spark, HBase, and more
Best For: Large-scale data storage and distributed processing across commodity hardware.
3.1. Airflow
Apache Airflow is a workflow orchestration tool used to schedule, automate, and monitor data pipelines. It helps data engineers manage complex workflows with clear visibility and control.
Key Features of Apache Airflow:
- Easy-to-build workflows using Python-based DAGs
- Strong scheduling capabilities for batch and recurring tasks
- Built-in monitoring with logs, retries, and alerting
- Integrates with cloud services, databases, APIs, and big data tools
- Ideal for automating repetitive ETL and data processing tasks
Best For: Orchestrating ETL pipelines and managing complex, multi-step data workflows.
4.1. Snowflake
Snowflake is a cloud-based data warehousing platform known for its speed, scalability, and ability to handle massive datasets without heavy infrastructure management. Companies use it for analytics, reporting, and storing structured and semi-structured data in one place.
Key Features Snowflake
- Integration with BI tools like Tableau, Power BI, and Looker
- Fully managed cloud data warehouse with zero maintenance
- Separates storage and compute for cost efficiency
- Scales up or down automatically based on workload
- Supports structured, semi-structured, and JSON-like data
- Secure data sharing across teams and external partners
- Built-in SQL support for analysis and transformations
Best For: Cloud data warehousing, scalable analytics, ELT workloads, and secure data sharing across teams and partners.
4.2. Amazon Redshift
Amazon Redshift is a fully managed cloud data warehouse on AWS, designed for fast SQL analytics across large datasets. It’s widely used by organisations that want high performance without managing on-premise hardware.
Key Features of Amazon Redshift
- Columnar storage for faster query performance
- Massively Parallel Processing (MPP) architecture
- Seamless integration with the AWS ecosystem (S3, Glue, QuickSight)
- Auto-scaling and workload management
- Supports standard SQL and BI tool integrations
- Concurrency scaling for heavy workloads
- Strong security with encryption and VPC isolation
Best For: High-speed SQL analytics on large datasets, especially for teams already using AWS services.
4.3. BigQuery
Google BigQuery is a serverless, fully managed data warehouse built for fast SQL analytics on massive datasets. It’s known for its speed, simplicity, and ability to scale automatically without infrastructure setup.
Key Features of Google BigQuery
- Serverless architecture, no provisioning or maintenance
- Real-time analytics with streaming data support
- Separates storage and compute for flexible pricing
- Native integration with Google Cloud services and Looker
- Supports standard SQL and machine learning with BigQuery ML
- Can query data directly from Google Cloud Storage
- Automatic scaling for large and unpredictable workloads
Best For: Teams needing fast, serverless analytics at scale, especially those working within the Google Cloud ecosystem.
4.4. Amazon Athena
Amazon Athena is a serverless, interactive query service that lets you analyse data directly in Amazon S3 using standard SQL. There’s no infrastructure to manage, just point to your data and run queries.
Key Features of Amazon Athena
- Serverless with zero setup or administration
- Uses SQL via Presto/Trino for fast, distributed querying
- Reads data directly from Amazon S3 (no ETL required)
- Supports multiple formats: CSV, JSON, Parquet, ORC, Avro
- Integrates with AWS Glue Data Catalog
- Pay-as-you-go pricing based on data scanned
- Works smoothly with QuickSight and other BI tools
Best For: Ad-hoc SQL analytics on S3 data without managing servers or a full data warehouse.
5.1. Tableau
Tableau is a leading data visualisation and BI tool known for its intuitive drag-and-drop interface. It helps teams turn raw data into clear, interactive dashboards without heavy coding.
Key Features of Tableau
- Easy, drag-and-drop dashboard and chart creation
- Connects to hundreds of data sources (SQL, cloud warehouses, spreadsheets)
- Real-time data updates and live connections
- Strong data storytelling features with interactive visuals
- Supports calculated fields and advanced analytics
- Robust sharing, publishing, and collaboration options
- Enterprise-grade security and governance
Best For: Creating interactive dashboards and visual analytics for business decision-making.
5.2. Microsoft Power BI
Power BI is Microsoft’s business analytics tool that helps users build interactive reports and dashboards. It’s popular for its ease of use, strong integrations with Excel and Azure, and enterprise-level governance.
Key Features of Power BI
- Drag-and-drop interface for quick report building
- Seamless integration with Excel, Azure, SQL Server, and Microsoft 365
- Large collection of pre-built visualisations
- Real-time dashboards and automated data refresh
- Power Query for powerful data cleaning and transformation
- DAX formulas for advanced calculations
- Strong collaboration and sharing through the Power BI Service
Best For: Teams using the Microsoft ecosystem and anyone who needs fast, interactive business dashboards.
6. Distributed Database & Storage Systems
6.1. Apache Hive
Apache Hive is a data warehousing and SQL-like query system built on top of Hadoop. It allows users to run SQL queries (HiveQL) on large datasets stored in HDFS without writing complex MapReduce jobs.
Key Features of Apache Hive
- SQL-like querying through HiveQL
- Designed for large-scale batch processing
- Works seamlessly with Hadoop and HDFS
- Supports partitioning and bucketing for faster queries
- Handles structured and semi-structured data
- Integrates with Spark, Tez, and other Hadoop ecosystem tools
- Scalable, fault-tolerant, and suitable for long-running queries
Best For: Batch SQL processing on massive datasets stored in Hadoop environments.
6.2. Apache Cassandra
Apache Cassandra is a highly scalable, open-source NoSQL database designed for handling large volumes of data across distributed systems. It ensures high availability with no single point of failure.
Key Features of Apache Cassandra
- Peer-to-peer distributed architecture for fault tolerance
- Handles massive amounts of structured and semi-structured data
- Horizontal scalability for growing workloads
- Tunable consistency levels for flexible data management
- Supports replication across multiple data centers
- High write and read throughput for real-time applications
- Integrates with big data tools like Spark and Hadoop
Best For: Scalable, high-performance NoSQL storage for real-time, distributed applications.
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6.3. MongoDB
MongoDB is a popular open-source NoSQL database that stores data in flexible, JSON-like documents. It’s designed for scalability, high performance, and handling evolving data structures.
Key Features of MongoDB
- Schema-less design for flexible data modelling
- Horizontal scaling with sharding for large datasets
- High availability with replica sets and fault tolerance
- Powerful indexing and aggregation framework
- Integrates with big data and analytics tools like Spark
- Supports multiple programming languages
- Strong community and enterprise support
Best For: Flexible, high-performance data storage and handling of dynamic or evolving datasets.
7.1. Amazon Kafka
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that makes it easy to build and run applications using Apache Kafka for real-time data streaming.
Key Features of Amazon MSK
- Fully managed Kafka clusters with automatic provisioning
- Handles high-throughput, real-time data streams
- Ensures data durability with replication across multiple nodes
- Integrates seamlessly with other AWS services
- Scales automatically to accommodate fluctuating workloads
- Supports stream processing for analytics and monitoring
- Reduces operational overhead for managing Kafka infrastructure
Best For: Real-time data streaming, event-driven applications, and analytics pipelines.
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Conclusion
Data engineering is all about making data useful. The tools we’ve covered, like Python, Spark, Snowflake, Redshift, and Kafka, help engineers organise, process, and analyse data efficiently.
If you want to get practical experience with these tools and learn how data pipelines work in real projects, check out our course on Data Engineering. It’s a great way to start building the skills companies are looking for.
1. Which data engineering tool should a beginner learn first?
Beginners usually start with Python because it’s simple, widely used, and supports almost every data engineering task, from scripting to automation to ETL. Once comfortable, they move on to tools like SQL, Spark, and cloud platforms.
2. Do data engineering tools require strong coding skills?
Not all tools require heavy coding. While technologies like Python, Spark, and Hadoop do involve programming, many others, such as Tableau, Power BI, and BigQuery, offer low-code or SQL-based interfaces.
3. Are cloud-based data engineering tools replacing on-premise tools?
Not entirely, but cloud tools like Snowflake, Redshift, BigQuery, and Athena are increasingly popular because they scale easily, cost less to maintain, and integrate well with modern analytics workflows.
4. What is the difference between ETL and ELT in data engineering?
ETL transforms data before loading it into a warehouse, while ELT loads raw data first and transforms it inside the warehouse. Modern tools like Snowflake and BigQuery are designed for ELT due to their high compute power.
5. How do companies choose the right data engineering tools?
Most organizations consider factors like data size, budget, cloud provider preference, security requirements, and team skill sets. Many end up using a mix of tools rather than relying on just one platform.