Data Engineering: Essential Skills and Tools for 2025
Data engineering is now crucial for all organizations, not just tech companies, as they seek to harness data and AI. A solid foundation in data engineering is key to unlocking that potential.
This guide covers the essential skills, tools, and technologies for becoming an effective data engineer. Whether you're just starting or looking to deepen your expertise, this is your roadmap to modern data engineering.
Programming Languages
SQL
Python and SQL are must-haves for both aspiring and experienced data engineers. SQL, especially in its ANSI-compliant forms, is widely used due to its simplicity and expressiveness, making it a fundamental skill.
Python
Python remains the top choice in the data and AI world. Its simple syntax and powerful built-in features make it ideal for scripting and data manipulation. With a vast array of libraries (e.g. pandas, spark, scikit-learn, pytorch) for data analysis, machine learning, and big data, Python continues to lead the pack in data engineering and AI.
Scala (Bonus)
Scala is an elegant cross between functional and object-oriented programming languages, and runs on the Java Virtual Machine (JVM). Great for robust and large enterprise projects, and still a major footprint in big data frameworks like Apache Spark, don’t sleep on this programming language.
Key Frameworks and Tools
Apache Spark
First and most importantly as far as frameworks go there is Apache Spark. Spark is an open-source analytics and big data processing engine that leverages a distributed computing architecture.
Spark supports Python, Scala, SQL, and R programming languages. You can deploy Spark applications on a variety of cloud computing services such as Amazon EMR, or deploy it yourself with its native Kubernetes support. Spark clusters can be anywhere from 1 to thousands of nodes, and can process data up to petabytes in scale.
>>> from pyspark.sql.functions import col, when
>>> df1 = df.withColumn(
"life_stage",
when(col("age") < 13, "child")
.when(col("age").between(13, 19), "teenager")
.otherwise("adult"),
)
>>> df1.groupBy("life_stage").avg().show()
+----------+--------+
|life_stage|avg(age)|
+----------+--------+
| adult| 53.5|
| child| 3.0|
| teenager| 13.0|
+----------+--------+
Note: PySpark also has strong interoperability with pandas, arrow, and koalas dataframes, making it a powerhouse for data science and machine learning with big data.
PyTorch
PyTorch is a leading deep learning framework, ideal for both research and production environments. PyTorch is extremely relevant for ML and data engineers working on AI-driven data pipelines, model training, and model inference.
Its support for dynamic computation graphs allows for efficient processing of complex data models, while libraries like TorchServe simplify deploying ML models. Don’t think ML frameworks like PyTorch are only for data scientists and ML engineers; data engineers will stand apart from the crowd when equipped with even a basic understanding of ML concepts and popular frameworks like PyTorch.
Streams: Kafka, Kinesis
Many business use cases demand data and updates real-time. Streaming tools like Apache Kafka and Amazon Kinesis are essential tools for modern data engineering. Kafka is widely used for its ability to handle high-throughput, real-time data streams in distributed systems—especially with Kafka being open-source and cloud agnostic.
Amazon Kinesis is a fully managed streaming service, with similar capabilities plus integrations into the AWS ecosystem. Both platforms are invaluable for applications requiring low-latency data processing and event-driven applications, such as IoT, financial systems, and fraud detection, positioning them as critical tools for data engineers navigating real-time data challenges.
Note: while streaming engines like Kafka aren’t going away anytime soon, they are often selected too hastily as the required tool for the job. With modern data formats (e.g. Delta Lake) and query engines (e.g. Spark), I would encourage you not to underestimate the capability of simpler micro-batch processing too.
Delta Lake
Delta Lake is an open-source storage layer that brings reliability and performance to data lakes. It allows data engineers to build scalable and robust pipelines by adding ACID transactions and schema enforcement to the flexibility of data lakes.
Delta Lake enables incremental data processing with time-travel capabilities, making it easy to track changes and roll back to previous versions of data. It also optimizes data lakes with features like compaction, z-order indexing, and liquid clustering, ensuring high performance for queries.
As data lakes grow in size and complexity, Delta Lake is essential for ensuring consistency, reliability, and efficiency in managing large-scale datasets.
Through the Delta Kernel project you can now use Delta Lake through a wide variety of programming languages and tools, such as Rust, Go, Python, Flink, Power BI, and many more, making it an even more versatile engine even in smaller datasets.
Databricks
Remember all the previous tools, frameworks, and languages we just covered? If you could take all of those and so much more, bundle it up into a single unified platform, that platform would be Databricks.
Databricks truly does unify the experience and solutions for data engineering, data analysis, data science, ML engineer / MLOps, and BI and reporting. Founded by original creators of Apache Spark, Databricks earned its respect initially as a highly efficient proprietary runtime for Spark, and made notebooks and jobs simpler and more collaborative than ever. Since then Databricks has continued to add new products to the platform including several favorites such as:
Notebooks with support for Python, Scala, SQL, and R.
Jobs / Workflows with advanced orchestration features.
SQL Dashboards.
Machine Learning model training, evaluation, logging, and model serving.
Gen AI development, evaluation, serving, and guardrails.
Data Governance through Unity Catalog with access control, row-level and column-level security, data lineage, and attribute-based access control (ABAC).
Serverless jobs, SQL, and model serving
Databricks is multi-cloud, supporting AWS, GCP, and partnered with Microsoft Azure. Note: some features may be more available on AWS and Azure than GCP.
Conclusion
There are many more tools I couldn’t list for risk of making this too long; the list above are some of my top picks, especially in 2024/2025.
Several other worthy mentions:
Apache Airflow
Iceberg (similar to Delta Lake)
DLT
Apache Airflow
DuckDB
MLFlow
Terraform
Kubernetes
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