The best data engineering tools make your life easier. Speed up processes, simplify complex operations, give you insights into the machinery, and maybe save some $ along the way.
In this article, we’ll give you an overview of the 7 best data tools for data engineering use cases. Concretely, we’ll analyze the best data tools for:
Data processing and pipeline building
Data analytics and BI
Machine learning engineering
General-purpose programming language
Be warned, the article is highly opinionated and offers a perspective on how to automate data-driven processes to the fullest extent.
Best tool for data processing: Keboola
Data processing is a wide term encompassing a wide range of data operations including data integration, ETL, ELT, reverse ETL, and building data pipelines for other purposes.
The best data engineering tool for data processing is Keboola, the data platform as a service. With its plug-and-play design, you can construct data processing workflows with a couple of clicks and fully extend and automate them with developer tools (CLI, CI/CD, etc.).
Key features of Keboola:
Set up and run your data pipelines in minutes using 250+ pre-built components. The platform automates all the data processing tasks. Extract, transform, and load data with a couple of clicks.
For both tech savvy users and business teams. Keboola offers both code (Python, SQL, R, Julia, dbt, CLI) and no-code (visual builder, canned transformations) solutions to build data pipelines. Effectively tearing down silos and empowering your technical and non-technical coworkers to build and automate data-driven flows by themselves.
Automated data infrastructure provisioning. No need to worry about DevOps and DataOps. Keboola provides job orchestrators, sets up monitoring, tracks data lineage, builds development branches, versions data and code, and deploys your jobs without having to lift a finger.
Extensibility. Other than data pipelines, Keboola offers a myriad of features out of the box: enterprise-grade security, machine learning toolboxes, data storage replication, the Data Catalog for documentation and sharing, … Effectively, you can extend Keboola to set up, run, and automate all your data operations.
Other tools we recommend you to consider for data processing are:
Apache Kafka - a messaging broker that is ideal for real-time data stream processing.
Segment - a customer data platform (CDP) that offers steep pricing, but works great for building customer profiling.
Informatica - an ETL tool like Keboola, but for huge enterprises (think IBM, Microsoft, etc.).
Best tool for data transformations: dbt
dbt (Data Building Tool) is an open-source tool that simplifies data transformation following software engineering best practices like modularity, portability, CI/CD, and documentation.
dbt empowers data engineers and data analysts to transform data in the warehouse through SQL code, which it then converts to models (datasets).
Key features of dbt:
SQL-based. With dbt, you write all your data transformation as SQL queries. The Structured Query Language is well known and it is easy to pick up by engineers, analysts, and business experts.
Dynamically generated documentation. dbt automatically generates documentation for all your transformations, so it also serves as a data management tool.
Out-of-the-box testing. Deploy tests in dbt SQL alongside your transformations to assert data integrity, referential constraints, and semantic validity.
Versioning. Integrate dbt with git to keep track of all data model changes. You can always revert to the previous version if you mess anything up.
Production-ready. Organize your work with dbt’s repositories so you can set up a development, staging, and production environment, to run transformations under the same production-level standards as other software projects.
The following tools were all contenders for the best data engineering tool for data transformations:
Matillion - a data integration tool that can build ETL data pipelines through a simple no-code/low-code drag-and-drop GUI. The graphical interface speeds up transformation building via canned transformations.
AWS Athena - a serverless query engine that runs as part of Amazon Web Services and empowers you to perform transformations and data analysis with SQL over Amazon-supported storages (S3 buckets, Hive, …).
Try dbt in Keboola for free. Set up and run your data process in minutes, no credit card required.
There are many file systems, databases, data warehouses, and data lakes to choose from as candidates for the best data storage solution. So why do we think Snowflake is the best?
Because it’s the all-in-one data storage and analytics engine that scales seamlessly with big data volumes. The cloud-based data warehouse can take care of all your storage and data analytics needs via a simple SQL interface that grows with your data needs.
Key features of Snowflake:
Universal storage. Snowflake can be used as a data lake (supports unstructured data storage), as multiple data warehouses, and even as a database. The universality of this data storage architecture is unprecedented.
Massively Parallel Processing (MPP). Snowflake processes SQL queries using MPP (massively parallel processing) compute clusters where each node in the cluster stores a portion of the entire data set locally.
Cloud-agnostic. Snowflake - unlike Google BigQuery, Microsoft Azure, Amazon Redshift, or other data warehousing solutions - can run on any cloud provider.
The following tools were all contenders for the best data engineering tool for data storage:
MongoDB - a distributed NoSQL database, that offers extremely low-latency consistency of write and read processes over its nodes (but doesn’t offer constant availability).
PostgreSQL - the best relational database that covers a wide variety of data analytics use cases via add-ons (e.g. PostGIS for spatial analytics)
Best tool for data analytics and business intelligence: Tableau
A good data analytics and business intelligence tool goes beyond pretty data visualizations. It helps you analyze data, track KPIs, and keep a finger on the pulse of the business.
Tableau is the best BI and data analytics tool. Its combination of intuitive user experience with a powerful analytic engine and striking visualizations make it the top contender for the best BI tool.
Key features of Tableau:
Beautiful and practical data visualizations. Beauty is in the eye of the beholder. But so is the truth. Tableau allows you to build and customize dashboards and visualizations that quickly explain the story behind the data.
Scales seamlessly. Tableau can compute metrics and generate reports over large datasets without compromising the speed of data visualization updates.
Intuitive user interface. Tableau is designed with the no-coder in mind. It can easily be picked up by technical and non-technical profiles alike, offering an intuitive user experience.
The following tools were all contenders for the best data engineering tool for data analytics and business intelligence:
Matplotlib and seaborn - Two open-source Python visualization tools that need more hands-on coding, but are extremely well-paired with analytics.
PowerBI - a staple among BI apps, PowerBI offers a powerful analytics platform (that, unfortunately, runs only on Windows).
Best tool for machine learning: JupyterLab
JupyterLab is an open-source web-based interactive development environment for Jupyter Notebooks, code, and data.
JupyterLab is centered around Jupyter Notebook, the favored tool among data scientists. It can run Python, R, Julia, or a dozen more data science workflows and incorporate the scientific and machine learning libraries and APIs from Python into the notebook.
Key features of JupyterLab:
Interactive output. The notebooks can be exposed as HTML, images, videos, LaTeX, and custom MIME types.
Integrates big data tools. JupyterLab easily leverages big data tools, such as Apache Spark, Python (TensorFlow, scikit-learn, and other libraries), R, and Scala.
Another tool is usually mentioned instead of JupyterLab. Namely, Apache Spark, the open-source analytics engine for big data processing. Apache Spark is usually preferred for extremely large datasets since its Apache Hadoop architecture scales more seamlessly. But because Apache Spark can be incorporated into JupyterLab, we chose the latter as the best machine learning tool.
Best tool for orchestrating workflows: Apache Airflow
Long gone are the days when Cron jobs were the best thing since sliced bread. Nowadays, the best workflow orchestrators provide fine-grained triggers, monitoring, data quality tests, and an extensibility framework.
This is why Apache Airflow is the best data tool for orchestrating workflows. The Python-based DAGs allow you to author, schedule, and monitor workflows.
Key features of Apache Airflow:
Dynamic pipeline definition. Workflows are written as Python DAGs, so they can dynamically cover a wide range of data pipelines.
Extensible. With operators, you can extend the functionality of Airflow to cover other use cases as well as use the data tool as a data integration platform.
Monitored. The visual UI helps you determine how your jobs are performing. But you can also set up alerts and re-tries for failed jobs.
Keboola - Keboola Orchestrators can run Python, R, Julia, SQL, dbt, and other workflows. But as we said before, Keboola is not just a data pipeline tool. Keboola is a data platform as a service that helps you automate all your data operations, not just orchestrate workflows.
Best programming language: Python | SQL | Scala
Unfortunately, there is not a single programming language that is the best one for all data engineering. Let’s check why one is preferred over another.
1. Python - the best data engineering language for scientific computing
Python is a high-level, general-purpose programming language. It has been established as the go-to language for scientific computing. And it is one of the preferred tools for data scientists since so many machine learning algorithms are available via Python.
It also doubles as a great language for data engineers. As a fully-fledged programming language and a rich set of libraries, you can build backends and frontends for your data engineering apps in a single language.
2. SQL - the best data engineering language for data modeling and engineering analytics
SQL cannot build apps as Python can. But it has become the Lingua Franca of data storage and analytics. Knowing SQL will empower you to query almost every data storage and build data models in data warehouses.
3. Scala - the best data engineering language for large-scale data workflows
Scala is harder to read and write than Python or SQL. As a descendant of Java, it combines both object-oriented and functional programming paradigms.
The more keystrokes are justified, though. Unlike Python or SQL, Scala can be used to author production-ready large-scale data workflows. It scales seamlessly. Producing code that runs at a fraction of the interpreted and declarative contestants.
One programming language to rule them all?
Ultimately, the choice between Python, SQL, or Scala depends more on your data architecture and company needs. All three are powerful languages but are best used for different purposes. Pick Python for machine learning pipelines, SQL for analytic engineering, and Scala for scalable backends with low latency and high throughput.
How to use all the best tools at once? With Keboola!
The problem with the best data engineering tools is that they often don’t work well together.
Data lineage breaks as data flows from one tool to another.
Security surfaces increase as you provision user roles for every tool under a different policy.
Observability becomes difficult with different alerting and monitoring systems.
Data governance is a pain because there is no ingrained system to manage all the data tools in one place.
Luckily, Keboola can help you join all your favorite tools into a single data platform, without worrying about security, observability, governance, or lineage.
Did we mention you can use Keboola for free?
Keboola offers an always free tier (no credit card required), so you can start automating your data engineering processes without breaking the piggy bank.