From best-in-class to hidden gems - these are the best tools a data engineer needs.
What’s the difference between knowledge and wisdom?
Knowledge is knowing Excel is Turing-complete. Wisdom is not using Excel as your database.
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:
Be warned, the article is highly opinionated and offers a perspective on how to automate data-driven processes to the fullest extent.
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.).
Other tools we recommend you to consider for data processing are:
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).
The following tools were all contenders for the best data engineering tool for data transformations:
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.
Not every organization will be able to run Snowflake (buy hey, you can do it for free in Keboola the Snowflake partner), so we showcase other data storage options.
The following tools were all contenders for the best data engineering tool for data storage:
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.
The following tools were all contenders for the best data engineering tool for data analytics and business intelligence:
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.
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.
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.
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.
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.
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.
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.
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.
The problem with the best data engineering tools is that they often don’t work well together.
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.
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