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What is Databricks and Why It Matters in Modern Data Work

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•5 min read
What is Databricks and Why It Matters in Modern Data Work
T
Toni Uimonen

Computer science, web & software development

Data is everywhere. Every click, purchase, and sensor generates a continuous stream of information that businesses aim to leverage for better decision-making. The challenge lies in the sheer volume of data and its dispersion across various systems.

Many organizations find that traditional tools can no longer keep pace, different teams use disparate tools, and data may be difficult to trust.

Databricks offers a novel approach to this problem. It is a platform that unifies data processing, analytics, machine learning, and even advanced AI models, agents, and tools in one place. Practically, this means data engineers, analysts, and data scientists can collaborate seamlessly without the need to transfer data between different tools.

Databricks in a Nutshell

Databricks is a cloud-based analytics platform built on Apache Spark technology. Spark is renowned for its ability to process massive amounts of data in parallel and at speed. The founders of Databricks were also the original developers of Spark at the University of California, Berkeley's AMPLab.

The core idea behind Databricks is simple:

Make data processing, analytics, and modeling easier and more collaborative, regardless of where the data resides.

In practice, Databricks operates in a browser, though it also offers API and CLI options. Work is conducted within a workspace, a shared environment for teams to manage data and resources. Users can create notebooks—interactive documents that combine code, text, and results—similar to Jupyter Notebooks but with a broader language selection. Code execution occurs either in an automated serverless cluster or a traditional user-managed cluster, which is a virtual computing environment running in the cloud (Azure, AWS, or Google Cloud).

Why Databricks is Compelling

Databricks' strength lies in its ability to integrate multiple facets of data work into a single platform.

Previously, data engineers built data pipelines, data scientists worked in their own environments, and analysts created reports elsewhere. Databricks brings these roles closer together.

Here are some reasons why Databricks has gained significant attention:

  1. Scalability – The same environment suits both small data experiments and massive datasets. The serverless feature enables dynamic scaling without the user needing to manage cluster settings.

  2. Collaboration – Multiple users can work in the same notebook, share code, comment, and manage projects within the team according to access rights.

  3. Reliability and Manageability – Databricks' proprietary Delta Lake technology allows for data versioning and integrity.

  4. Machine Learning and AI – The platform supports MLflow tools, traditional machine learning, generative AI (GenAI), and AI agent development. Training models on large datasets is straightforward, and serverless clusters can simplify computational management.

  5. Usability – Notebook-based workflows are intuitive, and the language selection (Python, SQL, R, Scala) makes the platform flexible.

A company can thus combine Databricks with data from, for example, ERP systems, e-commerce transactions, and sensor data into a single view, running both batch and real-time analytics reports and forecasts without separate transfer steps.

Key Concepts to Know

Several fundamental concepts in Databricks are encountered quickly:

  • Workspace – A workspace is where projects, notebooks, and files reside. The environment is shared among the team, facilitating collaboration.

  • Notebook – A document that combines code, text, and results.

  • Cluster – A distributed computing environment running in the cloud that executes code in parallel across multiple nodes. Both traditional and serverless clusters are available.

  • DBFS (Databricks File System) – Databricks' proprietary file system where data and resources can be stored.

  • Delta Lake – A file format that enhances data processing reliability. It supports ACID properties, versioning, and both batch and stream processing.

Practical Example

Here's a simple example.

You want to load store data and sales region data in Databricks and see how they relate:

from pyspark.sql.functions import col

# Read the CSV files and select/rename relevant columns
df_stores = spark.read.format('csv').option('header', 'true')\
    .load("/FileStore/data/stores.csv")\
    .select("store_id", col("store_name").alias("store"), "city", "region_id")

df_sales_regions = spark.read.format('csv').option('header', 'true')\
    .load("/FileStore/data/sales_regions.csv")\
    .select(col("region_id").alias("id"), col("region_name").alias("region"))

# Left join stores with sales regions
df_final = df_stores.join(df_sales_regions, df_stores.region_id == df_sales_regions.id, "left")\
    .select("store_id", "store", "city", "region")

# Save the final DataFrame as a Delta table
df_final.write.mode("overwrite").saveAsTable("retail_stores_with_regions")

This code accomplishes several tasks in a few lines: it reads the store and sales region CSV files from Databricks' file system, selects and renames the relevant columns, performs a left join to combine store information with region data, and prepares a final DataFrame. Finally, it saves the resulting table as a Delta table that can be queried directly in Databricks.

All of this runs in the cloud and there’s no need to execute the code on your local machine, as Databricks handles the distributed computation and storage in the background.

Why Databricks' Popularity is Growing

Many companies have transitioned to using Databricks because it integrates various stages of data work, making complex processes more manageable. Databricks' Lakehouse architecture allows for unified data processing and supports both batch and real-time analytics.

Databricks collaborates well with other tools. It can be used alongside Microsoft Fabric or Azure Synapse Analytics, and supports numerous popular libraries like Pandas, Matplotlib, and scikit-learn, as well as modern AI and ML tools such as PyTorch, TensorFlow, and Hugging Face models.

Summary

Databricks is primarily an integrative platform where data can be collected, processed, modeled, and shared collaboratively. It offers a technically efficient yet practically approachable way to work with large datasets.

As of 2025, Databricks has become one of the world's most valuable private technology companies, valued at $100 billion, and is on track to achieve $4 billion in annual revenue due to increased demand for its AI products.

If you're interested in learning more, the easiest way is to try out Databricks Free Edition. It’s a free trial of the Databricks environment where you can run your own experiments and get familiar with the interface.

Additional Reading

#databricks#data-engineering#machine-learning#big-data#pyspark

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