Apache Spark - Install on Databricks

Key Insights

• Databricks eliminates traditional Spark installation complexity by providing a fully managed environment where clusters are provisioned with a single click and Spark is pre-configured and optimized.
• Cluster configuration choices—particularly runtime version, node types, and autoscaling settings—directly impact both cost and performance, making these decisions critical even in a managed environment.
• The Databricks Community Edition offers a free tier sufficient for learning and prototyping, making it the fastest path from zero to running Spark code.

Why Databricks for Spark

Installing Apache Spark traditionally involves downloading binaries, configuring environment variables, managing dependencies, setting up a cluster manager, and troubleshooting compatibility issues. It’s tedious work that distracts from actually using Spark.

Databricks changes this equation entirely. As a managed platform built by the original creators of Spark, Databricks handles infrastructure provisioning, cluster management, and Spark optimization. You get a working Spark environment in minutes, not hours.

This article walks through setting up Spark on Databricks from account creation to running your first job. By the end, you’ll have a functional environment ready for data engineering and analytics work.

Prerequisites and Account Setup

You need a Databricks account. Two options exist:

Databricks Community Edition is free and sufficient for learning. It provides limited compute resources but includes all core functionality. Sign up at community.cloud.databricks.com.

Paid Databricks runs on AWS, Azure, or GCP. Your organization likely already has a workspace if you’re in an enterprise environment. If you’re setting up fresh, you’ll need an active cloud account with appropriate permissions.

For this guide, I’ll use Community Edition. The concepts translate directly to paid tiers.

After signing up, you’ll land in the workspace—Databricks’ web-based interface. The left sidebar contains the key navigation elements:

• Workspace: File browser for notebooks and folders
• Compute: Cluster management
• Data: Database and table explorer
• Workflows: Job scheduling

Spend a few minutes clicking through these sections. The interface is straightforward, but familiarity helps.

Creating a Spark Cluster

Clusters are the compute backbone of Databricks. A cluster is a set of virtual machines running Spark, managed entirely by the platform.

Navigate to Compute in the sidebar and click Create Cluster. You’ll see configuration options that determine your cluster’s capabilities and cost.

Cluster Name: Use something descriptive. I typically include the purpose and my initials: spark-dev-jsmith.

Cluster Mode: Choose between:

• Standard: Multi-node clusters for production workloads
• Single Node: One machine, suitable for development and small datasets

For learning, Single Node works fine and costs less.

Databricks Runtime Version: This is critical. The runtime (DBR) bundles Spark with additional libraries and optimizations. Higher versions include newer Spark releases:

Stick with LTS (Long Term Support) versions for stability. At the time of writing, DBR 14.3 LTS is a solid choice.

Node Type: Determines CPU, memory, and cost per hour. For development, smaller instances like m5.large (AWS) or Standard_DS3_v2 (Azure) suffice.

Autoscaling: Automatically adds or removes workers based on workload. Enable this for production; disable for predictable development costs.

Here’s what a cluster configuration looks like as JSON (useful for automation):

{
  "cluster_name": "spark-dev-cluster",
  "spark_version": "14.3.x-scala2.12",
  "node_type_id": "m5.large",
  "num_workers": 0,
  "autoscale": {
    "min_workers": 1,
    "max_workers": 4
  },
  "spark_conf": {
    "spark.databricks.cluster.profile": "singleNode",
    "spark.master": "local[*]"
  },
  "autotermination_minutes": 60
}

The autotermination_minutes setting is important—it shuts down idle clusters to prevent runaway costs. Set this to 60 minutes or less for development.

Click Create Cluster. Provisioning takes 3-5 minutes. The status indicator turns green when ready.

Verifying Your Spark Installation

With a running cluster, verify that Spark works correctly.

Create a new notebook: Workspace → Create → Notebook. Name it spark-verification and select Python as the default language.

Attach the notebook to your cluster using the dropdown at the top. The cluster name appears with a green dot when connected.

Run these verification commands in separate cells:

# Check Spark version
print(f"Spark Version: {spark.version}")

Expected output:

Spark Version: 3.5.0

# Verify SparkSession is active
print(f"App Name: {spark.sparkContext.appName}")
print(f"Master: {spark.sparkContext.master}")

# Simple computation test
spark.range(10).show()

This creates a DataFrame with numbers 0-9 and displays it:

+---+
| id|
+---+
|  0|
|  1|
|  2|
|  3|
|  4|
|  5|
|  6|
|  7|
|  8|
|  9|
+---+

If these commands execute without errors, your Spark installation is working. The spark variable is pre-configured in Databricks notebooks—no initialization code required.

Running Your First Spark Job

Let’s do something more realistic: read data, transform it, and output results.

Databricks includes sample datasets. We’ll use the diamonds dataset, a classic for demonstrations:

# Read the sample diamonds dataset
diamonds_df = spark.read.format("csv") \
    .option("header", "true") \
    .option("inferSchema", "true") \
    .load("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv")

# Display schema
diamonds_df.printSchema()

Output:

root
 |-- _c0: integer (nullable = true)
 |-- carat: double (nullable = true)
 |-- cut: string (nullable = true)
 |-- color: string (nullable = true)
 |-- clarity: string (nullable = true)
 |-- depth: double (nullable = true)
 |-- table: double (nullable = true)
 |-- price: integer (nullable = true)
 |-- x: double (nullable = true)
 |-- y: double (nullable = true)
 |-- z: double (nullable = true)

Now perform a transformation—calculating average price by cut quality:

from pyspark.sql.functions import avg, round

# Aggregate: average price by cut
price_by_cut = diamonds_df.groupBy("cut") \
    .agg(round(avg("price"), 2).alias("avg_price")) \
    .orderBy("avg_price", ascending=False)

price_by_cut.show()

Output:

+---------+--------+
|      cut|avg_price|
+---------+--------+
|  Premium| 4584.26|
|     Fair| 4358.76|
|Very Good| 3981.76|
|     Good| 3928.86|
|    Ideal| 3457.54|
+---------+--------+

This demonstrates the complete Spark workflow: read, transform, action. The code runs distributed across your cluster, even though it looks like ordinary Python.

Configuration and Optimization Tips

Databricks pre-configures Spark with sensible defaults, but you’ll often need to adjust settings.

Runtime Configuration

Set Spark configuration values within a notebook:

# Enable adaptive query execution (usually on by default)
spark.conf.set("spark.sql.adaptive.enabled", "true")

# Increase shuffle partitions for larger datasets
spark.conf.set("spark.sql.shuffle.partitions", "200")

# Check a configuration value
print(spark.conf.get("spark.sql.shuffle.partitions"))

For settings that must apply at cluster startup, add them to the cluster configuration under Spark Config:

spark.sql.shuffle.partitions 200
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.sql.adaptive.coalescePartitions.enabled true

Installing Libraries

Install Python packages directly in notebooks using the %pip magic command:

%pip install pandas-profiling pyarrow

This installs packages on all cluster nodes. The installation persists for the cluster session but resets on restart.

For Java/Scala libraries, use Maven coordinates in the cluster’s Libraries tab:

org.apache.spark:spark-avro_2.12:3.5.0

Init Scripts

For complex setup requirements—installing system packages, configuring environment variables, or running custom scripts—use init scripts. Create a script in your workspace:

#!/bin/bash
# init-script.sh
echo "export CUSTOM_VAR=production" >> /etc/environment
apt-get update && apt-get install -y jq

Reference it in cluster configuration under Advanced Options → Init Scripts.

Environment Variables

Set environment variables in the cluster configuration:

ENVIRONMENT=development
API_ENDPOINT=https://api.example.com
LOG_LEVEL=INFO

Access them in notebooks:

import os
env = os.environ.get("ENVIRONMENT", "default")
print(f"Running in {env} mode")

Next Steps and Resources

You now have a working Spark environment on Databricks. Here’s where to go next:

Official Documentation

• Databricks Documentation: Comprehensive reference for all platform features
• Apache Spark Documentation: Core Spark concepts and API reference

Structured Learning

• Complete the Databricks Academy free courses, particularly “Apache Spark Programming with Databricks”
• Work through the /databricks-datasets/ sample data to practice transformations

Practical Next Steps

1. Connect to your own data sources (S3, Azure Blob, databases)
2. Experiment with Delta Lake for reliable data lakes
3. Set up scheduled jobs using Databricks Workflows
4. Explore the MLflow integration for machine learning experiments

The managed nature of Databricks means you can focus on learning Spark’s programming model rather than fighting infrastructure. Use that advantage. Write code, break things, iterate quickly. The cluster will be there when you need it.