Apache Spark ETL: 2 Methods to Build Pipelines That Scale

Apache Spark has become the default engine for large-scale ETL. Thousands of companies, including 80% of the Fortune 500, run Spark. Uber alone runs over 2 million Spark applications per day, powering everything from ETA models to fraud detection.

In fact, many data engineers on Reddit still describe Spark as the “best general-purpose framework for distributed big data processing,” especially for heavy ETL jobs and advanced analytics workloads. 

But there is a practical question practitioners keep asking: do you build your Apache Spark ETL pipeline yourself using PySpark, or do you offload the pipeline management entirely to a tool like Hevo?

Both approaches work. They just suit different teams and different scales.

This post covers both paths. You’ll understand how to build an Apache Spark ETL pipeline with PySpark, and how Hevo’s no-code ELT platform handles the same job without a single line of code. By the end, you’ll know exactly which approach fits your use case.

What is Apache Spark?

Apache Spark is an open-source distributed computing engine built for large-scale data processing. It handles batch jobs, real-time streaming, SQL queries, machine learning, and graph analytics, all from a single platform. 

What sets Spark apart from older frameworks like Hadoop MapReduce is its in-memory processing approach. Instead of constantly reading from and writing to disk, Spark keeps data in memory whenever possible, making it dramatically faster for large-scale workloads. In fact, Spark has sorted 100 TB of data three times faster than Hadoop MapReduce. 

Spark can run on Hadoop, Kubernetes, EC2, or standalone. It also powers Databricks, which has become the dominant managed Spark environment for enterprise teams. 

Where Spark fits in your stack

In most modern stacks, Spark sits in the transformation layer. It pulls raw data from sources, processes it at scale, and loads it into a destination like Snowflake, BigQuery, or a data lake. That’s the ETL pattern.

Spark makes most sense when your transformations are too complex or too large for SQL alone. Think multi-terabyte joins, custom ML feature engineering, or unstructured data parsing. For straightforward structured data movement, a managed ELT tool often gets you there faster. That trade-off is exactly what this article unpacks.

Want to understand how ETL pipelines work before building one? Read our guide on what ETL pipelines are and how they’re structured.

Benefits of Building ETL Pipelines with Apache Spark

Apache Spark can process large datasets quickly. For teams dealing with large, complex, or mixed data workloads, it has some real structural advantages over traditional tools. Here is what matters most. 

In-Memory Processing Cuts Pipeline Time Significantly 

Spark processes data in memory rather than reading and writing to disk between steps. This makes it nearly 100 times faster than Hadoop MapReduce for many workloads. This makes a meaningful difference when you are running multi-stage transformations across large datasets. 

Batch and Streaming in One Engine 

Most pipelines start as batch jobs and eventually need real-time capability. With Spark, you don’t rebuild from scratch. The same engine handles both. Spark Streaming and Structured Streaming let you process live data with the same APIs you use for batch, which keeps your codebase manageable as requirements grow. 

Built-In Fault Tolerance 

Spark’s architecture is built around Resilient Distributed Datasets (RDDs). If a node fails mid-job, Spark recomputes only the lost partition, not the entire pipeline. This fault-tolerant architecture keeps data flowing even when individual nodes or sources fail. For production pipelines, that resilience matters.

Scales Horizontally as Your Data Grows 

You do not need to redesign your pipeline when your data doubles. Spark adds compute nodes and redistributes work across the cluster. It runs on Hadoop, Kubernetes, AWS EMR, or Azure HDInsight — so you are not locked into one infrastructure model. This is one reason why ETL at scale consistently points back to Spark as the default engine. 

Native CI/CD Integration 

Spark fits cleanly into automated deployment workflows. You can version your transformation logic, run tests against it, and deploy through the same CI/CD pipelines your engineering team already uses. This reduces manual errors and keeps data delivery consistent across environments.

One API Across Multiple Languages 

Your team isn’t locked into one language. Spark supports Python (PySpark), Scala, Java, and R. Data engineers can write in PySpark while data scientists work in the same ecosystem using Scala or R. No context switching, no separate toolchains.

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How to Create an Apache Spark ETL Pipeline 

There are two ways to build an Apache Spark ETL pipeline. The first is writing it yourself using PySpark. The second is using a managed ELT platform like Hevo to handle the pipeline for you.

Neither approach is universally better. 

The right choice depends on your data complexity, team size, and how much infrastructure you want to own. Here’s how they compare:

	PySpark (DIY)	Hevo (Managed ELT)
Setup time	Hours to days	Under 5 minutes
Code required	Yes, Python/Scala	No
Transformation flexibility	Full, custom logic	UI-based + Python transforms
Infrastructure management	You own it	Fully managed
Fault tolerance	Built-in, needs tuning	Automatic, out of the box
Best for	Complex, large-scale, custom pipelines	Structured data movement to a warehouse
Maintenance overhead	High	Low
Scalability	Manual cluster tuning	Auto-scales
Cost model	Compute + engineering time	Subscription, event-based pricing
Monitoring	Spark UI + custom setup	Built-in dashboards and alerts

If you’re moving structured data from a known source to a warehouse, Hevo gets you there faster with less risk. If you need full control over complex transformations at scale, PySpark is the right tool. 

2 Easy Methods to Create an Apache Spark ETL

Method 1: Using PySpark ETL to Set Up Apache Spark Integration

This method uses PySpark to extract JSON data, apply transformations, and load the output into a PostgreSQL database. It gives you full control over every step — useful when your transformation logic is complex or highly custom.

Before you begin, make sure you have Python, PySpark, and psycopg2 installed. You will also need access to a running PostgreSQL instance.

Step 1: Extract: Read JSON Data into a DataFrame 

Start by initializing a Spark session and reading your source data. Spark can infer the schema of a JSON file automatically, so you don’t need to define it manually.

python
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("ETL Pipeline") \
    .getOrCreate()

# Read JSON file into a DataFrame
df = spark.read.json("data/population.json")

# Preview the schema and data
df.printSchema()
df.show(5)

SparkSession became the primary entry point for Spark applications in Spark 2.0, replacing the older SQLContext pattern and simplifying Spark development. If you’re referencing older tutorials that still use SQLContext, they’re outdated. 

Step 2: Transform: Clean and Filter the Data 

Transformation is where you shape the data to match your business requirements. This might mean filtering rows, dropping nulls, renaming columns, or joining tables.

In this example, we filter the dataset to include only adults (age 18 and above) and drop any rows with null values:

python
df_transformed = df.filter(df["age"] >= 18).dropna()
df_transformed.show()

If you do not need to modify the data, you can skip this step and pass the raw DataFrame directly to the load stage. But in most real-world ETL pipelines, some transformation is always required — even if it is just schema normalization. 

Step 3: Load: Write to PostgreSQL 

Once the data is transformed, load it into your PostgreSQL database using psycopg2.

python
import psycopg2

conn = psycopg2.connect(
    dbname="your_db",
    user="your_user",
    password="your_password",
    host="localhost",
    port="5432"
)
cursor = conn.cursor()

cursor.execute("""
    CREATE TABLE IF NOT EXISTS population (
        name VARCHAR(100),
        age INT
    )
""")

rows = [(row["name"], row["age"]) for row in df_transformed.collect()]
cursor.executemany("INSERT INTO population (name, age) VALUES (%s, %s)", rows)

conn.commit()
cursor.close()
conn.close()

Note: collect() pulls data into driver memory and should only be used for small datasets or demos. For production-scale ETL pipelines, use Spark’s JDBC write method instead of collecting rows locally.

Best Practices for PySpark ETL Pipelines 

Follow these when moving from a local script to production:

• Partition your data thoughtfully. Too few partitions underuse your cluster. Too many create overhead. A good starting point is 2x the number of CPU cores available.
• Use schema enforcement. Don’t rely on schema inference in production. Define your schema explicitly with StructType to catch upstream changes early.
• Monitor with the Spark UI. Every Spark job exposes a web UI at localhost:4040 by default. Use it to identify slow stages, skewed partitions, and memory pressure.
• Handle failures gracefully. Wrap your load step in try/except blocks and log failures. Spark will retry tasks automatically, but application-level errors need explicit handling.

Need a deeper look at how ETL pipelines are structured? See our guide on ETL architecture and ETL best practices.

Method 2: Using Hevo for Setting Up Your ELT Pipelinee

Writing and maintaining PySpark code works well when you need custom transformations at scale. But for most structured data movement jobs, it’s more complex than the problem requires.

Hevo Data is a no-code ELT platform that connects 150+ sources to your data warehouse in minutes. It handles extraction, loading, and transformation without you writing or maintaining pipeline code. Fault-tolerant by design, it keeps data flowing even when sources fail or schemas change upstream.

Here’s how to set up a pipeline in Hevo.

Step 1: Connect Your Source

Log into Hevo and select your data source from the integrations library. This covers databases, SaaS tools, file storage systems, and streaming sources. Authentication and schema detection are handled automatically.

Step 2: Configure Your Destination 

Select your destination warehouse. Hevo supports Snowflake, BigQuery, Redshift, Databricks, and others. Enter your connection details and Hevo maps the source schema to the destination automatically.

Schema changes at the source? Hevo detects them and handles them without breaking the pipeline. No manual intervention required.

Step 3: Set Your Pipeline Frequency 

Choose real-time sync or a scheduled batch frequency depending on your use case. For most ETL automation workflows, Hevo defaults to near-real-time ingestion with incremental syncs after the initial historical load.

Step 4: Apply Transformations if Needed 

If you need to clean or reshape data before it lands in the warehouse, Hevo’s transformation layer lets you write Python or drag-and-drop logic in the UI. You can rename columns, filter rows, parse nested JSON, or apply custom business logic.

If your transformations are SQL-based, you’re better off applying them in the warehouse after loading. That’s the ELT pattern, and it keeps your pipeline simple and your transformation logic version-controlled in DBT or similar tools. For more on this, see ETL vs ELT.

Step 5: Monitor Pipeline Health

Once live, Hevo gives you real-time pipeline visibility: record counts, sync status, schema change alerts, and failure notifications across your devices. If a record fails, Hevo flags it and retries automatically. No manual intervention needed.

The entire setup, from source connection to first data sync, typically takes under five minutes for standard integrations.

When to Use Hevo Over PySpark:

• Your team does not have Python or Spark expertise in-house
• You are connecting standard SaaS sources or databases, not custom data formats
• You need a pipeline running today, not next sprint
• You want pipeline reliability without writing retry and monitoring logic from scratch

Thinking about the cost of building and maintaining ETL pipelines in-house? That is worth reading before you commit to the DIY path.

Challenges of building ETL pipelines with Apache Spark

The ETL challenges with Apache Spark are as follows-

• Resource Management: You will face performance bottlenecks if the resources are not managed properly. Memory management may be a bit tricky since a failure in Spark jobs could either be due to insufficient memory or inefficient use.
• Steeper Learning Curve: It is complex to understand, and Spark requires significant time and effort to understand its ETL architecture, APIs, and best practices.
• Performance Tuning: Sensitive parameters related to memory, parallelism, and data partitioning have to be fine-tuned to obtain good performance from Spark.
• Writing and Maintaining Code: The complexity can make the ETL code difficult to write and maintain over time. Additionally, Spark is difficult to tune, which can add to the complexity and maintenance challenges.

Conclusion

This article outlined a basic Apache Spark ETL process using PySpark from a single source to a Database. However, in reality, you will be dealing with multiple disparate sources. You will need several complex transformations performed on the fly. You might have to load data into a Cloud Data Warehouse. All of this can get very complicated. It requires a lot of expertise at every level of the process. But Hevo Data can guarantee you smooth storage and processing.

Hevo is a No-code data pipeline. It has pre-built integrations with 150+ sources. You can connect your SaaS platforms, Databases, etc. to any Data Warehouse of your choice, without writing any code or worrying about maintenance.

Want to take Hevo for a spin? Sign up for a 14-day free trial and experience the feature-rich Hevo suite first hand.

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