Serverless Data Pipeline: Components, Use Cases, Examples & Challenges

Traditional data pipelines often come with the burden of managing a large infrastructure and the inability to scale during traffic spikes.

Also, frequent patching and server maintenance add to operational overheads, leading to a complex billing system.

Enter serverless data pipelines.

Serverless data pipelines can eliminate the need for infrastructure management by offering a plug-and-play model that scales automatically and encourages usage-based pricing.

It ensures seamless hand-offs in data delivery and allows data teams to focus on reliable, real-time data insights and analytics.

This article discusses what serverless data pipelines are, their benefits, core components, use cases, and how to build them with Hevo.

Finally, it covers challenges, best practices, and real-world examples.

Continue reading to find out.

What Is a Serverless Data Pipeline?

A serverless data pipeline is an upgraded approach to managing data workflows using native cloud-based platforms like Microsoft Azure, AWS, or Google Cloud.

Unlike traditional ETL (extract, transform, and load) systems, these types of data pipelines can manage data operations without complex server infrastructure.

They offer real-time data processing, dynamic resource management, and integrated fault tolerance, which helps further improve the performance of data pipelines.

These capabilities are especially useful for modern businesses that want to simplify the data management process while minimizing overhead expenses and achieving agility and efficiency.
Here’s an example of a serverless data pipeline built on an AWS cloud platform:

Why Teams Are Moving Off Self-Managed ETLs

The demand for ETL tools is continuously rising. According to the Global Market Insights report, it is expected to grow at a CAGR of 13% from 2024 to 2032.

However, with the rise in cloud-based ETL, more teams are moving off self-managed ETLs to a serverless data pipeline.

Let’s understand why:

Cons of self-managed ETLs

• Teams need to set up the infrastructure and consistently monitor its performance.
• Scaling data pipelines is difficult because large volumes of data need manual configuration, which is time-consuming and error-prone.
• Self-managed ETLs have hidden overhead expenses like license fees, infrastructure maintenance costs, and expenses due to unexpected downtime.
• The continuous need to manage the infrastructure shifts the team’s focus from critical tasks, leading to delays in innovation and business progress.

Pros of serverless data pipelines

• Cost-efficient – Teams are billed on real usage, and no overhead expenses for maintenance and upgrades are included in the cost.
• No need to design and maintain infrastructure; the cloud provider maintains and upgrades the data storage system.
• Supports automatic pipeline scaling up and down depending on the data traffic. 
• It requires no manual intervention, as resources dynamically adjust to real-time data patterns. This lets data engineers dedicate their time and efforts to value-driven tasks instead of worrying about managing data manually. 

What Are the Key Components of a Serverless Data Pipeline? 

A serverless data pipeline consists of the following components:

1. Source layer

It is the data source layer in the form of cloud storage like Amazon S3, Google Storage, or Microsoft Azure Blob storage, which stores data in the form of raw files or logs.

This layer could also take structured data from databases like MySQL or MongoDB that support multiple applications.

Real-time data from event streaming platforms is also a hub of unfiltered data that can be processed through serverless data pipelines.

2. Event-based triggers

The system waits for an event like a file upload, system-generated activity, or an API call to start the data processing workflow.

Systems like AWS Lambda or Google Cloud functions can instantly detect events and respond to messages to automatically start data processing.

3. Ingestion layer

Different data pipeline tools like Hevo or AWS Kinesis can collect large amounts of structured and unstructured data from data storage and IoT devices.

This data is then ingested into serverless data pipelines for further processing and analysis.

4. Orchestration management

Data orchestration regulates how the task of processing data flows from one stage to another in a serverless data pipeline.

Teams can use modern tools like AWS Step Functions, Hevo, or Google Cloud Composer to control data workflows.

These smart tools can ensure the proper sequencing of data and automate coordination and consistency in pipelines.

5. Transformation layer

At this stage, the system filters, enhances, and reshapes the data into a format most suitable for business or analytical requirements.

Data teams can implement tools like dbt Cloud, Hevo Data, or AWS Glue to automate data transformation in real-time or in batches.

This layer ensures that the data received downstream is high-quality, organized, and reliable for reporting and decision-making.

Data destination storage and visualization

The system stores the final processed data at the destination layer, making it readily available for analysis and reporting. 

Teams utilize large data warehouses like Snowflake or BigQuery to support large-scale querying and performance.

To convert this processed data into dashboards and reports, you can connect visualization tools like Tableau, Power BI, or Looker and gain real-time data insights.

Security monitoring and logging

Data teams can utilize technologies like AWS CloudWatch, Hevo’s Observability dashboard, and Datadog to monitor the security and health of serverless data pipelines.

These tools continuously track the performance of data processing tools, identify mistakes, and issue security alerts and notifications in case of any failures or latency issues.

Monitoring and logging tools also help maintain data pipeline stability and data accuracy, and provide complete visibility across the system.

How Do Serverless Data Pipelines Work?

The serverless data pipelines create an interconnection between the existing data storage and the target system via a cloud-based platform that automatically maintains the entire data processing workflow.

Here is a breakdown of the process:

1. Event detection

A trigger, like a file upload to the server or a real-time data request initiated, activates the cloud-based service, like AWS S3, to pull the data and start processing.

2. Ingestion 

Pipeline tools like Hevo or AWS Glue immediately pick up the data and ingest it into the serverless data pipelines.

3. Data transformation

The transformation tools clean and modify data based on either pre-defined rules or on the fly in real time.

Hevo allows users to transform data through a visual UI or via code to generate enriched data according to the user’s requirements. 

4. Automatic data delivery

The system pushes the final data onto the destination storage, like a data warehouse or database, for further analysis.

5. Data monitoring

Finally, monitoring tools evaluate performance, log activities, and error encounters in case of delays, failures, or abrupt data anomalies. 

What Are the Everyday Use Cases of Serverless Data Pipelines?

Here are the top three everyday use cases of serverless data pipelines:

1. Automating ETL workflows 

Serverless data pipelines automate ETL workflows by taking raw data from multiple sources, processing, and standardizing it to meet business needs.

It is further pushed into cloud data warehouses like AWS RedShift.

This approach eliminates manual engagement, minimizes errors and delays, and ensures that clean, analytically ready data is always ready for reporting and decision-making.

2. Real-time automation in smart homes with IoT data 

Smart home systems utilize IoT sensors to track energy use, security, and environmental variables.

Serverless data pipelines capture and analyze data in real-time.

This allows the system to automate tasks like lighting adjustments, temperature regulations, and alarm triggering.

Their capacity to efficiently process massive amounts of sensor data enables seamless automation and a responsive smart home experience..

3. Monthly financial data processing with serverless data pipelines

Financial teams rely on serverless pipelines to efficiently process large data in batches using scheduled triggers and established logic.

This helps to deliver accurate and timely reports without the need for manual intervention.

How to Build a Serverless Data Pipeline With Hevo

Hevo offers a simple, robust, no-code, and serverless data pipeline architecture. 

Here’s a step-by-step process to build one with it:

1. Set up the source

Log in to your Hevo account, and the pipeline section will open by default.

If necessary, choose a different region from the user information box before creating your pipeline.

On the pipelines page, click + CREATE PIPELINE, then search for and choose PostgreSQL as the source type.

On the PostgreSQL setup page, input connection details in the following way:

• Pipeline Name (e.g, PostgreSQL Source 2)
• Database host and port (for example, postgresql.westeros.inc, port 5432).
• The username and password for a read-only user.
• Ingestion Mode (Logical Replication, Table, or Custom SQL- Select Logical Replication for database Sync).
• Database name to replicate.

For creating secure connections, enable SSH or SSL and upload the appropriate SSL certificates (CA file, client certificate, and key).

Change advanced settings such as:

• Load Historical Data- to retrieve the entire data on the first run.
• Merge tables- to combine tables with the same name from different databases.
• Include New Tables- to automatically sync new or re-created tables.

Click TEST & CONTINUE to confirm the connection.

If any errors appear, fix them and retest before proceeding to the next step.

2. Choose source objects

Select the tables or objects you wish to replicate.

By default, all are selected.

If any table lacks a primary key, select an appropriate Query Mode in the next step.

Hevo suggests optimal modes, but they may be customized.

3. Select and configure the destination

On the Destination Type page, select your target destination (eg, Amazon Redshift).

You may also click View All Destination Types to select a different option.

Fill in the needed Amazon Redshift connection information, including host, port, user credentials, and database name.

Optionally enter a table prefix for your destination tables, then click CONTINUE..

4. Final settings and launch

At the last stage, you can optionally specify Transformations to clean or improve your incoming data.

The Schema Mapper allows you to evaluate and adjust field mappings.

Once finished, Hevo constructs your pipeline and starts data ingestion from the PostgreSQL Source to your specified destination.

Challenges in Setting Up a Data Pipeline & Best Practices to Overcome Them

Setting up big data pipelines comes with its own set of challenges.

However, by implementing the following best practices, you can ensure a streamlined data processing workflow through the pipelines. 

Challenge 1: Ensuring data quality 

Problem

Inconsistent data formats, pipeline issues, and changing schemas can severely impact the data quality.

Problems like data duplication, transformation, and mistakes in data collection can lead to inaccuracies in business reports and erroneous decisions.

Best practice

To ensure good data quality, teams must include strict data validation and data cleaning levels throughout the pipeline.

Automated warnings with abnormality alerts can detect irregularities in the early stages, while regular audits and reviews can guarantee long-term accuracy even with massive data sets.

Challenge 2: Data integration from multiple sources

Problem

Companies typically gather data across multiple divisions like marketing, finance, sales, and customer support, which can have different formats and structures.

To gain a single data perspective, teams must standardize and integrate data from different sources.

This can lead to integration challenges and compatibility issues when connecting different systems.

Best Practice

To address these challenges, data teams must facilitate platforms that offer pre-built connectors, API support, and flexible mapping.

This practice can allow flawless data integration and processing between cloud services and internal systems.

Challenge 3: Complexities in operations and debugging

Problem

ETL pipelines frequently function on distributed systems that are prone to unpredictable issues like node crashes or network failures.

Additionally, when these issues occur at multiple stages of the pipeline, they become harder to trace.

Similarly, in the case of serverless pipelines, debugging issues without centralized logging could be a challenge.

Best Practice

To address these challenges, data teams can integrate with systems that offer observability and structured logging, like Hevo Data Systems.

While allowing users to gain advantages of data pipelines, these solutions offer unified dashboards and real-time alerts for monitoring critical parameters like error rate and throughput.

This enables faster error detection, analysis, and resolution of issues.

Real-World Examples of Serverless Data Pipelines

Here are the three relatable real-life examples of serverless data pipelines implemented using Hevo:

1. Favor Delivery

Favor Delivery integrated Hevo to strengthen its data infrastructure and automate real-time analytics.

The Hevo teams transformed their manual, Python-based ETL system with Hevo’s low-code, serverless data pipelines, which enabled easy and seamless integration with Snowflake.

This upgrade boosted ETA accuracy, encouraged faster new subscription rollouts, and minimized manual data operations and tasks.

With Hevo controlling the backend infrastructure, Favor’s data team was able to concentrate on developing strategic initiatives, data quality, and delivering business insights more efficiently.

2. Deliverr

Deliverr, a fast-growing e-commerce company, utilized FlyData and RedShift to address data management difficulties resulting from inadequate scalability and manual workflows.

To address this, Emmet Murphy, their staff software engineer, migrated their system to SnowFlake and implemented Hevo for serverless data integration.

This resulted in improved query performance by 25-40% and reliability by 99.98%.

The company also achieved saving two man-weeks of maintenance by using Hevo’s automated pipelines and real-time monitoring.

This shift allowed teams to build data pipelines independently, which could automatically scale seamlessly according to the business requirements.

3. TextExpander

TextExapander, a San Francisco-based company, implemented Hevo to design serverless, cloud-based data pipelines that eliminate the need to maintain infrastructure and integrate data from sources like MongoDB and Google Sheets into SnowFlakes.

Jeff Olsen, the team’s data specialist, leveraged Hevo’s seamless interface and real-time capabilities to empower marketing teams and achieve customer success.

Implementing this strategy also benefited the company by saving time, increasing retention, tracking credits, and making quick data-driven choices.

So, are you ready to build your first serverless data pipeline?

Start your free Hevo trial today and see how easy it is. No infrastructure to manage. Instant scaling or pay-as-you-go.

FAQ’s