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How to Handle the Challenges of Running CI/CD Data Pipelines in the Cloud

From handling the dynamism of big data to privacy, DR and efficient scaling, running a healthy automated data pipeline in the cloud isn’t trivial.

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Sooter SaaluData Engineer
How to Handle the Challenges of Running CI/CD Data Pipelines in the Cloud

Building and operating your CI/CD data pipelines on public clouds can enhance the flexibility and scalability of your development and deployment operations. You can create resources to fit specific use cases and scale up and down as needed.

However, the distributed nature of cloud resources means it's challenging to build and operate these pipelines. You must:

  • Handle the dynamic needs of big data
  • Ensure optimal security and data privacy in the pipeline
  • Design your pipeline to scale efficiently with growing data volumes and processing requirements
  • Ensure consistent data quality with your monitoring and logging standards
  • Integrate existing infrastructure and APIs
  • Embed fault tolerance into your deployed pipelines while implementing disaster recovery measures

This article explores these challenges using the example of a retail company that wants to build CI/CD data pipelines to optimize the speed and efficiency of developing, testing and deploying features for its e-commerce platform's online storefront and backend systems.

Dynamic Handling of Large Data Sets and Models

Your CI/CD data pipelines are meant to function consistently despite workload changes and increased data traffic. Achieving this in practice involves conducting rigorous stress tests while setting adaptable scaling standards to respond to surges in data traffic.

You can use both vertical and horizontal scaling on public clouds. Vertical scaling (scaling up) increases resource capacity, whether by expanding computational power or storage, so that a single system operates better under larger workloads. Horizontal scaling (scaling out) uses parallel data processing to share the workload across multiple systems or nodes. Mixing these scaling methods allows you to meet different needs.

In the retail company example, you can vertically scale components in your CI/CD data pipeline to handle more storage or deal with specialized tasks that require high computation power. Horizontal scaling can be used for tasks and jobs that can be partitioned or split between parallel functioning nodes, such as data ingestion, transformation and model training.

Depending on your CI/CD platform, horizontal scaling may require more skill and effort to implement and maintain because both upstream and downstream services in your pipeline need to be built to handle distributed and parallel operations. More configuration might be needed to load balance your distributed workloads, ensure backups for failed nodes and maintain coordination between your multiple systems.

Vertical scaling, however, can be less resilient with its single point of failure. It's more limited, as there is a physical limit to how much capacity you can scale to, and the price to implement and maintain it gets progressively higher. You get diminishing returns on performance as more resources are attached due to other bottlenecks. For example, no matter how high the bandwidth, throughput can still be a limiter, as the actual transfer time for the data would depend on factors like the efficiency of the network infrastructure, the protocols used for data transfer and any processing overhead.

Data Privacy and Security

Since CI/CD data pipelines operate with sensitive data, they need to be protected from unauthorized access. Particularly in industries with strict data regulations, your infrastructure for processing and integrating data should be subjected to security controls and meet data sovereignty and residency standards.

For example, your fictional retail company must take care with processing customer data, especially payment data and other personally identifiable information (PII). Retail companies must also comply with regulations such as the Payment Card Industry Data Security Standard (PCI DSS) to ensure the security of cardholder data.

Virtual private networks and segmented networks that isolate sensitive data traffic help ensure the security of your CI/CD data pipelines. Integrating encryption into your pipeline's network further protects your data in transit and mitigates the risk of data interception. Dedicated hardware, such as Hardware Security Modules (HSMs) or Trusted Platform Modules (TPMs), adds cryptographic operations and secret key management as an extra layer of protection to sensitive data processing.

When it comes to data sovereignty, residency and localization standards, region-specific cloud infrastructure and cloud-adjacent storage help you maintain control over your sensitive data, minimize data movement across geographical boundaries and ensure data proximity. It also benefits the scalability and agility of cloud services.

Performance Bottlenecks in Data Processing and Storage

Your CI/CD data pipeline consists of different components that are ideally integrated to operate well together. However, performance bottlenecks can occur when certain components cannot effectively handle workloads due to surges or inefficient resource allocation.

You can use your cloud provider's autoscaling features to optimize your pipeline's resource allocation more effectively. Autoscaling your compute instances and containerized environments, among other components, allows resources to be dynamically allocated based on changing workload demands. For example, the retail company mentioned earlier will use autoscaling to deal with demand surges during peak customer seasons. On-demand services for flexible provisioning can help optimize performance and manage costs.

Using data partitioning and sharding to distribute workload across nodes and storage partitions can also minimize the load on individual machines and reduce bottlenecks. In the example retail company, partitioning and sharding measures can be used to enable parallel processing and distributed computing for customer data and internal workloads.

Also, consider high availability for your mission-critical components. It involves implementing redundancy to remove single points of failure and adding failover mechanisms to ensure as little downtime as possible during service failures and disruptions. In the example retail company, key customer-facing or internal components might use high-availability systems for near-zero downtime assurance. 

And don't neglect interoperability between your pipeline components since clashing components can lead to inefficient data processing and performance bottlenecks in your pipeline. Prioritizing cloud provider options that offer direct integrations with your existing tools and services means you can implement automation and workflow orchestration faster and more efficiently. It also improves data transfers between components in your CI/CD data pipeline.

Data Quality and Monitoring

Errors or inaccuracies in your CI/CD data pipeline can cumulatively affect pipeline performance and negatively impact decision-making processes and business outcomes. Unreliable data also introduces operational risks such as compliance violations, regulatory penalties and customer dissatisfaction, especially in stricter industries such as health and finance.

For your retail company, errors in the CI/CD pipeline can lead to inaccurate product information, pricing discrepancies or inventory inaccuracies. This impacts the efficiency of operations, customer experience and organizational revenue, and it can lead to issues with compliance and damage the brand's reputation.

Implementing monitoring mechanisms and data quality checks in your pipeline with third-party tools or custom-built solutions helps mitigate these risks by identifying, fixing and recovering from any issues quickly. Anomaly detection algorithms that use performance trends and benchmarked metrics can help spot inconsistencies and outliers in real time. Monitoring your data flow in real time and analyzing trends obtained from this observability data leads to better predictive algorithms and proactive alerting systems for your infrastructure.

Integration with Existing Infrastructure and APIs

In most organizations, the lack of compatibility and interoperability between the databases, applications, services and external APIs in your infrastructure and your CI/CD data pipeline makes integration challenging. You're dealing with various data formats, schemas and query languages across different databases and data warehouses. Each component could have dependencies for its operations, and you must consider the versions of external or third-party services and APIs using mechanisms that prevent disruptions due to product updates.

To address this challenge, start by considering each component's output to downstream services and input from upstream services. Prioritize standardization in input and output formats, schemas, and processes. For example, the example retail company will have standard data fields across its order processing, inventory management, and delivery systems to enable efficient communication between them. You can implement data transformation layers and employ schema management tools in your pipeline to reduce conflicts. When adding new services or components, assess their fit and interoperability with existing pipeline components.

For external APIs, implement automated testing and validation processes to check their compatibility and detect regressions. Establish version control practices that document version dependencies and maintain backward compatibility where possible to mitigate risks associated with service disruptions or changes.

Fault Tolerance and Disaster Recovery

To mitigate the risk of data loss and maintain data consistency, your CI/CD data pipeline should be continually available, resistant to failures and disruptions, and easy to recover in case of downtime. It's a considerable challenge, especially in distributed environments where data must be harmoniously replicated across multiple nodes or storage systems.

To address these challenges, implement automated backup mechanisms to regularly capture and store critical data and pipeline configurations. Replicating data across multiple geographically distributed nodes safeguards against single-point failures in your data storage infrastructure.

For the example retail company, customer data is critical. To protect it and prevent data loss, it must regularly back up customer and order data. Multi-region replication of its systems’ data must also be employed to prevent significant downtime in cases of data center outages.  

Designing CI/CD data pipelines with redundancy in mind enhances its fault tolerance. Load balancers, failover clusters and redundant data stores minimize the impact of potential component failures. Fault-tolerant design principles like stateless microservices and idempotent processing further fortify your pipeline against disruptions by ensuring continuous operation even when individual components encounter issues.


This article explored the challenges involved in building and deploying CI/CD data pipelines on public clouds and how to mitigate them.

Organizations with stringent data security, compliance and performance needs can integrate dedicated cloud infrastructure, such as Equinix’s, into their data pipelines. They can store data on fully managed high-performance arrays by Dell, NetApp or Pure Storage. These arrays are consumed as a service, available globally (to meet any data sovereignty and latency needs) and can be connected directly and privately to all major cloud platforms’ network onramps that are located in the same data centers, a solution known as cloud-adjacent storage.

Published on

11 June 2024


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