Data processing is complex and rapidly evolving, driven by the efficient and reliable handling of vast datasets. Data originates from diverse sources, which requires structured processing to inform crucial business decisions, drive product innovation, and fuel business growth. Maintaining data accuracy and timeliness in these data pipelines is crucial, as delays or errors can trigger costly operational disruptions and customer dissatisfaction.
Organizations rely on service level agreements (SLAs) to ensure consistency and reliability in data processing. SLAs define the level of service quality that customers can expect from their data providers or service operators.
But why do data pipelines really need SLAs? And what happens if an SLA is breached?
Let’s find answers to these important questions in the article below!
What is SLA in data pipelines?
An SLA in data pipelines is a formal contract between a data provider or team and its users. It specifies the expected standards and responsibilities for data services, such as data availability, accuracy, timeliness, etc. SLAs set clear guidelines for data handling, meeting performance standards, and taking corrective measures for potential issues, such as data delays, inaccuracies, or system failures.
There are two types of SLAs: internal and external. Internal SLAs are agreements between teams within an organization. External SLAs, also known as customer SLAs, are agreements between an organization and a vendor providing data services.
Many products, like Google Cloud Platform (GCP), list SLA promises on their sites, ensuring high availability and reliability for their data services. For instance, the BigQuery SLA guarantees 99.9% uptime, meaning the service will be available 99.9% of the time in any given month.
Amazon Web Services (AWS) also governs the use of its services through SLAs. For instance, AWS CodePipeline has an SLA that ensures the service is available 99.9% of the time, excluding scheduled maintenance.
Let’s talk about the key components of data SLAs:
- Data freshness – means ensuring data is up-to-date and readily accessible when needed. This guarantees that users can rely on the data pipeline to be operational and to provide the most recent data for timely decision-making.
- Data quality – focuses on the accuracy, consistency, and reliability of the data processed and delivered by the pipeline. SLAs might include metrics for data accuracy, completeness, and freshness, ensuring that the data meets specific quality standards.
- Performance metrics – are acceptable levels of different metrics, such as latency and throughput, to ensure the pipeline operates efficiently and meets the required performance standards.
- Error handling and resolution time – specifies how data teams will manage errors and the time frame within which they must be resolved. This includes setting expectations for error detection, communication with stakeholders, and the maximum allowable time to fix issues to minimize disruption.
- Compliance and security requirements – ensure the data pipeline complies with relevant regulatory and security standards. SLAs may include provisions for data encryption, access controls, and compliance with laws such as the General Data Protection Regulation (GDPR).
Why are SLAs necessary for data pipelines?
Similar to IT teams using SLAs to ensure reliable vendor services, data teams need SLAs to manage complex data environments. The growing reliance on data across various business sectors demands dependable data sources and pipelines. However, as data chains become longer and more complex and as the number of data producers and consumers increases, the likelihood of issues arises.
SLAs help overcome such issues by setting clear exceptions for both service providers and consumers. They align service delivery with business goals, establish measurable standards, and reduce risks and disputes.
SLAs in data pipelines are important for:
- Enhancing operational efficiency.
- Ensuring data reliability and consistency.
- Mitigating risks and minimizing downtime.
- Meeting business requirements and expectations.
- Enabling better decision-making through timely and accurate data.
- Facilitating collaboration between data engineers, data scientists, and business stakeholders.
What happens when an SLA is breached?
An SLA breach occurs when the service provider fails to meet the agreed-upon standards and performance metrics outlined in a Service Level Agreement. This failure to meet the agreed terms can affect the service provider and the customer.
When an SLA is breached, the immediate effects can include decreased reliability and trust in the service, financial penalties, and strained customer relationships. Operational efficiency may suffer as teams scramble to address the breach.
Let’s talk about different scenarios to understand the impact of SLA breaches.
- Telecom companies: Failing to meet SLAs can lead to significant revenue losses due to service downtime. Customers may switch to competitors due to poor service reliability. SLA breaches can also cause delays in billing cycles, disrupting cash flow.
- Banking: In the banking sector, breaching SLAs can lead to account balance errors, causing customer mistrust and dissatisfaction. When transactions fail or are delayed, it can result in financial loss for the customers and regulatory penalties for the bank.
- Investment apps: Breaches can cause inaccurate or delayed data updates, leading to poor investment decisions. Users may lose trust in the app’s reliability, which can decrease engagement and increase customer churn.
- Ride-hailing companies: Breaching SLAs can lead to delays in ride availability or cancellations, affecting user experience. This leads to declined customer usage and revenue.
- Compliance violations: SLA breaches that result in non-compliance with regulatory standards can lead to heavy fines and legal penalties. Non-compliance can damage the company’s reputation, making it difficult to regain market standing and customer trust.
How to avoid SLA breaches in data pipelines?
After defining SLAs and sharing them with customers, managing time and monitoring metrics are essential to avoid SLA breaches. This helps ensure service quality, maintain customer trust, and avoid potential penalties.
Here are key steps to ensure compliance:
1. Set realistic and clear SLAs
Define achievable and transparent SLAs that align with the capabilities of your data pipeline and the needs of your business. Clear SLAs help set proper expectations for both service providers and customers. Your goal shouldn't only be to meet the basic service level. Strive to set goals that exceed compliance.
2. Implement alerts for an early SLA warning
Using proactive alerting mechanisms and preemptive warning systems is an effective starting point to avoid SLA breaches. Use monitoring tools to set up alerts that notify you of potential SLA violations before they occur. Early warnings allow you to address issues promptly, minimizing the risk of breaches.
3. Implement OLAs
Operational level agreements (OLAs) define the interdependent relationships between internal support groups. Implement OLAs to ensure all parts of the organization are aligned and working towards meeting the SLAs.
4. Establish redundancy and backup strategies
Create redundancy and backup plans to maintain service continuity in case of failures. This includes having backup data pipelines and systems to take over in case of primary system failures. You should also set up a contingency plan outlining the steps to be taken during an SLA breach.
5. Ensure open communication to prevent SLA violations
Maintain open lines of communication between all stakeholders involved in the data pipeline. Regular updates and transparency help in identifying potential issues early and collaboratively finding solutions to prevent SLA breaches.
6. Regularly review and update SLAs
Periodically review and update your SLAs to reflect any changes in business needs, technological advancements, or performance capabilities. Regular updates ensure the SLAs remain relevant and achievable.
Tools data engineers can use in their workflows to manage SLAs
Organizations need reliable tools to manage SLAs in data pipelines that can help monitor, schedule, and ensure the quality and performance of data processes.
Here are some commonly used tools, both open-source and commercial, available to data engineers to manage their SLAs:
1. Apache Airflow
Apache Airflow is an open-source tool for task scheduling and workflow orchestration. It allows data engineers to define, schedule, and monitor complex workflows. Using standard Python features, data engineers can easily create workflows.
With Airflow, you can automate and visualize the flow of data tasks, ensuring they run on time and according to the defined SLAs.
2. New Relic
New Relic uses advanced AI technology to enhance alert quality and reduce false alarms. If any metric falls below the acceptable threshold, New Relic sends instant notifications, allowing teams to take corrective actions before SLA breaches occur.
The AI-powered alert system in New Relic helps developers by reducing unnecessary alerts. It uses smart thresholding and anomaly detection to send only necessary, actionable alerts. This reduces alert fatigue and helps developers maintain SLAs.
3. Secoda
Secoda is an all-in-one data management platform for data search and governance. It helps data engineers maintain data quality for SLAs, monitor the health of the entire data stack, and prevent data asset sprawl. The Data Quality Score (DQS) in Secoda helps measure, track, and improve data quality.
Moreover, the automated workflows in Secoda integrate data discovery into your processes, reducing manual errors and building trust in your data. By setting thresholds and receiving alerts, Secoda helps you resolve data quality issues.
4. Datadog
Datadog offers infrastructure monitoring with complete visibility into performance and security to maintain data SLAs. Its software-as-a-service (SaaS) -based monitoring provides metrics, visualizations, and alerts. This helps engineering teams maintain, optimize, and secure data environments.
With a one-click correlation of related metrics, traces, logs, and security signals, troubleshooting becomes faster and more efficient, ensuring SLA compliance and optimal performance of your data pipelines.
Manage SLAs and avoid SLA breaches with Secoda
As data volume grows, ensuring high quality becomes crucial. Poor data quality can affect decision making. Maintaining data quality and reliability also helps manage SLAs and avoid breaches. Data engineers use reliable tools to monitor their data pipelines and quality issues, and Secoda is one of them.
Secoda is a data management platform that helps data teams manage SLAs by providing a centralized hub for data discovery and monitoring. It implements Data SLAs based on the following principles:
- Comprehensive: Covering all data assets.
- Automated: Minimizing manual efforts.
- Actionable: Providing clear improvement steps.
- Multi-dimensional: Assessing accuracy, reliability, stewardship, and usability.
Furthermore, Secoda’s DQS offers a comprehensive scoring system for evaluating and improving data quality. Initially focusing on tables, DQS provides actionable steps to enhance scores across various categories, resulting in a total score out of 100 points.
Key features of Secoda include:
- AI-powered features: Enhance data team efficiency and SLA management.
- Slack integration: Enable easy communication and collaboration among stakeholders.
- No-code integrations: Seamlessly connect with various data sources without complex coding.
- Track and evaluate data quality metrics: Efficiently monitor and assess data quality to ensure SLA compliance.
Need help managing your data SLAs? Schedule a demo today and explore how Secoda can optimize your data management!
