Production-Grade Data Pipelines
Distinguishing between production-grade data pipelines and amateur setups is crucial for organizations aiming to leverage their data efficiently. Here’s what sets them apart according to a recent Reddit thread:
1. Handling Late-Arriving Data
Late-arriving data can disrupt the accuracy and timeliness of analytics. Production-grade pipelines address this challenge by implementing mechanisms to accommodate data that arrives after the expected time window (e.g. reprocessing, lambda architecture, data versioning)
2. Keeping Raw Data to Support Reprocessing
Retaining raw data is a cornerstone of robust data management. It allows for the reprocessing of data in light of new business requirements or to correct processing errors. This approach ensures that data can be reanalyzed or restored to its original state, providing flexibility and security.
3. Handling Occasional Bursts from Reprocessing, Backfilling, Etc.
Production systems must be equipped to manage sudden increases in load, such as those caused by reprocessing historical data or backfilling missing information. Scalable infrastructure and efficient resource management strategies ensure that these bursts do not impact the ongoing operations.
4. Support for Comprehensive Unit-Testing and Constraint-Checking
Quality assurance is non-negotiable in production-grade pipelines. This involves comprehensive unit-testing (e.g. of transformation logic) and constraint-checking (e.g. uniqueness-, range- or format-constraints) to catch errors early in the development cycle, reducing the risk of data corruption or loss and ensuring the reliability of the data pipeline.
5. Minimize Tight-Coupling by Subscribing to Domain Objects
Tight coupling between systems is a risk to stability and agility. Instead of replicating physical schemas, the preferred approach is to subscribe to domain objects. This means publishing a denormalized domain object (like customer, invoice, shipment) to a medium (e.g., Kafka, Kinesis, S3 files) and using a data contract enforced by JSON Schema for testing. This method reduces dependency, allowing upstream systems to evolve without breaking the data pipeline.
This is the one of the core ideas behind the “Data Mesh”.
6. Manage Costs on Cloud Databases
Cost management is vital in cloud-based environments. Production-grade pipelines optimize resource use and query performance, ensuring that the infrastructure scales cost-effectively. Techniques include selecting the right data storage formats, leveraging data compression, and monitoring usage to avoid unnecessary expenses.
7. Provide a Low-Enough Data Latency to Satisfy Their Users
The value of data often diminishes with time. Production-grade pipelines prioritize low latency, ensuring that data is available for decision-making processes as quickly as possible. This involves optimizing every stage of the data pipeline, from ingestion to analysis.
8. Automate Everything
Automation is key to reducing manual errors and increasing efficiency. Production-grade pipelines automate repetitive tasks such as data ingestion, transformation, and monitoring. This not only speeds up processes but also allows data teams to focus on more strategic tasks.
9. Observability & Alerting & KPIs
Visibility and transparency are critical for monitoring the health and performance of data pipelines. This includes implementing observability tools, setting up alerting for anomalies, and tracking key performance indicators (KPIs) to identify areas for improvement. Effective metadata management and logging enable issues to be identified and resolved proactively, often without needing to inspect the data directly.
10. Data Dictionaries & Documentation
Comprehensive documentation is essential for maintaining and scaling data pipelines. This includes maintaining data dictionaries and catalogs that provide clear definitions, lineage, and usage instructions for the data assets. It ensures that teams can collaborate effectively and that new members can quickly come up to speed.
Source
[1] What distinguishes production-grade data pipelines from amateur setups?
