Data pipelines are the hidden heroes of the modern data-driven world! They’re the tireless workhorses behind all the cool data analysis and AI applications you see everywhere. They are a series of automated processes that move data from one place to another, transforming it along the way. Imagine a long, connected series of pipes, each responsible for a specific step in the data journey.
What do data pipelines do?
Data pipelines can help users accomplish some important tasks, such as:
- Gather data from various sources like databases, applications, sensors, websites, etc.
- Scrub data and remove inconsistencies, errors, as well as irrelevant data
- Convert data into formats that can be better analyzed
- Filtering, aggregating, and joining data sets to be more coherent, useful, and insightful
- Load and deliver data to final destinations, say data warehouses, analytics platforms, machine learning models, etc.
With the power to automate and accomplish such a wide range of tasks, data pipelines are immensely useful. What makes data pipelines so important?
- It automates tedious tasks
- Ensures and maintains the quality of data
- Enhances the data accessibility
- Powers real-time insights
Suppose you have a room full of documents and random objects spread everywhere, on the floor, on the shelves, all over the place. Now, if you must organize it, you will sort everything according to some parameters, then put them properly in order on shelves and racks. Data pipelines do the exact same thing. Data pipelines sort, clean, and label everything and make it easy to find and use what data you need.
However, like with every process and protocol, there are some best practices for using and managing data pipelines too.
Best Practices for Working with Data Pipelines
Let us look at some of the top best practices to follow when building and managing data pipelines. Using these best practices, one could see a significant improvement in data quality as well as reduce the risk of pipeline breakage enormously.
Idempotency
It is important that when a data pipeline is being used, no matter how many times it is run, the data should not be duplicated. Also, in case of an incident or failure, once the pipeline is back up and running again, no data should be lost or altered. Data pipelines usually run on a fixed schedule. As a practice, one should collect logs of previous successful runs of the pipeline and use that data to define accurate parameters for future runs. So, if a pipeline is scheduled to run at 4 PM but encounters failure and it is run on an hourly basis, the next run should automatically capture the data from the 3 PM run and the timeframe should be tampered with or incremented until the current run is successful.
Consistency
In scenarios where data flows from upstream to downstream databases, consistent pipeline execution plays a crucial role in maintaining data integrity. If a pipeline run completes successfully without modifying, adding, or deleting records, subsequent runs should strategically adjust the processing timeframe to incorporate data potentially missed due to upstream latencies. This proactive approach mitigates the risk of data loss and ensures consistent synchronization between source and target databases, even when dealing with time-sensitive data updates. So, in the same example as above, if the last time the pipeline ran successfully was at 3 PM and no data records were added, modified, or deleted during that run, then the next run should capture the entire data from 3 PM to 5 PM, instead of from 4 PM to 5 PM.
Concurrency
To enhance pipeline stability and maintain data consistency, particularly in scenarios where increased execution frequency coincides with potentially elongated runtimes, proactive measures are essential. Triggering subsequent runs while the preceding pipeline execution remains active can exacerbate performance bottlenecks and introduce inconsistencies within the data stream. Therefore, robust pipelines should incorporate intelligent logic to detect ongoing runs. Upon identifying a concurrent execution, appropriate actions should be implemented, such as raising an exception to halt the subsequent run or gracefully exiting to prevent resource conflicts and data integrity issues. In case there is a dependency between data pipelines, then Directed Acrylic Graphs or DAGs can be used to manage the dependencies.
Evolution of the Schema
As source systems undergo natural evolution in response to shifting requirements or technological advancements, schema modifications become inevitable. If left unaddressed, these structural changes can introduce inconsistencies in data types, field additions, or modifications, potentially leading to pipeline disruptions and data loss. To mitigate these risks and safeguard the integrity of data flows, proactive schema reconciliation strategies are paramount. This involves implementing robust logic within data pipelines to meticulously compare source and target schemas. Upon identifying discrepancies, the pipeline should seamlessly adapt its operations to accommodate the modifications, ensuring uninterrupted data flow and preserving the consistency of downstream data stores.
Another good practice on this front is to take a schema-on-read approach instead of the schema-on-write approach. There is also a range of tools available in the market to help with this, such as UpSolver SQLake which enables the data pipeline to dynamically adapt to the schema evolution.
Performance Monitoring and Logging
Managing numerous data pipelines presents a significant operational challenge. While manual monitoring of every individual pipeline might seem a tempting route, it rapidly becomes untenable as the network expands. To ensure the continued effectiveness and data integrity of complex data processing, embracing automated monitoring solutions is crucial. Robust logging and real-time performance metric capture empower proactive interventions, enabling preemptive identification and resolution of potential issues. Alerts and notifications provide a vital early warning system, safeguarding against data quality degradation by anticipating and addressing anomalies in data volume, latency, throughput, resource consumption, performance declines, and error rates.
Configure Timeout and Retry Protocols
When data pipelines rely on external APIs for data exchange, unforeseen network disruptions can pose significant challenges. These disruptions, ranging from slow connections and dropped packets to complete communication loss, can jeopardize pipeline execution and data integrity. To mitigate such risks, proactive implementation of robust network handling mechanisms is essential. By equipping pipelines with clearly defined timeout periods for individual API requests and a well-calibrated retry mechanism with defined back-off intervals, we can effectively prevent scenarios where pipelines remain indefinitely suspended in a non-productive state, safeguarding against downstream data inconsistencies, and ensuring operational resilience.
Validation
Data quality stands as the cornerstone of valid insights and informed decision-making. In this pursuit, data validation emerges as a critical safeguard, ensuring that processed information adheres to predefined rules and established standards. By seamlessly integrating validation checks throughout the data pipeline, particularly at key stages like ingestion, transformation, and loading, we can meticulously verify data integrity, reliability, and consistency. This comprehensive approach not only upholds data quality but also empowers downstream data applications with trusted and accurate information, ultimately facilitating the extraction of meaningful insights.
Handling Errors and Testing
Resilient data pipelines necessitate a proactive approach to potential disruptions. This starts with robust error handling, employing meticulous analysis to anticipate exception scenarios, possible failure modes, and even fringe cases that could cause pipeline disruption. By seamlessly integrating comprehensive error-handling routines within the pipeline itself, we can effectively mitigate these risks and prevent costly data processing breakdowns. Furthermore, rigorous testing procedures play a crucial role in solidifying pipeline reliability. Implementing a battery of unit, integration, and load tests provides invaluable insights into individual component functionality and overall pipeline performance under varying data volumes. This proactive testing strategy instills confidence in the pipeline’s stability and operational capacity, paving the way for seamless data flow and accurate downstream insights.
The intricate landscape of data pipeline construction presents a diverse array of language and tooling options, encompassing both batch and streaming architectures. Navigating this vast ecosystem necessitates a systematic approach informed by comprehensive needs analysis. By meticulously evaluating your specific use case requirements, desired functionalities, and potential tool limitations, you can strategically select the platform best suited to your objectives. Regardless of your chosen platform, however, adhering to the best practices will prove invaluable in the successful construction, diligent monitoring, and robust maintenance of your data pipelines.
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