Data pipelines feed and transform data for consumption and are increasingly growing in complexity. As enterprises continue to add data sources, they run the risk of breaking their data pipelines due to data errors, flawed logic or insufficient resources for processing. The challenge for every modern data team is to establish data reliability as early as possible in the data journey and to create optimized data pipelines capable of performing and scaling to meet the business and technical needs of the enterprise. In the context of data observability, “shift left” refers to a proactive approach that incorporates observability practices during the early stages of the data lifecycle. This concept is borrowed from software development methodologies and emphasizes addressing potential issues and ensuring quality from the outset.
Shifting left involves integrating observability practices and tools into the data pipeline and infrastructure from the beginning. This approach avoids treating observability as an afterthought or applying it only in later stages to identify and resolve data reliability, integrity, and performance issues as early in the development process as possible, minimizing the likelihood of problems cascading downstream.
The Economic Impact of Poor Data
To effectively manage data and optimize data pipelines, it is crucial to detect and address data incidents as early as possible within the supply chain.
The “1 x 10 x 100 Rule” is applicable in a variety of processes, including software development. It states that the cost of fixing a problem increases exponentially, depending on the stage at which it is detected. The rule can be extended to data pipelines and supply chains, where it suggests that detecting and rectifying a problem in the data landing zone (i.e., where source data is fed) incurs a cost of $1, whereas identifying and addressing the issue in the transformation zone (i.e., where data is transformed into its final format) increases the cost to $10. If the problem is only detected in the consumption zone (where data is in its final format and is accessed by users), the cost rises significantly to $100.
Shift Left Data Reliability
The process of identifying data pipeline issues has become more challenging for data teams as data supply chains continue to evolve and grow in complexity, mainly resulting from the following factors:
● Expanding sources: The number of data sources (such as Databricks, Snowflake, Redshift, Teradata and many others) being incorporated into the supply chain has significantly increased. Organizations now integrate data from a wider range of internal and external sources, contributing to the complexity of data management and processing.
● Advanced data transformation logic: The logic and algorithms used to transform the data within the supply chain have become more sophisticated. Complex transformations and calculations are applied to raw data to derive meaningful insights, necessitating robust systems and processes to handle the intricacies involved.
● Resource-intensive processing: The processing requirements for data within the supply chain have significantly escalated. With larger volumes of data and more complex operations, organizations must allocate substantial computing resources, such as servers, storage and processing power, to handle the data processing workload effectively.
By proactively addressing data incidents at an early stage, organizations minimize the potential impact and cost associated with data issues. This not only ensures the reliability and accuracy of data consumed by users but also safeguards the integrity of downstream processes and decision-making. Ultimately, the shift left approach to data reliability promotes efficiency, reduces costs, and enhances overall data quality and trustworthiness.
How Can Data Teams Shift Left?
To effectively shift left in your data reliability solution, certain capabilities are crucial; these include:
● Early Data Reliability Checks: Conduct data reliability tests before data enters the data warehouse and data lakehouse. This ensures that bad data is identified and filtered out early in the data pipelines, preventing its propagation to the transformation and consumption zones.
● Support for Data-in-Motion Platforms: Enable support for data platforms like Kafka and monitor data pipelines within Spark jobs or Airflow orchestrations, which enables real-time monitoring and metering of data pipelines.
● File Support: Perform checks on different file types and capture file events to determine when incremental checks should be performed.
● Circuit-Breakers: Integrate APIs that incorporate data reliability test results into your data pipelines, which can halt data flow when bad data is detected. By preventing the spread of bad data, circuit breakers protect downstream processes from being affected.
● Data Isolation: Identify and isolate bad data rows, preventing their continued processing.
● Data Reconciliation: Implement data reconciliation capabilities to ensure consistency and synchronization of data across multiple locations.
Shifting left your data reliability practices empowers data teams to focus on innovation, reduces costs, enhances data trust and enables agile business processes. Embracing this approach makes data more usable and resilient and delivers a more robust and reliable data ecosystem that supports informed decision-making and drives organizational success.