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Data Pipelines: How to Optimize at Scale with Data Observability

December 17, 2023
10 Min Read
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It’s an overused trope, but true nonetheless: data is an essential element – perhaps the MOST essential element – to operating any business. With bad data, you’re flying blind. With good data, you know the state of your organization, you can make smart predictions, and you can capitalize on gaps in the marketplace that rapidly-developed (but always high quality) data products can fill.  

Guiding data throughout an enterprise’s data environment are data pipelines, which are the backbone of modern data environments. The sheer volume that is created, integrated, processed, shared, and transacted creates challenges to managing and scaling all of that data, especially when a typical enterprise can have thousands (and in many cases, even tens of thousands) of data pipelines. For it to be usable and to deliver meaningful insights, enterprises need these data pipelines to be able to scale as data volume increases. Additionally, that scale must happen automatically and without any negative impact to operational efficiency or loss of data quality and reliability.

Modern data teams rely on data observability to support data pipeline optimization because it provides data monitoring, alerts data teams to issues, and ensures data pipeline quality and accuracy of the enterprise’s data. The following provides an in-depth understanding of how to optimize data pipelines at scale with data observability. It covers the following key topics:

  • Data Observability & Data Pipelines
  • Why are Data Pipelines Important?
  • Causes of Broken Data Pipelines

Introduction to Data Observability and Data Pipelines

Data pipelines are the tools and processes used to move data from its source to its destination, often involving multiple stages of transformation and processing. Data pipelines can be complex and fragile, and even small errors or issues can lead to significant data reliability problems. A well-designed data pipeline is essential for ensuring that data is collected, processed, and delivered accurately and efficiently.

Data observability refers to the ability to understand and monitor the behavior and performance of data environments in real-time. It involves collecting and analyzing data about the pipeline's input, processing, and output, and identifying any issues or anomalies that could impact the quality or reliability of the data. Data observability is critical for ensuring that data pipelines are operating as expected and that any issues or errors are detected and resolved quickly.

Data pipelines, on the other hand, are the tools and processes used to move data from its source to its destination, often involving multiple stages of transformation and processing. Data pipelines can be complex and fragile, and even small errors or issues can lead to significant data quality and reliability problems. A well-designed data pipeline is essential for ensuring that data is collected, processed, and delivered accurately and efficiently.

Data observability provides the means to monitor and maintain the performance and quality of data pipelines. By using tools and processes that enable real-time monitoring and analysis of pipeline behavior, organizations can quickly detect and resolve any issues or anomalies that could impact the accuracy or reliability of the data. This allows organizations to ensure that their data pipelines are performing as expected and that the data being collected and analyzed is accurate and reliable.

Why Are Data Pipelines Important?

On an economic scale, it’s hard to overstate the importance of data pipelines. They allow businesses to make informed decisions, identify opportunities, and drive growth. But when these pipelines break, the consequences can be disastrous. At best, it leads to incorrect analysis, wasted time and resources. At worst, it can lead to major financial losses, compliance issues, and reputational damage.

As data pipelines become more complex and distributed, the risk of failure and the possibility of data inconsistencies also increase. This can have severe consequences, particularly at scale, where even small errors can result in significant losses in revenue, reputation, and customer trust.

“Data observability provides a granular understanding of how pipeline jobs will interact with infrastructure elements such as data stores, containers, and clusters...” 
-Eckerson Group, Top Ten Use Cases for Data Observability

Preventing broken data pipelines is crucial for businesses to maintain a competitive edge in today's data-driven landscape. Consider the ways that data observability provides the necessary tools and capabilities to prevent data pipeline issues and how it provides a structure for efficient data pipeline scaling:

Timely Insights:

Data pipelines ensure the timely delivery of data to analytics and decision-making systems. If a data pipeline breaks or encounters errors, it can disrupt the flow of data, leading to delays in generating insights. In a fast-paced business environment, timely insights are critical for identifying market trends, customer preferences, and emerging opportunities. By preventing broken data pipelines, businesses can stay agile and make data-driven decisions quickly, gaining a competitive advantage over slower competitors.

Data Quality and Accuracy:

Broken data pipelines can introduce errors or inconsistencies in the data, compromising its accuracy and quality. Inaccurate or incomplete data can lead to flawed analyses, misleading insights, and erroneous decision-making. Maintaining data integrity is essential for businesses to rely on accurate information when developing strategies, launching products, or optimizing operations. By ensuring data pipelines are functioning properly, organizations can trust the quality of their data and make informed decisions that drive their competitive edge.

Operational Efficiency:

Efficient data pipelines streamline data processing and delivery, optimizing the use of resources and minimizing operational costs. When a data pipeline breaks, it causes delays, bottlenecks, and even failures in data processing. This leads to inefficiencies and resource wastage. Proactively preventing broken data pipelines results in data (accurate data) being available when needed. When that happens, data teams can maintain operational efficiency, allocate resources effectively, and leverage data for business growth more efficiently than their competitors.

Insights That Drive Business Activity:

Real-time data insights and actions are increasingly valuable for enterprises who can capitalize on that information to create new data products that differentiate them against their competitors. Broken data pipelines can hinder real-time data processing and prevent businesses from promptly detecting anomalies, identifying emerging patterns, or triggering automated actions. By ensuring data pipelines are reliable and functional, enterprises can leverage real-time data insights to make proactive decisions and stay ahead of their competitors.

Customer Experience and Personalization:

Data pipelines play a vital role in delivering a personalized customer experience. By analyzing customer data in real-time or near real-time, businesses can tailor their products, services, and marketing efforts to individual preferences and needs. Broken data pipelines can disrupt the collection and processing of customer data, leading to delays or failures in delivering personalized experiences. By preventing broken data pipelines, businesses can maintain a seamless and personalized customer experience, enhancing customer satisfaction and loyalty.

Innovation and Adaptability:

Data pipelines are critical for innovation and adaptability in a rapidly evolving business landscape. They facilitate the integration of new data sources, enable the adoption of emerging technologies like machine learning and artificial intelligence, and support the development of data-driven applications. Broken data pipelines can hinder these innovation efforts, preventing businesses from leveraging new data sources or technologies effectively. By maintaining reliable data pipelines, organizations can stay at the forefront of innovation and adapt to market changes swiftly, securing their competitive edge.

Data pipelines are an ever-evolving endeavor that requires continuous building and maintenance. No organization operates within a fixed dataset as the modern data stack is designed to constantly receive and process new data, incorporating additional data sources while also discarding irrelevant data (provided they practice data maintenance). To accomplish this, data teams are consistently integrating new applications and directing data flow through Snowflake data pipelines, Databricks, Redshift, and other data sources. Data engineers responsible for managing data pipelines do not adhere to a "set it and forget it" approach. Instead, they are actively involved in guaranteeing the accuracy, timeliness, and reliability of the data.

Managing data pipelines in a modern data environment 

Common Causes of Broken Data Pipelines

Why do data pipelines break? Is it because of bad data, or are there operational aspects of data environments that contribute to certain types of failures? The short and simple answer is that the prevalence of erroneous data can be attributed to numerous factors, all of which can be traced back to the fundamental transformations in data pipelines. These include the following:

Data Pipeline Networks are Becoming Increasingly Complex

You can see the original simplicity of the data lifecycle in the top part of the chart below (from an Eckerson Group data observability whitepaper). Data would flow from an application or SQL database, pass through a straightforward process of ingestion into a data warehouse using basic ETL or CDC tools. Along the way, there might be transformations, cleansing, and quality testing. Subsequently, the data would be stored and transmitted to a straightforward analytics application that would analyze the data and generate weekly, monthly reports, or dashboards.

Source: The Definitive Guide to Data Observability for Analytics and AI, Eckerson Group

Creating a data pipeline for new analytics reports or incorporating new data sources used to be a labor-intensive process, with careful considerations made by business analysts and database administrators as gatekeepers. Consequently, data remained on the static side, subject to cautious modifications.

However, in response to the growing abundance of data within organizations, software vendors have introduced a plethora of tools that simplify the creation of data pipelines like never before. As a result, modern data architectures have expanded rapidly, becoming larger and more intricate within a short timeframe.

Today, there exists a multitude of data sources, including real-time streaming ones. Additionally, a wider range of data storage options are available, notably cloud data lakes, which are favored by IT for their flexibility and scalability. Alongside these, there are NoSQL data stores and traditional legacy data warehouses that remain crucial to business operations, making migration impractical due to cost and mission-critical nature. While some data connects directly to analytics applications, a significant portion traverses data pipelines, undergoing transformations or aggregations before being stored again.

During the past few years, many businesses scrambled to construct extensive networks of data pipelines, often neglecting the implementation of corresponding data observability and management tools. Now they are facing the consequences of their actions, dealing with a proliferation of data errors.

Data Pipelines Are Increasingly Fragile

Kevin Petrie of the Eckerson Group, says that today's data pipelines can be described as "fragile webs of many interconnected elements." But why is this the case? After all, the latest generation of data pipeline tools, such as ETL, ELT, APIs, CDC, and event streamers like Apache Kafka, offer more powerful features for data ingestion, transformation, and quality control compared to their predecessors.

However, this progress also contributes to the challenge. Companies are now using their data in ways that would have been unimaginable a decade ago, simply because they have the capability to do so. They are combining data sets from diverse sources, in different formats and time periods, and performing multiple transformations and aggregations, resulting in extensive data lineages (which we'll delve into later). Furthermore, they are interconnecting data sources and analytics applications from various parts of the business that were previously disconnected.

Yet, with each data aggregation or transformation, the potential for problems arises. Issues such as missing or incorrect data, duplicated or inconsistent data, poorly-defined schemas, and broken tables can occur. 

The emergence of new forms of data pipelines introduces additional vulnerabilities. For example, real-time event streams are susceptible to data loss if there are hardware or network disruptions. Additionally, the sheer diversity of tools increases the likelihood of data engineers, particularly those with inadequate training, inadvertently compromising the quality of a dataset.

Data Lineages Are Getting Longer

Data is not uniform in its value or significance. Certain datasets hold greater importance and are consistently utilized, transformed, and combined with other datasets. Consequently, their data lifecycle can be remarkably lengthy.

However, what happens when an error arises within this critical dataset? To identify and rectify the error, one must trace the data lineage all the way back to the original dataset. This process is not only time-consuming but can also become impossible if crucial information, such as the dataset's metadata, is incomplete or missing.

Unfortunately, this is a common occurrence. Traditional data catalogs and dictionaries typically provide minimal support in creating comprehensive data lineage metadata, including details on data usage and connections with other assets. Instead, the responsibility of categorizing and tagging data was often left to data engineers, who would manually undertake these tasks. Consequently, in the haste to construct data pipelines, many engineers neglected proper metadata documentation, only to regret it later when confronted with data quality issues that required extensive tracking and analysis.

Legacy Approaches to Data Quality Testing Are No Longer Effective

Traditionally, data quality tests were performed solely during the data ingestion phase into the data warehouse. However, as highlighted earlier, the quality of data can deteriorate at various points along the data pipeline due to the reasons mentioned above.

To ensure data integrity, it is crucial to conduct ongoing quality testing throughout the entire data lifecycle. Furthermore, taking a proactive approach to data quality testing is essential, rather than relying solely on reactive measures when a data error occurs. Delaying the resolution of such errors can result in significant financial consequences for your business. Conventional data quality monitoring tools are limited to monitoring for problems after they have arisen, rather than assisting in the identification and prevention of issues before they manifest.

Data Pipelines Require Data Democracy

It is critical to have citizen data scientists and self-service BI analysts working on behalf of designing and managing your enterprise data strategy. In order for analytics to effectively permeate every crucial operational decision and customer interaction, it must be accessible to a broader range of job roles within the business.

However, it is essential to acknowledge that data democratization can inadvertently lead to compromised data quality and reliability. One aspect of this is the fact that employees who do not work with data on a full-time basis may possess less expertise in creating robust data pipelines.

A more significant concern lies in the loosening of previously tightly-controlled processes and workflows due to data democracy. For instance, a citizen data scientist might lack historical knowledge about missing or incomplete values within a specific data source. Alternatively, they may overlook the inclusion of essential metadata when creating a new dataset, thereby complicating the tracking of data lineage.

When these pipelines break or deliver inconsistent data, it can have a significant impact on business operations and decision-making. At Acceldata, we’re helping some of the world’s most respected brands use data observability to overcome data pipeline issues, such as: 

  • Data quality issues: Data quality issues, such as missing or inconsistent data, can cause data pipelines to fail. Data teams must ensure that data sources are consistent and meet the required data quality standards before importing the data.
  • Changes in data sources: Changes in data sources, such as schema changes or new data fields, can cause data pipelines to fail, so data source changes and updates to the pipeline must be tracked accordingly.
  • Incompatible data formats: Incompatible data formats can cause issues when transferring data between systems. It's crucial to ensure that data formats are compatible between systems and that data transformations are performed correctly.
  • Connectivity issues: Connectivity issues, such as network outages or firewall restrictions, can cause data pipelines to fail. Data teams have to monitor connectivity and address any corresponding issues promptly.
  • Inadequate testing: Inadequate testing can lead to data pipelines that fail in production, so data pipelines must be thoroughly tested before being deployed into production environments.
  • Human error: Human error, such as incorrect data entry or incorrect configuration, can cause data pipelines to fail. It's essential to have processes in place to minimize the risk of human error.
  • Hardware or software failure: Hardware or software failures can cause data pipelines to fail. Redundancy and backup systems must be in place to mitigate the impact of hardware or software failures.

Understanding the common causes of broken data pipelines is clearly important, as it enables data teams to take proactive measures to prevent pipeline failures and minimize their impact when they do occur. But building a framework to prevent broken data pipelines, at scale, requires an understanding of why they occur and a thoughtful approach to building an environment that makes them resilient.

The Impact of Broken Data Pipelines

The impact of broken data pipelines can be significant on business operations and decision-making. Data pipelines are in a continuous state of ETL, transacting and processing data between and among different applications and systems. If they fail, this can result in missing, inconsistent, or incomplete data and that typically delivers harmful outcomes.

For example, if a financial institution's data pipeline fails to extract data from its trading system, they won’t be able to report financial results accurately. This creates regulatory issues, financial losses, and reputational damage.

In addition to the direct impact on business operations, the failure of a data pipeline can also have indirect effects. If data pipelines fail regularly,  data cannot be trusted, which can result in delays in decision-making and lost opportunities. This lack of trust can also lead to additional work for data engineers and analysts who have to troubleshoot and fix the pipeline, diverting resources away from other tasks.

Innovative data teams recognize that it is essential to have robust data pipeline monitoring, testing, and maintenance processes in place to prevent data pipeline failures. This can include regular checks for data quality issues, compatibility testing, and monitoring for connectivity and hardware or software failures. By prioritizing the reliability of data pipelines, businesses can ensure that their operations run smoothly and that they can make informed decisions based on accurate and reliable data.

Measuring the Financial Costs of Broken Data Pipelines

The economic impact of broken data pipelines can be significant; there are opportunity costs to missing market and customer signals, and there are actual bad outcomes that happen when bad data is used to make decisions. In late 2022 bad data being used by Credit Karma resulted in customers erroneously getting pre-approved for credit cards for which they were not actually eligible. This not only damaged the Credit Karma brand and created trust issues with customers, but the FTC also slapped the company with a $3 million fine. 

These examples are, unfortunately, fairly common. Consider other examples of scenarios where broken data pipelines have delivered bad data, which has led to considerably bad outcomes for companies and their customers:

  • Facebook’s Overstated Metrics: In 2016, Facebook admitted that, because of bad data, they had overstated the average view time for video ads on its platform for two years. Some estimates suggested that average viewing time was inflated by 60-80%. This caused significant distrust among advertisers and sparked concerns about the lack of third-party verification of Facebook’s metrics.
  • Wells Fargo’s Fraudulent Accounts Scandal: In 2016, Wells Fargo faced severe backlash and financial penalties when it was discovered that millions of unauthorized bank and credit card accounts had erroneously been created on behalf of Wells Fargo clients. The data inaccuracies that resulted from these illicit activities eventually resulted in massive regulatory fines, the resignation of the CEO, and a significant hit to the bank’s brand.
  • The 2008 Financial Crisis: Inaccurate ratings of subprime mortgage-backed securities by ratings agencies contributed to the 2008 financial crisis. These inaccuracies, often due to poor quality data regarding the underlying assets and their associated risks, led to significant losses for businesses and individuals, and they severely damaged the reputations of the ratings agencies involved.

Think about it from the standpoint of retailers. If a retailer's data pipeline fails to update their inventory levels, they may be unable to fulfill customer orders or may overstock certain products, resulting in lost sales or increased costs due to excess inventory. This can lead to a decrease in revenue and lower profit margins, which are already razor-thin in this market.

In the financial industry, broken data pipelines can result in missed trading opportunities, delayed settlements, or inaccurate financial reporting. All of these scenarios lead to financial losses, regulatory fines, and damage to the institution's reputation, which can further impact revenue and profitability.

In the healthcare industry, broken data pipelines can lead to delayed diagnoses, incorrect treatment plans, or billing errors, resulting in increased costs and potential legal liabilities.

The costs associated with fixing a broken data pipeline can also be significant. Data engineers and analysts may have to spend hours troubleshooting and fixing the pipeline, diverting resources from other critical tasks.

The Path to Building Resilient Data Pipelines

Creating a framework for resilient data pipelines involves establishing a set of guidelines, best practices, and tools that enable data engineers to build and maintain pipelines that are robust, scalable, and fault-tolerant. 

The first step is to establish a set of standards and guidelines for designing and building data pipelines. This could involve defining a common set of technologies, platforms, and processes that can be used across the organization to ensure consistency and reduce complexity. For example, standardizing on a specific data integration tool or ETL framework can help ensure that pipelines are built consistently and can be maintained and scaled more easily.

Data teams will also need to develop a set of best practices for testing, validating, and monitoring data pipelines. They should establish KPIs for data pipeline performance and set-up automated monitoring and alerting systems to ensure that any issues are detected and addressed promptly. Regular testing and validation of the pipeline under different load conditions and failure scenarios can help ensure that it remains resilient in the face of changing requirements and evolving threats.

Next, it is critical to establish a culture of continuous improvement and innovation. This requires regularly reviewing and updating the framework to incorporate new technologies, best practices, and lessons learned from previous pipeline failures. Encouraging collaboration and knowledge sharing across teams and departments can also help foster innovation and enable the organization to stay ahead of emerging trends and technologies.

A resilient data pipeline can provide a solid foundation for data-driven decision-making and enable organizations to achieve their business objectives more effectively and efficiently. These are some key principles that data teams should follow:

  • Build with scalability in mind: Data volumes can grow rapidly, and it's crucial to design data pipelines that can handle the increased volume. This requires using distributed systems that can easily scale and add additional nodes as data volumes grow.
  • Use fault-tolerant architectures: Using a fault-tolerant data pipeline architecture can ensure that the pipeline can continue running even if one component fails. This can involve using redundant hardware, load balancing, and backup systems.
  • Monitor and manage pipeline performance: Regular monitoring of data pipelines can detect issues before they cause significant problems. Data teams need to create alerts for critical metrics, such as data throughput and latency, and having a centralized logging and monitoring system.
  • Use automated testing: Automated testing can help catch issues before they are deployed to production environments, and setting up automated tests for data quality, data consistency, and pipeline functionality.
  • Plan for disaster recovery: Having a disaster recovery plan in place can help ensure that data pipelines can quickly recover from disasters, such as hardware failures, network outages, or natural disasters.

With these data pipeline infrastructure principles, enterprises can build resilient data pipelines that can handle growing data volumes, recover from failures quickly, and ensure that data is available and reliable for decision-making.

How to Build Redundancy and Scalability Into Your Data Pipelines

Once a structure has been created for data pipeline design, data teams will want to also include redundancy and scalability into their plans. Doing so is an important consideration for ensuring high availability and reliability. Redundancy is the duplication of critical components or data in a system to ensure that if one component or data source fails, another is available as a backup. Scalability, on the other hand, is the ability of a system to handle increasing workloads and to expand as demand grows. To build redundancy and scalability into your data pipelines, there are several strategies you can use.

First, using distributed systems such as Hadoop, Spark, or Kafka, can enable horizontal scaling as data volumes and processing requirements grow. These systems distribute the processing load across multiple nodes, allowing for increased processing capacity without adding more resources to individual nodes. Load balancing can also be used to distribute the processing load evenly across multiple nodes, ensuring that no single node becomes overloaded.

Secondly, implementing replication and backup can ensure redundancy and fault tolerance. Replicating data across multiple nodes and backing up data to multiple locations ensures that if one node or location fails, the data can be retrieved from another node or location. Fault tolerance measures such as checkpointing and retry mechanisms can also ensure that data pipelines can recover from failures and continue processing without interruption.

Thirdly, regular monitoring and alerting can detect issues before they become critical, enabling proactive intervention to prevent downtime and ensure high availability. Monitoring can involve tracking key metrics such as data throughput, latency, and error rates, and setting up alerts for any abnormal or anomalous behavior.

Lastly, cloud-based services such as AWS EMR, Azure HDInsight, or Google Cloud Dataflow can provide scalability, redundancy, and fault tolerance out of the box. These services can scale automatically based on demand, replicate data across multiple regions, and provide fault tolerance features such as automated backups and failover.

Data Pipeline Monitoring Best Practices

Monitoring data pipelines is a critical aspect of ensuring that they are functioning correctly and meeting the needs of the business. Here are some best practices for monitoring data pipelines:

  1. Define key metrics: Define the key metrics that need to be monitored to ensure the smooth operation of the data pipeline. Metrics should include data throughput, latency, error rates, and processing times.
  2. Set up alerts: Set up alerts to notify the appropriate team members or stakeholders when any of the key metrics exceed predefined thresholds. Alerts can be set up for a wide variety of scenarios, such as when data ingestion rates fall below a certain threshold or when processing times exceed predefined limits.
  3. Monitor data quality: Ensure that the data flowing through the pipeline is of high quality and meets the required standards. This requires monitoring the completeness, accuracy, and timeliness of the data.
  4. Monitor resource utilization: Monitor the utilization of resources, such as CPU, memory, and storage, to ensure that the pipeline is not overburdening the underlying infrastructure.
  5. Establish baselines: Establish baselines for the key metrics to provide a reference point for future monitoring and to detect any deviations from the expected behavior.
  6. Conduct root cause analysis: Conduct root cause analysis when issues are detected to identify the underlying cause of the problem and to prevent similar issues from occurring in the future.
  7. Automate monitoring: Automate the monitoring process as much as possible to reduce the risk of human error and to ensure that monitoring is performed consistently and continuously.

Keep in mind that even with highly functioning pipelines and fewer problems, attention and alerting will always be necessary. It only takes a single instance of data pipeline failure to create a snowball effect of issues that can quickly spiral out of control.

The Importance of Preventing Broken Data Pipelines

As we’ve seen, when data pipelines fail, it can lead to significant business disruptions, loss of revenue, and damage to the organization's reputation. The impact of broken data pipelines can be felt across the organization, from data scientists and analysts to business leaders and stakeholders.

Preventing broken data pipelines is critical for ensuring the accuracy, reliability, and security of an organization's data. By establishing robust data pipeline architecture, implementing best practices for testing and monitoring, and fostering a culture of continuous improvement, organizations can minimize the risk of pipeline failures and ensure that their data remains a valuable asset for driving business success.

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