Nowadays, data is everywhere - in transactions, customer behavior, third parties, and even in IoT sensor readings. To manage it, organizations are beginning to use storage services; the most popular is the Data Lake. Such platforms provide centralized repositories for storing different types of data, including both raw, unstructured, and structured data at scale. Data Lake Data Lake Data lakes are beneficial in business since they surpass traditional analytical methods. However, at the same time, they introduce new layers of complexity, necessitating that the data is fresh, the dashboards are trustworthy, and the reasons for a pipeline break are accurately identified. To help answer those questions, the term "data lake observability" emerged as a discipline focused on delivering visibility and traceability in modern data infrastructures. The approach clarifies and organizes the data, allowing teams to detect issues and resolve them immediately. data lake observability What is Data Lake Observability? What is Data Lake Observability? For a better understanding of observability in data lakes, it is helpful to differentiate this discipline from traditional monitoring. As such, monitoring flags familiar failure conditions, usually job or service-related. Observability, on the other hand, refers to diagnosing unknowns by examining system outputs, even when failures were not anticipated. monitoring Observability Applied to data lakes, observability involves collecting, organizing, and surfacing telemetry across the entire data lifecycle. collecting, organizing, and surfacing telemetry across the entire data lifecycle. This approach helps navigate the following issues: Ingestion: Are we pulling in the correct data from the credible sources?
Transformation: Are our pipelines behaving as expected?
Storage: Is our data organised, accessible, and within budget?
Consumption: Are analysts, dashboards, and ML models getting the correct data? Ingestion: Are we pulling in the correct data from the credible sources? Ingestion Transformation: Are our pipelines behaving as expected? Transformation Storage: Is our data organised, accessible, and within budget? Storage Consumption: Are analysts, dashboards, and ML models getting the correct data? Consumption This telemetry can include: Metrics: pipeline runtimes, table sizes, event volumes, success/failure rates
Logs: detailed records of pipeline execution, errors, retries, and warnings
Traces: contextualized paths that follow a data request or job across services
Data quality indicators: null rates, schema drift, duplication, freshness Metrics: pipeline runtimes, table sizes, event volumes, success/failure rates Metrics Logs: detailed records of pipeline execution, errors, retries, and warnings Logs Traces: contextualized paths that follow a data request or job across services Traces Data quality indicators: null rates, schema drift, duplication, freshness Data Analyzing the combination of these signals, data observability platforms provide real-time, high-fidelity insights into the health of data. This framework enhances confidence in governance and decision-making. It also enables faster debugging and troubleshooting. Why Observability is a Must-Have for Modern Data Lakes Why Observability is a Must-Have for Modern Data Lakes There is a trend of data platforms transitioning from batch ETL workflows to real-time, event-driven microservices, and the requirements for observability are growing exponentially. Consequently, data flows are unpredictable; a single source table can spread across 15 dashboards and multiple data products while training ML models. batch ETL batch ETL Many teams managing cloud-native data platforms often mention the chaos that occurs when observability is lacking. For example, changes to KPIs might lead stakeholders to doubt the accuracy of the data, and DEV teams to spend hours diagnosing pipeline failures, sifting through logs, and running SQL queries. SQL SQL Most troubling, however, is how low-quality data quietly spreads, breaking downstream models and insights in ways that are difficult to trace, and propagating silent failures. However, observability can help solve these problems with: Proactive detection: identifying anomalies before they impact business users.
Cause analysis: tracing issues to their origin with complete data lineage.
Data trust: ensuring that decisions are based on complete, up-to-date, and accurate data.
Operational efficiency: empowering engineers to resolve issues faster and focus on innovation rather than firefighting. Proactive detection: identifying anomalies before they impact business users. Proactive detection Cause analysis: tracing issues to their origin with complete data lineage. Cause analysis Data trust: ensuring that decisions are based on complete, up-to-date, and accurate data. Data trust Operational efficiency: empowering engineers to resolve issues faster and focus on innovation rather than firefighting. Operational efficiency Since data reliability is a competitive advantage, observability is mandatory. data reliability is a competitive advantage Key Pillars of Data Lake Observability Key Pillars of Data Lake Observability Achieving observability is a layered architecture composed of interconnected capabilities. The list below describes those pillars: layered architecture 1. Metrics and Dashboards. Data lakes constantly change. Jobs run, data lands, schemas evolve, and users query. Tracking these activities via metrics is essential to understanding the lake’s health. These are the most important metrics and questions they help to answer: Ingestion frequency: How often is data updated?
Pipeline success rates: How reliable are scheduled jobs?
Latency: How long does it take for data to become available?
Record counts: Are we ingesting the expected volume? Ingestion frequency: How often is data updated? Ingestion frequency: Pipeline success rates: How reliable are scheduled jobs? Pipeline success rates Latency: How long does it take for data to become available? Latency Record counts: Are we ingesting the expected volume? Record counts For example, Apache Airflow or AWS Glue integrates well with Prometheus or CloudWatch, allowing teams to build real-time dashboards. These visualizations form the first layer of observability and help teams spot unusual trends quickly. Apache Airflow AWS Glue Prometheus CloudWatch 2. Logs and Traces Metrics highlight the problems that happened, while logs and traces explain their reasons. As such, logging can provide execution details: SQL queries, error stacks, and retry attempts. This information may later help in understanding why the system failed, giving the necessary context to resolve issues efficiently. error stacks error stacks Using trace IDs and tracing helps specialists to relate service-level pipeline failure dependencies and identify the exact stage or microservice where the problem originates. The combination of structured logs and traces is required to untangle the myriad modern data systems. trace IDs trace IDs Modern logging stacks - ELK (Elasticsearch, Logstash, Kibana) or Datadog - provide log collection and analysis. For distributed tracing, OpenTelemetry or Jaeger helps track how data flows across microservices, which is mandatory for debugging in event-driven or serverless architectures. ELK (Elasticsearch, Logstash, Kibana) Datadog OpenTelemetry Jaeger 3. Data Quality Monitoring Even a perfectly operating pipeline can result in everything downstream breaking due to erroneous data. Data quality monitoring addresses this specific problem by ensuring that the most essential datasets are checked for null values, unexpected values, schema drift, duplication, data loss, and inconsistent formats or time zones. Data quality monitoring null values, unexpected values, schema drift, duplication, data loss, and inconsistent formats or time zones. Monte Carlo, Great Expectations, and Bigeye are some of the tools that allow teams to set expectations and rules that automatically flag anomalies. Moreover, organizations significantly enhance the integrity and reliability of the data ecosystem by embedding checks in CI/CD pipelines. These tools ensure new jobs or schema changes do not introduce regressions. Monte Carlo, Great Expectations, and Bigeye CI/CD pipelines CI/CD pipelines 4. Lineage and Impact Analysis Data lineage helps answer questions about data relationships: What upstream tables feed this report?
If this pipeline fails, who gets impacted?
Has this field’s definition changed recently? What upstream tables feed this report? If this pipeline fails, who gets impacted? Has this field’s definition changed recently? Lineage tools, such as DataHub, Amundsen, or Apache Atlas, automatically discover relationships in different systems and present them in interactive graphs. When an anomaly emerges, these tools help trace its upstream source and downstream effects. This allows organizations to minimize downtime and improve collaboration between teams. DataHub, Amundsen Apache Atlas 5. Cost and Storage Optimisation 5 Regarding the last pillar, observability can greatly reduce or rationalize costs. In cloud settings, data lakes can become financial black holes if not adequately monitored. For its part, observability enables tracking critical metrics - storage growth over time, query execution patterns, redundant or orphaned datasets, and frequent scans or inefficient joins that inflate compute costs. Storage and performance metrics on AWS S3, Google BigQuery, and Databricks are monitored natively. Deeper insights into user behavior and dataset usage are available through Select Star and Snowflake’s Resource Monitor. These insights help make decisions that optimise performance and spending. WS S3, Google BigQuery, and Databricks Select Star Snowflake’s Resource Monitor Case Study: Samsung Securities Dividend Mishap Observability is also highly beneficial in financial operations, as demonstrated by the case study of Samsung Securities, one of South Korea’s most influential financial services companies. Samsung Securities Samsung Securities In 2018, the organization faced a catastrophic data mishap due to inadequate observability. During a typical dividend payout, an employee mistakenly issued 2.8 billion shares instead of ₩2.8 billion in dividends. This case was a staggering error caused by a simple yet undetected schema or data entry issue. 2.8 billion shares ₩2.8 billion The mistake was undetected in time due to the absence of real-time validation and monitoring of sensitive numerical fields. The cost of such a mistake was severe: the company’s stock plummeted by approximately 12%, erasing nearly $300 million in market capitalization. Big clients severed ties, regulatory bodies imposed a freeze on new client intake for six months, and top executives were forced to resign. This incident highlighted the necessity of observability. Without data schema enforcement, anomaly detection, and real-time alerts, insignificant pitfalls can escalate into large financial and reputational disasters. Samsung’s Securities issue could have been detected faster with better observability, and this case is a reminder of why proactive data governance is essential for every organisation working with data. Implementation To avoid similar pitfalls and build a resilient data ecosystem, organizations should develop observability as a structured approach. One of the possible procedures is described below: Phase 1: Foundation Instrument pipeline success/failure metrics.
Centralise logs and set up alerting.
Define freshness checks for high-value tables. Instrument pipeline success/failure metrics. Centralise logs and set up alerting. Define freshness checks for high-value tables. Phase 2: Quality and Lineage Add schema and null-value checks using tools like Great Expectations.
Integrate lineage mapping into your data catalogue.
Standardise metadata tagging (for instance, PII, owner, SLA). Add schema and null-value checks using tools like Great Expectations. Integrate lineage mapping into your data catalogue. Standardise metadata tagging (for instance, PII, owner, SLA). (for instance, PII, owner, SLA) Phase 3: Governance and Cost Monitor query frequency and storage usage.


Set up data SLAs and automated documentation.


Review unused datasets for deletion or archiving.
Conclusion
Data observability implies building trust in data as a product. Now, organizations need real-time insights provided. Because it is crucial to transfer data into a visible, understandable, and monitored format, these insights can be retrieved with the help of observability.
The risks and stakes rise because the architecture scales and the data volume increases accordingly. Delayed insights can accrue significant costs, automated systems can fail silently, and schema errors can lead to breakdowns. It is not possible to eliminate the risk of failures, but it is possible to mitigate them by making failures explainable, detectable, and manageable. Monitor query frequency and storage usage. Monitor query frequency and storage usage. Set up data SLAs and automated documentation. Set up data SLAs and automated documentation. Review unused datasets for deletion or archiving.
Conclusion
Data observability implies building trust in data as a product. Now, organizations need real-time insights provided. Because it is crucial to transfer data into a visible, understandable, and monitored format, these insights can be retrieved with the help of observability.
The risks and stakes rise because the architecture scales and the data volume increases accordingly. Delayed insights can accrue significant costs, automated systems can fail silently, and schema errors can lead to breakdowns. It is not possible to eliminate the risk of failures, but it is possible to mitigate them by making failures explainable, detectable, and manageable. Review unused datasets for deletion or archiving. Conclusion Conclusion Data observability implies building trust in data as a product. Now, organizations need real-time insights provided. Because it is crucial to transfer data into a visible, understandable, and monitored format, these insights can be retrieved with the help of observability. The risks and stakes rise because the architecture scales and the data volume increases accordingly. Delayed insights can accrue significant costs, automated systems can fail silently, and schema errors can lead to breakdowns. It is not possible to eliminate the risk of failures, but it is possible to mitigate them by making failures explainable, detectable, and manageable.

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Apache

Diving Deep Into Data Lake Observability: Why It Matters More Than Ever

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