As more data systems and technologies have become accessible and interactive, organizations have increasingly come to rely on data to make business decisions. Since data comes from various sources, and in various different formats, turning data into insights requires you to prepare, enrich, and monitor it properly to be able to gain insights from it.
Engineering and Analytics Teams use techniques of Data Observability to ensure a reliable, timely, and high-quality Data Flow inside and outside their organization. What is Data Observability? It refers to understanding the data’s internal state by looking at its external aspects. More specifically, it refers to the process of understanding the state and health of your data in your systems.
This article provides a comprehensive overview of Data Observability, Data Pipeline Observability, and the benefits of using Data Observability. We also discuss the best seven platforms that can be used for performing Data Pipeline Observability practices in your company.
Table of Contents
What is Data Observability?
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Data Observability is an umbrella term that defines an organization’s ability to understand the health of its enterprise data by tracking, monitoring, and troubleshooting it. This helps maintain a constant data influx to all the teams, by providing complete visibility into their Data Pipelines. Using practices of Data Observability, companies can drill down into their Data Pipeline problems and resolve them as soon as they get known.
Data Observability also helps in eliminating data downtime through the use of Data Pipeline Observability tools. These tools enable you to automate the monitoring, altering, and triaging of enterprise data and apply best practices of DevOps technologies to deliver greater trust in data. FirstEigen provides autonomous data observability, trustability, and quality solutions for data lakes, pipelines, and warehouses.
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What is Data Pipeline Observability?
Data Pipeline Observability refers to applying Data Observability concepts to your Data Pipelines to analyze the performance, improve the availability, and help you achieve maximum utilization of the resources of your pipeline.
Data Pipeline Observability enables your Data Engineers to monitor their Data Pipelines and optimize their performance by altering the parameters and resources like computation units, storage requirements, network resources, and many more.
Data Pipeline Observability also provides a comprehensive overview of your Data Pipeline’s workings, enabling you to reduce downtime (timeframe where the data is incomplete or unusable) by applying DevOps principles. It helps your organizations discover Data Quality issues, Data Integrity issues, and broken parts of your Data Pipelines, among a few, and provide a quick resolution to all these issues.
What are the Business Outcomes of Data Observability?
Data Observability practices enable your business to monitor and manage the health of your enterprise data. It allows your teams to become more efficient and focused. The following are major outcomes of implementing Data Observability practices in your business:
- Data Observability enables automated Data Discovery and helps simplify Data Lineage problems.
- Using Data Observability Pipelines, dependency on data is continuous, as opposed to one-time testing, which provides a holistic and complete overview of the quality of your enterprise data.
- Data Observability practices feature Data Pipeline cost analysis which is useful for teams and allows them to focus on optimizing their Data Pipeline performance. This also allows businesses to find any sluggish Data Pipelines and optimize them.
- Data Observability helps in creating a comprehensive perspective and a primary source of truth throughout your organization.
- Data Observability provides a framework for conceiving reliable and accurate findings related to the quality of data.
What do we Track with Data Pipeline Observability?
Data Delivery Timelines
Data Delivery Timeline refers to the timely delivery of data to your teams. This metric helps in ensuring that your teams get to work on fresh and real-time data and produce up-to-date results. This metric also measures the rate at which your data tables are updated. Better Delivery Timeliness can be achieved by automating your Data Pipelines and using infrastructural readiness monitoring.
Data Volume
Abnormal volumes of data can be an indication that your Data Pipeline is broken, which can result in unforeseen results. The volume can also refer to the completeness of data received by your Data Pipeline. This can be managed by setting up checkpoints at different levels and identifying the part where your Data Pipeline is not producing the desired results.
Data Pipeline ingests data from multiple sources, and this data can be in different formats. Data Observability Pipeline allows monitoring of these varied formats of data to ensure that your data is not broken.
Data Lineage
Data Lineage metric provides answers to questions like where the data breakdown took place, which teams are accessing the data, which injectors were impacted, and other information. A properly planned Data Lineage also provides information on Data Governance and Compliance, technical aspects and metadata, and a few more parameters. This information allows you and your teams to create a single source of truth.
Data Risk
The Data Risk metric mainly measures the risk associated with exposure to data. All the parameters like security and regulations, privacy laws, and regulatory controls are all monitored during the process to ensure less risk. Data Pipeline Observability procedures also enable the teams to segregate the levels and find the data risks on a regular basis.
Data Quality & Consistency
Data Quality & Consistency metric helps your teams to uncover data that is inconsistent or incomplete. This data has the potential to lead to business decisions that are low in trust and not optimal. Data Observability helps your teams to monitor Data Quality from the source to the destination allowing them to uncover problems and fix them quickly.
Data Completeness
The Data Completeness metric is used to increase the accuracy and context of your business decisions. If the data is not complete, it does not provide a holistic overview and chances are your decisions will be biased. Data Observability helps in overcoming this by enabling teams to monitor Data Analytics and indeed increase the usefulness of their data.
Why is Data Observability Essential For Your Data Pipelines?
Data Pipeline Observability enables businesses to utilize the full potential of their Data Pipelines and provide the utmost value. Following are a few advantages that Data Pipeline Observability incorporates:
- Every expanding business needs Data Observability Pipelines as it ensures that their business decisions are accurate. It also ensures that data reaching their analytics and business teams is of the highest quality and has very less risk of getting exposed.
- Data Pipeline Observability keeps your Data Engineers in sync. This also allows your teams to contribute toward a singular goal and better understand the process and functionality of your Data Pipelines.
- Data Pipeline Observability enables teams to maintain Data Quality and prevent Data Pipeline failures, well ahead of time by providing insights into Data Pipeline health, its performance, and user, or infrastructure issues.
Is Data Observability a Part of Data Governance?
To understand if Data Observability is a part of Data Governance, let us first understand what Data Governance is.
Data Governance deals with managing the data and making it available to the teams. It also ensures the data is secure and maintains its integrity throughout. An effective Data Governance strategy for your organization may include set strategies and directions for your data products to ensure that data-related work is performed according to your business operating procedures.
Data Observability deals with having an overview of the health of your data and ensuring that your business decisions are not biased or damaged. It aims at providing complete visibility into your Analytical and Operational workloads to help you resolve any performance, user, or infrastructure issues across your systems.
When it comes to Data Observability and Data Governance, Data Observability and Data Governance do not completely overlap. They have some convergence and work together harmoniously in order to provide maximum benefit to your Data Pipelines. The final focus may be completely different, which is again largely dependent on the form and function of your business, and the teams performing both tasks can also differ.
What Makes a Data Pipeline Observability Effective?
Data Pipeline Observability allows you to monitor your data before it enters the system and prevents bad, incomplete, or risky data from defining business decisions.
Following are a few features of Data Pipeline Observability that make it effective for businesses:
- Connection to Existing Tech Stack: Data Pipeline Observability can be connected to your existing Data Pipelines by requiring you to write or modify your codes. This allows for maximum throughput and fewer investments.
- Monitor Data: Data Pipeline Observability enables your teams to efficiently monitor your data. This framework is also scalable based on volumes of ingested data and ensures the highest security and compliance.
- Machine Learning Tools: Data Pipeline Observability requires minimal configuration and uses Machine Learning Algorithms for self-learning and anomaly detection.
- Earlier Detection of Issues: Data Pipeline Observability helps in identifying key dependencies, and provides a broad overview of your Data Flow.
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- Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision-making.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
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Monte Carlo
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Monte Carlo is an end-to-end Data Pipeline Observability service that prevents, finds, and fixes Data Pipelines. This tool helps in preventing Data Downtime and ensures Data Reliability.
It comes with features like Monte Carlo catalogs, automated alerting, and observability features out of the box. The platform has raised $80M in startup Series C.
Databand
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The DataBand Data Pipeline Observability enables efficient Data Engineering in modern infrastructure. This tool provides an AI-powered platform that helps in smooth operations and unified visibility of your Data Flows.
It can also be combined with cloud-native tools like Apache Airflow and Snowflake.
Honeycomb
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The Honeycomb Data Observability Pipeline tool is capable of providing a complete overview of your Data Pipeline problems in distributed systems. It is a full-stack cloud-based tool that shows events, logs, traces, and more. It also supports OpenTelemetary data for generating instrument data.
Datafold
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Datafold Data Pipeline Observability tool provides a platform to monitor anomaly detection, profiling, and more. It enables data QA, table comparisons, creating intelligent alerts, and many more through a single click of a button.
Data Teams can use Datafold to monitor their ETL code and integrate it with CI/CD using instant review.
SigNoz
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SigNoz is an open-source Data Pipeline Observability tool that is excellent at catching all the traces and metrics of data. It provides a full-stack APM feature suite. It includes features like telemetry data generation, a storage solution, a visualization layer, and many more.
SigNoz effectively generates telemetry data using OpenTelemetory using vendor-agnostic instrumentation.
DataDog
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Data Dog is a Data Pipeline Observability tool that provides functionalities like infrastructure monitoring, application performance management, security management, and many more. This tool provides access to open-source libraries and end-to-end distributed systems for tracing requests and logs monitoring.
Accel Data
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Accel Data is a Data Pipeline Observability tool that is effective for Data Monitoring and ensuring Data Reliability. This tool is used by Data Engineering teams to gain a comprehensive overview of their Data Pipelines.
Accel Data combines data from multiple layers and presents them on a single platform. This tool is more focused on the finance industry’s needs.
Conclusion
With organizations depending largely on data from various sources, it becomes extremely important to manage and monitor data and maintain its health. This can be done with the help of Data Pipeline Observability techniques, to ensure that the best quality data is used throughout. This article provided a comprehensive guide on Data Pipeline Observability practices and tools that can provide correct Data Observability for your data.
There are various Data Sources that organizations leverage to capture a variety of valuable data points. But, transferring data from these sources into a Data Warehouse for a holistic analysis is a hectic task. It requires you to code and maintains complex functions that can help achieve a smooth flow of data. An Automated Data Pipeline helps in solving this issue and this is where Hevo comes into the picture. Hevo Data is a No-code Data Pipeline that has 150+ pre-built Integrations to choose from.
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Hevo can help you Integrate your data from 150+ data sources and load them into a destination to analyze real-time data at an affordable price. It will make your life easier and Data Replication hassle-free. It is user-friendly, reliable, and secure.
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