{"id":5236,"date":"2026-01-08T06:25:52","date_gmt":"2026-01-08T06:25:52","guid":{"rendered":"https:\/\/gurukulgalaxy.com\/blog\/?p=5236"},"modified":"2026-03-01T05:28:57","modified_gmt":"2026-03-01T05:28:57","slug":"top-10-data-observability-tools-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Data Observability Tools: Features, Pros, Cons &amp; Comparison"},"content":{"rendered":"\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"559\" src=\"https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/278.jpg\" alt=\"\" class=\"wp-image-5237\" srcset=\"https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/278.jpg 1024w, https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/278-300x164.jpg 300w, https:\/\/gurukulgalaxy.com\/blog\/wp-content\/uploads\/2026\/01\/278-768x419.jpg 768w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Top_10_Data_Observability_Tools\" >Top 10 Data Observability Tools<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#1_%E2%80%94_Monte_Carlo\" >1 \u2014 Monte Carlo<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#2_%E2%80%94_Bigeye\" >2 \u2014 Bigeye<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#3_%E2%80%94_Acceldata\" >3 \u2014 Acceldata<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#4_%E2%80%94_Metaplane\" >4 \u2014 Metaplane<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#5_%E2%80%94_Soda_Soda_Cloud_Soda_Library\" >5 \u2014 Soda (Soda Cloud &amp; Soda Library)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#6_%E2%80%94_Anomalo\" >6 \u2014 Anomalo<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#7_%E2%80%94_IBM_Databand\" >7 \u2014 IBM Databand<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#8_%E2%80%94_Great_Expectations_GX_Cloud\" >8 \u2014 Great Expectations (GX Cloud)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#9_%E2%80%94_Kensu\" >9 \u2014 Kensu<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#10_%E2%80%94_Telmai\" >10 \u2014 Telmai<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Comparison_Table\" >Comparison Table<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Evaluation_Scoring_of_Data_Observability_Tools\" >Evaluation &amp; Scoring of Data Observability Tools<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Which_Data_Observability_Tool_Is_Right_for_You\" >Which Data Observability Tool Is Right for You?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Frequently_Asked_Questions_FAQs\" >Frequently Asked Questions (FAQs)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/gurukulgalaxy.com\/blog\/top-10-data-observability-tools-features-pros-cons-comparison\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span>Introduction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Data observability is the ability of an organization to understand the health and state of their data across its entire lifecycle. Unlike simple data quality testing, which checks data at a specific point in time, data observability provides continuous, end-to-end monitoring. It focuses on what industry experts call the &#8220;Five Pillars of Data Observability&#8221;: <strong>Freshness<\/strong> (is the data up to date?), <strong>Distribution<\/strong> (is the data within expected ranges?), <strong>Volume<\/strong> (is the dataset complete?), <strong>Schema<\/strong> (has the structure changed?), and <strong>Lineage<\/strong> (where did the data come from and who does it impact?).<\/p>\n\n\n\n<p>These tools are important because they drastically reduce &#8220;Data Downtime&#8221;\u2014the periods when data is partial, erroneous, or missing. Real-world use cases include preventing financial reporting errors, ensuring ML models aren&#8217;t trained on &#8220;garbage&#8221; data, and saving data engineers from the &#8220;fire drills&#8221; that occur when pipelines break silently. When choosing a tool, users should evaluate the ease of integration with their existing stack (e.g., Snowflake, Databricks, Airflow), the depth of ML-driven anomaly detection, and the quality of the automated data lineage.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Best for:<\/strong> Data engineers, analytics leaders, and data platform teams at mid-market to enterprise companies. It is especially vital for organizations where data drives automated decision-making, customer-facing products, or strict regulatory reporting (e.g., FinTech, HealthTech, E-commerce).<\/p>\n\n\n\n<p><strong>Not ideal for:<\/strong> Very small startups with simple, single-source data pipelines where manual checks are still feasible, or teams that do not yet have a centralized data warehouse or lake. In these cases, simple open-source validation scripts may be more cost-effective.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Top_10_Data_Observability_Tools\"><\/span>Top 10 Data Observability Tools<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1_%E2%80%94_Monte_Carlo\"><\/span>1 \u2014 Monte Carlo<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Monte Carlo is widely recognized as the pioneer of the data observability category. It offers an end-to-end platform that requires minimal configuration, using machine learning to automatically learn your data&#8217;s fingerprints and alert you when something looks &#8220;off.&#8221;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Automated Monitoring:<\/strong> No-code ML models that automatically detect anomalies in volume, freshness, and schema.<\/li>\n\n\n\n<li><strong>Full-Stack Lineage:<\/strong> Visualizes the journey from the ingestion layer down to the BI dashboard (e.g., Looker, Tableau).<\/li>\n\n\n\n<li><strong>Incident Management:<\/strong> Collaborative workspaces to assign, track, and resolve data issues.<\/li>\n\n\n\n<li><strong>Data Health Insights:<\/strong> Executive-level reporting on data reliability trends and SLAs.<\/li>\n\n\n\n<li><strong>Deep Integrations:<\/strong> Native support for Snowflake, Databricks, BigQuery, Airflow, and dbt.<\/li>\n\n\n\n<li><strong>Field-Level Lineage:<\/strong> Extremely granular view of how specific columns impact downstream reports.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The most mature &#8220;all-in-one&#8221; solution on the market with a very polished UI.<\/li>\n\n\n\n<li>Requires almost zero manual &#8220;rule-writing&#8221; to get started, providing immediate value.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Positioned as a premium enterprise solution with a high price point to match.<\/li>\n\n\n\n<li>Can sometimes produce &#8220;alert fatigue&#8221; if not tuned properly during the initial weeks.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2 Type II, HIPAA, GDPR, and SSO integration. Data stays in your warehouse; only metadata is processed.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Industry-leading customer success teams, extensive documentation, and a highly active community of data leaders.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2_%E2%80%94_Bigeye\"><\/span>2 \u2014 Bigeye<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Bigeye focuses on data reliability for high-growth data teams. It stands out for its &#8220;Autometrics&#8221; feature, which suggests the most relevant metrics to track for every single table in your warehouse.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Autometrics:<\/strong> Automatically scans your warehouse and recommends specific data quality checks.<\/li>\n\n\n\n<li><strong>SLA Tracking:<\/strong> Define and monitor Service Level Agreements for your data consumers.<\/li>\n\n\n\n<li><strong>Issue Templates:<\/strong> Standardized workflows for investigating and documenting root causes.<\/li>\n\n\n\n<li><strong>Delta Tracking:<\/strong> Compares data across different environments (e.g., Prod vs. Staging).<\/li>\n\n\n\n<li><strong>Extensive API:<\/strong> Fully programmable for teams that want to build custom automation on top of Bigeye.<\/li>\n\n\n\n<li><strong>Smart Throttling:<\/strong> Intelligently groups alerts to prevent noise.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The recommendation engine makes it very easy for small teams to cover large data estates.<\/li>\n\n\n\n<li>Excellent balance between automated ML and manual &#8220;expert&#8221; overrides.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The lineage capabilities are solid but arguably less deep than Monte Carlo\u2019s.<\/li>\n\n\n\n<li>Some users report a steeper learning curve for the advanced programmatic features.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2 Type II, GDPR, and encryption at rest\/transit.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> High-quality technical support and a growing library of &#8220;Data Reliability&#8221; educational resources.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3_%E2%80%94_Acceldata\"><\/span>3 \u2014 Acceldata<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Acceldata takes a broader approach by combining data observability with &#8220;Data Compute&#8221; and &#8220;Data Pipeline&#8221; observability. It is a favorite for large enterprises managing massive hybrid-cloud or on-premise Hadoop\/Spark environments.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Multi-Layer Observability:<\/strong> Monitors the data, the processing engine (Spark\/Snowflake), and the pipeline.<\/li>\n\n\n\n<li><strong>Cost Optimization:<\/strong> Specific tools to identify and reduce &#8220;wasteful&#8221; spend in Snowflake or Databricks.<\/li>\n\n\n\n<li><strong>Open Architecture:<\/strong> Highly extensible for legacy on-premise systems as well as modern cloud stacks.<\/li>\n\n\n\n<li><strong>Automated Data Reconciliation:<\/strong> Ensures data matches perfectly across different stages of a migration.<\/li>\n\n\n\n<li><strong>Real-time Alerting:<\/strong> Low-latency notifications for critical production failures.<\/li>\n\n\n\n<li><strong>Data Quality Circuit Breakers:<\/strong> Automatically stops a pipeline if data fails a critical check.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The best choice for organizations that need to monitor both data health and infrastructure costs.<\/li>\n\n\n\n<li>Unmatched support for &#8220;Big Data&#8221; legacy environments like Hadoop.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The broad feature set can feel overwhelming for teams only interested in data quality.<\/li>\n\n\n\n<li>UI is more &#8220;industrial&#8221; and functional rather than consumer-grade &#8220;slick.&#8221;<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2, HIPAA, ISO 27001, and support for VPC deployments.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Strong enterprise support with dedicated technical account managers for large contracts.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4_%E2%80%94_Metaplane\"><\/span>4 \u2014 Metaplane<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Metaplane is often called the &#8220;Monte Carlo for SMBs.&#8221; It focuses on extreme ease of use and a fast setup time, making it the go-to choice for teams using the &#8220;Modern Data Stack&#8221; (Snowflake, dbt, Fivetran).<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>10-Minute Setup:<\/strong> Connect your warehouse and BI tool to start monitoring almost instantly.<\/li>\n\n\n\n<li><strong>dbt Cloud Integration:<\/strong> Automatically syncs metadata and tests from your dbt runs.<\/li>\n\n\n\n<li><strong>Slack-First Workflow:<\/strong> Alerts and incident management happen directly within Slack.<\/li>\n\n\n\n<li><strong>Automated Lineage:<\/strong> Simple, effective visualization of how warehouse tables map to BI dashboards.<\/li>\n\n\n\n<li><strong>Usage Analytics:<\/strong> Identifies &#8220;ghost&#8221; dashboards that no one is looking at.<\/li>\n\n\n\n<li><strong>Schema Evolution Tracking:<\/strong> Immediate alerts when a source table adds or removes a column.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Highly affordable and transparent pricing compared to enterprise rivals.<\/li>\n\n\n\n<li>One of the best user experiences in the category; very low friction to adopt.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Lacks some of the &#8220;deep&#8221; enterprise governance features found in Collibra or IBM.<\/li>\n\n\n\n<li>Not designed for complex on-premise or non-cloud-native environments.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2 Type II, GDPR, and secure metadata-only access.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Friendly, fast support and a very active Slack community for users.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5_%E2%80%94_Soda_Soda_Cloud_Soda_Library\"><\/span>5 \u2014 Soda (Soda Cloud &amp; Soda Library)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Soda is unique because it bridges the gap between open-source testing and enterprise observability. It uses a human-readable language called &#8220;SodaCL&#8221; (Soda Check Language) to define data quality rules.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>SodaCL:<\/strong> A YAML-based language that allows both engineers and business users to write tests.<\/li>\n\n\n\n<li><strong>Soda Library:<\/strong> An open-source CLI tool for running checks within CI\/CD or orchestration.<\/li>\n\n\n\n<li><strong>Soda Cloud:<\/strong> A centralized platform for visualizing results, managing alerts, and tracking history.<\/li>\n\n\n\n<li><strong>Data Contracts:<\/strong> Tools to help data producers and consumers agree on data standards.<\/li>\n\n\n\n<li><strong>Anomaly Detection:<\/strong> ML-powered checks that supplement manual &#8220;threshold&#8221; tests.<\/li>\n\n\n\n<li><strong>Multi-Source Support:<\/strong> Works with SQL databases, Spark, and streaming data.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>&#8220;Developer-first&#8221; approach that fits perfectly into existing GitOps workflows.<\/li>\n\n\n\n<li>Excellent for companies that want to start with open-source and scale to a cloud platform.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Requires more manual &#8220;rule-writing&#8221; than the purely ML-driven tools.<\/li>\n\n\n\n<li>The setup is more technical, requiring knowledge of YAML and CLI tools.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2 Type II, GDPR, and support for air-gapped or private cloud deployments.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Extensive open-source community on GitHub and Slack; professional support for Cloud customers.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"6_%E2%80%94_Anomalo\"><\/span>6 \u2014 Anomalo<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Anomalo focuses on &#8220;Deep Data Quality.&#8221; While other tools check if data <em>arrived<\/em>, Anomalo uses sophisticated ML to look <em>inside<\/em> the data to find subtle issues that traditional tests miss.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Unsupervised Learning:<\/strong> Automatically finds anomalies without needing any rules or thresholds.<\/li>\n\n\n\n<li><strong>Root Cause Analysis:<\/strong> Automatically identifies which segments or columns are causing an issue.<\/li>\n\n\n\n<li><strong>Data Validation for GenAI:<\/strong> Specific tools to monitor the quality of unstructured data for LLMs.<\/li>\n\n\n\n<li><strong>Visual Profiling:<\/strong> Automatically generates a visual &#8220;health check&#8221; for every table.<\/li>\n\n\n\n<li><strong>No-Code UI:<\/strong> Designed so that data analysts can manage observability without writing SQL.<\/li>\n\n\n\n<li><strong>Historical Analysis:<\/strong> Compares current data against months of historical patterns.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Exceptional at finding &#8220;needle in a haystack&#8221; issues that standard volume\/freshness checks miss.<\/li>\n\n\n\n<li>The root cause analysis saves hours of manual investigation.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Can be computationally expensive for very large datasets if every column is monitored deeply.<\/li>\n\n\n\n<li>Less emphasis on &#8220;pipeline&#8221; or &#8220;compute&#8221; observability compared to Acceldata.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2 Type II, HIPAA, and GDPR. Data never leaves your VPC.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Strong focus on customer success and technical deep-dives for enterprise clients.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"7_%E2%80%94_IBM_Databand\"><\/span>7 \u2014 IBM Databand<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Acquired by IBM in 2022, Databand is a pipeline-centric observability tool. It is specifically designed to help engineers catch &#8220;bad data&#8221; at the moment it is being processed in Airflow, Spark, or Snowflake.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Pipeline Health Monitoring:<\/strong> Tracks the success, duration, and resource usage of every job.<\/li>\n\n\n\n<li><strong>Deep Airflow Integration:<\/strong> Provides an &#8220;Airflow-native&#8221; view of pipeline failures.<\/li>\n\n\n\n<li><strong>Data Profiling in Transit:<\/strong> Checks data quality <em>during<\/em> the execution of a Spark job.<\/li>\n\n\n\n<li><strong>Automated Lineage:<\/strong> Maps dependencies based on actual execution logs.<\/li>\n\n\n\n<li><strong>Incident Tracking:<\/strong> Integrated with Jira, Slack, and PagerDuty for fast response.<\/li>\n\n\n\n<li><strong>Metadata Repository:<\/strong> Keeps a historical record of every pipeline run and data snapshot.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The best choice for teams that are &#8220;Airflow-heavy&#8221; or use complex Spark jobs.<\/li>\n\n\n\n<li>Focuses on the &#8220;root cause&#8221; of a pipeline failure, not just the data symptom.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The UI can feel a bit more &#8220;enterprise-heavy&#8221; (IBM style).<\/li>\n\n\n\n<li>Not as focused on &#8220;business-user&#8221; discovery compared to Atlan or Alation.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2, ISO 27001, GDPR, and backed by IBM\u2019s global security standards.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Extensive enterprise support and integration into the broader IBM Data &amp; AI ecosystem.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"8_%E2%80%94_Great_Expectations_GX_Cloud\"><\/span>8 \u2014 Great Expectations (GX Cloud)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Great Expectations is the most popular open-source tool for data validation. With the launch of GX Cloud, it has evolved into a full-fledged observability platform that combines testing with centralized management.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Expectations:<\/strong> A massive library of pre-built &#8220;tests&#8221; (e.g., <code>expect_column_values_to_not_be_null<\/code>).<\/li>\n\n\n\n<li><strong>Data Docs:<\/strong> Automatically generates clean, human-readable documentation of data quality.<\/li>\n\n\n\n<li><strong>GX Cloud Dashboard:<\/strong> A centralized place to view all test results across different environments.<\/li>\n\n\n\n<li><strong>Profiler:<\/strong> Automatically scans data and suggests a baseline set of &#8220;expectations.&#8221;<\/li>\n\n\n\n<li><strong>Integration Flexibility:<\/strong> Works with Python, SQL, Spark, and almost every modern orchestrator.<\/li>\n\n\n\n<li><strong>Checkpoints:<\/strong> Allows you to &#8220;stop the line&#8221; if data fails to meet expectations.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The industry standard for data testing; if you hire a data engineer, they likely already know GX.<\/li>\n\n\n\n<li>Massive open-source community ensures constant updates and support for new sources.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>The open-source version can be difficult to manage at scale without the Cloud version.<\/li>\n\n\n\n<li>Still feels more like a &#8220;testing framework&#8221; than an &#8220;automated monitoring&#8221; tool.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> Varies (OSS); GX Cloud is SOC 2 compliant.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Unrivaled community size on Slack and GitHub; professional support for GX Cloud.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"9_%E2%80%94_Kensu\"><\/span>9 \u2014 Kensu<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Kensu takes a &#8220;real-time&#8221; approach to data observability. It is designed to provide visibility into the data as it moves through pipelines, rather than just checking it once it reaches the warehouse.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>In-Pipeline Monitoring:<\/strong> Observes data quality as it is being processed by Spark or Python.<\/li>\n\n\n\n<li><strong>Data Circuit Breakers:<\/strong> Automatically stops faulty pipelines to prevent corrupted data from spreading.<\/li>\n\n\n\n<li><strong>Developer-Centric:<\/strong> Focuses on helping engineers debug issues during the development lifecycle.<\/li>\n\n\n\n<li><strong>Contextual Alerts:<\/strong> Tells you not just <em>that<\/em> something failed, but <em>where<\/em> in the code it happened.<\/li>\n\n\n\n<li><strong>Schema Evolution:<\/strong> Monitors for &#8220;silent&#8221; schema changes that might break downstream apps.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Excellent for preventing &#8220;data pollution&#8221; by stopping issues at the source.<\/li>\n\n\n\n<li>Strong alignment with DataOps and CI\/CD best practices.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Smaller market presence compared to Monte Carlo or IBM.<\/li>\n\n\n\n<li>Integration requires more &#8220;instrumentation&#8221; (adding code to your pipelines).<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> GDPR compliant and SOC 2 ready.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> Fast, engineering-led support and a focused user community.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"10_%E2%80%94_Telmai\"><\/span>10 \u2014 Telmai<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Telmai is an architectural-first observability tool. It is built to handle massive scale and cross-platform monitoring, specifically designed for heterogeneous environments where data moves between many different types of systems.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Key features:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Low-Code ML:<\/strong> Uses AI to find anomalies across massive, multi-petabyte datasets.<\/li>\n\n\n\n<li><strong>Cross-Source Analysis:<\/strong> Compares data health across different systems (e.g., Kafka to Snowflake).<\/li>\n\n\n\n<li><strong>Data Profiling:<\/strong> High-speed scanning that provides a statistical overview of your data estate.<\/li>\n\n\n\n<li><strong>Incident Management:<\/strong> Full lifecycle tracking of data outages.<\/li>\n\n\n\n<li><strong>Open Metadata:<\/strong> Allows you to export Telmai&#8217;s findings to other governance tools.<\/li>\n\n\n\n<li><strong>Time-Travel Analysis:<\/strong> Easily compare current data quality to any point in the past.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Pros:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Built for &#8220;unlimited&#8221; scale; does not struggle with extremely wide or deep tables.<\/li>\n\n\n\n<li>Great for &#8220;Hybrid&#8221; teams moving data between on-premise and cloud.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Cons:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Less &#8220;brand recognition&#8221; than the top 3 players.<\/li>\n\n\n\n<li>The interface is powerful but requires some training to navigate effectively.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Security &amp; compliance:<\/strong> SOC 2 Type II, HIPAA, and GDPR compliant.<\/li>\n\n\n\n<li><strong>Support &amp; community:<\/strong> High-touch support for enterprise customers and a detailed technical knowledge base.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Comparison_Table\"><\/span>Comparison Table<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><td><strong>Tool Name<\/strong><\/td><td><strong>Best For<\/strong><\/td><td><strong>Platform(s) Supported<\/strong><\/td><td><strong>Standout Feature<\/strong><\/td><td><strong>Rating (Gartner\/TrueReview)<\/strong><\/td><\/tr><\/thead><tbody><tr><td><strong>Monte Carlo<\/strong><\/td><td>Enterprises \/ All-in-One<\/td><td>Snowflake, DB, BQ, etc.<\/td><td>End-to-End Visual Lineage<\/td><td>4.7 \/ 5<\/td><\/tr><tr><td><strong>Bigeye<\/strong><\/td><td>High-Growth Teams<\/td><td>Cloud Warehouses<\/td><td>Autometrics Recommendations<\/td><td>4.6 \/ 5<\/td><\/tr><tr><td><strong>Acceldata<\/strong><\/td><td>Hybrid \/ Cost Mgmt<\/td><td>Hadoop, Spark, Cloud<\/td><td>Compute Cost Observability<\/td><td>4.5 \/ 5<\/td><\/tr><tr><td><strong>Metaplane<\/strong><\/td><td>SMBs \/ Fast Setup<\/td><td>Snowflake, dbt, BI<\/td><td>Slack-Integrated Incidents<\/td><td>4.8 \/ 5<\/td><\/tr><tr><td><strong>Soda<\/strong><\/td><td>Developer-First Teams<\/td><td>Multi-platform, OSS<\/td><td>SodaCL Testing Language<\/td><td>4.4 \/ 5<\/td><\/tr><tr><td><strong>Anomalo<\/strong><\/td><td>Deep Data Validation<\/td><td>Cloud Warehouses<\/td><td>ML Root Cause Analysis<\/td><td>4.7 \/ 5<\/td><\/tr><tr><td><strong>IBM Databand<\/strong><\/td><td>Pipeline\/Airflow Users<\/td><td>Airflow, Spark, Cloud<\/td><td>Pipeline-Centric Monitoring<\/td><td>4.3 \/ 5<\/td><\/tr><tr><td><strong>Great Expectations<\/strong><\/td><td>Testing Standards<\/td><td>Python, Spark, SQL<\/td><td>Massive Test Library (Expects)<\/td><td>4.5 \/ 5<\/td><\/tr><tr><td><strong>Kensu<\/strong><\/td><td>Real-Time \/ Developer<\/td><td>Spark, Python, Cloud<\/td><td>In-Pipeline Circuit Breakers<\/td><td>4.2 \/ 5<\/td><\/tr><tr><td><strong>Telmai<\/strong><\/td><td>Massive Scale \/ Hybrid<\/td><td>Multi-cloud, Kafka, DBs<\/td><td>Cross-System Data Health<\/td><td>4.4 \/ 5<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Evaluation_Scoring_of_Data_Observability_Tools\"><\/span>Evaluation &amp; Scoring of Data Observability Tools<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To help you decide, we have evaluated these tools against a weighted scoring rubric that reflects the priorities of modern data organizations.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><td><strong>Category<\/strong><\/td><td><strong>Weight<\/strong><\/td><td><strong>Evaluation Criteria<\/strong><\/td><\/tr><\/thead><tbody><tr><td><strong>Core Features<\/strong><\/td><td>25%<\/td><td>Lineage, anomaly detection, schema monitoring, and incident management.<\/td><\/tr><tr><td><strong>Ease of Use<\/strong><\/td><td>15%<\/td><td>Time-to-value, UI intuitiveness, and no-code capabilities.<\/td><\/tr><tr><td><strong>Integrations<\/strong><\/td><td>15%<\/td><td>Depth of support for the &#8220;Modern Data Stack&#8221; and legacy systems.<\/td><\/tr><tr><td><strong>Security &amp; Compliance<\/strong><\/td><td>10%<\/td><td>SOC 2, HIPAA, data residency, and metadata-only privacy.<\/td><\/tr><tr><td><strong>Performance<\/strong><\/td><td>10%<\/td><td>Impact on warehouse costs and ability to scale to petabytes.<\/td><\/tr><tr><td><strong>Support &amp; Community<\/strong><\/td><td>10%<\/td><td>Documentation, Slack communities, and enterprise support response.<\/td><\/tr><tr><td><strong>Price \/ Value<\/strong><\/td><td>15%<\/td><td>Predictability of cost and ROI for small vs. large teams.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Which_Data_Observability_Tool_Is_Right_for_You\"><\/span>Which Data Observability Tool Is Right for You?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The &#8220;best&#8221; tool is the one that fits your technical maturity and your most painful problem.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Solo Users vs SMB vs Enterprise:<\/strong> If you are a solo data engineer at a startup, <strong>Great Expectations<\/strong> (open-source) or <strong>Metaplane<\/strong> (free tier\/low cost) are perfect. For a mid-market team, <strong>Bigeye<\/strong> or <strong>Anomalo<\/strong> offer the best automation. For a massive enterprise, <strong>Monte Carlo<\/strong> or <strong>Acceldata<\/strong> provide the governance and scale you need.<\/li>\n\n\n\n<li><strong>Budget-conscious vs Premium:<\/strong> <strong>Soda<\/strong> and <strong>Great Expectations<\/strong> allow you to start for free. <strong>Metaplane<\/strong> is very affordable for small teams. <strong>Monte Carlo<\/strong> is a premium investment for teams where &#8220;data downtime&#8221; costs thousands of dollars per hour.<\/li>\n\n\n\n<li><strong>Feature depth vs Ease of use:<\/strong> If you want the deepest &#8220;inside the data&#8221; ML, go with <strong>Anomalo<\/strong>. If you want something that &#8220;just works&#8221; with your BI tool in 10 minutes, go with <strong>Metaplane<\/strong> or <strong>Monte Carlo<\/strong>.<\/li>\n\n\n\n<li><strong>Integration and scalability:<\/strong> Teams with complex Airflow\/Spark pipelines should look at <strong>IBM Databand<\/strong> or <strong>Kensu<\/strong>. Teams on purely Snowflake\/Databricks should stick with <strong>Monte Carlo<\/strong> or <strong>Bigeye<\/strong>.<\/li>\n\n\n\n<li><strong>Security and compliance:<\/strong> If you are in a highly regulated field and cannot allow any metadata to leave your environment, check for tools that offer VPC or on-premise deployments, such as <strong>Acceldata<\/strong> or <strong>Soda<\/strong>.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions_FAQs\"><\/span>Frequently Asked Questions (FAQs)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>1. What is the difference between Data Quality and Data Observability?<\/p>\n\n\n\n<p>Data quality is a &#8220;snapshot&#8221; check (e.g., is this column null?). Data observability is a &#8220;continuous&#8221; process that looks at the health of the entire pipeline, including lineage, schema, and performance.<\/p>\n\n\n\n<p>2. Does data observability slow down my warehouse?<\/p>\n\n\n\n<p>Most modern tools (like Monte Carlo and Metaplane) are &#8220;agentless&#8221; and use metadata or lightweight queries, resulting in negligible impact on your warehouse performance or costs.<\/p>\n\n\n\n<p>3. Do I need to write SQL for these tools?<\/p>\n\n\n\n<p>It varies. Tools like Anomalo and Monte Carlo are largely no-code, while Soda and Great Expectations are designed for engineers who prefer writing YAML or Python.<\/p>\n\n\n\n<p>4. How does lineage help with data observability?<\/p>\n\n\n\n<p>Lineage allows you to see the &#8220;blast radius&#8221; of an issue. If a table fails, lineage tells you exactly which dashboards and executive reports will be incorrect as a result.<\/p>\n\n\n\n<p>5. Can these tools prevent data errors before they happen?<\/p>\n\n\n\n<p>Some can. Tools like Soda, Kensu, and Great Expectations allow you to set &#8220;circuit breakers&#8221; that stop a pipeline if the data fails a check, preventing the &#8220;bad&#8221; data from reaching your production warehouse.<\/p>\n\n\n\n<p>6. What is &#8220;Data Downtime&#8221;?<\/p>\n\n\n\n<p>Data Downtime is the amount of time that data is inaccurate, missing, or otherwise unusable. Data observability tools aim to reduce this to near-zero.<\/p>\n\n\n\n<p>7. Are these tools compatible with dbt?<\/p>\n\n\n\n<p>Yes, almost all the top 10 tools have deep dbt integrations, often surfacing dbt test results and model documentation directly within the observability dashboard.<\/p>\n\n\n\n<p>8. How much do these tools cost?<\/p>\n\n\n\n<p>Pricing ranges from free (open-source) to $15k\u2013$20k per year for mid-market teams, and $50k+ for large enterprise deployments. Most are priced based on the number of tables or datasets monitored.<\/p>\n\n\n\n<p>9. Can I build my own observability tool?<\/p>\n\n\n\n<p>You can, but it is often a &#8220;hidden cost.&#8221; Building a robust matching engine, lineage visualization, and alerting system usually takes months of engineering time that could be spent on core data products.<\/p>\n\n\n\n<p>10. How does ML-based anomaly detection work?<\/p>\n\n\n\n<p>The tool analyzes historical metadata (e.g., &#8220;this table usually gets 10k rows at 8 AM&#8221;). If only 5 rows arrive, or they arrive at 10 AM, the ML identifies this deviation and sends an alert.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The transition from reactive data quality to proactive data observability is a milestone for any data-driven organization. By implementing a tool like <strong>Monte Carlo<\/strong>, <strong>Metaplane<\/strong>, or <strong>Anomalo<\/strong>, you are not just buying software; you are building trust. In 2026, a dashboard that no one trusts is worse than no dashboard at all. Choose a tool that fits your current stack and scales with your ambition, ensuring that your data &#8220;water&#8221; stays pure, no matter how fast it flows.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Data observability is the ability of an organization to understand the health and state of their data across its&hellip;<\/p>\n","protected":false},"author":32,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3253,3269,3279,3280,1877],"class_list":["post-5236","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-bigdata","tag-dataengineering","tag-dataobservability","tag-dataquality","tag-dataops"],"_links":{"self":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts\/5236","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/users\/32"}],"replies":[{"embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/comments?post=5236"}],"version-history":[{"count":1,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts\/5236\/revisions"}],"predecessor-version":[{"id":5238,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/posts\/5236\/revisions\/5238"}],"wp:attachment":[{"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/media?parent=5236"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/categories?post=5236"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gurukulgalaxy.com\/blog\/wp-json\/wp\/v2\/tags?post=5236"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}