Documents
  • Invariant Documents
  • Platform
    • Data Platform
      • Install Overview
      • System Requirement
      • Software Requirement
      • Prepare the Environment
      • Installing Ambari Server
      • Setup Ambari Server
      • Start Ambari Server
      • Single Node Install
      • Multi-Node Cluster Install
      • Cluster Install from Ambari
      • Run and monitor HDFS
    • Apache Hadoop
      • Compatible Hadoop Versions
      • HDFS
        • HDFS Architecture
        • Name Node
        • Data Node
        • File Organization
        • Storage Format
          • ORC
          • Parquet
        • Schema Design
      • Hive
        • Data Organization
        • Data Types
        • Data Definition
        • Data Manipulation
          • CRUD Statement
            • Views, Indexes, Temporary Tables
        • Cost-based SQL Optimization
        • Subqueries
        • Common Table Expression
        • Transactions
        • SerDe
          • XML
          • JSON
        • UDF
      • Oozie
      • Sqoop
        • Commands
        • Import
      • YARN
        • Overview
        • Accessing YARN Logs
    • Apache Kafka
      • Compatible Kafka Versions
      • Installation
    • Elasticsearch
      • Compatible Elasticsearch Versions
      • Installation
  • Discovery
    • Introduction
      • Release Notes
    • Methodology
    • Discovery Pipeline
      • Installation
      • DB Event Listener
      • Pipeline Configuration
      • Error Handling
      • Security
    • Inventory Manager
      • Installation
      • Metadata Management
      • Column Mapping
      • Service Configuration
      • Metadata Configuration
      • Metadata Changes and Versioning
        • Generating Artifacts
      • Reconciliation, Merging Current View
        • Running daily reconciliation and merge
      • Data Inventory Reports
    • Schema Registry
  • Process Insight
    • Process Insight
      • Overview
    • Process Pipeline
      • Data Ingestion
      • Data Storage
    • Process Dashboards
      • Panels
      • Templating
      • Alerts
        • Rules
        • Notifications
  • Content Insight
    • Content Insight
      • Release Notes
      • Configuration
      • Content Indexing Pipeline
    • Management API
    • Query DSL
    • Configuration
  • Document Flow
    • Overview
  • Polyglot Data Manager
    • Polyglot Data Manager
      • Release Notes
    • Data Store
      • Concepts
      • Sharding
    • Shippers
      • Filerelay Container
    • Processors
    • Search
    • User Interface
  • Operational Insight
    • Operational Insight
      • Release Notes
    • Data Store
      • Concepts
      • Sharding
    • Shippers
      • Filerelay Container
    • Processors
    • Search
    • User Interface
  • Data Science
    • Data Science Notebook
      • Setup JupyterLab
      • Configuration
        • Configuration Settings
        • Libraries
    • Spark DataHub
      • Concepts
      • Cluster Setup
      • Spark with YARN
      • PySpark Setup
        • DataFrame API
      • Reference
  • Product Roadmap
    • Roadmap
  • TIPS
    • Service Troubleshooting
    • Service Startup Errors
    • Debugging YARN Applications
      • YARN CLI
    • Hadoop Credentials
    • Sqoop Troubleshooting
    • Log4j Vulnerability Fix
Powered by GitBook
On this page
  1. Discovery
  2. Inventory Manager

Reconciliation, Merging Current View

PreviousGenerating ArtifactsNextRunning daily reconciliation and merge

Last updated 5 years ago

Inventory manager sources data to the reconciliation view nightly for all of the tables from the various data sources configured right after midnight. This data is used to for validating the incremental data which is sourced near-real time nightly. Any errors in the pipeline are addressed as part of the validation using the data. The reconciliation data is kept by default for 30-day period and this time period is configurable. It is expected to record the inventory counts and reconciliation fixes within that period. The data for reconciliation is sourced into the recon schema as configured in the target data store configuration.

The data from incremental view is merged into current view periodically for all of the configured tables by data source. This merge leverages the sourced date time, database action to effectively maintain the current view to reflect the state of the records in the source system. In addition, the date column is updated to reflect the time of the update in the current view so downstream data jobs can use that to drive processing logic i.e. select data changed for a particular time window.