Support for Incremental Replication
CREATED FOR:
- Beginner
- Intermediate
- Admin
- Developer
- User
If your tables currently do not allow for incremental replication, refer to the following recommendations for possible solutions.
Modifications for Modified At
The Modified At
method, which is the most ideal replication method, uses a datetime
column to detect new and/or updated data. Remember that the datetime
column in tables using this method must be indexed and cannot contain null values at any time.
If your table does not have a datetime
column, you can add an index modified at
column. Null values are not allowed in a modified at
column. Check that the column is populated for every row.
To ensure the Modified At
method works as intended, you cannot delete rows from the table. Rather, you should mark the row as invalid by adding a deleted
column to the table. This column returns a 1
if the row is invalid and 0
otherwise. You can then use this column to filter out invalid rows when you are building metrics and reports.
Modifications for Single Auto Incrementing Primary Key
If the Modified At
method cannot be enabled, then the Single Auto Incrementing Primary Key is the next best option. New data is discovered in tables using this method by searching for primary key values that are higher than the current highest value in the Data Warehouse.
Remember, tables using this method are single column with integer auto incrementing primary keys. To use this method in your database, make the following modifications:
- If the primary key is either a composite key or a non-integer, change the primary key to an auto incrementing integer
- If the primary key is a single integer column but keys can be assigned non-sequentially, change the primary key to auto increment
Wrapping Up
By making minor modifications to your tables, you can take advantage of the faster, more efficient Incremental Replication Methods. However, if this is not possible, you can still take other steps to reduce your update time and optimize your database.
Commerce
- Commerce Intelligence User Guide
- Commerce Intelligence Introduction
- Getting Started
- Administrator
- Analyze Data
- Data Analyst
- Data Warehouse Manager
- Introduction
- Advanced Calculated Column Types
- Building Google Ecommerce dimensions
- Calculated Column Types
- Configuring Replication Methods
- Configuring Data Rechecks
- Changing a metric’s operational table
- Creating and Using Data Warehouse Views
- Creating / Deleting paths for calculated columns
- Creating / Using a SQL Calculated Column
- Creating calculated columns
- Data and Updates Information
- Guest orders
- How Commerce Stores Data
- Entity Relationship Diagrams
- Managing data dimensions in metrics
- MongoDB data modeling guide
- Replicating Google Analytics channels
- Standardizing data with mapping tables
- Translating SQL queries into Commerce Intelligence reports
- Understanding and Evaluating Table Relationships
- Using the Date Difference Calculated
- Using Dashboard Wide Filtering
- Using the Event Number Calculated Column
- Using the Sequential Comparison Calculated Column
- Common Commerce Tables
- SQL Report Builder
- Using the Cohort Report Builder
- Using the Cohort Report Builder for Non-Date Based Cohorts
- Creating a qualitative cohort analysis
- Explore special filter operators
- Export the results of my query
- Using Formulas in the [Report Builder]
- Create Google Analytics charts
- Importance of the Lifetime Revenue Cohort Analysis
- Ordering data using the Show Top/Bottom feature
- Using the SQL Report Builder
- First purchase report
- Understanding the Repeat Order Probability Report
- Auditing Metrics using the SQL Report Builder
- Differences in Columns Between SQL and Data Warehouse Manager
- Connecting Data
- SaaS Integrations
- SaaS Integrations
- Understand Results Between Database and SQL Editor
- Connecting Adobe Analytics
- Expected Adobe Analytics Data
- Connecting Facebook Ads
- Expected Facebook Ads data
- Connecting Google Adwords
- Expected Google Adword data
- Auditing Google Adwords data
- Connecting Google Analytics Warehoused
- Expected Google Analytics Warehoused Data
- Connecting Google Analytics
- Expected Google Analytics data
- Connecting Google ECommerce
- Expected Google ECommerce data
- Connecting Mixpanel
- Expected Mixpanel data
- Data Validation in Mixpanel
- Connecting PrestaShop
- Connecting Quickbooks
- Expected Quickbooks data
- Connecting Salesforce
- Expected Salesforce data
- Connecting Spree
- Expected Spree Data
- Connecting Stripe
- Expected Stripe data
- Connecting WooCommerce
- Connecting Zendesk
- Expected Zendesk data
- Analyzing Zendesk data
- Auditing Zendesk data
- Database Integrations
- Connecting Amazon RDS
- Connecting Databases via VPN
- Connect Your MySQL Database to Commerce Intelligence
- Connecting Adobe Commerce
- Expected Commerce Data
- Connecting Microsoft SQL Server
- Connecting MongoDB via SSH Tunnel
- Connecting MySQL via a direct connection
- Connecting MySQL via cPanel
- Connecting MySQL via SSH Tunnel
- Connecting PostgreSQ
- Analyze Campaigns
- Analyze Customers
- Calculating Commerce churn rates
- Defining customer concentration
- Defining customer churn
- Expected Lifetime Value (LTV) analysis (basic)
- Expected Lifetime Value (LTV) analysis (advanced)
- Track User Acquisition Source Data Overview
- Track User Device and Browser Data in your Database
- Analyzing customer repurchasing behavior
- Analyzing Website Activity and Customer Conversion Rates
- Recency, frequency, monetary (RFM) analysis
- Analyze Business Performance
- Tracking goals against actual metrics
- Analyzing returned orders
- Year-over-year, month-over-month, week-over-week
- Analyzing holiday season performance
- Analyzing repeat probability decay and churn
- Understanding and building a basic analytics
- Identifying your most valuable marketing sources and channels
- Understanding Google Analytics UTM attribution
- Analyzing inventory levels
- Reporting a retail calendar
- Forecasting
- Build Reports and Share Data
- Data User
- Reports
- Dashboards
- Create Dashboards
- Out-of-the-box dashboards
- Dashboard Pro
- Importing charts from another user
- Permanently deleting a chart
- Using Dashboard Groups
- Managing Dashboards
- Deleting Dashboards
- Renaming Dashboards
- Setting a default Dashboard
- Adding charts to Dashboards
- Removing charts from Dashboards
- Sizing and arranging charts in a Dashboard
- Bulk-editing charts in Dashboards
- Cloning Dashboards
- Searching for Dashboards
- Sharing Dashboards with other users
- Sharing Dashboards organization-wide
- Accessing shared Dashboards
- Changing access to shared Dashboards
- Leaving (unsharing) a Dashboard
- Sharing Data
- Best Practices
- Working with Data
- Working with data
- UTM tagging in Google Analytics
- Formatting and Importing Financial Data
- Recommended Data Dimensions for Segmentation and Filtering
- Checking the Update Cycle Status
- Reducing Your Update Cycle Time
- Modifying Your Database to Support Incremental Replication
- Optimizing your Database for Analysis
- Optimizing Your SQL Queries
- Understanding your Commerce Intelligence Environment
- Project Organization
- Working with Dashboards
- Working with Data
- Tutorials