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Prachi Sharma
Solution Analyst, 4DAlert
12 articles
4 Critical Pitfalls That Are Killing Your DevOps Pipeline (And How to Effectively Mitigate Them)
· AI-Powered MDM
Database change management is the biggest barrier to true continuous delivery, because schema updates are versioned, tracked and deployed with the same rigor as application code
Inside 4DAlert’s AI-Driven Matching Engine: Speed, Accuracy, and Confidence in Master Data Management
· AI-Powered MDM
4DAlert’s AI-driven matching engine pairs machine learning with configurable business logic to resolve entities across SAP, Salesforce and Microsoft Dynamics 365
Rethinking MDM Implementation: Why It Doesn’t Have to Be Expensive or Time consuming
· AI-Powered MDM
Traditional MDM carries multi-million dollar investments and multi-year timelines, driven by a lack of pre-built content for systems like Salesforce, Microsoft D365 and SAP
Are MDM and Data Quality Two Sides of the Same Coin?
· AI-Powered MDM
MDM structures and governs core entities, while data quality tools make sure the content inside those entities is trustworthy, accurate and complete — neither capability replaces the other
When Good Data Goes Bad: Understanding the Risks and Solutions
· Data Quality
Most data quality problems go undetected because errors enter at the source and corrupt data keeps flowing into dashboards, ML models and customer systems after pipelines look green
AI-driven Data Reconciliation or Legacy processes—who wins on cost and efficiency?
· Data Reconciliation
Traditional rule-based reconciliation adapts poorly to evolving schemas, misses relationships across datasets and cannot reconcile in real time across multiple systems
CI/CD Pipeline Automation with Snowflake and Azure Synapse
· CI/CD
Declarative database CI/CD keeps the latest CREATE DDL in source control, compares it against the target database, and auto-generates the ALTER scripts for deployment
GitOps: Automating Database DDLs, Schema Comparison & Change Deployment
· CI/CD
GitOps extends core DevOps principles to databases by making Git the single source of truth for DDL scripts and schema configurations
Understanding Imperative vs Declarative Database Management approaches
· CI/CD
Imperative management spells out every step of a schema change by hand, while declarative management defines the desired end state and lets the system execute it
Solving the 7 Biggest Obstacles in CI/CD Pipeline for Database Change Management
· CI/CD
Database CI/CD means frequently integrating database object changes into a shared repository, generating CREATE versus ALTER deployment scripts and automatically deploying them into production environments
Why Unified Data Reconciliation and Data Observability should be your best next move?
· Data Observability
Data observability watches data quality and flow as it moves between systems, while data reconciliation aligns records across sources and resolves discrepancies