ShiftFlo Accelerator | Shorthills AI

Modernizing your Datalake is Powerful but Complex

Migrating to Amazon S3 Tables is a strategic move. It unlocks the performance, cost-efficiency, and open standard of Apache Iceberg, managed seamlessly by AWS. However, the migration journey from an existing Delta Lake is filled with risks.

Service Integration Complexity

Building robust data pipelines requires expertly integrating multiple powerful AWS services, each with its own interface and configuration.

Risk to Operational Continuity

Synchronizing live data with a historical snapshot during cutover is extremely complex, risking data loss, duplication, and additional operational downtime.

Risk to Data Quality & Integrity

Migrations risk silent data errors, like lost timestamp precision or numeric accuracy, that manual checks miss and can compromise the entire dataset.

Orchestration Overhead

Managing pipelines, deployments, and creating custom scripts for migration manually is inefficient and error-prone.

AI-Assisted Migration Engine: The ShiftFlo Advantage

ShiftFlo is an intelligent automation platform designed to handle the entire end-to-end migration from Delta Lake to Amazon S3 Tables. We turn a complex, high-risk infrastructure project into a predictable, automated, and successful process

Automates the full tech stack migration - from design to deployment—not just code snippets

Guarantees data integrity through built-in, automated reconciliation and validation

De-risks complex data migrations with our tested Automated Data Validation scripts

Eliminates the error-prone manual configuration of IAM roles, Glue triggers, and S3 buckets

Migrated both bulk historical data and live, incremental streams with zero downtime

Protects your production data with automated rollbacks to a last-known-good state

Applies schema evolution on-the-fly, modernizing your historical data during migration

Unlocks your trapped data to accelerate the journey to modern analytics

and Al

Generates complete technical specs and project plans before a single line of code is written

Challenges

Mid-Job Failures

An initial table copy or a large batch job fails due to a transient error (e.g., S3 throttling).

Flawed Incremental Logic

A MERGE job misses updates, duplicates records, or mishandles deletions on re-runs.

Data Mismatches & Corruption

Post-migration data has lost timestamp precision or has different numeric type handling.

High-Velocity Streaming & Small Files

High-frequency writes create millions of tiny data files, crippling query performance and bloating metadata storage costs.

Mid-Migration Schema Drift

The source table schema is altered after the initial snapshot has started, causing future incremental jobs to fail.

Proactive Risk Mitigation for Your Mission-Critical Migrations

Solutions

Idempotent & Resumable Scripts

Our migration and ETL scripts are designed to be idempotent. A failed job can be safely re-run from the beginning without creating duplicate or corrupt data, ensuring a clean and predictable state.

Watermarking & State Management

ShiftFlo's incremental scripts use robust watermarking and state management, often leveraging Delta Lake's Change Data Feed (CDF). This ensures we process each change exactly once and correctly apply it.

Automated Data Reconciliation

Our built-in validation suite goes beyond simple row counts. It performs deep schema checks and data-level comparison queries to ensure perfect fidelity between source and target, flagging any discrepancies.

Optimized Micro-Batching & Compaction

ShiftFlo implements an intelligent micro-batching architecture. It buffers streaming data and writes it in optimized, larger files. Furthermore, it automates the scheduling of Iceberg's compaction procedures to keep tables performant and cost-effective.

Automated Schema Reconciliation & Gating

Before every incremental run, ShiftFlo's script automatically compares the live source schema against the target Iceberg table's schema. If a drift is detected, the job is safely paused with a detailed alert, preventing data corruption and allowing for a controlled, managed schema evolution.