The Impetus Data Warehouse Workload Migration product identifies and offloads data and workflows from existing enterprise data warehouses to Hadoop-based data lakes. It converts SQL scripts to equivalent HiveQL scripts and executes them on Hadoop while performing data quality checks. This automated process saves 30-60% of the time and cost of manual offloading while reusing existing tools and reducing risks in migrating to Hadoop.
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Simplified Workload Migration to Big Data Warehouse
1. Data Sheet
Simplified Workload Migration
to Big Data Warehouse
Advances in Open Source Hadoop distributions have led to quicker
installations of Data Lake. However, migrating the workloads and data
from existing enterprise data warehouse to Hadoop-based Data Lake
may involve error and trial, which is not suitable for critical production
environments.
Impetus identifies this key enterprise need and offers a unique workload
migration solution to offload, transform, and analyze existing data and
workloads from the enterprise data warehouse to the Big Data
warehouse. The solution also provides an advanced data science library
for solving difficult traditional data quality problems.
The Impetus Data Warehouse Workload Migration product is a proven,
cost-effective, and low-risk solution to offload traditional data warehouse
to Big Data warehouse.
Enhanced Productivity
• Automated Offloading
Reduced Cost
• Lower Migration Cost
Minimized Risk
• Inbuilt Quality Checks
Advanced Monitoring
• Error Check and Restart
Optimized Performance
• Partitioning, Clustering and Buckets based
on Dataset
Key Features
Overview
Key Components
• Intelligent Identification of “Offload-able” Entities
• Automated Schema and Data Migration
• Automated Quality Check for Data Migration
• Automated SQL/ Procedural Language Scripts Migration
• Automated Post ETL Quality Checks
• Enablement of End-to-end ETL Offload Pipeline
Automation Tool Sets for Quick and Reduced Risk in Migration
• Data Quality using Advanced Data Science Algorithms
• Optimizations for Hadoop-based Data Architecture
• Data Security and Governance Enablement in Hadoop
Advanced Offerings
• Teradata, Netezza, MS SQL Server, Oracle
Out-of-the-box Support for:
Click-based Data Lake Creation
• Simplified UI for Design and
Orchestration