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.

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

2. Overview of the Automation Toolset
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may be trademarks of their
respective companies.
Impetus is focused on creating big business impact through Big Data Solutions for
Fortune 1000 enterprises across multiple verticals. The company brings together a
unique mix of software products, consulting services, Data Science capabilities and
technology expertise. It offers full life-cycle services for Big Data implementations and
real-time streaming analytics, including technology strategy, solution architecture,
proof of concept, production implementation and on-going support to its clients.
To learn more, visit www.impetus.com or write to us at inquiry@impetus.com.
The Impetus Data Warehouse Workload Migration product identifies and
offloads data and ETL workloads from the enterprise data warehouse to
Hadoop. The core strength of the product is its automated utility that
converts SQL transformation scripts into equivalent HiveQL and executes
them on Hadoop environment. It also allows users to run a set of data
quality functions to standardize, clean, and de-dupe data. Finally, the
processed data can be uploaded back to the source enterprise data
warehouse for reporting.
• Saves 30%-60% manual offloading
time and cost
• Faster parallel and scalable SQL
processing using Hadoop along with
streaming ELT options
• Maximize the existing investments and
reuse of tools
• Reduced risk in Hadoop journey with
automated QA checks for data/ logic
migration
• Library of advanced Impetus data
science machine learning algorithms
for enhanced data quality
Key Benefits
Workload Migration is a component of Impetus Data Warehouse (IDW)
product, which offers a complete, modern enterprise Big Data Warehouse
that operates at petabyte scale powered by Open Source technologies.
Impetus Data Warehouse Workload Migration Tool
GUI
EDW BDWExecutionIngestion Transformation Data Quality
Procedures
Tables
SQL
Roles
Metadata
Data