Analytics Acceleration for OLTP Data Stores

From Legacy to Modern Architectures

Traditional databases optimize transactions but struggle with analytics. Here’s how MemSQL can accelerate analytic performance for OLTP data stores.

Legacy Architecture

Step 1
database
Data Source
OLTP Databases
SQL Server, Postgres or Oracle
Step 2
gear
Transform
ETL / CDC
Attunity, GoldenGate, Informatica, Talend
Step 3
database
Analyze
Data Marts / EDW
Oracle, SQL Server Teradata, SybaseIQ
Step 4
piechart
Visualize
Dashboard
Microstrategy, Tableau, Looker
The risks and costs of modernizing an operational database often results in offloading transactions to another database platform to support faster or highly concurrent analytics.

Modern Architecture with MemSQL

Step 1
database
Data Source
OLTP Databases
SQL Server, Postgres or Oracle
Step 2
gear
memsql
Transform + Analyze
MemSQL
Directly connect Attunity, GoldenGate, Informatica, or Talend to MemSQL to analyze data in real time
Step 3
piechart
Visualize
Dashboard
Microstrategy, Tableau, Looker

Customer Snapshot

Leading Energy Company

MemSQL delivers operational consistency with live data syncronization along with breakthrough analytic performance without impacting application response time

Customer Snapshot

Leading Energy Company

A leading energy company struggled to perform critical invoice validation rules without disrupting application performance. With MemSQL and an efficient change data capture process, the validation rule runs more frequently to eliminate costly invoice errors.

Invoice Validation Before MemSQL

Step 1
invoice
Invoice Created
Vendor inputs invoice into Oracle
Step 2
gear
Deduplication
In-database rules engine runs on operational database checking for duplicate invoice entries
Step 3
invoice cleared
Invoice Cleared
Throttling rules engine maintains application performance while processing duplicate invoices
Step 4
invoice
Duplicate Invoices
Duplicate invoices sent to vendors and customers
Optimizing application performance results in throttling data validation rules, resulting in inaccurate records and added costs.

Invoice Validation After MemSQL

Step 1
invoice
Invoice Created
Vendor inputs invoice into Oracle
Step 2
gear
memsql
Transform + Deduplication
Real-time data syncronization using change data capture ensures exact replica of operational system to enable continuous validation of accurate invoice records
Step 3
invoice cleared
Accurate Invoices
Accurate invoices viewed in BI reports are accurately cleared for payment
Implementing MemSQL with a real-time change data capture process enabled an exact replica of the Oracle application for fast cost-effective processing of invoices.

Ready to get started?

See how MemSQL can modernize your data analytics