Analytics Acceleration for

Data Lakes

From Legacy to Modern Architectures

Hadoop and cloud object store data lakes are optimized for storing large data volumes but struggle with real-time analytics at-scale. MemSQL enhances and accelerates analytic performance for Hadoop, AWS S3, and more.

Legacy Architecture

Step 1
database
Check Icon
Application Source
OLTP, NoSQL Datastore
Oracle, SQL Server, Cassandra
Step 2
gear
Exclamation Icon
Transform
Data Integration
Flume, Scoop, Spark, Kafka
Step 3
datalake
Exclamation Icon
Store
Data Lake
Hadoop, NoSQL, AWS S3
Step 4
piechart
Exclamation Icon
Visualize Batch Data
Dashboard
Tableau, Looker, Microstrategy
Accelerating analytics on existing data lake infrastructure requires a database with scalable rapid data ingestion and fast queries of large data sets leveraging the simplicity of SQL.

Modern Architecture Augmented with MemSQL

Step 1
database
Check Icon
Application Source
OLTP, NoSQL Datastore
Oracle, SQL Server, Cassandra
Step 2
gear
Check Icon
memsql
Check Icon
Transform + Analyze
MemSQL
Directly connect Kafka, Spark or a change data capture tool to MemSQL
Step 3
piechart
Check Icon
Visualize Real-Time Data
Dashboard
Tableau, Looker, Microstrategy
Data Integration with Sparkarrows
Works with Legacy Architecture for Archiving and Data Science processing
Date Lake
The modern database solution from MemSQL provides real-time analytic performance across several data sources with scalable SQL for an integrated cost effective platform.

Customer Snapshot

Consumer Packaged Goods

A global consumer packaged goods company struggled to provide an accurate real-time view of their logistics, point of sale, and sentiment analysis applications. Use of Hadoop prevented rapid analysis and up-to-date visibility for their operations. MemSQL enabled real-time analytics across multiple applications leveraging rapid data synchronization and scalable SQL.

Data Analysis Before MemSQL

Step 1
Multiple Data Sources
Check Icon
Multiple Data Sources
Pulling data from Factory, Warehouse, Shipping, Point of Sale and Distribution Data
Step 2
gear
Exclamation Icon
Transform
Several SAP Data Services jobs required to transform disparate data formats
Step 3
datalake
Exclamation Icon
Store
Data stored in HDFS leveraging Apache Hive for analysis
Step 4
piechart
Exclamation Icon
Logistics and Distribution dashboard
Visualized data with Tableau mobile, SAP Business Objects, and Python
The batch data movement architecture and slow query performance of Hadoop resulted in incomplete data views and a frustrating user experience for analysts and data scientists.

Data Analysis After MemSQL

Step 1
Multiple Data Sources
Exclamation Icon
Multiple Data Sources
Pulling data from Factory, Warehouse, Shipping, Point of Sale and Distribution Data
Step 2
gear
Check Icon
memsql
Check Icon
Transform + Analyze
All data sources syncr real time using Apache Spark, AWS S3, and SAP Data Services with standard SQL for analysis
Step 3
Logistics and Distribution Dashboard
Check Icon
Logistics and Distribution Dashboard
Interactive visualation and analysis with Tableau mobile, SAP Business Objects, and Python
Implementing MemSQL with real-time data syncronization and fast query processing on standard SQL resulted in an accurate and responsive data lake environme

Ready to get started?

See how MemSQL can modernize your data analytics