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. SingleStore enhances and accelerates analytic performance for Hadoop, AWS S3, and more.
Step 1
Application Source
OLTP, NoSQL Datastore
Oracle, SQL Server, Cassandra
Step 2
Transform
Data Integration
Flume, Scoop, Spark, Kafka
Step 3
Store
Data Lake
Hadoop, NoSQL, AWS S3
Step 4
Visualize Batch Data
Dashboard
Tableau, Looker, Microstrategy, IBM Cognos Analytics
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.
Step 1
Application Source
OLTP, NoSQL Datastore
Oracle, SQL Server, Cassandra
Step 2
Transform + Analyze
SingleStore
Directly connect Kafka, Spark or a change data capture tool to SingleStore
Step 3
Visualize Real-Time Data
Dashboard
Tableau, Looker, Microstrategy, IBM Cognos Analytics
Data Integration with Spark
Works with Legacy Architecture for Archiving and Data Science processing
Date Lake
The modern database solution from SingleStore provides real-time analytic performance across several data sources with scalable SQL for an integrated cost effective platform.
Customer Snapshot
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. SingleStore enabled real-time analytics across multiple applications leveraging rapid data synchronization and scalable SQL.
Data Analysis Before SingleStore
Step 1
Multiple Data Sources
Pulling data from Factory, Warehouse, Shipping, Point of Sale and Distribution Data
Step 2
Transform
Several SAP Data Services jobs required to transform disparate data formats
Step 3
Store
Data stored in HDFS leveraging Apache Hive for analysis
Step 4
Logistics and Distribution dashboard
Visualized data with Tableau mobile, SAP Business Objects, IBM Cognos Analytics 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 SingleStore
Step 1
Multiple Data Sources
Pulling data from Factory, Warehouse, Shipping, Point of Sale and Distribution Data
Step 2
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
Interactive visualization and analysis with Tableau mobile, SAP Business Objects, IBM Cognos Analytics and Python
Implementing SingleStore with real-time data synchronization and fast query processing on standard SQL resulted in an accurate and responsive data lake environme
Ready to get started?
See how SingleStore can modernize your data analytics
