BIG DATA AND FAST DATA

Leverage Fast Data to Get More from Big Data

GAIN MORE VALUE FROM BIG DATA WITH A STREAMING DATA ARCHITECTURE

Big Data Lakes are often a random collection of large volumes of data for uncertain use cases. Because of this, many Big Data solutions struggle to keep up with large data volumes and the need to drive clear value. With batch integration and after-the-fact analytics, Big Data solutions cannot discover urgent and perishable operational insights that high-velocity data (a.k.a Fast Data) offers. Using real-time data integration and streaming analytics, you can feed pre-processed streaming data into enterprise data lakes while getting the maximum value from your high-velocity, high-volume data with context-rich, real-time insights.

  • Build a smart data architecture that supports use-case-driven Big Data analytics
  • Extend the lifetime of existing Big Data solutions by storing only the data you need
  • Gain more operational value from Big Data and immediate insights from your Fast Data
  • Bring insights from Big Data analytics to life by easily integrating machine learning algorithms into operational decision making

WHY STRIIM FOR BIG DATA

Feed your Hadoop and NoSQL solutions continuously with real-time, pre-processed data from enterprise databases, log files, messaging systems, and sensors to support operational intelligence. By loading and storing up-to-date, filtered, transformed, and enriched data in enterprise data lakes, you gain insights faster and easier, while better managing limited data storage capacity. Striim also integrates easily with 3rd-party machine learning solutions to automate operational decisions where appropriate using Big Data insights.

 

BUILD A USE-CASE DRIVEN DATA LAKE
Support operational use-cases by keeping your data lake consistent with transactional systems, and pre-process incoming data to make analytics faster and easier.
ADOPT A SMART DATA ARCHITECTURE
Perform continuous non-intrusive data ingestion at the speed of life. Use in-flight data filtering and aggregation before storing the data to reduce the data storage footprint.
TRANSFORM YOUR BUSINESS
Use streaming analytics before storing high-velocity data to capture the value of perishable insights. Easily integrate machine learning algorithms to make automated operational decisions.
Customer Use Case

AEROSPACE MANUFACTURER

The leading aerospace and defense manufacturer chose Striim to support its modernization of analytics solutions. The company moved to a Hadoop-based Big Data environment to provide richer and more timely analytics to its employees and partners. Striim integrates its HP NonStop OLTP systems with their Hadoop ecosystem by delivering transactional data to HDFS, Kafka, and HBase in real time. With the ability to contain up-to-date airplane parts and schema data in the Hadoop environment, the company moved operational reporting processes from HP NonStop to Hadoop.


Offloaded operational reporting from transactional systems to Hadoop, reducing overhead on the OLTP systems.


Supports critical operational decision-making for production and supply-chain management using real-time airplane parts and schema data.


Hadoop environment serves a large ecosystem including suppliers and partners, with timely, operational data.

How Striim Works

REAL-TIME DATA INTEGRATION AND PRE-PROCESSING

Striim ingests changed data continuously from a wide variety of sources including transactional databases, messaging systems, log files, and sensors. For transactional databases, Striim uses non-intrusive change data capture (CDC) to minimize the impact on source systems. After performing filtering, transformation, aggregation, enrichment, and analytics on data-in-motion via continuous queries, it delivers the streaming data to Hadoop, Kafka, and NoSQL environments – on-premise or in the cloud – with sub-second latency. Striim can also feed real-time data to other targets, and easily integrates with machine learning solutions to bring Big Data insights into real life.


Real-time Continuous Integration from Databases, Log Files, Messaging Systems, and Sensors


Filtering, Aggregation, Transformation, Enrichment, and Analytics for Data-In-Motion


Delivery to All Major Hadoop and NoSQL Solutions, On-Premise or In the Cloud

GET STARTED