Streaming Data Integration for Hadoop
With Striim’s streaming data integration for Hadoop, you can easily 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.
Ingest Real-time, Pre-Processed Data for Operational Intelligence
Striim is a software product that continuously moves real-time data from a wide range of sources into Hadoop, Kafka, relational and NoSQL databases — on-prem or in the cloud — with in-line transformation and enrichment capabilities. Brought to you by the core team behind GoldenGate Software, Striim offers a non-intrusive, quick-to- deploy solution for streaming integration so your Hadoop solution can support a broader set of operational use cases.
With the following capabilities, Striim’s streaming data integration for Hadoop enables a smart data architecture that supports use-case-driven analytics in enterprise data lakes:
- Ingests large volumes of real-time data from databases, log files, message systems, and sensors
- Collects change data non-intrusively from enterprise databases such as Oracle, SQL Server, MySQL, HPE NonStop, MariaDB, Amazon RDS
- Delivers data in milliseconds to Hadoop (HDFS, HBase, Hive, Kudu), Kafka, Cassandra, MongoDB, relational databases, cloud environments, and other targets
- Supports mission-critical environments with end-to-end security, reliability, HA, and scalability
- Uses low-latency data for operational use cases
- Accelerates time to insight with a continuous flow of transformed data
- Ensures scalability, security, and reliability for business-critical solutions
- Achieves fast time-to-market with wizards-based UI and SQL-based language
- Enterprise-grade and fast-to-deploy streaming integration for Hadoop
- Real-time integration of structured and unstructured data
- In-flight filtering, aggregation, transformation, and enrichment
- Continuous ingestion and processing, at scale
- Integration with existing technologies and open source solutions
Striim enables businesses to get the maximum value from high-velocity, high-volume data by delivering it to Hadoop environments in real-time and in the right format for operational use cases.
Real-time, Low-impact Change Data Capture
Striim ingests real-time data from transactional databases, log files, message queues, and sensors. For enterprise databases, including Oracle, Microsoft SQL Server, MySQL, and HPE NonStop, Striim offers a non-intrusive change data capture (CDC) feature to ensure real-time data integration has minimal impact on source systems and optimizes the network utilization by moving only the change data.
In-Flight Data Processing
As data volumes continue to grow, having the ability to filter out and aggregate the data before analytics becomes a key way to manage the limited storage resources. Striim enables in-flight data filtering
and aggregation before it delivers to Hadoop to reduce data storage footprint. By performing in-line transformation (such as denormalization) and enrichment with static or dynamically changing data in memory, Striim feeds large data volumes in the right format without introducing latency.
Striim is designed to meet the needs of mission-critical environments with end-to-end security and reliability — including out-of-the-box exactly once processing — high-performance, and scalability. Users can focus on the application logic knowing that from ingestion to alerting and delivery, the platform is bulletproof to support the business as required.
Fast Time to Market
Intuitive development experience with drag-and-drop UI along with prebuilt data flows for multiple Hadoop targets from popular sources allow fast deployment. Striim uses an SQL-based language that requires no special skills to develop or modify streaming applications.
Operationalizing Machine Learning
Striim can pre-process and extract features suitable for machine learning before continually delivering training files to Hadoop. Once data scientists build their models using Hadoop technologies, these can be brought into Striim, using the new open processor component, so real-time insights can guide operational decision making and truly transform the business. Striim can also monitor model fitness and trigger retraining of models for full automation.
Differences from ETL
Compared to traditional ETL offerings that use bulk data extracts, Striim enables continuous ingestion of structured, semi-structured, and unstructured data in real time delivering granular data flow for richer analytics. By performing in-memory transformations on data-in-motion using SQL-based continuous queries, Striim avoids adding latency and enables real-time delivery. While ETL solutions are optimized for database sources and targets, Striim provides native integration and optimized delivery for Hadoop, Kafka, databases, and files, on-prem or in the cloud. Striim also offers stream analytics and data visualization capabilities within the same platform, without requiring additional licenses.