DataJitsu
Don't let your Big Data push you around
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Master it with DataJitsu
What is DataJitsu?
Building a high-performance, real-time analytics environment can feel like you’re in a street fight with a nasty opponent.
Assembling the specialized skills, building the technology, collecting data from multiple sources, and productizing the environment, all take precious time and resources away from your core business.
DataJitsu helps by integrating all the components into a smoothly operating unit.
Just add data.
Features
DataJitsu's architecture helps you build or transition your analytics to a cloud-based,
high-performance environment with a minimum of fuss.
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Collect, wrangle, munge, QA, and deliver actionable analytics quickly and continuously.
Administrative Support
Spark-based streaming architecture for high-volume and low-latency.
High Performance
Customer Support
Seamlessly maps and applies updates from incoming data sources to a data model optimized for analytics, exploration and reporting.
Cloud Data Warehouse
Project Management
Whether from file transfers, database CDC, or real-time streaming, the cloud data warehouse is continuously and transparently updated with new information.
Continuous Ingestion
Social Media Management
Unlike a standard big data system, DataJitsu keeps a consolidated, near real-time representation of the cloud data warehouse ready for querying.
State Tracking
Data & Research
Multi-Stage Data Pipeline
Customizable processing steps in the streaming pipeline permit cleaning, transformation, and enrichment on-the-fly during data ingestion.
Personal Assistant
Match data organization needs to specific analytical requirements, keeping optimized datamarts always ready for your analytics.
Dynamic Datamarts
Learn More about DataJitsu
Any of these challenges ring a bell?
Interference between operational and analytical platforms – Operations and analytics often share – and therefore compete for – access to key databases. Many common production problems stem from this interaction, creating complexity, scalability issues, and increased time-to-market for new analytical products.
Uncooperative data models – Legacy data models are often pressed into doing double-duty, trying to simultaneously serve the needs of operational apps with thousands of small, optimized tables, and analytics functions requiring rich consolidated structures. A system which accommodates both ends of the spectrum allows operations and analytics to coexist harmoniously.
Proliferating databases – Business growth usually results in a proliferation of databases, causing spiraling complexity and data replication issues. Automatic simplification and consolidation of databases lowers costs and reduces errors.
The rising cost of legacy analytics – Much of the world’s existing analytics, written in SAS or other legacy environments, is concerned with cleaning and munging of the underlying data, rather than business analytics. Migrating the work to a DataJitsu pipeline improves performance and lowers licensing costs.