Lyftron's vision & goals
We make BI & Big Data analytics work easier and faster.Our goal is to empower business users and make always-busy business and heavily loaded BI specialists less dependent on each other when solving data-driven business problems.
Lyftron modern data Hub
Lyftron's vision and goals
We make BI & Big Data analytics work easier and faster. Our goal is to empower business users and make always-busy business and heavily loaded BI specialists less dependent on each other when solving data-driven business problems. If you have ever experienced a lack of data you needed, time to consuming report generation or long queue to your BI expert, consider Lyftron.
Steroids for your BI
Typical real-life challenges
Struggling too much?
Lyftron uses a built-in Big Data engine to handle growing data volumes. Repeatable queries can be cached or calculated in advance. Optimization needs less effort as Lyftron automatically suggests query improvements.
My reports crawl
Report update depends on IT
Lyftron empowers business analysts and data scientists by putting self-service in their hands. They can easily discover and prototype data models, add new data sources, experiment with query optimization and dig in raw data. Less IT is needed.
Now users can get live data no matter where it is stored. If databases are too busy to be queried live, Liftron will cache the data. Now waiting for a monthly refresh of a report is no longer needed.
I need real-time data
One question, inconsistent answers
The right data is mined in Liftron thanks to user-friendly names added and shared by data analysts when discovering the data. Raw data can be previewed to clarify doubts.
360 degree view on business needed
Looking for remedies for IT problems?
Layers of a Logical Data Warehouse in Lyftron are just views. No need to design ETL processes, it is enough to decide which views are cached, when and where.
ETL is no blocker
Lyftron self-service interface makes it simple to connect new data sources, explore data and prepare data marts for reports. You can decide what should be cached or stored in EDW later.
A cloud data warehouse may be quickly selected as a caching database. Data loading for a selected view is just one button click. A cloud warehouse is protected by Lyftron Windows Integrated authentication, access rights, and data masking.
Cloud off loading
Lyftron Data Virtualization layer is a single access point to all data sources. Authentication, role-based authorization, multilevel access rights, and data masking is enforced on all data sources.
Slow queries may be accelerated using caching, in-memory processing on Apache Spark or query retargeting to a different execution engine. Query rewrite will automatically choose the best preaggregate.
Future data volume
Lyftron supports multiple SQL execution engines at the same time. You can simply decide which data is cached on which caching database and what is stored in Hadoop.
Experienced business analysts and data scientists may be granted rights to manage their area of the data warehouse. Now a data scientist can perform actions that previously required IT help.
Requests from business users
Hot topics on BI journey
Real time access
Real-time access brings instant view on real data from source systems. No need to wait for capturing, processing and data aggregation. We live in a fast-paced environment where no-latency decision making is a king. Smart decisions must be made promptly to get ahead of the competition.
Agile BI is a groundbreaking view of Business Intelligence. The new idea of BI development biased towards rapid adaptation to business changes. It turns BI classical approach upside down. It shifts an effort from data sourcing to data modeling and provides more self-service for business analysts. Agile BI brings empowerment for business analysts, shorter development cycles, lower development costs, faster feedback and more.
Separation of data model and presentation
When data structure for reporting is centrally defined, tangible benefits occur. In a data-driven world, people count on reporting, collaborate and make collective decisions. They need freedom of choice: where, when and how they get access to data. Separation of the data model and presentation tools makes it all possible lowering the costs of implementation and ensuring data governance at the same time.