Accelerate your advanced analytics journey and enable AI and prescriptive insights using Azure services
Analytical Data Foundation solution from Tiger Analytics enables the customer to unlock full potential of enterprise data in a matter of weeks, and not months. The solution leverages the power of Microsoft Azure cloud platform to provide access to timely and accurate data and delivers a quick return on advanced analytics and AI projects.
The solution is built to quickly scale using these five pillars:
1. Enterprise wide data ingestion, or "no silo" (Azure Data Factory, IoT / Events Hubs)
2. Data ready for machine learning (Azure Data Lake Storage, Delta Lake, Azure Databricks)
3. Powerful analytics solutions (Azure ML)
4. Track business critical metrics in near-realtime (Power BI)
5. Cost effective framework
Tiger’s solution is based on a robust data platform (the core foundation) to implement efficient data operations and get the business ready data for insights.
* Batch & Streaming Data Processing: Tiger employs best practices-based approach to support batch as well as streaming data ingestion and processing using Azure Data Factory, HDInsight, and Azure Databricks.
* Advanced Analytics & AI: Tiger supports the enablement of customized Delta Lake layers (Bronze., Silver, Gold) to facilitate fast and accurate analytics and machine learning outcomes by utilizing Azure Synapse Analytics and Azure Machine Learning services. Insights can be surfaced via Power BI
* Security, Governance, and Cost: Tiger utilizes a host of Azure services such as Azure Monitor, Policies, AD, Cost Explorer etc. and tools such as Azure Data Catalog to build a secure, well-governed and cost-effective solution.
Tiger will work with the business to understand the requirements and partner to deliver a customized solution to ensure success. Our Open IP philosophy believes in a "glass box" approach. The solution is transparent to the customers and there is no requirement for a lock-in. The solution can easily scale to include rapid development and deployment of enterprise-grade ML models.