From legacy migration and cost optimization to data quality and AI readiness, Tiger Analytics helps enterprises unlock more value from Snowflake - faster.
Many enterprises are investing in Snowflake but still face challenges turning that investment into measurable value - from complex legacy migrations and rising cloud costs to slow data pipelines and data quality gaps that limit AI adoption. Modernizing hundreds or thousands of workloads while making the most of Snowflake Cortex can feel equally daunting.
Join Tiger Analytics for a complimentary, 60-minute executive workshop to:
Most organizations are trying to modernize data platforms while simultaneously preparing for AI initiatives.
Yet many struggle with:
Legacy migrations taking months
Poor data quality
Manual engineering effort
Limited governance
Slow AI adoption
High migration risk
A leading U.S. P&C insurance provider partnered with Tiger Analytics to transform its legacy data ecosystem into a scalable, governed Snowflake platform - accelerating modernization while creating a foundation for advanced analytics and AI.
WHAT CHANGEDSnowflake's latest innovations enable enterprises to move beyond cloud data warehousing into AI-ready data platforms.
Powered by capabilities such asOrganizations can modernize faster while embedding governance, automation, and AI directly into the platform.
Automate dbt code quality, maintain architectural standards, and optimize warehouse performance. This tool runs automated audits and AI-driven checks across naming conventions, DAG integrity, and Snowflake costs, offering one-click refactoring and Git PR integration.
Identify root causes and optimize long-running, complex SQL queries automatically. Powered by Snowflake Cortex LLMs, this engine analyzes query profiles and warehouse performance metrics to rewrite code, reduce compute costs, and boost execution speed.
Turn data profiling statistics into production-ready validation rules in seconds. Utilizing Cortex LLMs, this tool analyzes dataset metadata to automatically recommend context-aware Great Expectations and native SQL quality checks without manual effort.
Gain end-to-end data quality governance directly within Snowflake. Built on Snowpark and Great Expectations, this solution offers an intuitive Streamlit interface, automated inline pipeline checks with circuit-breaker capabilities, and natural-language querying of DQ results.
Accelerate data pipeline development by instantly converting Source-to-Target Mapping (STTM) documents into production SQL. Leveraging Snowflake Cortex LLMs within Snowflake's governance boundary, it automates pseudo-logic translation to eliminate manual coding errors and speed up release cycles.
Gain complete visibility, cost intelligence, and AI-powered diagnostic insights across your dbt pipelines. Running directly in Snowflake via Streamlit, this platform tracks model-level Snowflake credit costs, enforces SLA compliance, detects performance regressions, and provides automated root-cause analysis for pipeline errors.
A view of your modernization readiness and where AI can accelerate the journey
Practical examples of AI-led migration, engineering, governance and optimization
A look at proven accelerators that can reduce engineering effort and modernization timelines
Actionable next steps for building an AI-ready Snowflake data platform
As a Snowflake Elite Services Partner, Tiger Analytics helps enterprises accelerate data modernization with deep platform expertise, AI-led engineering, and proven delivery accelerators. Together with Snowflake, we enable organizations to build trusted, governed, and AI-ready data ecosystems.
We will get in touch with you shortly.
The brochure has been emailed to you.