Databricks | Partnerships | Tiger Analytics

Empowering Enterprises with Lakehouse and Data-driven Transformation

Tiger Analytics’ partnership with Databricks is a collaboration that brings together cutting-edge capabilities to empower enterprises in solving complex problems and accelerating solutions for today’s dynamic world and future challenges. Harness the power of Databricks Lakehouse Platform harmoniously fused with Tiger Analytics’ expertise in advanced analytics and AI/ML. Enable real-time processing and analysis of immense datasets with centralized governance and seamlessly integrate data engineering, data science, and business analytics by eradicating data silos to maximize performance gains and reduce costs & complexity.

Recognitions and Specializations

Our Capabilities

Data Foundation

Implement a modern data platform with Tiger’s expertise in data ingestion from disparate sources, data processing and building Raw, Processes & Prepared layer for faster consumption leveraging Delta Lake, and Unity Catalog as part of Tiger Fata Fabric Accelerator.

Data Modernization

Embark on a successful data modernization journey with Tiger’s migration competency and dedicated in-house accelerators that enhance data accessibility, improve data quality, and enable data-driven decision making. Utilize Databricks Autoloader to seamlessly migrate data from disparate legacy subsystems into a unified Databricks Lakehouse platform.

Data Governance and DataOps

Safeguard your data assets and ensure compliance with industry regulations with Tiger’s expertise in Unity Catalog implementation leveraging Delta Sharing. Tiger’s advanced DataOps solution and accelerators like TigerML enable you to enhance agility, reliability, and cost-effectiveness in delivering business value at scale.

Model and Serve

Efficiently optimize the complete data science/ML lifecycles spanning data preparation, modeling, and insights sharing via your SQL and BI applications with the help of Tiger’s in-house accelerator – Tiger MLCore with tools such as MLflow, Databricks Notebooks and Databricks Workflows powered by Unity Catalog.

Our Accelerators

Tiger Data Fabric
Tiger MLCore
Data Quality & Profiling Framework
Databricks Operations Monitoring
DataOps Observability

Tiger Data Fabric

Accelerator for Implementing Modern Lakehouse leveraging Databricks and the underlying cloud in a highly scalable manner with powerful Governance & Automation Capabilities at its core.

Tiger MLCore

This accelerator built on top of Databricks Delta Lake, MLFlow, and Unity Catalog brings a collection of end-to end MLOps workflow modules to accelerate the development of MLOps platforms and model management.

Know more

Data Quality & Profiling Framework

End-to-end solution for implementing configuration driven data testing with a library of more than 50 testing rules and the ability to add many more custom rules. This accelerator also has dashboarding capability for analyzing test and data profiling results.

Databricks Operations Monitoring

This accelerator built on top of Databricks Overwatch feature helps in implementing Governance best practices at the workspace level with a consolidated view of cost and infrastructure utilization patterns enabling 360° reporting for optimization.

DataOps Observability

The Observability accelerator captures vital operational and runtime metadata, encompassing pipeline jobs, runs, and datasets, including data quality (DQ) assertions. It offers comprehensive end-to-end data lineage across diverse cloud services, delivering insights into the versions of pipelines, datasets, and more.

Customer Stories

Enabling Easier Data Consumption and Improving Data Lake Adoption for a Fortune 500 Logistics Service Provider

The client had multiple data sources feeding into their data lake but the data dictionary and understanding of data lineage was missing. They didn’t have a silver layer having clean and standardized data before moving into the data lake. Besides, the performance of Databricks and Power BI was not good. All of these led to an incomplete road map for data lake adoption. The client wanted our help in cleaning the data from the raw/bronze layer and move it to the silver layer from where the data can be pulled by the data science and analytics team.