Google Cloud Expertise
Smart Analytics- Data Lakes Modernization
Smart Analytics- Data Processing
Smart Analytics- Streaming Data Analytics
Conversational Design- AI Conversational Design
Tiger Specialization
Data Ingestion & Orchestration
- Google Cloud
- Pub/Sub
- Data Transfer Service
- Cloud IOT Core
- Storage Transfer Service
- Cloud Composer
Data Processing
- BigQuery Compute
- Cloud Data flow
- Cloud Dataproc
- Cloud Dataprep
- Cloud Data fusion
Data Storage
- Google Cloud Storage
- Big Query Storage
Model & Serve
- Cloud SQL
- BigQuery
- Cloud Bigtable
- Cloud Spanner
Consumption & Egress
- Looker
- Vertex AI
- Data Studio
- BigQuery ML
DevOps
- Cloud Build
- Cloud Composer
Management & Governance
- Data Catalog
- Datastream
- Cloud Composer
- Cloud Security
- Command Center
Data Ingestion & Orchestration
- Google Cloud
- Pub/Sub
- Data Transfer Service
- Cloud IOT Core
- Storage Transfer Service
- Cloud Composer
Data Processing
- BigQuery Compute
- Cloud Data flow
- Cloud Dataproc
- Cloud Dataprep
- Cloud Data fusion
Data Storage
- Google Cloud Storage
- Big Query Storage
Model & Serve
- Cloud SQL
- BigQuery
- Cloud Bigtable
- Cloud Spanner
Consumption & Egress
- Looker
- Vertex AI
- Data Studio
- BigQuery ML
DevOps
- Cloud Build
- Cloud Composer
Management & Governance
- Data Catalog
- Datastream
- Cloud Composer
- Cloud Security
- Command Center
Why choose Tiger Analytics? Our Accelerators
Great Expectations – Data Quality Framework
Open-Source framework for Data Quality. It is highly configurable with table & field level rules, integrated with Airflow and monitoring tools.
GCP Self-Service Data Lake Management
Provides end to end capability right from Data Ingestion, Data Quality and transformation capabilities required for a Data Lake.
Accelerate Innovation using
Tiger’s AI & Advanced
Analytics Solutions
Our Experience
A large retail chain in the US, developed sales and revenue forecasting models and operationalized and scaled these across 2000+ locations.
A leading technology vendor in the US, migrate Enterprise Data Lake, historical data, Databricks notebooks and data pipelines from Azure to GCP
A leading media house, built data pipelines enabling large scale analytics for optimizing ad revenue.
A large Financial Services Company in the US, developed a data pipelines framework for feature creation and streaming data pipelines for anomaly detection models.
A large US based insurance major, built several use cases covering Recommender Systems, Risk Analytics, Peer Positioning, Customer Analytics leveraging datasets from Google Marketplace to augment internal data.
A US based retailer, enabled the cloud transformation journey from Azure to GCP and managed the end to end development & deployment in GCP
