AI Solution

Data Engineering & Analytics presentation

Data engineering and analytics involve the processes and technologies businesses use to gather, store, process, and analyze large volumes of data. It focuses on designing and building systems for collecting and storing data, ensuring it is accessible, reliable, and scalable.

Use cases of AI/ML in Data Engineering & Analytics

Key Features

Components of Data Engineering

Data Collection

Data Storage

Data Processing

Data Integration

Data Quality and Governance

Data Security

Data Infrastructure

Data Monitoring and Maintenance

Data Visualization and Reporting

Components of Data Analytics

Data Collection

Data Cleaning and Preparation

Exploratory Data Analysis (EDA)

Data Modeling

Predictive Analytics

Business Intelligence

Technologies Used in Data Engineering

Explore the range of services we offer to improve your technical outcomes.

Snowflake

Snowflake

Use Snowflake to injest/feed data to snowflake datalake and warehouse.

Snowflake

Snowpark

It is used to write codes directly.

NVidia

NVIDIA Rapids

GPU libraries for effecient data handling.

NVidia

Snowpark & NVIDIA

Plug AI models in Snowpark to analyze data in real time.

Technologies Used in Data Analytics

Explore the range of services we offer to improve your technical outcomes.

Snowflake

Snowflake

Store data in snowflake datalake and warehouse.

NVidia

NVMIDIA GPU

Computational tasks and performance of predictive models

snowflake

Snowflake & NVIDIA

It is used for data management solutions and deploy predictive models.

NVidia

NVIDIA GPU

Train models using Snowflake stored data.