Lentiq

Additional features

Multi-Cloud Support

Lentiq allows you to deploy and manage data pools across different cloud providers simultaneously, offering a flexible and scalable solution for data management.

Jupyter as a Service

Provides an easy-to-use interface for data scientists and developers to work with Jupyter notebooks, enabling seamless integration with data lakes.

Spark as a Service

Lentiq supports Apache Spark for scalable data processing and big data analytics, ensuring high-performance processing for large datasets.

Data Pools

Lentiq introduces the concept of interconnected mini-data lakes, known as data pools, which work together to provide a stable, secure, and fast data science environment.

Data Management

Simplifies the management of data and metadata across multiple clouds, without requiring code changes, enhancing the workflow for teams working on distributed datasets.

ML Pipeline Support

Lentiq enables the creation, training, and sharing of machine learning models, making it easier for teams to build and deploy AI solutions.

Model Serving

Lentiq allows teams to easily share and deploy trained models internally, promoting collaboration and making it easier to integrate AI solutions into existing processes.

No Hadoop

Lentiq moves away from the centralized Hadoop system, offering a more modern and flexible approach to handling big data in the post-Hadoop era.

BI Tools Integration

The platform integrates seamlessly with business intelligence tools, making it easier to analyze and visualize data across different platforms.

Real-Time Data Ingestion

Lentiq supports the real-time ingestion of data, enabling fast and efficient processing as data streams in.

ETL Capabilities

The platform provides ETL (Extract, Transform, Load) capabilities for data cleaning, preprocessing, and transformation, ensuring that the data is ready for analysis and machine learning.

Schema-on-Read

Lentiq uses schema-on-read technology, allowing data scientists and analysts to work with raw, unstructured data without requiring predefined schemas.

Collaborative Data Science

Lentiq fosters team collaboration by enabling users to share data, notebooks, and models in a shared environment, making it easier for teams to work together on projects.

Flexible Data Processing

The platform supports a range of data processing technologies, including Apache Kafka, Jupyter, and Apache Spark, providing the flexibility to choose the right tool for the task.

Notebooks to Code Conversion

Lentiq allows users to convert Jupyter notebooks into executable, reproducible code, which can be integrated into workflows for seamless deployment.

Cross-Cloud Data Sharing

Lentiq’s multi-cloud environment enables the sharing of data and models across different cloud providers, ensuring data freedom and flexibility for teams working in diverse cloud ecosystems.

Efficient Model Deployment

Once models are trained, Lentiq enables their easy deployment, ensuring that machine learning solutions are available for business use.

AI Solution Building

Lentiq provides all the tools necessary to build AI solutions, from data ingestion and cleaning to model training and deployment, making it a complete solution for data science teams.

Ad-Hoc Project Support

Lentiq’s ease of use and quick setup make it ideal for ad-hoc data science projects, allowing teams to start analyzing data and building models without worrying about complex setup and deployment tasks.