Essential toolset for analytics
Self-service, fail-safe exploratory environment for collaborative data science workflow
Unified single sign-on experience between cloud and
on-premise tools
Similar user experience across AWS, GCP, and Azure clouds
Easy to use Web Interface
Leverage the power of analytical tools
Project level collaboration environment across multiple Clouds
Aggregated billing with cost comparison across cloud providers
Problems We Solve
Data access problem
Problems with tools
Increased IT handholding
Lack of security
Data access problem
- Access to full datasets is on “need to know” basis
- Access to production data is limited
- Data access is governed by corporate/BU/department policies
Problems with tools
- Installation of tools difficult and expensive (lack of ops & security skills)
- Latest & greatest tool versions aren’t available in the shared environment
- Lack of collaboration: everyone has its own silo
Increased IT handholding
- Data sciences are bound to local machines, due to the cost of IT support
- Lack of self-services leads to stream of requests to ops teams
- Resources allocation monitoring and security control in the shared environments
Lack of security
- Local data copies are not secure (DS get copies of data on their local machines)
- Cloud security policies for data protection are difficult to implement
- Cutting edge tools require enterprise hardening (tools have to be secured)
Our Solution
Self-service and customization
- Self-provisioning of the environment
- Plug-in proprietary components; change tool versions
- No dedicated IT Support is involved
Secured and fail safe
- Break it and simply re-provision a fresh environment
- Provides a client’s security perimeter
- Security policies and access controls
Collaborative workflow
- Collaboration between data science team members
- Collaboration between data science and engineering teams
- Easy path from R&D to Production
Exploratory environment
- Best open source data tools
- Horizontally scalable compute
- Easy access to data and metadata
- Training / workshops
Flexible deployment architecture
- Public cloud: Azure & AWS, GCP (initial version)
- No platform or vendor lock-in
Features
Unified single sign-on experience between cloud
Similar user experience across clouds
Easy to use Web Interface
Leverage the power of analytical tools
Data visualization with data science libraries
Easy Life Cycle management
Install libraries and dependencies
Multiple Git repositories support
Control Cloud Services Usage
Manage Cloud services quota limits
Role permissions administration
Project level collaboration across сlouds
Track key activities
Storage space collaboration
Unified single sign-on experience between cloud and on-premise tools
- SAML and OAuth2.0 support
Similar user experience across AWS, GCP, and Azure clouds
Easy to use Web Interface
- Single-click life-cycle management of analytical tools and computational resources
- Dashboard and in-grid filtering
- Built-in “how-to” pages
Leverage the power of analytical tools
- Apache Zeppelin
- Deep Learning
- Jupyter
- Jupyter with TensorFlow
- JupyterLab
- RStudio
- RStudio with TensorFlow (implemented on AWS)
- Superset (implemented on GCP)
- Add computational power to your jobs by deploying Standalone Apache Spark cluster/EMR (AWS)/Dataproc (GCP) on memory, storage, compute, GPU optimized instances
Run your analytics effectively
- Python v2/v3, Scala, Apache SparkR support
- Data visualization with pre-provisioned data science libraries
- Cloud Storage compatibility
- Amazon S3
- Azure Blob Storage, Azure Data Lake
- Google Buckets
Easy Life Cycle management
- Once setup, create a snapshot and save time setting up new environment
- Start / Stop / Terminate notebooks
- Configure start/stop schedule for analytical tool and computational resources
- Add/Remove computational resources (Cloud provider’s Data Engine Service or Apache Spark standalone cluster)
- Simply create an AMI image based on required tool and save time for further provisioning and configuration
- Fine-tune default Spark configuration
Easy Life Cycle management
- Enrich analytical experience installing libraries and dependencies
Collaboration capabilities
- Shared codebase
- Shared Storage
- Shared Wiki
- DataLab Web UI for repositories access management
- Build in UnGit for easy to use source collaboration
Aggregated billing with cost comparison across cloud providers
- In-grid billing functionality
- Details billing report
- Admin mode to see all users
Manage Cloud services quota limits
- Set cost limits for DataLab’s infrastructure
- Set limits per user
- Alert users on reaching quota
- Stop/terminate environment when quota gets depleted
Role permissions administration
- Assign users to DataLab groups
- Grant users ability to create analytical tools/clusters/leverage particular instance shapes
- Admin mode to manage infrastructure of the whole team
Project level collaboration environment across multiple Clouds
- Connect you analytical environment to multiple cloud endpoints
- Project level resource management
Provide a quick overview of the key activities in DataLab
- Check all user activities on DataLab
File management in Cloud storage via Bucket browser
- Possibility to upload/download/delete file on clouds storage
- Access any bucket from the assigned projects
