Cloud data management
Modern data architectures (like data lakes, data hub) aim at building platforms which can serve as a single unified, trusted data source, providing quick insights for driving businesses, while adapting to changing needs of data in today’s digital world.
Cloud technologies can help enterprises to create self-service, pay-as-you-go, auto-scalable platforms, depending on their varying data needs. With readily deployable infrastructure and services required for a data processing lifecycle, the cloud can serve all the needs of a modern data architecture encompassing:
- Ingestion & storage
- Orchestration and integration/pipelining of various components
- Reporting, analytics and data science
- Data security and governance
It can also be agile with all types of:
- Data sources (stream or batch)
- Data formats (structured or unstructured)
- Data volume (small or big)
Additionally, it can provide the flexibility of interaction with different data environments – on-premise, hybrid or multi-cloud.
Applications
- Scalable data lakes
- Data warehousing
- Machine Learning applications
- Real-time IoT
- Enterprise data hub
Benefits
- Elasticity, scalability
- Pay only for what’s used
- Availability of diverse set of state-of-the-art tools and technologies
- Easy and fast deployment of infrastructure and applications
- Easy management of resources (DevOps)
- Accessible over geographies
Considerations
- Data not on-premise
- Requires network access
- Security risks
- Disaster and recovery
Our offerings
Data architectures for the cloud
- Discover data & flow of the data within an enterprise – real-time, batch
- Understand business requirements from the data
- Recommendations for public, private, hybrid or multi-cloud environments
- Design data storage, processing & integration pipelines
- Design security & governance measures for the data
- Design backup & recovery policies as needed
- Design Master Data Management (MDM) solutions
- Recommend best tools and technologies for the designed cloud data architecture
Data migration to cloud
- Migration from on-premise to cloud OR cloud to cloud
- Understand existing datasets (on-premise or existing cloud provider) – complexity, relationships and sizes
- Recommend tools and cloud technologies to be used
- Devise a strategy for migration with minimal impact on existing processes & procedures
- Design and implement new or existing structures, pipelines in the cloud
- Automate, implement and monitor the data & process migration
- Provide support services after migration to cloud
ETL / Data integration pipelines in cloud
- Understand the data flow and integration (ingress/egress) points
- Select the best integration tool available in the respective cloud provider (real-time or batch)
- Design, implement and test the data flow pipelines
- Recommend and implement the best orchestration methods for the flow
Data security and governance in the cloud
Understand the existing or new cloud data architecture
- Understand the existing or new cloud data architecture
- Recommend the best security measures for data access & data storage
- Devise governance strategies for data compliance
- ML (machine learning) algorithms that can help in implementing smart strategies around data
- Select the right set of tools in the respective cloud provider to implement governance/security measures
Data analytics on the cloud
- Discover use cases and data for analytics
- Select the best tool/technology for analytics in the respective cloud provider
- Implement scalable and efficient solution for the use case
Technologies
Amazon Web Services - provides on-demand cloud computing platforms and APIs, tools like Glue, Kinesis etc.
GCP- cloud computing services running on Google infrastructure tools like Data Flow etc.
Open-source distributed cluster computing framework, Popular for analytics
Cloud computing services created by Microsoft. Tools like Data Factory, Talend on Azure, SQL Warehouse etc.
Kafka - open-source stream processing
Talend Cloud - data integration tool from Talend in cloud environment
