Cloud data management

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

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; to provide 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, payas-you-go,  auto-scalable platforms, depending on their varying data needs. With readily deployable infrastructure and services required for a data processing lifecycle, Cloud can serve all the needs of a modern data architecture – FROM ingestion & storage TO orchestration and integration/pipelining of various components TO reporting, analytics and data science TO data security and governance. It can also be agile with all types of data sources (stream or batch) , data formats (structured or unstructured) and data volume (small or big).

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