Data platform modernization and migration to Azure

Overview

Accelerating the intelligent enterprise journey with cloud and data

Enterprises are sitting on a wealth of data. What’s more, they need to constantly work with data coming in from myriad sources. While there’s an abundance of data, for enterprises to be able to gain a competitive edge with data, accessibility to data is critical. Companies also need to have the capability to glean and use the insights from data effectively to make intelligent decisions and drive growth. They can accelerate their journey to becoming data-driven, intelligent enterprises by leveraging the power of cloud and modern data management techniques. 

TCS can help enterprises realize greater value from their data strategy with Machine First^TM  led innovative solutions and services on Microsoft Cloud.

Solution

With services ranging from advisory to build and run based on the agile methodology, we make the data on cloud journey seamless.

We offer a combination of solutions and frameworks focusing on different data aspects like data ingestion and validation, Hadoop, database migration, and archival and ERP data storage using our proprietary Machine FirstTM principles. These include:

• A framework to migrate data from online database transaction processing (OLTP) sources like Oracle, SQL server, Postgres, MongoDB, and DB2 to Azure Cloud databases like Azure SQL, Azure PostgreSQL, and Azure Cosmos DB using Azure-supported tools.

• Self-service accelerated data ingestion solution and AI-driven data quality and data validation tool, which is used to ingest data from various sources such as cloud, file folders, FTP/SFTP and file servers, databases, and distributed file systems like HDFS into Azure Data Lake and Operational Data Store. 

• A framework to accelerate and simplify migration of data from appliances such as Netezza, Exadata, and Teradata to Azure Synapse and Snowflake.

• A framework to migrate HDFS and applications from the Hadoop ecosystem to the Azure environment. 

• A framework to ingest ERP data into Azure Cloud and integrate it with other data to gain meaningful business insights. 

• A solution to archive on-premise data using Azure cloud storage.   

We provide integrated advisory, build and run services based on the agile methodology, particularly for: 

• Data standardization: Ready frameworks, architecture designs, and re-deployable connectors ensure quick integrations with legacy applications and databases.

• Data integrity: Next-gen data warehouse with architecture re-platforming process, pre-build modeling, online sustenance analysis, and automation at each step ensure data integrity for voluminous data.

• Automation: Auto-provisioning of secured Azure services enables unified visualization and cost-efficient deployments. 

• Data factory model: Defined agile processes drive optimal utilization.

Benefits

Edge ahead with data democratization, improved productivity, faster application development, and speed to market—all at a lesser cost. 

Here’s what you can gain:

Faster go-to-market through dynamic load handling, minimal preparation for setup, data ingestion accelerators, and quick service deployments.

Cost control and lower total cost of ownership by leveraging Microsoft Azure PaaS services and ready calculators enabling cost forecasting, and embedded FinOps solutions that drive 40% cost optimizations. 

Improved productivity and a 50% effort reduction. Embedded Unified Industry Governance practices provide 40% automation and 100% seamless integration of various data sources to build enterprise data catalog, lineages, and glossaries.

Seamless data migration, quality check, archival and validation. Data migrations are done with user-friendly, self-service interfaces, bringing 45% efficiencies in operations and productivity.

Democratization of software development with preconfigured data migration code and user-friendly UI for easy configurations

Limited human intervention with our Machine First-based approach for data ingestion, transformation and testing.