GlobalLogic Data Platform

Accelerate the journey from data silos to generating actionable insights using GlobalLogic’s Inhouse Data Platform Accelerator

Overview

Although extracting value from data across an enterprise is a modern-day imperative, many businesses continue to struggle with the challenges of big data architecture, technology stacks, and hosting capabilities required for ramp-up. GlobalLogic Data Platform enables businesses to implement a fully functional, end-to-end data platform within a target or greenfield cloud account within days instead of months. Leveraging cloud native PaaS technologies, our data platform helps businesses gather enterprise-wide data, transform and enrich it, and deliver it to analysts for extracting value and insights.

Starting version 2.0, GlobalLogic Data Platform is now available with pluggable and modular Accelerators

Supported Platforms

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Industries

Industry Agnostic

Technologies/Works well with

Azure: DataBricks, Data Lake Storage, Blob Storage, Data Factory, Spark, Hive
AWS: S3, Glue, Managed Airflow, Lambda, Athena

Globallogic Data Platform now consists of smaller accelerators which integrate well together to provide a larger functionality set or can also be used individually. With V2.0, users now have the option to go with the complete GlobalLogic Data Platform or pick and choose from the catalogue of Accelerators within the Data Platform.

Conceptual Architecture

It has been designed in a way that it allows modularity, flexibility and extensibility

  • Modularity: Take the complete Data Platform or pick and choose the specific Accelerators within the Data Platform based on your needs
  • Flexibility: Configuration based implementation that allows you to configure and use it for different technology options. Few examples given below
    • RDBMS connector – gives the flexibility to the users to configure, connect and ingest data from various RDBMS databases.
    • Spark Execution Engine – users will have the flexibility to configure and run the spark based transformations either on Glue or EMR based on the needs.
  • Extensibility: Using popular languages like python the functionality can be enhanced or new functionality can be added as per the requirements.

As of now, Globallogic Data Platform contains the following accelerators

Data Platform

Business Needs

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Eliminate silos of data that create roadblocks in generating insights and value

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Implement consistency around the infrastructures, technologies, pipelines & processes used to work on data projects

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Provide transparency around available data / adaptors / pipelines to eliminate inconsistences & duplications

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Manage the tedious, non-trivial and lengthy process to provision, configure, integrate, setup security, access control

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Enable role-based access control across data, data processing, platform configurations and APIs

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Accelerate data processing & align with business requirements

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Jump-start the data science team's ability to create and experiment with AI/ML models

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Create a next-gen data platform and future strategy with limited expertise

Value Proposition

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Enabling consistency and reuse around infrastructures, technologies, pipelines, and processes

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Providing transparency around available data/adaptors/pipelines to eliminate inconsistencies and duplications

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Being modular, flexible and extensible that allows customisations at run time, enabling easy to tailor solutions

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Enabling role-based access control across data, data processing, platform configurations and APIs

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Jump-starting the data science team’s ability to create AI/ML models

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Saving time, effort and cost through consistency, standardization and reusing data components/pipelines.

Features

Core Platform Capabilities

  • Data Integration & Transformation
    • Out-of-box data integration & transformation powered by PySpark component library
    • Ability to integrate with Reporting solutions and Analytical tools
  • Data Quality & Validation
    • PySpark based framework with pre-built component library for common validation rules
    • Run validation jobs separately and persist validation report across all entities and rules
  • Data Orchestration
    • Template based pipeline orchestration accelerator based on Apache Airflow / Azure Data Factory
    • Speeds up, simplifies and unifies DAGs development experience
  • Data Ingestion
    • Supports structured batch-push from blob storage
    • Supports data files in CSV, JSON, Avro, Parquet and ORC data formats
    • Supports structured batch-pull from RDBMS / RDS and Snowflake Data warehouse
  • Polyglot Storage
    • Standard data integration zones (raw, staging, refined)
    • Well-scoped hot and cold / archive zones
    • Supports Blob, RDBMS and data warehouse storage

Portal Capabilities

  • Deployment
    • Automated installation
    • Fully automated resource provisioning for Data Pipeline execution
    • Fully automated resource provisioning to enable Consumption
  • Control Plane
    • Data Source Management and Schema registration
    • Project Management
    • User registration
  • Data Plane
    • Profile the data source and generate common statistics on relevant columns.
    • Identify entities and columns with sensitive data based on column names and column value patterns via configurable regex patterns.
    • Mask or encrypt the sensitive column values using predefined masking algorithms.
  • Security
    • Flexible Role Based Access Control based on Azure AD or AWS IAM
    • Ownership and Access control
    • Integration with AWS KMS or Azure Keyvault
  • Monitoring
    • Job Monitoring and Management
    • High-level job tracking and monitoring
    • Self-service analysis and troubleshooting
  • Ingress / Access endpoints
    • API and JDBC/ODBC endpoints for enabling ingestion and data usage by external analytics solutions

Let’s Work Together

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