Mosaic Decisions

Self-Service Data Platform for Your Hyper-Distributed Data Needs

#cloud-migration #multicloud #computing-at-scale #modern-data-platform #data-on-cloud #data-integration

Mosaic Decisions, LTI’s modern data logistics platform, harnesses valuable data to generate actionable insights, which can facilitate business transformation and enhance the quality and speed of decision-making. Equipped with capabilities of data ingestion & integration, storage & governance, advance analytics, the platform decodes structured & unstructured data at enterprise speed and scale.

Data on Cloud

Simplifying and accelerating an organization’s journey onto Cloud (AWS, Azure, GCP) by utilizing pre-configured, ready-to-assemble platform kit for data and purpose-built, cloud-native ETLs for cloud warehouse technologies (Snowflake, BigQuery).

Accelerated Data Ops & Data Lake Setup

Enable setting up of Big Data Lake, using intuitive drag & drop features to connect & ingest, integrate & prepare, process & transform and manage & govern by tapping into varied formats of hyper-distributed silo-ed data and reduce time to harness enriched analysable data.

Streaming Data Analysis

Capture, store and process data-in-motion, as well as data-at-rest, to enable real-time & batch analytics and insights generation.

Optimized Cost Investment

Multi-cloud support enables no cloud-vendor lock-in and optimizes and controls IT spend by enabling optimized data processing and technology abstracted self-served features reduces skilled resource investment.

Enterprise Governance & Security

Integrate with enterprise authentication & authorization to better govern and manage the workloads and lineage to demystify data to decisions journey with an effective, collaborative and centralized platform management & controls.

Enterprise Data & Analytics Hub

Integrated data-science workbench enabled with rapid feature engineering capabilities and flexibility to create and access data APIs for external analytical consumption.

Product Features

Data Connectivity & Ingestion

Connect to internal & external data sources and preserve source metadata information.

Extensive pre-built data connectors library for - RDBMS, file systems, cloud storages, NoSQL, streams and advance connectors for external applications.
Connect, process and ingest structured, semi-structured and unstructured data formats.
Readily available data profiling & data quality dashboard.
Manage, maintain and discover source metadata and share data assets based on roles and permissions.
Centrally curate, manage and organize enterprise data assets
Smart data type validation and correction during ingestion process.

Data Orchestration & Processing

Easy-to-use, self-served intuitive drag & drop features for data orchestration and processing.

No Code – Low Code features enables faster design & development.
Batch and real-time data processing enable accelerated analytics & insight.
Ever-evolving prebuilt function library to build complex business rules for data validations.
Workflows to manage and build complex business transformation and error-handling scenarios.
Runtime column propagation to address code reusability.
Extensibility to execute custom codes.
Workflow checkpoints allows for recoverability of data pipelines from point of failure.
Agnostic to data storage layer

Runtime Execution and Integration

Multiple runtime environment support and microservice-based platform architecture provides flexibility to plug-and-play.

Platform-native runtime execution support for Spark.
Cloud-native pushdown ETLs optimized for Snowflake and BigQuery.
Integrated data science workbench to operationalize data science activities.
Communicates using RESTful web services to provide standardized way of interoperability of information in JSON format with other application using https protocols.
Multiple SDKs for defining custom functions.
Interoperability with applications ensured using https protocols, and communication using RESTful web services.
Integration with GIT repository for code version control.

Scalability & Availability

Serverless platform architecture to execute workloads on any infrastructure.

Serverless platform architecture.
Deployable on-cloud and on-premise.
Multi-cloud support for spark-based workloads to run anytime anywhere.
Cloud PaaS adoption caters high availability of the platform.
Kubernetes adoption enables high scalability for workloads.

Collaboration & Change Management

Collaborate, share and manage data operations across enterprise-wide data community.

Collaborative environment for data engineering teams to manage, share and control.
Centralized metadata management.
Share, authorize and control the access of data assets and pipelines across data engineering teams.
Manage workload resource configuration for optimal infrastructure utilization.
Configure cloud data warehouses to manage utilizations for application specific cloud-native transformations.
Lineage to map data transformation path from source to target.

Governance & Security

Integrate with enterprise governance and security measures and standards.

Centralized user management service to manage users, roles and actions on the platform.
Integrate with enterprise single sign-on (SSO), LDAP or AD.
Centrally curate, manage and organize enterprise metadata data.
Centralized scheduling and logging mechanism to monitor cluster workload execution.
Alerts, notification and grant access management and mechanism.
Audit control checks and reports to keep a tap at user activities on the platform.
Versioning to manage and control code migration.

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Simplified Data-to-Decisions platform for accelerated business outcomes @Speed and @Scale

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