DIGIXT

A cognitive digital transformation platform for businesses to gain a competitive edge, improving operational resilience and enabling flexibility in this new digital reality

Excel in the Digital Era

Connect any data sources to Construct business-driven models and Contain the data in the simplest and most efficient way for clients to Consume in a multitude of ways.

Connect

This layer provides an end-to-end data engineering solution, allowing data engineers to connect to any type of data source, such as SQL, NoSQL, IoT devices, APIs, streaming data, etc. while also supporting structured, semi-structured, and unstructured data.
Module Highlights :

Construct

This layer offers declarative ETL pipelines for both stream and batch ingestion, as well as visible end-to-end pipeline tracking for health performance quality.

Module Highlights :

Contain

This layer enables industry-first polyglot storage, allowing clients to choose storage based on their use-cases.

Module Highlights :

Consume

This layer serves as a unified gateway, exposing and aggregating data from polyglot storage. It also incorporates an MPP (Massively Parallel Processing) distributed query engine which can be used with SQL queries

Module Highlights :

Control

This layer provides a simple and single console for monitoring and managing all DigiXT modules.

Module Highlights :

Platform Architecture

A digital transformation platform architecture that integrates state-of-the-art technologies and capabilities to deliver a holistic digital experience

Structured

Unstructured

Semi Structured

Streaming

DigiXT AI Market Place

DigiXT ML LAB

DigiXT Connect

DigiXT Connect

DigiXT Consume

DigiXT Compose

DigiXT Compose

DigiXT Contain

DigiXT Control

Authentication / Access Management

Audit Management

Security Management

Monitoring

Structured

Unstructured

Semi Structured

Streaming

DigiXT AI Market Place

DigiXT ML LAB

DigiXT Connect

DigiXT Connect

DigiXT Consume

DigiXT Compose

DigiXT Compose

DigiXT Contain

DigiXT Control

Authentication / Access Management

Audit Management

Security Management

Monitoring

What Do We Deliver!

DigiXT incorporates a comprehensive modular system with ready-to-deploy use cases that accelerates the Big Data journey with promising results.
Modular system

“Invest less for more”

End-to-end solutions

“Ready to deploy use-cases”

Guaranteed results

“Delivering outcome-based solutions”

How Can We Help?

Re-imagine the path to

digital transformation

Our AI implementation methodology streamlines your AI journey.
Finalize use-cases
1

Finalize use-cases

Select data sources
2

Select data sources

Platform Deployment
3

Platform Deployment

Data Ingestion
4

Data Ingestion

Data Management
5

Data Management

Deploy AI/ML Platform
6

Deploy AI/ML Platform

Discover Our Core capabilities

Given the sheer size of the data sets, a big data solution must often process data files using long-running batch tasks to filter, combine, and otherwise prepare the data for analysis. These jobs often involve reading source files, processing them, and publishing the output to new files.

If the solution has real-time sources, the architecture must include a mechanism for capturing and storing real-time messages for stream processing. This can be a basic data storage, with incoming messages being dumped into a folder for processing. Many systems require a message ingestion store to operate as a buffer for messages to allow scale-out processing, dependable delivery, and other message queuing semantics.

Change Data Capture (CDC)  is a data Integration technique that enables high-velocity data to accomplish reliable, low-latency, and scalable data replication while utilising fewer computation resources. We use CDC to provide fresh data updates in real-time to BI tools and team members, keeping them up to date. The most significant benefit of DigiXT’s CDC is that it can stream updates without directly querying the underlying source which is a significant performance boost.

Exploratory Data Analysis (EDA) assists us in identifying the underlying structure of data and its dynamics, allowing us to maximise insights. EDA is also essential for extracting key variables and detecting outliers and abnormalities. Despite the fact that there are several algorithms in Machine Learning, EDA is regarded as one of the most significant components for understanding and driving the enterprise. Through search, lineage, and striking visualisations, the DigiXT platform tells a captivating story about your data.

As there are several databases accessible to address various issues, relying on a single database to meet all programme needs might result in a non-performing, “jack of all trades, master of none” solution. The storage in DigiXT’s architecture is polyglot, which means it may be of many kinds depending on the use case. This layer supports some of the most complex persistence architectures.

This layer or module provides a contemporary data layer that allows users to access, combine, transform, and deliver datasets with unprecedented speed and efficiency. Data virtualization technology provides users with quick access to data stored throughout the enterprise, including traditional databases, big data sources, and cloud and IoT systems, in a fraction of the time and expense of physical warehousing and extract/transform/load (ETL).

This module takes your experience in management operations from reactive to proactive. DigiXT empowers you to anticipate how changes to your product, brand, or customer and employee engagement will impact the bottom line.

Most big data solutions aim to bring insights into the data via analysis and reporting. This layer allows for access-controlled data retrieval for MIS reasons. Standard access techniques, such as APIs and ODBC/JDBC, are available for downstream access, from mobile applications to web applications.

 

Typical big data solutions consist of repeated data processing operations included in workflows that convert source data, transfer data across many sources and sinks, as well as load, processed data into an analytical data repository or convey the findings directly to a report or dashboard. This layer is responsible for automating these workflows.

This layer ensures the success of the platform which is determined by the efficiency with which the separate components are handled, with a management console for creating users, granting access, monitoring, and implementing alerts and notifications.

Testimonials

What Our Clients Say

DigiXT stays ahead of the curve with the digital age.

Blog

Latest News

Fetching Polyglot Data is Cool, but How About Persistence?

Fetching Polyglot Data is Cool, but How About Persistence?

The term “big data” refers to a large volume of data. There are millions upon millions of records in this...
An Essential Guide to Our Scalable Data Ingestion Platform

An Essential Guide to Our Scalable Data Ingestion Platform

Introduction We recently acquired a solution request from a valuable customer to ingest data from multiple sources into...

Embrace the new digital world by working with DigiXT to embark on a digital transformation journey.