Over the last decade, most organizations have completed a digital transformation, resulting in a burgeoning volume of data. Initially, data scientists were expected to build an adequate data infrastructure and pipeline before making sense of it all. This led to data modeling not being done correctly. The organizations were further prevented from being able to extract optimal value from the data projects.
Today, with organizations generating insurmountable amounts of data, it is apparent that they need a data engineering team to organize and ensure data quality, security, and availability.
Companies today generate a significant amount of data on a daily basis. However, without proper access to correct data at the right time, organizations may find it challenging to make decisions across the business value chain.
It has been observed that data analysts spend a large portion of their time in data processing instead of analysis. Extracting apt insights can be challenging as it is entirely dependent on the quality and reliability of the data infrastructure.
Trigent gives organizations the technology prowess to organize, optimize and process data to draw insights and capitalize on opportunities and mitigate risks. Our qualified team, experienced in Data Engineering, can build high-performance infrastructure, implement efficient cleansing processes, optimize data and extract actionable insights to help achieve your business goals.
Terabytes and Petabytes of Data is generated every day from IoT Sensors, eCommerce sites, social media, CRM and TMS platforms which provide insightful patterns & trends. Trigent’s Data engineering consulting services assess your current state of data collection, operations, and strategic needs and implement automated pipelines for data collection, storage, and analysis. Our consultants will present and implement solutions that produce deep and actionable insights that can redefine your organization and even help you disrupt the market.
Our Data Engineering Consulting services will focus on:
Many organizational initiatives are becoming cloud-based. Cloud engineering enables organizations to build and maintain data lakes and data pipelines. Organizations aspiring to be driven by data need to focus on cloud-driven analytics.
Trigent enables organizations to not only leverage the power of cloud data warehouses and data lakes but also build solutions with cloud-first architecture. Trigent’s cloud data management services help organizations capture, store, and analyze large volumes of data and deliver high performance of real-time data analytics workloads. Our team of data analysts enables the organization to optimize cost and resources and design a cloud adoption strategy.
Data silos are a big challenge for organizations. The major focus of any organization generating humongous amounts of data daily is to extract them from numerous sources and integrate and load them into a data warehouse. Our architecture will integrate new and existing data sources together into an effective data lake either from scratch or leveraging services provided by major cloud platform vendors. Transform data from your existing systems into intelligence that enables you to ask questions and discover new opportunities that drive progress. Implement upstream and downstream ETL pipelines for batch processing or real-time processing.
Trigent’s team of seasoned experts leverages its expertise in the end-to-end data engineering ecosystem and selects or recommends the apt technology stack for building the data pipelines that are robust and generate faster insights.
Data lakes can ingest large volumes, variety, and velocity of data and catalog them centrally. Data lakes in the cloud are a centralized repository of large amounts of structured and unstructured data and provide end-to-end services that reduce the time, effort, and overall cost. The aim of a data lake is to make organizational data accessible to numerous end-users like data scientists, data analysts, and data engineers, to name a few. Data lakes are built on low-cost hardware, making it economically viable to store data.
Big data infrastructure on the cloud adapts seamlessly, and cloud elasticity enables organizations to focus less on managing data platforms.
Ingest data from all sources into scalable Data Lakes. We assess and architect the ideal cloud architecture for your business across major providers like AWS Redshift, Microsoft Azure, Snowflake, and Google BigQuery.
Data lakes and Data Warehouses (DWH) aggregate data in different ways. Data warehouses structure and package data for quality, consistency, reuse, and performance. Data lakes complement warehouses and focus on original raw data fidelity, structured for analytical agility.
The rapid adoption of cutting-edge technologies is resulting in voluminous data. Organizations are continuously looking to convert the data points into actionable insights. Moreover, as organizations explore methods to manage data, there is a significant improvement in database engines. However, these databases often lack the required agility due to these existing manual procedures.
Our accelerators reduce the time to operationalize modern data platforms on the cloud. They cover all aspects of Data Ops, which include ingesting data from multiple sources, provisioning data for insights, smart catalog, data quality, testing, and deployment. Automation of DataOps improves availability, accessibility, and integration of data bringing people, processes, and technology together for reliable and high-quality data for all stakeholders.
DataOps is more than DevOps in data analytics. DevOps combines software development and IT operations to ensure continuous delivery, while DataOps includes data analysts, data scientists, and data engineers in the process. This ensures greater collaboration in the development of data flows, resulting in a transformation of analytics models and intelligence systems.
Data visualization in data science is about effectively communicating the insights leading to timely action. Insights garnered from churning large volumes of data are often complex. Additionally, it is even more challenging to communicate the insights to an audience that may have the interest but not the time to go into technical details.
Trigent combines its domain knowledge and versatile technology solutions to transform your data into clear and actionable recommendations. Create insightful reports and dashboards visualization to drive business growth using Grafana, PowerBI, Tableau, Google Charts, and Looker.
Our data visualization solutions support a variety of data formats and structures. See patterns in your business activities, connect the dots, identify emerging trends and turn your data into intuitive and informative charts and graphs. Simplify the process of making data-driven decisions and improve communication of business insights to your employees and clients.
AI is the defining technology of the age, and several industries are leveraging AI in one form or another. Trigent exploits the promise of AI to enhance customer experience by anticipating customer needs and optimizing work to provide effective outcomes.
Trigent’s AI operating model includes:
More than half of the ML models are unsuccessful to make a move from proof of concept to production. Lack of coordination leads to difficulties in creating, managing, and deploying machine learning models.
Trigent’s team provides the right mix of data science, data engineering, and data ops to build an effective AI and deliver business value, thereby providing an apt MLOps practice.
Ensure the success of AI/ML solutions by bringing in seasoned experts in IT/DevOps, software engineering, and AI/ML. Trigent can help businesses apply AI, engineering, and MLOps to meet their business requirements.
We work towards:
— An asset-based third-party logistics provider