In today's data-driven world, enterprises constantly seek ways to streamline their operations and improve their bottom line. With the increasing demand for cloud-based solutions, many organizations are turning to Amazon Web Services (AWS) to help manage their data processing needs. But for many, the journey to the cloud can be a complex and challenging process.
As an AWS partner, Tredence is helping enterprises accelerate their migration journeys to the AWS cloud while ensuring they receive the highest quality and expertise.
With our deep experience in data analytics and data science, we fortify the AWS practice, bringing together the cutting-edge capabilities of the AWS cloud data platform with Tredence's expertise in ensuring successful analytics adoption.
Core Service Capabilities
Cloud Data Warehouse Advisory Services
Platform Administration, Governance and Operating Model
Data Engineering Services
Analytics and Adoption
Infra and Application Adoption
Scale your data processing capability with Tredence’s AWS products and service expertise
Scale your data processing capability with Tredence’s AWS products and solutions expertise
Developed using CloudWatch, EventBridge, and SNS, and Lambda, it prevents concurrent execution of step functions and sends automated alerts in case of job failures. Helps track job performance with an intuitive dashboard.
Leverages DMS and Glue to transform and move data from on-premise sources to the cloud, specifically Redshift and S3.
Uses Glue to integrate with third-party marketing tool APIs to extract data into S3 and then transfer it to Redshift for downstream consumption.
An interactive dashboard built on AWS Lambda to trigger Python scripts to execute complex stored procedures from AWS RDS scheduled using AWS EMR-Spark Integration to forecast health metrics and alert systems based on the metrics and other advanced analytical capabilities.
Predictive supply risk management is a white box purpose-built solution that can be tailored to the supply chain's specific needs. Built using AWS, it helps ingest batch and streaming data from internal data sets, clean and refine data, etc. It provides predictive recommendations through web-based applications or business intelligence systems.
A leading warehouse club retailer faced the challenge of finalizing product prices in a highly competitive market. They relied on an expensive third-party pricing vendor subscription to accomplish this task. Eager to reduce costs, the company sought a solution to replace the vendor and set product prices on its own.
Tredence developed a robust, cloud-based data analytics solution on AWS to efficiently determine the final product's price.
99% match achieved with the third-party vendor's output data
50% reduction in vendor cost
The company is able to confidently determine product prices weekly without vendor dependence.
AWS services used
Cloud Storage –S3
PySpark jobs in EC2 Instances
The Client's Challenge
Variance in Cement Quality: The client was facing issues with consistency in the quality of their cement product. This was leading to customer complaints and affecting their brand image.
Keeping Cost at an Acceptable Level: The client was also looking to reduce their clinker and energy costs in order to stay competitive in the market.
Tredence migration and modernization experts helped the client build a robust ML-based watch tower solution hosted on the AWS Enterprise Smart Factory Platform. The solution provided near real-time predictions of short (1d, 2d) and long-term (7d, 28d) cement strengths, which helped stabilize cement product quality and make more informed operations decisions. The results were integrated into the central plant control room UI and equipment plant Human-Machine Interface (HMI) for easy access and monitoring.
$150k reduction in the production cost annually per plant
Reduction in operational cost
Reduced CO2 emissions of 12000 tonnes and electrical energy consumption.
Improvement in product quality, leading to lesser customer complaints
The Client's Challenge
Tredence was brought in to help build and support a robust AI/ML-powered data quality management solution, Sancus, developed on AWS. This solution provided predictions related to membership activities such as acquisition, renewal, and personalization, and helped improve coverage and personalize the member shopping experience.
The solution included a data engineering pipeline and data science models deployed and executed using a DevOps approach. PySpark scripts were used for ETL and data enrichment, and interaction with the business team helped assign direct mail to households. The entire pipeline was version controlled with Atlassian’s Bitbucket and Jenkins was used for DevOps deployment and execution of recurring campaigns.
Improvement in market share and sales
Increased customer loyalty due to more personalization
Increased effectiveness in membership-related activities
The Client's Challenge
The customer wanted a unified data platform to enhance team collaboration, streamline cost tracking, and consolidate components, offering a comprehensive customer view from diverse sources.
Our team developed a resilient Ingestion Data platform using the AWS ecosystem, enabling near-real-time batch ingestion from diverse sources, with raw data stored in RAW zone. The data, processed using the Data Build Tool, is securely transferred to a trusted zone and integrated with Treasure Data for Customer Data Platform segmentation. The segmented data is rerouted to the Redshift distribution zone, providing a centralized repository for downstream stakeholders to build comprehensive Business Intelligence and Analytics solutions.
170B records, ingested from ~116 sources.
Up to 70% accelerated implementation and time to value
Advance analytics features and supported customers to monetize the data.
The customer data platform (CDP) helped customers achieve a unified customer view, enhanced customer engagement, and optimized marketing ROI.
The Client's Challenge
The data originates from the BOG application or API and may be structured, semi-structured, or unstructured. As a result, it was difficult to aggregate data from various sources and compute KPIs that were connected to the data's insights and to create a centralized data storage unit.
Our team developed a resilient AWS data lake solution, providing real-time health and safety metrics insights. This system improved cement product quality and equipment safety, enabling informed operational decisions and utilizing advanced machine-learning techniques.
The solution uses AWS Glue for efficient data processing, Redshift for fact tables, Lambda for data pull, SQS messages for threshold mechanisms, and Step Functions for orchestration.
Improved Decision-Making in Operations
Enhanced Quality of Products and Equipment
Scalability and Reusability.