Modernizing & mining analytics capabilities onto data platforms with our AWS partnership
Get StartedModernizing & mining analytics capabilities onto data platforms with our AWS partnership
Modernizing & mining analytics capabilities onto data platforms with our AWS partnership
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
Migration Services
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.
Transform your business with core system migration, modernization, and advanced travel & hospitality technologies. Enhance hyperlocalized targeting, customer identity resolution, data governance, marketing resource management, and marketing & service channels optimization. Utilize AI and ML for real-time dashboards and personalized marketing. We use AWS tech stack such as AWS Systems Manager Agent, EC2 instance, S3 bucket, Amazon MSK cluster, AWS Lambda, AWS Glue, SageMaker & Amazon EMR.
Our ATOM.AI digital accelerators redefine retail innovation by accelerating platform modernization and implementing tailored solutions across consumer experience, marketing, and supply chain domains. For instance, our Unified Retail Data Model (URDM) supports over 60 retail use cases and tracks 300+ KPIs, ensuring comprehensive insights for strategic decision-making. Additionally, our suite of 110+ ML algorithms, optimized for AWS Cloud, offers scalable solutions with minimal customization. By significantly reducing time to value, ATOM.AI drives cost efficiencies and enhances technology adoption, empowering retailers to achieve rapid business transformation.
The Client's Challenge
The client's existing Pricing Engine heavily depends on data from MS Access and the third-party service Revionics. With plans to decommission Revionics, the client needed to minimize manual intervention and automate over 30 processes currently managed through MS Access.
The Solution
We successfully migrated over 30 MS Access processes to the AWS environment, ensuring seamless integration within the client's system. A robust custom user interface was developed, backed by AWS RDS, to enable efficient data management and streamlined operations. The application was meticulously developed and tested. The final deployment to production was facilitated using CloudFormation Templates (CFTs), ensuring a smooth transition and reliable performance.
AWS Tech Stack
Amazon EMR, AWS Lambda, AWS Glue, Amazon S3, AWS Step Functions
Business Impact
~30 MS Access processes migrated to AWS
Developed a custom UI backed by AWS RDS
Achieved a seamless production deployment using CloudFormation Templates (CFTs)
The Client's Challenge
The client faced challenges transitioning from Teradata to Snowflake due to limitations in near real-time processing and machine learning capabilities. They sought to establish a data product-oriented Data Mesh using AWS and Databricks to enhance data accessibility, processing efficiency, and governance across their new architecture.
The Solution
We implemented a resilient AWS architecture with a hub-and-spoke model, secure VPC connectivity, and isolated subnets for Databricks clusters. Key AWS services, including Transit Gateway, Lambda, Glue, S3, and EMR, were leveraged, ensuring high availability, security, and scalability for optimized data processing and governance.
AWS Tech Stack
Amazon DynamoDB
Business Impact
70+ reusable library functions developed for standardized data processing
4x cost and runtime reduction achieved by migrating workflows to Databricks + AWS
10+ near real-time data pipelines operationalized for continuous data processing
Automated provisioning of 6 shared Databricks workspaces
Created dashboards on DBX for insightful data visualization
The Client's Challenge
A leading American cruise line faced difficulties in establishing a real-time data ingestion pipeline to handle a high volume of messages from their microservices-based source application to their Operational Data Store (ODS).
The Solution
Tredence developed a real-time streaming ingestion framework using Amazon MSK, Confluent Schema Registry, and Snowpipe, seamlessly integrating data into Snowflake. This enabled processing up to 10,000 messages per second with a scalability potential of 1 million.
AWS Tech Stack
gRPC, Protobuf, Amazon MSK, Confluent Schema Registry, AWS EC2, Confluent Kafka Connect, AWS S3, AWS IAM, AWS SNS, Snowpipe, Snowpark Dataframe, Python, Shell Scripting
Business Impact
Real-time data integration for personalized experiences and operational efficiency
Easy handling of increasing message volumes
Optimized data processing leading to a reduction in time to insights with an annualized savings of appx. $100K
~10,000 messages processed per second, a capability not previously available
The Client's Challenge
A global hospitality leader faced performance issues with its Revenue Management dashboards due to increasing data volume, hindering decision-making and data visualization across properties.
The Solution
Tredence implemented a scalable data solution on AWS, leveraging serverless architecture and Snowflake for efficient data consolidation. An ETL pipeline using AWS Glue and Lambda streamlined data ingestion into Snowflake, while interactive Tableau dashboards provided real-time data updates across properties.
Technology Stack
PySpark, PostgreSQL, AWS Glue, AWS Lambda, Amazon S3, Apache Airflow, Snowflake, Tableau
Business Impact
Better dashboard performance and flexibility
Centralized data repository with frequent updates, ensuring access to the latest insights
Empowered revenue managers and helpdesk teams with actionable insights
The Client's Challenge
The client, a renowned global hospitality company specializing in luxurious resorts and vacation experiences, aimed to enhance marketing effectiveness by implementing hyper-personalized customer targeting. They faced challenges with slow data pipelines, data inaccuracies, and low customer engagement, deterring their ability to deliver targeted campaigns with high click and conversion rates.
The Solution
Tredence developed a leading-edge machine learning (ML) and artificial intelligence (AI) pipeline for customer targeting. Tredence accurately mapped customers to specific campaigns by leveraging diverse datasets and sophisticated algorithms, increasing click and conversion rates. This solution was seamlessly integrated with AWS.
Technology Stack
AWS (SageMaker, EMR, EC2), Snowflake
Business Impact
2% increase in campaign click rates
Reduced pipeline processing time from multiple days to ~16 hours
Faster ML/optimization pipeline
Better model retraining and management
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.
Business Impact
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
CloudAWS EMR
AWS RDS
PySpark jobs in EC2 Instances
IAM
Secrets Manager
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.
The Solution
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.
Business Impact
$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
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.
The Solution
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.
Business Impact
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.
