AWS Kinesis provides a fast and efficient way to transform and analyse streaming data in real-time using Apache Flink. It also allows customers to query and analyse data in real-time and continuously produce insights and stateful computations to trigger real-time actions like anomaly detection based on historical data trends. Such live data can then be visualised in the form of BI dashboards using Tableau or PowerBI.
Experience spot-on visualisation for dynamic data input flow.
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A cloud readiness assessment is a process where an organisation looks at its resources and IT environment and determines if it is capable of migrating to the cloud. It is a best practice for any organisation migrating all of its IT infrastructure, or even a single app, to the cloud to perform a cloud readiness assessment in order to smoothen the migration process and set up a cloud environment that fits the organisation’s requirements.
Embrace the next big tech revolution and validate if your organisation is cloud-ready.
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The SageMaker ML platform is designed to support the end-to-end ML model lifecycle, from model data preparation to model deployment. Its modular architecture makes it flexible as well. This means customers can choose to utilise SageMaker independently for model building, training, or deployment, enabling the creation of predictive analytics models directly on Tableau or Power BI dashboards.
Get a seasoned consultant to ingest data and create ML models based on KPIs provided.
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Redshift helps companies overcome the issues of data platform modernisation by providing a cloud-based suite of data management, processing, and analytics tools. This enables customers to tap into Redshift’s massive parallel processing, federated queries, result caching, petabyte scaling, concurrency to create an agile data warehouse infrastructure, ready for BI and analytics consumption with three times the performance when compared to traditional on-premises data warehouses.
Experience your workflow simplified and redundancies removed.
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For this energy utility major based out of France, close to 10,000 pieces of equipment will be linked and every single one of them with dozens of models, providing an estimated savings of 800,000 Euros per year for the company.
The company encountered the issue of Predictive Maintenance with several thousand pieces of equipment functioning and those too of multiple models.
It decided to tap into the AWS suite to develop, train and deploy predictive maintenance models to foresee breakdowns of equipment parts.
In a significant win compared to the past, a next-generation pharmacy organisation successfully reduced operational costs by one-fifth.
To help itself with its growing data requirements and to honour its SLAs, the organisation was tasked to migrate from its data warehouse on-premises to a high-octane data hosting solution.
By employing AWS services, a centralised data solution was created by the pharma organisation, which brought down the total cost of ownership of its database environments by 30%. Near-real-time data from transactional systems on the cloud was accessed for quicker data discovery and advanced analytics.
An American multinational mass media and business information conglomerate had many weeks of engineering work saved, thanks to Kinesis Data Streams and Firehose that made the entire clickstream data pipeline work extremely simple and, yes, reliable.
Real-time clickstream events such as readership statistics, impressions, and page views were required to be analysed for no less than 300 global websites and apps. They also wanted a system to monitor and analyse trending content to facilitate sharing and enhance consumer engagement.
Creation of a clickstream analytics platform that has the power to transmit and process over 30 TB of data a day.
An automotive sector service framework created by this multinational engineering and technology firm enabled car manufacturers and their suppliers to share vehicle functioning data confidentially, selectively and securely for advanced fault detection. Data merging, in some cases, led to fault detection months in advance!
In the automobile industry, it is important that causes of problems in new vehicles be identified ASAP. By not understanding the issues, the mass manufacturers of vehicles face needless repairs and avoidable expenses. To gather multiple data points and to store, process and analyse them securely, there was this need to migrate to an overarching cloud platform. It was of paramount importance to maintain a discrete data-sharing arrangement by determining beforehand which parts of data should be made available to others in the system.
A bespoke cloud platform was the need wherein several vendors, and suppliers of parts joined in. The data had to be stored in the AWS instance of the supplier providing the data and should be made open to availability for other partners only for service purposes, and that too individually.
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