Data is more than ever the key enabler in the digital era, but agility and the ability to deploy new technologies are crucial too. The digital disruption forces companies to transform if they want to stay in the game.
The convergence of PAAS Cloud platforms - offering a huge set of managed services - with mature data architectures and agile methods, creates the perfect environment for businesses to meet those challenges.
Micropole’s Advanced Analytics in the Cloud (A2C) expertise, helps you build highly scalable Enterprise Data Platforms in the Cloud by combining traditional BI and Big Data processes together with advanced analytical capabilities.
Micropole’s team has more than 30 years of experience with BI, Big Data and Analytics solutions.
Our passion for data and our highly skilled experts enable our customers to migrate their Enterprise Data platform to the Cloud and benefit from a huge set of pay-as-you-use managed services. We guide you through all the steps of your project enabling you to quickly become agile, launch new services and address virtually any type of new data use case!
We're proud to partner with both AWS - Amazon Web services to provide you with the uniquely adapted solution that fits your specific needs. They are key in helping us provide you with these advanced data and analytics solutions and cloud capabilities.
Interested to find out more about this global player on the market and how we work with them? Read on below and discover our unique approach to data and analytics in the cloud.
The realization of a data landscape or data lake can take many forms, include many different services and has many flavours.
To fasten the realisation of your data landscape or data lake and be future proof, Micropole designed it’s A2C reference architecture.
This reference architecture is based up-on some corner stone data services of AWS.
Data ingestion with S3, Simple Storage Service
Data can easily and in various ways be provisioned towards S3, that further has many integration points in the AWS landscape.
Once the data is available in S3, it becomes ”consumable” by:
Analytics with Redshift
AWS Redshift is a fully managed data warehouse solution. Offering standard ODBC and JDBC access.
Redshift is an MPP (massive parallel processing) column store.
A column store database is much better suited for analytics purposes compared to ”traditional” row organized databases.
Redshift is a multi node cluster architecture that allows you to scale (and pay) in function of your data volume.
Fully managed also meaning, any kind of upgrade is totally “behind the scenes”, all aspects of backups are handled by the solution.
Data visualization with QuickSight
As a fully managed service, QuickSight lets you easily create and publish interactive dashboards that include ML Insights. Dashboards can then be accessed from any device, and embedded into your applications, portals, and websites.
Orchestration with StepFunctions
StepFunctions are able to orchestrate the whole data chain. StepFunction being a fully managed AWS service allows you to scale without boundaries, this while having milliseconds latency between step transition.
Machine learning with Sagemaker
Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Amazon SageMaker is a fully-managed service that covers the entire machine learning workflow to label and prepare your data, choose an algorithm, train the model, tune and optimize it for deployment, make predictions, and take action. Your models get to production faster with much less effort and lower cost.
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