What we're about

When I first entered the world of Big Data as an engineer, I didn’t realize what an enormous technological challenges I will be facing. As time went by, despite my experience, I’m still struggling to keep up with new technologies, frameworks and architectures.

Consequently, I created this meetup, detailing all the challenges of Big Data, especially in a cloud environment . I am using AWS, GCP and Data Center infrastructure to answer the technical questions of anyone navigating their way in the big data world.

In this meetup we will try to answer questions regarding: big data best practice, data science, data engineering, BI, how to manage costs, performance tips, security tips and Cloud best practices.

We shall present lecturers working on several cloud vendors, various technologies and frameworks, and startups working on data products. Basically - if it is related to data - this is THE meetup.

Some of our online materials (mixed content from several cloud vendor):







You tube channels:




Data Engineers
Data Science
DevOps Engineers
Big Data Architects
Solution Architects

Upcoming events (2)

Data integration, ETL, ELT, ... challenges, and complexities

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In today's fast-paced business world, companies face a variety of challenges when it comes to collecting, integrating, and managing data from different sources. These challenges include dealing with legacy systems, managing data security, and handling data in real-time. At DatAtlas, we understand the complexity of these challenges and have created a SaaS platform that delivers data from various sources in real-time, securely and at scale.

During my presentation, I will discuss the importance of data integration, the challenges that companies face when dealing with data integration, I will also provide real-world examples of how businesses have successfully overcome data integration challenges using our platform.

Lecturer: Meryem Chafry, Founder & CEO @DatAtlas.
I worked for 3 years as a data scientist and data engineer in the Telco industry before launching my own start-up, that helps businesses manage, move, and make decisions on the freshest data in seconds. Since then, I have had the opportunity to work with other sectors to automate their data flows and achieve their business goals with data.

Language- English

How to design ML Observability for high-risk AI use cases

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MLOps simplified the baseline processes making it easy to build models at scale today. But there has little or no focus on ML acceptance. Any AI/ML model can fail, models are not explainable by design, models can carry the risk of usage during production and model auditing is very complex. Deploying AI for mission-critical use cases requires additional layers like explainability, monitoring, auditability, data privacy and risk mitigation to ensure the AI solution is acceptable to all stakeholders.


  1. Introducing ML Observability
  2. Using ML Observability for model monitoring, model explainability and auditing.
  3. Designing the policy layers to manage model usage risk in ML Observability.

Lecturer: Vinay Kumar Sankarapu- the Co-Founder and CEO of Arya.ai. He did his Bachelor's and Masters in Mechanical Engineering at IIT Bombay. He started Arya.ai in 2013, along with Deekshith while in college. Vinay Kumar leads R&D of AryaXAI product. He wrote multiple guest articles on ‘Responsible AI’, ‘AI usage risks in BFSIs’ and ‘AI Governance framework’. He is presented technical and industry presentations across multiple conferences globally - Nvidia GTC, ReWork, Cypher, Nasscom, TEDx etc. He was the youngest member of ‘AI task force’ set up by the Indian Commerce and Ministry in 2017 to provide inputs on policy and to support AI adoption as part of Industry 4.0. He was listed in Forbes Asia 30-Under-30 under the technology section. He represented India in Worldcup Technology Challenge in 2015 among 54 other countries in the finals.

Language: English

Past events (64)

Love At First Sight? More Like Data At First Sight