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At a time when we are collectively homebound, thanks to the pandemic, the Dream11 Indian Premier League (IPL) 2020 came like a breath of fresh air for cricket fans everywhere. Not only did the **Dream11 IPL 2020** fill an otherwise gaping void in the realm of sports this year, but it also kept our passion for cricket, a thread that binds us together, burning as brightly as ever.

Building batch data analytics platform traditionally is not a new trend. While the industry is moving towards agile and shorter development cycles, scope of building data platform is no more limited to batch processing. Businesses aim for real time updates on-the-go. No one wants to know something that has broken after an hour.

Data is the new oil…only if you can reach meaningful insights out of it. Getting the most relevant insights in the fastest possible way, makes a business stand apart from the crowd. In other words, reliability and speed are the two key metrics when it comes to assessing the quality of insights. However, with growth in user base and resultant data, supporting deep analytics at a large scale becomes a challenge.

Scale to serve over 500,000+ events per second, 5 TB+ ingestion capacity and provide logging, search for micro services, security analytics along with anomaly detection 'Elasticsearch as a Service' for our microservices , security, data services logging and analytics needs in the face of high events frequency and data ingestion requirements_

Over the years, mobile application developers have experimented with various standard architecture patterns like Model View Controller (MVC), Model View Presenter (MVP), Model View ViewModel (MVVM), and clean architecture et al. These patterns need improvisations to implement it for specific requirements of the mobile app. While designing the architecture, the first step is to identify and state the objectives. Below were the objectives identified by us:

Big Data is much more than simply a matter of size — it presents an opportunity to discover key insights and emerging trends in data, makes businesses more agile, board room decisions better informed, and answer questions that have previously been considered unanswerable. With all the hype around big data, insightful data is eventually most important to business.

Trust and transparency have always been (and will always be) the cornerstones of what makes Dream11 a great product and a great organisation. When we're not busy creating a strong, positive work culture and a killer product, we spend our time and energy in trying to figure out the answer to a single question: How do we build more trust among our users (and Dreamsters too)?

Our Leaderboard has already served 1 million requests per minute, serving 250k concurrent users at its peak. As our user base grows, we expected the request pattern to grow by a much larger magnitude. So, we needed to design a system that would linearly scale as our traffic increases. It should be able to crunch gigabytes of data using distributed sorting within a SLA (Service Level Agreement) of under a minute, while maintaining strong consistency of different user views across multiple platforms. The persistent systems used should support millions of input/output operations per second, while maintaining throughput and latency under the strict SLA desired.This is done to ensure that our users have a seamless experience across platforms and devices at all times.