Data engineering

  • There was curiosity— a thirst to unravel the potential concealed within data's labyrinth for Salman Dhariwala, Director of Data Engineering at Dream11. His focus was clear - to build robust pipelines and transform raw information or data into priceless insights. More than just a tech pursuit, Salman has been keen to shape how organizations leverage data for strategic victories. Salman shares with us how his transition into the sports-tech industry allowed him to pursue his two greatest passions – technology and sports.
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  • In a record-breaking feat, Dream11's app has taken fan engagement to new heights this year, managing a concurrency of 10.56 million users during the Indian Premier League (IPL). This time of the year, the Dream11 Stadium is nothing short of stepping onto the cricket field itself, and it's all thanks to the extraordinary efforts by our super talented teams of engineers, data scientists, product developers, designers, customer experience and ops experts. But the real magic happens when we dive into the minds of our #Dreamsters. Join us as we unveil their gameplan and insights leading upto India’s biggest sporting event in Indian cricket – the TATA IPL 2023. Get ready to go #BehindTheDream!
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  • Technology is our backbone to delivering a world-class user experience. In the first edition of our #BeyondTheAlgorithm series, we embarked on a journey to highlight the remarkable achievements of our Technology and Engineering teams at Dream11. In this blog, Pradip Thoke, VP of Data Engineering, Dream11, shares his inspiring story of dedication, and perseverance that has helped shape the company’s engineering prowess.
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  • The blog covers the importance of real-time data processing in gaining a competitive advantage in various industries. It introduces Streamverse, Dream11's in-house real-time data processing platform and its core primitives: Streams and Operators, and provides a detailed overview of the platform's architecture. It also gives examples of how real-time data processing can improve user engagement, personalisation and real-time analytics, empowering a product to take business critical decisions.
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  • At Dream11, we have seen tremendous growth in terms of our user base to become the world’s largest fantasy sports platform. Our users can demonstrate their knowledge and skills in sports and connect deeper with the sport they love. With an ever-growing user base that currently stands at over 120 million, we work meticulously on our data platform to handle the sheer scale of generated data every second. Our goal is to build self-serve data products, be a data-obsessed team, and promote the concept of ‘data as a service within the organisation.
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  • As the world’s largest fantasy sports platform with 110 million+ users, Dream11 hosts hundreds and thousands of fantasy sports contests every day. Here, our users can actively engage with real-life sporting events and showcase their knowledge of sports. However, enabling so many users to have the best experience possible on Dream11 every day can seem challenging. At such a large scale, one of the common behavioural analytics requirements that we have is Funnel Analytics, to understand user behaviour and preferences. With our in-house Data Platform where we collect, process and serve terabytes of data per day, we have solved this easily. And in this journey, our hero has been DataAware — a Funnel Analytics tool that we use to recognise and know user behaviour trends at such a large scale as Dream11. We developed DataAware with the following features:
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  • 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.
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  • 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.
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  • 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.
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