Bikash Singh
MIQ Digital India Pvt Ltd - Technical Lead
Engineering Lead with an expertise in working on Programmatic Media Buying space for Ad-Tech domain.
Skilled in Application Development and building a highly scalable Big Data Analytics platform. Data Pipeline builder responsible for optimizing data stores and building Big Data solutions from the ground up.
Experienced in performing root cause analysis and optimizing Data Pipelines at scale with cost effective approach.
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Scaling Proximity Targeting via Delta Lakehouse based Data Platforms Ecosystem
Proximity Targeting is a marketing technique that uses mobile location services to reach consumers in real-time when they are around a store location or point of interest. This is done by defining a radius around a specific location. If a consumer has opted into location services on their mobile phone and enters within this radius, proximity targeting helps in triggering an advertisement or message to consumers in an effort to influence their behaviour. This can be combined with the ability to purchase impressions through programmatic ad platforms that are powered by real-time bidding which can help businesses formulate the right strategy of influencing their users on a particular geographical area. They can build user groups based on certain characteristics (such as neighbourhoods, demographics, interests, and other data), and subsequently launch another campaign that targets anyone with those characteristics.
The growth of mobile devices has led to enormous data generation which offers tremendous potential when used effectively for business. Thus we need an efficient platform where we can process such huge data efficiently and with minimum latency and cost. This talk describes MIQ's journey into building a fast, scalable & cost effective processing platform using Spar, MLLib, Kafka's Event Driven microservices, Delta Lakehouse architecture, delivering faster and actionable insights for Proximity targeting which has empowered the creation of a product generating ~30 million dollar revenue on a year to year basis.
Building Identity Graph at Scale for Programmatic Media Buying Using Apache Spark and Delta Lake
The proliferation of digital channels has made it mandatory for marketers to understand an individual across multiple touchpoints. In order to develop market effectiveness, marketers need have a pretty good sense of its consumer’s identity so that it can reach him via mobile device, desktop or a big TV screen on living room. Examples of such identity tokens include cookies, app IDs etc.A consumer can use multiple devices at the same time and so the same consumer should not be treated as different people in the advertising space. The idea of identity resolution comes with this mission and goal to have an omnichannel view of a consumer.
Identity Spine is MIQ’s proprietary identity graph, using identity signals across our ecosystem to create a unified source of reference to be consumed by product, business analysis and solutions teams for insights and activation. We have been able to build a strong data pipeline using Spark and Delta Lake, thereby strengthening our connected media products offerings for cross channel insights and activation.
This talk mostly highlights :
* The journey of building a scalable data pipeline that handles 10TB+ of data daily.
*How we were able to save our processing cost by 50%
* Optimization strategies implemented to onboard new dataset to enrich the graph
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