Day 1 | HALL 2 - Practical Insights and Best Practices

This is a hands-on interactive technical workshop on real-time analytics using SingleStoreDB on AWS. Ramanuj Vidyanta from AWS and SingleStore’s Sr. Solutions Consultant Rakesh Puttaswamy will guide through the development of an operational real-time dashboard for a digital marketing use case. We’ll use SingleStoreDB Cloud on AWS, Amazon S3, and Amazon Quicksight to drive real-time analytics from streaming data.
Discussion Topics
How to build an operational database based on a real-time digital marketing use case
How to use SingleStoreDB Cloud on AWS, Amazon S3 and Amazon Quicksight
*Pre-requisites: Laptop with Wi-fi connectivity (Wi-fi is available onsite)

Traditionally, data landscapes in an enterprise have been tech heavy and ensured that they are catering to the business needs as and when they evolved. However, with the higher rate of data generation through various mediums and shrinking time to make a data driven decision, approach to data management has also gone through a constant change. Slowly industry is converging towards a trend where we are generating more “closure to business” data assets. I’ll be sharing my first-hand encounter of the evolving data ecosystem, and would love to have you as my audience.

Modern businesses have vast amounts of data at their fingertips and are acutely aware of how enterprise data strategies positively impact business outcomes. Despite this, only a handful of organisations interact with all stages of the data life cycle process to truly distill information that distinguishes future-ready businesses from the rest. Companies have not treated the collection, movement, and tracking of data as a first-class problem resulting in an unmanageable number of disparate point-to-point integrations. It’s time to learn how to tame your streaming data pipelines and simplify your data movement.

What is building?

• Building a logistics service using Event-Driven architecture

• Event sourcing for better inter-process communication in

• Microservice-based architecture

• Building Data Lake using a message broker to decouple the application dependency with Data Lake pipelines.

Why are we building?

• Reduce operating costs

• Reduce ease of management by using Kafka infrastructure.

• Denormalize real-time data in motion to take comprehensive decisions quickly, as well as store this data in Data Lake for analytical purposes with reduced cost while querying.

How are we building?

• Democratizing the Data Pipelines and Event sourcing with the Kafka-Connect ecosystem

• Change data collection using Kafka-Connect Source connector

• Using kafka State-Stores for Data Denormalization in kstreams

• Doing the entire Data Lake integration using an S3 sink

connector

• Setting up Inter Process

communication using various Kafka-connect connectors like HTTP, sns, dynamodb etc.

What benefits it will bring?

• Ease of management on managing the entire Kafka-connect framework, especially on a large-scale setup with observability

• Low overhead of running partition redistribution, auditing etc on Kafka cluster.

• Realtime Data Aggregation and Denormalization for taking comprehensive real-time decision-making.

• Ease of integration in microservice-based architecture especially around event

sourcing to help to take decisions in near real-time

Why we are unique?

• Doing real-time computation through our Addfix and Geocoder engine to correctly identify the address with lat-long to deliver the shipment at a defined location and all this is happening through Kafka.

• Empowering operational dashboards to take decisions in near real-time to plan the operations accordingly on the ground.

• Created a near real-time data lake to take decisions quicker and faster

What did you learn? How others can be benefited

• Extendable and developer-friendly ecosystem to onboard and get started with real-time data very quickly

• We have powered multiple applications/3rd party applications/data lake all through event sourcing in Kafka, without the need to worry about scale.

We will be discussing scalable data lake solutions that can accelerate the decision-making process
for descriptive or advanced analytics. We will be discussing two use cases. The first use case will be
about a streaming solution that can efficiently ingest, process, and score prospective
customers/leads to enhance the overall customer experience. The second use case discusses the
creation of a semantic layer within Azure Synapse that can facilitate reporting on leads, target
marketing, and conversion funnel metrics.

The abundance of data in today's world has transformed the way businesses
operate, and MarTech has become a crucial part of a business's success. In this
session, we will explore how data is essential to making AI and ML effective in the field
of MarTech and provide real-world examples of how data-driven marketing
strategies have led to increased customer engagement, higher conversion rates,
and greater revenue growth. Discover how retail customers are leveraging data to
drive their MarTech strategies and take their marketing efforts to the next level.

Data is essential but the whole ecosystem is fragile, and as we are moving from era of consent to the cookieless world, data is also getting scarce. As consumers are becoming more tech savvy and regulations are getting tighter, for good reasons, it raises a legitimate question on the ability of brands to target and personalize experience for customers.
In this era of ever-changing regulations to address data privacy and yet connect with users, it becomes an opportunity for Data Engineers to solve for:
Creating first party data platform
Developing the ecosystem of tools, technologies and data flow pipelines that can help targeting under stringent data governance regulations
How to create Privacy safe APIs across self-owned and partner-owned data sharing platforms
Helping to measure impact both effectively and accurately
In the session we are going to cover the topics of First Party Data Platform, Data Clean Room, Conversion APIs along with identity mapping algorithms to build a future proof data ecosystem to empower organizations to help them target, personalize and measure their strategies in the evolving data governance landscape.