A Modern Data Stack: From Capture to Visualization

In today's rapidly evolving technological landscape, companies are in a relentless pursuit to harness their data and turn it into actionable insights. But the journey from raw data to useful information is a complex one. Here, we delve deep into our state-of-the-art data stack, breaking down each component and its role in transforming data into knowledge.

1. Change Data Capturing (CDC) and Kafka Streams (Extract and Load)

At the very beginning of our data journey is the CDC mechanism. This captures changes made in our product, ensuring that we always have the most up-to-date information. But capturing is just the first step. The changes captured need to be efficiently disseminated. This is where Kafka Streams come in.

Kafka Streams provides a robust way to continuously process and transmit the changes. It's like the nervous system of our data infrastructure, ensuring that every part of the system knows about every change almost instantly.

2. BigQuery: The Data Warehouse (Storage and Query)

Once the data is captured and transmitted, it needs a home, a place where it is stored and can be accessed for further analysis. This is the role of BigQuery.

Our system consumes the Kafka Streams and publishes these events into BigQuery. Think of BigQuery as a vast library where every event, every change, and every piece of data is a book. And just like a library, it's not just about storing books; it's about organizing them so that they can be easily found and read.

3. DBT: The Data Janitor (Transformation)

Storing data is one thing; ensuring it's clean, unified, and aggregated is another. DBT (Data Build Tool) is our solution to this challenge.

DBT is like a janitor for our data. It ensures that the data is clean, gets rid of any inconsistencies, and transforms it into a more understandable and usable format. With its SQL-first transformation workflow, DBT enables our data team to follow software engineering best practices, ensuring that our data pipelines are robust, efficient, and reliable.

4. Accessing the Data Treasure (Visualization, Data Applications)

With the data captured, transmitted, stored, and cleaned, it's now ready to be accessed, analyzed, and visualized. We offer three primary ways for our customers to interact with this data:

1. LookerStudio

Google's BI tool, LookerStudio, provides an intuitive interface for data exploration and visualization. It's like a magnifying glass that lets users zoom in on specific data points, patterns, or trends.

2. Tenant-Separated BigQuery DataSets

For those who need more granular access, we offer tenant-separated BigQuery DataSets. This ensures that each tenant has a dedicated space, providing both security and customization.

3. Embedded Analytics with Cube and Highcharts

For a seamless user experience, we offer embedded analytics right inside our UI. Here's how it works:

Cube: Serving as our Semantic Layer, Cube ensures that the data is presented in a way that's meaningful to the users. Additionally, it takes care of caching and provides a tenant-separated embedded analytics solution. It's like a translator, converting the technical language of data into something more user-friendly.
Highcharts: Once the data is ready to be presented, Highcharts steps in. Built on JavaScript and TypeScript, Highcharts offers a plethora of visualization options, from simple bar graphs to complex heat maps. And with its wide range of wrappers, it ensures compatibility with virtually any backend or server stack.

4. Future Applications (Data Applications)

In the realm of Affiliate Marketing, leveraging a unifying semantic layer like Cube can revolutionize data-driven decision-making and strategy formulation. Cube's semantic layer acts as a bridge, translating raw

data from various affiliate platforms into a structured and understandable format. When integrated with Large Language Models (LLMs), this transformed data can power sophisticated data apps tailored for affiliate marketers. For instance, an LLM can analyze consolidated performance metrics from Cube to provide real-time content recommendations, predict affiliate trends, or even auto-generate marketing copy that resonates with target demographics. Additionally, LLMs can assist in identifying high-performing affiliate partnerships by analyzing historical data, enabling marketers to allocate resources more efficiently. Such a synergy between Cube's semantic layer and LLMs paves the way for intelligent affiliate marketing tools, optimizing campaigns, enhancing content strategy, and maximizing ROI through data-driven insights.

The concept of conversational data questions translated to queries against a unified semantic layer heralds a transformative approach to data interaction. By allowing users to pose natural language questions, this method bridges the gap between intricate database structures and non-technical stakeholders. Imagine a scenario where a marketer, without any SQL knowledge, inquires, "Which product had the highest sales last month?" Through the unified semantic layer, this conversational question is seamlessly translated into a structured query, fetching the precise information from the underlying data sources. Such a system not only democratizes data access but also enhances efficiency, eliminating the need for intermediaries or extensive training. It empowers individuals across an organization to directly engage with data, fostering a culture of informed decision-making and real-time insights. The marriage of natural language processing with a semantic layer ushers in an era where data becomes as conversational and accessible as chatting with a colleague.

In essence, our data stack is a harmonious blend of cutting-edge tools and technologies, each playing a pivotal role in turning raw data into actionable insights. Whether you're a business analyst looking for trends or a developer aiming for integration, our stack ensures that data is always at your fingertips, ready to guide you towards informed decisions.