• MovingLake offers seamless, real-time interactions in plain English, making data accessible to all.
• Co-founded by Andres Paez and Edgar Cabrera, MovingLake addresses critical use cases, including real-time ML, overcoming state skipping, and enabling real-time workflows and dashboards.
• The startup's event-driven architecture bridges the gap between Data and Transactional teams, providing a unified solution for businesses.
In data management, businesses often grapple with the complexities of integration. Event-driven architectures emerge as modes of efficiency. Enter MovingLake, a startup founded in 2022, hailing from the vibrant tech hub of Mexico City, Mexico.
What sets MovingLake apart is its inception. The founders—Andres Paez and Edgar Cabrera, are seasoned tech experts with impressive collective experience.
Cracking the Code with Andres and Edgar
Andres Paez, the CTO of Casai and Valoreo in recent years, brings to the table a rich background as a Software Engineer and Product Manager at Amazon Prime, Google Search, and Google Cloud Platform.
On the flip side, Edgar Cabrera, with a decade of Software Engineer experience at various startups, exudes a passion for developing products from scratch and witnessing them evolve into robust, scalable systems.
The MovingLake symphony unveiled
With a team size of three, MovingLake steps into the spotlight with its revolutionary product—an LLM-enabled BI tool.
First of all, what is LLM and what is BI?
Large Language Models (LLMs) are advanced computer programs that can comprehend and generate human language. These models, such as GPT-3, can process and generate text in a manner that is strikingly comparable to how humans interact.
LLMs are important in the context of Business Intelligence (BI) because they excel at analyzing large volumes of unstructured data, such as text, and extracting significant insights.
They may assist you in making sense of consumer evaluations, social media conversations, market reports, and other data, transforming it into actionable intelligence.
Now, this isn't your run-of-the-mill BI tool. Why? Because it lets users query their databases using plain English prompts. Imagine the power of interacting with your data warehouse seamlessly, asking questions in natural language, and witnessing the data flow effortlessly. For those not versed in SQL or traditional BI tools, this is pretty valuable.
MovingLake's genesis
Three critical use cases stood out, and they all pointed to the paramount importance of real-time capabilities. First in line is "ML for ML"—the need for real-time data for machine learning.
Traditional models often faced challenges transitioning from training to production, especially in synchronizing offline and online data. MovingLake strides into this arena, offering a real-time by-default solution. Ingest, store, train, and predict—all seamlessly in real-time.
No more skipping states
The second hurdle that MovingLake gracefully overcomes is what they term "State Skipping." Think of a car rental business operating through a marketplace. Daily batch pipelines, a common practice, could lead to missing data.
Imagine a reservation created and canceled within an hour; traditional pipelines might lose that intermediate state. With MovingLake's connectors, syncing happens as often as possible, ensuring no intermediary states are lost.
Now, your database tells the complete story—reservations made and canceled, offering valuable insights into customer behavior.
Realtime dashboards and beyond
The need to create real-time workflows, alarms, and dashboards often arises but is not easily fulfilled by existing solutions. This is where MovingLake takes center stage.
Even if you start with simple data syncing, the platform provides the flexibility to branch into real-time systems. Automation, alarms, dashboards, and complete system integrations become more than possibilities—they become realities.
Bridging the gap with MovingLake
One of MovingLake's goals is to bridge the gap between Data and Transactional (Backend) teams. In many scenarios, companies end up purchasing data connectors and then resort to additional solutions for integration.
MovingLake disrupts this with its event-driven nature. A single connector source serves as the bridge between data warehouses, databases, data lakes, and microservices. The beauty lies in its simplicity—one connector to rule them all.
The startup's commitment to simplicity, real-time prowess, and inclusivity sets the stage for a data revolution and we are excited to witness it!
Edited by Shruti Thapa
