July 10, 2023

We all know how the story begins. The adoption of the internet generated tons of data, which lead to an explosion in volume, velocity, and variety of information. For the sake of making data-driven decisions, organizations have increasingly turned to Data Lakes – flexible and scalable Object Storages designed to hold all this heterogeneity of sources.

However, all possible value is lost if we don’t know where to find it. While LLMs are driving adoption for AI models, the Analytics team is unable to find answers to basic questions, and critical information would appear at the wrong time and place.

Data layout refers to how your data is structured and organized within storage systems. This organization critically influences the speed and efficiency with which data can be retrieved and processed. An optimal data layout can facilitate quick access to data, while a sub-optimal layout can slow down data retrieval and increase processing time.

Let’s imagine this vast haystack with a needle hidden in it. This needle represents a piece of critical data that can unlock insights into your organization. If it is randomly scattered within the haystack, finding this piece would be incredibly difficult, time-consuming, and exhausting. 

But what if the hay is well partitioned and arranged by its type, size, origin, and other features? Rather than digging through the entire haystack, you can focus on a specific section where you know the needle would be much likely to be. 

This is the same challenge that Data Lakes are facing. Raw formats lack a predefined structure that would facilitate data extraction. It is not easy to understand changes or to search for a particular group of insights if everything is everywhere. As you keep adding data, the complexity, and difficulty of managing it are augmenting exponentially. This means slower data retrieval, increased computational costs, and a greater possibility of bottlenecks in data engineering.

Maintaining an inefficient data layout has real-world implications for data-driven solutions. When we aren’t capable of navigating the Data Lake efficiently, it directly affects the speed of analysis. Scraping the whole dataset to find a particular set of characteristics can result in missed opportunities and lower ROI on data investments. 

On the other side, having to manage an unorganized Lake forces teams to create complex ETL processes that difficult the maintenance of clean and fresh SQL Tables. According to this use case from Xendit, using S3 Bucket Versioning was penalized by 75% of the amount of extra money spent on the Cloud. Poor formatting is inflating Cloud Computing Costs which most likely would suffer a cut in organizations due to the current macro-economic situation. 

Recognizing the initial challenges, table formats like Delta Lake, Apache Iceberg, and Apache Hudi have emerged. These formats brought a degree of structure and reliability to Data Lakes, making them more navigable to the end consumer. While they certainly provide a level of organization, these solutions still struggle to keep pace with the increasing volume and complexity of data. 

They have adapted mechanisms for compaction and order, but since the same data serve different purposes, the mechanisms aren’t broader enough –ZOrder loses effectiveness with each additional column– or lack precision during optimization -they need to re-shuffle the whole data recurrently- so the maintenance still relies on the expertise of the engineer.

For those experiencing slower (or heavily complex) data navigation, consider a new integration in your data management stack: Qbeast Format. Designed to sit on top of existing Table Formats like Delta, Iceberg, and Hudi, Qbeast optimizes the Data Layout to facilitate faster, more fine-grained data retrieval with lesser user friction.

Using multi-dimensional indexing techniques, the Qbeast layout ensures that related elements are grouped together for executing a proper data-skipping, and can maintain the most consulted sub-areas optimized for analysis. It gets rid of traditional partitioning strategies and takes care of distributing evenly the data within files. 

Searching for a specific needle gets easy-peasy by filtering those blocks that do not matter. Reading less data would effectively speed up queries and ultimately, decision-making processes.

The Data Layout is the compass that directs your journey through the Data Lake. By giving it the attention it deserves, you can enable innovative ML solutions, accelerate your analytics, and make the most out of your data investments.

Consider trying the open-source Qbeast Format for writing new data on the Data Lake. It would take no more than a line of code to unlock the full potential of your data. 

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