The hottest data debate in years is upon us: Data Nets or Data Mesh?
In this essay, we will explore both sides of the debate, and attempt to come to a conclusion about which approach is best suited for the needs of today's data teams.
Data Nets are a new breed of data architecture that aim to take advantage of the latest advances in Neural Nets and Generative AI.
The Four Fundamental Ideas of Data Nets:
The implication of a fully AI-first Data Net architecture is that data engineering as we know it today will cease to exist. Data engineers will be replaced by AI-powered data pipelines that are able to automatically ingest, cleanse, transform, and aggregate data from multiple sources and formats. These data pipelines will also be able to automatically detect and recover from downtime or schema changes, minimizing the impact on business operations.
Data Nets are composed of two main components:
The advantages of Data Nets over traditional approaches are numerous:
The disadvantages of Data Nets are that they are still a relatively new technology and thus have not been battle-tested in production environments. Furthermore, because they rely on Neural Nets, which are themselves a black box algorithm, it can be difficult to understand and debug issues when
Data Nets are the final nail in the coffin for the modern data stack. With data ingestion orchestrator, neural networks and generative AI all working together, Data Nets provide the most complete picture of what is possible with data today.
In contrast, Data Mesh and Data Contracts are both limited in their scope. Data Mesh only addresses the issue of how data is managed within an organization, while Data Contracts focus on defining the interface between different software components. Neither of these approaches take into account the advances made in artificial intelligence or how to best utilize data for predictive purposes.
There are many factors to consider when deciding whether or not a Data Net is right for your organization. In general, a Data Net can be beneficial if you need to:
Data Nets are also well suited for training on large datasets, which would be prohibitively expensive with traditional methods.
Some examples of when you might need a Data Net include:
A Data Net is a distributed data processing platform that uses neural networks and generative AI to automatically cleanse, transform, and aggregate data from multiple sources. Data Nets are designed to be highly scalable and fault-tolerant, making them ideal for use in mission-critical applications.
The 5 layers of Data Net architecture are:
Data Nets have been controversial for a number of reasons. Firstly, people have been critical of the Data Net approach, arguing that it fails to take into account the need for precision and flexibility when defining business metrics. Secondly, some data engineers have argued that the Data Net approach is too reliant on artificial intelligence and neural networks, which they believe are not yet mature enough technologies to be used in production data pipelines. Finally, there is concern that the use of generative AI could lead to unpredictable results and potentially introduce bias into data products.
The controversy is really because they represent a departure from the modern data stack, which has been relied upon for many years. Data Nets are a new way of managing and processing data, made possible by the confluence of data ingestion orchestrators, neural networks and generative AI. This new stack is more flexible and scalable than the traditional stack, but it also requires a different set of skills to manage effectively. As such, there has been some pushback from those who are comfortable with the status quo.
Data Nets could be the future of data management and offers several reasons why
The new Data Net architecture is a concern because it threatens to replace the existing data stack with something that is more AI-driven and less reliant on manual intervention. This would reduce the need for data engineers, because Data Nets could also automate many of the tasks that data engineers currently perform, such as cleansing and transforming data, aggregating data, and generating realistic mocks for testing purposes.
In this section, we'll take a look at some of the main trends enabling Data Nets and the AI-first data stack.
Data virtualization is a technique that allows data from multiple sources to be accessed and combined as if it were all stored in a single location. This enables data consumers to access the data they need without having to worry about where it is physically located or how it is formatted.
Data federation is a related technique that allows data from multiple sources to be combined into a single logical view. This can be done using either physical or logical techniques. Physical federation involves replicating the data into a single location, while logical federation leaves the data in its original location and uses special software to combine it into a single view.
Both of these techniques are important for enabling Data Nets, as they allow data from multiple sources to be easily accessed and combined. This makes it possible to build complex applications that use data from many different places without having to worry about where the data is coming from or how it is organized.
Data orchestration is the process of managing and coordinating data pipelines. It includes tasks such as extracting data from multiple sources, transforming it into the desired format, and loading it into a data warehouse or other target system.
Data orchestration is a critical part of any data engineering solution, as it ensures that data flows smoothly through the various stages of the pipeline. It also makes it possible to easily modify or add new steps to the pipeline as needed.
There are many different tools available for performing data orchestration, including Apache Airflow, Prefect, Dagster, and AWS Step Functions. Data Nets make use of these tools to automatically set up and manage complex data pipelines with very little input from data engineers. This frees up time for them to focus on more important tasks.
Neural networks are a type of artificial intelligence that is inspired by the way the brain works. They are composed of a large number of interconnected processing nodes, or neurons, that can learn to recognize patterns of input data.
Generative AI is a type of AI that is focused on generating new data rather than just recognizing patterns in existing data. It can be used for tasks such as creating realistic mockups of data for testing purposes or generating new images from scratch.
Both neural networks and generative AI are important for Data Nets, as they allow complex applications to be automatically built and maintained with very little input from humans. This frees up time for data engineers to focus on more important tasks.
Swyx here. Last I checked, I am human. In case you somehow missed our blog title, this blog post was an artistic/joke exercise responding to a real-life Twitter meme that sprang up during Coalesce 2022 (prompted, lightly edited and formatted by humans, but otherwise entirely written by AI). Any references to companies and individuals are completely made up and not intended to reflect anything remotely close to reality.
However we did attempt to indulge this meme a little as a means of exploring the nature of data discourse between data practitioners and, for want of a better word, data thought leaders:
Data lakes, data swamps, data meshes, data contracts. The more concepts we invent, the more conceptual load we heap on the industry. It is all well intentioned, but the lack of clarity on what ideas apply to whom based on a 2 word analogy starting with “data” leads to a lot of confusion, which then leads to demand for content, which then leads to popularity, which then feeds the next round of debate.
Brandolini’s law is often described: "The amount of energy needed to refute bullshit is an order of magnitude larger than to produce it." Data jargon is not bullshit, because it usually comes from real problems, so we need a new term.
We propose Catanzaro’s law: "The amount of energy needed to define, compare, contrast, implement, and get real value out of new data paradigms in context of our people, processes, tools, and platforms is an order of magnitude larger than to merely coin them."
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