data subTLDR week 1 year 2026

The Senior Data Engineer shared their recent job-hunting experience in 2025, emphasizing that the job descriptions didn't necessarily mirror the actual process. Their application to 100+ companies led to full recruitment loops at several tech industry giants, and they received offers from six, eventually accepting one from DoorDash. The hiring process focused heavily on SQL & Python, data modeling, system design, and behavioral aspects. Preparations involved practicing complex joins, window functions, handling messy inputs, and data structure manipulation. They also designed and reasoned about different data models and data system designs, including the trade-offs involved. Their behavioral preparations involved storytelling, handling disagreements, and influence. The competition was intense, necessitating swift and thorough preparation.

The new paper by DeepSeek on Manifold-Constrained Hyper-Connections (mHC) presents an approach that enhances the residual connection paradigm, yet has drawn mixed reactions. The addition of a new scaling dimension within the architecture, despite a minor compute increase, received positive feedback due to its potential for significant performance improvements. However, some were skeptical about the use of doubly stochastic matrices and their small eigenvalues, sparking technical discussions about the propagation of information in deep neural networks. Critics stressed the importance of understanding the strengths and limitations of the approach before application. Overall, the paper is seen as a promising step toward understanding topological architecture design.

The creator of a Deep Convolutional Generative Adversarial Network (DC-GAN) shared their successful model and asked for advice on reducing GPU usage. Users praised the work and shared their experiences with GANs. One user found the learning rate from the shared code useful for their own project, highlighting the importance of determining the correct learning rate. Some users suggested ways to optimize the model, like using Separable Convolutions. There were also discussions about the challenges of working with specific datasets and the value of adversarial learning. The overall sentiment was positive and encouraging.

The online community shows strong appreciation for the continuously updated machine learning research notes published on GitHub. They value the dynamic, evolving format, seeing it as a necessary adaptation to the fast-paced industry. The resource is particularly praised for its inclusion of Chinese language content. Users find the PDF format of the notes surprisingly pleasant, despite expectations of markdown or LaTeX. Beginners express interest in how to best utilize the resource for learning. The notes' explanation of attention mechanisms since 2017 also sparked curiosity, with users seeking simplified explanations for such complex concepts.

There's a strong sentiment among users that the focus of data engineering has shifted towards tool usage rather than the underlying concepts and principles. Many users expressed frustration that the field has become more about configuring software components with minimal coding, leading to an oversimplified understanding of data engineering. Concerns were also raised about overpriced tools like DataBricks and Microsoft being pushed by consultants, resulting in businesses missing out on more effective solutions. However, some users defended the current state of affairs, arguing that common terminology is useful and the field's expansion naturally leads to more basic questions. Overall, the sentiment was mixed.

The discussion centers on Bill Inmon's critique of modern data technology like Databricks and Snowflake. Participants generally agree with Inmon's claim that companies neglect data warehousing fundamentals, confusing storage for centralization and ingestion for integration. However, they assert that the issue lies with people and processes, not the platforms. There's a tendency to chase modern tools and certifications at the expense of core concepts like data modelling and integration. They also note that organizations often misuse these platforms, attempting to use them as a panacea for all data problems, which are often rooted in processes, roles, ownership, and culture. The sentiment is mixed, emphasizing the need for stronger data management and governance.

A UK-based data scientist offered free SQL mentorship, triggering a wave of interest among data enthusiasts. The mentor, who has extensive interview experience, aims to fill the skill gaps often found in candidates. Responses have been overwhelmingly positive, with many aspiring data scientists expressing their eagerness to learn from a seasoned professional. Users shared their struggles in a field where even junior roles often demand significant experience. They seek guidance on enhancing their SQL and Python skills and navigating the job market. The mentorship offer was seen as particularly beneficial for those in small companies with limited learning resources.

The user shared their experience of understanding SQL reporting better after building a full dashboard from scratch, which received a positive response, especially from those eager to replicate the project using Docker. The post resonated with beginners and those struggling to apply SQL concepts in real-world scenarios, as well as retired professionals who agreed on the value of hands-on projects. The lack of practical learning in university programs was also highlighted. However, there were also comments expressing interest without offering further discussion. Overall, the sentiment was positive, reflecting the value of practical projects in learning advanced technical skills.

The future of SQL jobs remains promising despite the rise of AI, according to a discussion among tech professionals. They emphasized that SQL is typically one part of a broader skill set, and that roles like Database Administrator (DBA) are challenging to replace with AI. Skills in orchestration tools, data analysis, business analysis, and reporting tools were recommended alongside SQL. Some contributors noted that AI hindered their learning process in database management and that it still fails to grasp the business logic required for SQL output. However, the adoption of cloud solutions and AI-driven systems could potentially impact some DBA roles. The overall sentiment was positive, encouraging the learning of SQL in conjunction with other skills.

The sole comment on this thread humorously suggests the implausible idea of solving Leetcode problems using HTML, following the original post about solving the Advent of Code in SQL. The sentiment is light-hearted and jesting, reflecting a whimsical approach to the unexpected use of programming languages.