The hottest Data Infrastructure Substack posts right now

Starting a career as a data team manager involves challenges and new skills, with resources like books to aid in the transition.
Assisting team members in their career growth involves sharing helpful articles, guides, and videos.
Improving project management, team culture, and communication are key elements in running successful data teams.

Models like GPT4 have been replicated in many organizations, leading to a situation where moats are less significant in the language model space.
The open LLM ecosystem is progressing, but there are challenges in data infrastructure and coordination, potentially leading to a gap between open and closed models.
Despite some skepticism, Language Models have been consistently enhancing their reliability making them increasingly useful for various applications, with potential for new transformative uses.

Defense tech landscape is evolving, and startups are now collaborating with the DOD.
Government contracts are key for defense tech startups, offering revenue and validation.
Innovation in AI/ML, data infrastructure, cybersecurity, vertical solutions, and autonomous systems are driving the defense technology industry.

All companies, regardless of size or industry, have data infrastructure challenges.
Data infrastructure can become messy over time if not managed properly.
Regular maintenance and monitoring of data infrastructure are essential to prevent messiness.

Data quality is a tradeoff in balancing stability and agility.
Data resiliency tools like SDF focus on tracing data lineage to improve debugging and fixing issues.
Managing messy data often requires making choices between stability and adaptability in data infrastructure.

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Experimentation Environments in MLOps are crucial for improving ML model development velocity.
Efficient Experimentation Environments should provide access to raw and curated data for Data Scientists.
MLOps tooling has matured, with a focus on point solutions rather than end-to-end platforms.

State of the Future is a deep tech tracker covering a wide range of technologies like computer vision, generative AI, and quantum hardware.
The three main trends identified in the future include solving productivity paradox, the shift from software in digital world to real world, and having optimism for the future.
Important news includes suppressing quantum errors, challenges faced by Amazon's drone delivery project, and closures of vertical farming startups due to high costs.

Data quality is crucial for successful AI implementation.
Robust data infrastructure is needed to support effective data management.
Establishing a data governance framework and a positive data culture are essential for long-term success.

Data as Code is a mental model to improve quality & productivity of data pipelines
Exchange 'data pipelines' for 'data delivery pipelines'
Data as Code is like Infrastructure as Code, defining data as source code for reproducibility & auditability

The difference between OLTP and OLAP systems can cause miscommunication among data producers and consumers.
OLTP systems focus on serving end users quickly with specific product features, while OLAP systems handle complex analytics by scanning large amounts of data.
Empathy and communication between OLTP and OLAP teams are crucial to building scalable data products.