Join Brycen at Japan DX Week Fall 2025 to discover advancements in data engineering for manufacturing and logistics industries.

Definity introduces its groundbreaking data engineering platform, designed to optimize enterprise lakehouse and Spark environments, backed by a $12 million Series A funding.

New Math Data's recent achievement of AWS Premier Tier Services Partner status showcases its commitment to AI and modern data solutions.

ALKU has unveiled ALKU Intelligence, a dedicated division focused on AI solutions, data engineering, and advanced analytics. This move addresses enterprise AI challenges effectively.

Nous Infosystems has officially rebranded as Artizent, highlighting its engineering-focused approach and commitment to critical industries.

A comprehensive applied statistics text featuring extensive graphical examples of probability distributions, inference, and real‑world data analysis.

Brycen is set to present at the 'THE AI 2026' online event, discussing the current state and future of data engineering for Physical AI in manufacturing.

Brycen has unveiled a dedicated Data Engineering Solutions site to meet evolving data utilization needs and support businesses effectively.

Dresner Advisory Services has released the 2026 Data Engineering Market Study, emphasizing data engineering's crucial role in modern analytical infrastructures. Explore the findings.

Trainocate introduces AWS Certified Training's new course, 'Data Engineering on AWS,' starting March 11, marking its first offering in Japan.

ArcSpan has announced the appointment of Akpanoluo Etteh as Senior Director of Data Engineering to improve its audience monetization platform.

Astronomer's State of Airflow Report 2026 reveals in-depth data on the growing importance of Airflow in enterprises, highlighting key trends and user insights.

CARTA HOLDINGS engineers will present at the 2025 Data Engineering Summit, discussing RevOps and data-driven marketing strategies in today's economy.

DataOps.live earns the title of DataOps Company of the Year by Data Breakthrough, highlighting its innovative approach to data product reliability and speed.

CARTA HOLDINGSが主催する「Data Engineering Summit」で、エンジニア2名が独自の視点からデータ活用の未来を語ります。

submitted by jupyter to data_engineering 1 points | 2 comments I am creating a couple of bigger database tables with at least hundreds of millions of observations, but growing. Some tables are by minute, some by milliseconds. timestamps are not necessarily unique. Should I create separate year, month, or date and time columns? Is one unique datetime column enough? At what size would you partition the tables? Raw data is in csv. Currently I aim for postgres and duckdb. Does timescaledb make a significant difference?