Prediction
#bigdata
This article is soft-marketing piece by founder of a database vendor startup, MotherDuck. Here are the summarized takeaways created by HARPA AI
- #BigData Era Over: The author argues that the era of Big Data is over, and the focus should shift from data size to using data for better decision-making.
- Data Size Misconception: The belief that most people have massive datasets is challenged; analysis reveals that the majority of customers, even in large enterprises, have moderate data sizes, often less than a terabyte.
- Storage-Compute Separation: Modern cloud data platforms separate storage and compute, allowing independent scaling. The shift to shared disk architectures and scalable object storage has revolutionized data architecture in the last 20 years.
- Workload Sizes vs. Data Sizes: Workload sizes for analytics are smaller than perceived. Analyzing BigQuery queries shows that 90% processed less than 100 MB of data, challenging the notion of large data processing needs.
- Big Data Frontier Receding: The definition of "Big Data" as what doesn't fit on a single machine becomes outdated. Advances in cloud computing provide substantial resources, making the number of workloads that qualify as "Big Data" decrease every year.
- Data as a Liability: Keeping data incurs costs beyond storage, including regulatory compliance, legal exposure, and the challenge of maintaining data quality and relevance over time. The author advocates for understanding why data is kept and the associated costs.
- Big Data One-Percenter: The article suggests questions to identify if an organization truly falls into the "Big Data One-Percenter" category, emphasizing the importance of using tools tailored to the actual data size and needs.