| Title | : | Building the Data Lakehouse |
| Author | : | |
| Rating | : | |
| ISBN | : | 1634629663 |
| ISBN-10 | : | 1634629663 |
| Language | : | English |
| Format Type | : | and 2 more , Kindle Edition, Paperback |
| Number of Pages | : | 254 pages |
| Publication | : | Technics Publications (Oct. 1 2021) |
The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.
Building the Data Lakehouse Reviews
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Grey scale pictures described with colours in the text 20+ pages falling out of binding Numerous spelling and grammatical errors Content is very verbose, could be slimmed down to about 50 pages. Lots of rambling about what data can go into a Lake House but very light on Lake House best practices etc
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Tratándose de un texto de uno de los padres del data warehouse uno esperaría un manual que aunque no cubra los detalles, sí que aclarara el roadmap general.En varios de los puntos se queda en un nivel generalísimo en el que no se aclaran los conceptos que escuchamos mucho y muy a menudo , y no terminamos de hacernos una idea definida de lo que significa o como se aplica en la praxis.Distinto de otros textos del estilo del mismo autor, como el DW 2.0, que aún siendo general aclara cada concepto y permite al lector anticipar escenarios prácticos o implementaciones.
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I expected from a thought leader to go beyond the “basic” ideas. Good as a primer. An advanced data modeler and architect like myself will consider this like a point of view that lacks the details of “how” that support their arguments.
