![]() There should also be a way to enforce schemas and ensure governance with ways of reasoning about data integrity.īut the data lakehouse idea is also in part a response to the rise of unstructured (or semi-structured) data that could be in a variety of formats, including those that could potentially be analysed by artificial intelligence (AI) and machine learning (ML) tools, such as text, images, video and audio. Between the large, amorphous mass of the lake with its myriad formats and lack of usability in day-to-day terms, and the tight, highly structured and relatively costly data warehouse.įundamentally, the data lakehouse idea sees the introduction of support for ACID (atomicity, consistency, isolation, and durability) – transactional processes with the ability for multiple parties to concurrently read and write data. The data lakehouse attempts to bridge the gulf between data lake and data warehouse. Meanwhile, compute and storage in the data warehouse architecture will be optimised for the types of access and processing required. It will have been explored, assessed, wrangled and presented for rapid and regular access, and is almost invariably structured data. The data warehouse puts data into a more packaged and processed format. ![]() Here, datasets – possibly after exploratory phases of work in the data lake – are made available for more regular and routine analytics. Processing capabilities are not likely to be critical or optimised to particular workflows, and the same goes for storage.ĭata warehouses, on the other hand, are at the opposite extreme of things. There could be search-type functionality perhaps via metadata and some ad hoc analysis could take place by data scientists. It’s where all the organisation’s data flows to and where it can live in more or less raw format, ranging from unstructured to structured, image files and PDFs to databases, via XML, JSON, and so on. Let’s recap on the key features of the data lake and data warehouse to make it plain where the data lakehouse idea fits in.ĭata lakes are conceived of as the most upstream location for enterprise data management. In this article, we’ll look at the features of the data lakehouse and give some pointers to the suppliers making it available.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |