This is an ELT architecture (extract, load, transform) as opposed to a more traditional ETL architecture, and can support companies of all sizes (perhaps with the exception of extremely large enterprises). There is a prevailing model of a data stack that we consistently see the world moving toward, that’s probably best summed up by this diagram. Similarly in one or two cases I’ve shared my reasons for not recommending them. Where I have significant experience with a product, I’ll let you know and provide more detail on why. Innovative new products that may not tick the above boxes, I personally believe are worth a mention.We generally hear our customers speak highly of.Have generally high adoption and awareness amongst startups. In general, I’ve chosen to highlight products that: It’s impossible for me to give a completely fair trial to every product in this space. Dataform is a data modeling platform for cloud data warehouses, and while only one small part of the overall data stack, is often the glue that ties many things together and as a result, we spend a lot of time talking about overall data architecture with customers and prospective clients. I’m CTO and co-founder at Dataform, I was previously an engineer at Google, where I spent most of my 6 years there building big data pipelines with internal tools similar to what is now Apache Beam. Here’s a breakdown of your options, trade offs, pricing and some thinking points around which you can make your decision, as well as some personal thoughts on the options. There are many choices out there, and navigating them all can be tricky. I advise a lot of people on how to build out their data stack, from tiny startups to enterprise companies that are moving to the cloud or from legacy solutions.
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