Part of the problem, of course, is that many of the most advanced technologies are complex, and it can be very difficult to bring them to life.
In context: When it comes to tech products, concepts are often more elegant than reality. Capabilities and functions that sound logical and straightforward often prove to be much more complicated or arduous than they first appear.
Part of the problem, of course, is that many of the most advanced technologies are complex, and it can be very difficult to bring them to life. But an even more common problem is that pre-existing requirements aren’t fully explained, or the number of steps required can prove to be much more challenging than first appears.
To put it simply, « the devil is in the details. »
This is true of many cloud and AI technologies. High-level product ideas, such as the ability to quickly analyze any type of data to help generate artificial intelligence (AI) or machine learning (ML)-driven models using the new types of hardware accelerators, have been talked about for years.
As Google made clear through several announcements at its Cloud Next event, however, there are a lot of important details that need to be in place in order for these ideas to become reality.
To start with, not all data analysis tools and data platforms can work with any type of data. That’s why the ability to import, or ingest, new and different format data types into a wider range of analytics tools is so important. Opening up the ability for data platforms like Elastic to get access to data stored on Google Cloud, and Google bringing support for Elastic into its newly expanded Looker line of business analytics tools, are just two of the many open data-related announcements made at Cloud Next.
Similarly, different types of data are often stored in different formats, and analytics tools have to specifically enable support for these data structures in order to make them more useful to a wider variety of users and application developers.
In the growing field of data lakehouses, for example, where large « lakes » of unstructured data, such as video and audio, are allowed to be queried with the kinds of tools found in structured data warehouses, the open-source Apache Iceberg table format is becoming increasingly popular.
