Yes. And there is a small possibility that something even worse happens for those previously infected/vaccinated called antibody-dependent enhancement (ADE). ADE occurs when the antibodies generated during an immune response recognize and bind to a pathogen, but they are unable to prevent infection. Instead, these antibodies act as a “Trojan horse,” allowing the pathogen to get into cells and exacerbate the immune response.
The worst case with a new covid strain would be if people who were vaccinated by a previous vaccine or infected by an older strain experience antibody-dependent enhancement after being infected with the new strain. This is where the body recognized the new strain as the old and starts producing anti-bodies. These anti-bodies actually assist the new strain in infecting your cells, making the disease worse.
ADE has not been detected with any covid strains/vaccines so its not something to worry about for now but who knows what may happen in the future. I've been keeping my eye out for any news of ADE with any of these new strains.
Certain viruses like dengue fever can be much worse if you had previously caught a different strain due to ADE.
I don’t understand what capability you are saying Databricks lacks. This capability is literally the entire premise of the Data Lakehouse. With Snowflake you need to export data out/or pipe data over jdbc/odbc to an external tool. With Databricks you can use SQL for data warehousing and when you need you can work with that same data using python to train an ML model without piping data out over jdbc (using the spark engine). One security model, one dataset, multiple use cases (AI/ML/BI/SQL) on one platform.
Here is the thing with the lakehouse though, you have flexibility and don’t need to use multiple engines to achieve the lakehouse vision. Databricks has all the security features a redshift / snowflake does so you can secure databases and tables rather than s3 buckets. It does get more complex if you want to introduce multiple engines but at least you have the option to make that trade off if you want to.
If you want simplicity, you can limit your engine to Databricks. You can also use JDBC/ODBC with Databricks if you want to use other tools that don’t support the delta format/parquet but piping data over JDBC/ODBC doesn’t scale with any tool to large datasets. Databricks has all the capabilities of big query/snowflake/redshift but none of those tools support python/r/scala. Their engines need to be rewritten from the ground up in order to do so.
With a datawarehouse, you can only interface with your data in SQL. With big query and snowflake, your data is locked away in a proprietary format not accessible by other compute platforms. You need to export/copy your data to a different system to train an ML model in python or R.
With the lakehouse, you can use python, R and Scala, (not just SQL) to interface with your data. You can use multiple compute engines (spark, Databricks, presto) so you are not locked into one compute engine.
I recall being a junior programmer, and wishing I could talk to my MySQL database in python code to do some processing that was difficult to express in SQL, that day is finally here.
Interesting. I've always wondered why tomatoes in the US taste like shit. Why is there a trade off between high yield and taste? Is there not a high yield + great taste variety?
This comment doesn't make any sense. I don't see how Cloudflare publishing the source code to their own hosted s3 service would help prevent lockin when an open source alternative to s3 is out there with hdfs. While s3 is a proprietary system, Any programs you write to operate against s3 can also easily be migrated to other object stores (Azure ADLS, Google Object store) with relative ease.
The thing that keeps people locked into s3 are egress/bandwidth cost. Until Cloudflare came along, no hosted object store (Google,Azure, including self hosted HDFS onprem or in the cloud) had economical bandwidth/egress costs.