Apache Spark is like Python’s Pandas and is like SQL databases. It can manipulate datasets, filter, integrate, transform.

But Spark was designed from scratch with horizontal scalability and parallelism in mind, which makes it capable of handling datasets with billions or even unknown number of rows — even if a bit less flexible than Pandas.

This is not new in the industry. Enterprise editions of commercial SQL databases are parallel and scalable since a very long time, being also very expensive in all levels of the stack: service/support, software and hardware.

But Spark is free software. And can use Hadoop — also a free software — as scalable and highly available storage, on cheap commodity hardware. In addition, it has a vibrant community and a democratic ecosystem of services and support.

As with all Open Source, Apache Spark changes the economic landscape of massive data processing systems market, taking money out of a few proprietary HW and SW vendors and pulverizing it locally on people and support.

From my LinkedIn of 2021-03-18 07:02:56

I’ve seen companies saying they have Big Data because they implemented Hadoop or a data lake and maybe Spark.

That’s just wrong.

Big Data, or more precisely, to be Data Driven, is a state where the data a company produces can be reused, as soon as possible, to optimize itself. And there are many ways to reuse data: all meetings and decisions happen with abundance of data, or recently generated data instantly feeds machine learning algorithms to optimize transactions, just to name a few situations.

To be Driven by Data is part culture and part infrastructure. On the infrastructure side, IT teams still struggle with limited visions about how data should flow pervasively and how access should be granted. They fear about security and performance while they should fear of missing out the data opportunity.

Data Streaming is a breakthrough recent technology that is here to help with more fluent data access. For an agile and effective data architecture, Data Streaming is much more strategic and important than just a bigger data warehouse because it is the component that can unleash your data and finally make it useful.

On my LinkedIn of 2021-05-30 15:11:58

This is what GPT “knows” about me. More precisely, this is the sequence of words GPT generates when asked with that specific prompt.

First paragraph is 100% correct.

Second is kind of 50% (in)correct and outdated. I do Fedora, not Debian nor Ubuntu, I’ve contributed to several FOSS projects, but never to Apache HTTPD, and I did work for IBM, but never to Red Hat.

Third paragraph he completely confused me with one of my relatives that have same last name but different first name.

Also, I think GPT would have a different perspective about me if blog posts in social media, such as Facebook, would be part of its training dataset. But it can’t because Meta won’t allow open access to their platform even if I post openly there.

Also on my LinkedIn.