In this next module, we will highlight the five common tasks of any data analyst, and map those to their respective tools on Google cloud platform. After that, we'll explore the big query feature set itself and end with the discussion comparing data analysts, data scientists, and data engineers. Okay, so before we get into the really cool part which is showing you the useful big data tools on Google cloud platform, we first have to talk about the data analyst tasks themselves as a whole. So here are the five things that any data analysts worth their salt is going to perform. You're going to ingest data, you're going to transform and clean it up, all data is dirty data. And then you're going to be creating some reporting data tables and storing that data for analysis which is that forth step. Now, finally look how far we've come into four steps to actually take to get to the analysis portion, or you're writing these cool sophisticated queries to get insights from your data. And then you're pairing that potentially with a visualization tool or platform to really make those insights shine and explain them to people, but the road is fraught with challenges. So with each of these different steps as we saw with some of the challenges that organizations face or data analysts have faced earlier on, each of these different steps has their own pitfalls. So ingestion, you've got petabytes of data, it's going to bottle like your tool, you don't even begin to imagine loading all of your data at once. So unfortunately, you're loading only in a sample, or you're looking only at a small amount of your data. So you can't really make amazing progress with loading all your data in at once or it just takes forever. Second, transforming your data, it's slow going, perhaps, you have to either rely on another team, data engineering team to write sophisticated pipelines to transform your data. And you wish there was an easier way to either write it yourself, or some kind of cool tool that will help you build these things up in just a little bit of an easier way. And that was a clear spoiler alert for one of the tools you're going to be learning in the next slide. So onto storage, scaling up the amount of data that you need to store as we mentioned before has been a problem for organizations that have managed their own hardware internally, or relied on things that aren't as inherently scalable as relying on Google cloud platform analysis. Your queries are bottlenecking your data is in many different places, and there are no easy way to mash it together. Visualizing your insights, you have amazing insights that you want to show, and as soon as you go to present it to your stakeholders and your peers, your tool starts to lag. You want to filter down, and drill down in a particular insight, and then you have a 30 minutes meeting. And unfortunately, it takes the tool to ten minutes to load and drill down into the inside, and then you've lost the audiences attention by that point as well. Let's see where the Google cloud platform can step in. So here's the right tools for scalability, and this will help you to address and overcome a lot of these challenges. So ingestion, Google cloud platform, big query in particular is a petabyte scale data analytics platform. And one of the great things that are going to cover in the ingestion part, or the pricing lab that you're going to do is actually importing data into big query, and batch form is free, which is great transforming your data. So say, you wanted to write some simple sequel, you can just do that directly inside a big query. Or if you didn't even want to write any sequel, one of the cool labs we're going to do later on he's using a tool called cloud data prep. Where you can chain together through a graphical user interface, a neat visual flow of how you want to process the data. So you wanted to drag and drop a deduplication, and then parse this particular field. You can do that visually, and you'll get a lot of practice with that as part of this of course. Storing data, again, we've mentioned it a lot Google called storage inexpensive big query itself. You're going to see in the pricing lab, it's as of the time of this recording is two cents per gigabyte per month. And if the data is there for a long time, that storage costs cut in half analysis, that's really where big query shines. And we're going to really go into the nine core parts of its feature set shortly, and this is managing scale, right? Fully managed, no DevOps managing it without you managing your servers, just right cool sequel. Last but not least, visualization tools, Google has built Google data studio, which is one of the free visualization tools that can sit on top of big query. And then you let all the big query processing, do all the hard, heavy lifting. And then rely on a tool that Google data studio, or tableau, or look, or click for you to do that visualization for you as well. So each tool for a different use case.
