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 BigQuery feature set itself and with a discussion and comparing data analysts, data scientists, and data engineers. 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 analysts tasks themselves as a whole. Here are the five things that any data analysts worth their assault is going to perform. You're going to ingest data, you're going to transform it, clean it up. All data is dirty data. Then you're going to be creating some reporting data tables and storing that data for analysis, which is that fourth 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. 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 at 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. Ingestion, you've got petabytes of data, it's going to bottleneck 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 and 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 cool tool that will help you build these things up in just a little bit of an easier way. That was a clear spoiler alert for one of the tools you're going to be learning in the next slide. Onto storage, scaling up the amount of data that you need to store, as we've 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 into a particular inside. You have a 30-minute meeting, and unfortunately, it takes the tool at 10 minutes to load and drill down into the inside, and then as you've lost the audience's attention by that point as well. Let's see where Google Cloud Platform can happen. Here's the right tools for scalability, and this will help you to address and overcome a lot of these challenges. Ingestion, Google Cloud Platform, BigQuery in particular is at petabyte scale data analytics platform. 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 BigQuery in 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 of BigQuery. Or if you didn't even want to write in the sequel, one of the cool labs are going to do later on is using a tool called Cloud data prep. We can chain together through a graphical user interface, a neat visual flow of how you want to process the data. Say you wanted to drag and drop a duplication 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 course. Storing data again, we've mentioned it a lot, Google Cloud Storage, inexpensive BigQuery itself. You're going to see in the pricing lab, it's as of the time of this recording is $0.02 per gigabyte per month, and if the data is there for a long time, that storage cost is cut in half. Analysis, that's really where BigQuery shines and we're going to really go into the nine core parts of its feature set shortly. This is managing scale. Fully managed, no, DevOps, managing it without you managing your server is just right, cool sequel. Last but not least, visualization tools are Google has built Google Data Studio, which is one of the free visualization tools that can sit on top of BigQuery. Then you let all the BigQuery processing do all the heavy lifting and then rely on a tool at Google Data Studio or Tableau or Looker or QlikView to do that visualization for you as well, so each tool for a different use case.