Data analytics tools. In this lesson, we'll identify tools essential for conducting data analysis, including spreadsheets and databases. One of the great things about data analytics is that it takes very little to get going, and that which it does take is often very accessible in a public or free manner. But of course, to conduct any data analytics we need data. In a moment of philosophical introspection the other day, I was pondering what came first, data or analysis? But wherever you fall on that question, data is a requisite of conducting data analytics. That data can come in many, many different forms. It can be structured, it can be unstructured, it can be at rest, it can be in transit, it can be natural language, it can be programmatic language. But at a very simple level, data is descriptive, numeric, or other, and I say other because it could be an image, it could be a audio file. But much of data analytics is done using spreadsheets, looking at rows and columns of data. Those rows and columns of data, we can think of as rows and columns in tables and those tables in a database and the use of all of these make up our different system. Those rows and columns and tables generally make up our databases. Just as data itself can come in many different flavors and types, databases too are varied. Things like relational databases where we're storing our information in independent tables and writing in logic and conditions that relate those tables together. As we'll see when we look at our sample data in the hands-on portion, you may have tables that store customer details separate from transactional detail. Different types of databases and systems and architectures and applications for storing data. Data warehousing as you can probably surmise from the descriptive nature of the term, is about storing large volumes of information in a very organized way where information can be archived and retrieve. Or data mart where smaller subsets of a database or data sources are created to serve specific targeted needs. Things like data lakes which are large environments oftentimes with information being fed into the lake from other sources. When we talk about databases, oftentimes information is stored in multiple databases. We as analytics professionals, end users, consumers, developers, access that information through any number of ways. But information is oftentimes fed or automated from a source to a file or from a file to a source, this idea of data at rest versus in transit. Of course, nowadays it's also, is it on-premise data or is it in the Cloud data? Really, the data in spreadsheets are all that you need. As we'll see again in the hands-on portion, the analytics capability that are built into simple spreadsheets these days is pretty compelling in and of it's own right. Really, every sheet, workbook becomes a very powerful analytic environment. Of course, to do more sophisticated enterprised grade professional data analytics, advanced data analytics, you need more. You need more from a infrastructure perspective, from a programmatic perspective. But for those of us that are just beginning our journey, there is so much that is available to us in terms of what we can do from an analytics perspective. Our ability to build skills, build knowledge, expertise, and create professional grade analytics assets is pretty impressive. Tools, tools, tools are everywhere, which is a good thing. Of course, the bad thing is that there's tools everywhere. So how do you pick those to use? Well, the simple advice is use the tool that you have access to. Don't pine over tools that you don't have, use the ones that are at your service and the ones that meet your needs. There good tools out there, there bad tools out there, there expensive tools, there cheap tools out there, there are free tools out there. Somewhere between free and paid services for some of the application providers, there's a wide ocean of very powerful analytics capabilities out there. It's a pretty staggering and pretty awe-inspiring suite of technologies from simple data management and data storage to data reporting and visualizations to building predictive models and accessing different libraries and system architecture assets. But at the end of the day it's about the data and we best not forget that. The best tools again, are the ones that you have accessible to you and the ones that allow you to most effectively and efficiently process, manage, understand the data. Think about it, engage in the group discussion, what tools have you found and are you currently using? Do you have specific needs when it comes to analytics tool set? Share with the group. In this lesson's reading, A Brief History of Database Management, you'll learn more about data analytics, tools, and the history of data management systems, including the origins of the database, NoSQL, which stands for not only SQL. As we'll learn in the following lesson, SQL or S-Q-L is a query language that's used to query, search, filter, manage data, while NoSQL, as you'll learn, is a way to query information in a database-less environment. Documents stores and a lot more in this very interesting reading. After completing this lesson's reading, research and share with the group an interesting article or website that you find about data analytics tools. Happy reading.