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Data Analytics

Data Analytics

Data Analytics refers to the database querying language’s ability to interact with multiple databases at once, as well as its use of relational databases. SQL is one of the most commonly used and flexible languages, as it combines a surprisingly accessible learning curve with a complex depth that lets users create advanced tools and dashboards for data analytics. To create and interact with databases quickly, SQL has been adapted into a variety of proprietary tools, each with its own focus and niche market, including the popular MySQL, Microsoft Access, and PostgreSQL.

While it remains largely popular for its ability to create and interact with databases quickly, SQL is also commonly used because it is a simple language capable of performing surprisingly complex data analysis. The language’s internal logic and the way it interacts with data sets are quite like tools including Excel and even the popular python library Pandas.

SQL is accessible, can build complex models and analyses quickly, and offers a deep ability for data manipulation. The ability to give SQL simple commands in English for complex procedures means that it is highly popular for users who require complex analytics but don’t know more advanced computer languages.

We offer our services to assess your data analytics needs applied to various asset management and project management operations.

SQL for Analytics

Perhaps the most popular use for SQL today (in all its varieties) is as a base infrastructure to build its and easy-to-use dashboards along with reporting tools, or what is called SQL for data analytics. Because it is so easy to communicate complex instructions to databases and manipulate data in seconds, SQL makes intuitive dashboards that can display data in a variety of ways. Moreover, SQL is an excellent tool to build data warehouses thanks to easy accessibility, clear organization, and ability to interact effectively.
Another way many use SQL data analytics is by integrating them directly into other frameworks, offering additional functionality and communication abilities without having to build entire structures from scratch. Indeed, SQL analytics can be used within languages like Python, Scala, and Hadoop, three of the most popular currently in use for data science along with big data management and manipulation.
The ability to interact directly with databases built in these languages means that SQL can be used as an intermediary between end-users and a more complex data storage system that would be more accessible by experts and data scientists.

Why SQL?

There are many reasons why SQL data analytics is the way to go in many industries and applications:

  1. SQL Analytics. The pure amount of data produced globally continues to grow at an exponential rate, from data science to marketing, to healthcare to government organizations. What’s more, all major DBMS (Database Management Systems) have integrations with SQL, making this language beneficial to teams that consistently utilize more than one.
  1. SQL is considered industry standard. In the 1970s, there were many different types of databases, all with their own operating systems, making migration extremely difficult. As a result, things got messy. Birthed from the idea of relational databases and algebra, SQL was designed to be the industry standard platform, making databases easier to use for everyone, everywhere. Over time, it became clear that every issue couldn’t be solved by relational databases alone, resulting in the development of different versions of SQL such as MySQL, SQLite, and Firebird, all of which are still popular today.
  1. SQL is easy to use. With over 250 different programming languages to choose from, why is SQL the one so many choose to work with? Well, it’s simple and easy to learn compared to some of the more complex processes. For example, languages like Java require memorization of a series of steps to complete a task, whereas SQL uses declarative statements to pull data. In SQL’s case, either the query works, or it doesn’t. You don’t need to understand how it pulls the data, because SQL does all the heavy lifting. For instance, when you search for an item on Amazon, it generates a series of suggestions based on that search. This is done using a relational database, which provides a list of similar products based on broad variables. These data-points, which might include your purchase history, similar product descriptions, other users’ preferences, and more, are each taken into consideration in order to provide you with personalized content. Your search essentially acts like a SQL query, as it drags information out of the database based on what you input.
  1. SQL allows for quick access to useful information. With millions or even billions of cells of data, businesses face the challenge of leveraging hordes of information effectively. From product prices to sales to customer information, this information can be used to help make better, more strategic, business decisions. For example, if an organization wants to test how many purchases happened in a particular area during a sale, with SQL, they can pull that information in seconds. The data can then provide helpful insight for running that sale in a different region or possibly trying a different sale altogether in another region. These statistics can help the company make more informed decisions and ensure efforts aren’t wasted. This is especially useful for analyzing large sets of data and pulling only the information you want to see. SQL is a core component of data practices for many organizations. Even popular business applications like Salesforce or Microsoft Dynamics use SQL as a foundation for their other data practices.
  1. SQL provides a gateway to programming. Due to SQL simple language structure, status as an open-source “free application”, and the fact that it is already heavily used in businesses, SQL can be a great first step toward programming. It is a simple language using English words to draw information out of a database, where other coding languages are often made up of strings of numbers and letters that require heavier memorization. SQL can provide you practice in using and interpreting data and give you an idea of the mindset needed to grasp more intricate coding languages down the line.

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We are here to help make sure your application development team is on the right track. To book a Data Analytics or SQL Best Practices Assessment or to learn how our application development consulting can help your business, contact us.