New Training: Describe Types of Core Data Workloads
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New Training: Describe Types of Core Data Workloads

In this 7-video skill, CBT Nuggets trainer Ben Finkel covers two types of data processing workloads in the cloud: streaming and batch. Gain an understanding of the pros and cons of each and learn when to use each method. Watch this new Programming and Development training.

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This training includes:

  • 7 videos
  • 47 minutes of training

You’ll learn these topics in this skill:

  • Describe Types of Core Data Workloads
  • Understanding Relational Data
  • Understanding NoSQL Data
  • Relational Data vs. NoSQL
  • Processing Batch Data
  • Processing Streaming Data
  • Data Storage and Processing in The Cloud

A Simple Explanation of the Difference Between Relational Data and Non-Relational

The concept of rows and columns for storing data is familiar to most people. Even non-technical people have used enough spreadsheets to get the idea of finding a piece of information when you know 1) the name of the spreadsheet, 2) which table to look in, 3) the column name, and 4) the row number. Storing data like that is called relational. Relational databases are the overwhelming favorite in data storage. But there are times when that 1:1 relationship between the piece of data and the row/column combination isn’t the best way to store or retrieve the data.

That’s where the idea of non-relational databases comes in. They’re also called NoSQL. Like the name suggests, the only thing that different non-relational databases have in common is that they’re not relational in nature. The data they store doesn’t get compartmentalized or squeezed down to fit into the row/column combination of a relational database.

Other than that, how non-relational databases store data and the rules they follow to do so differ. The key trade-off for non-relational databases is that they sacrifice precision for speed and they can be purpose-built to store exactly the right kind of data for a certain need. Social media and big data applications are good examples of services with huge amounts of unstructured data. They tend to choose NoSQL because the effort of following a strict model of data compartmentalization would slow data retrieval down.

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