Databricks Read CSV Simplified: A Comprehensive Guide 101

on Data Integration, Data Loading, Databricks, ETL • November 18th, 2021 • Write for Hevo

Understanding Databricks Read CSV

Are you trying to import a CSV file into Databricks? Have you scoured the Internet for the most convenient way to do it? If so, you’ve come to the right place. A majority of businesses use CSV format data as plain text files. CSV Files are easier to work with, smaller in size, and provide a variety of advantages, all while maintaining a standard format for representation.

CSV files are frequently used in Data Engineering Platforms, such as Databricks, for easy Data Handling and Manipulation. CSV Files are used by many organizations for Storage Optimization, Standard Representation, and other reasons. This article will provide you with a step-by-step guide on how to perform Databricks Read CSV. You will also explore more about Databricks and the nature of CSV Files and the salient features they offer. Read along to learn more about Databricks Read CSV.

Table of Contents

Prerequisites

  • An active Azure account.

Introduction to Databricks

Databricks Logo
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Databricks is one of the most popular Cloud-based Data Engineering platforms that is used to handle and manipulate vast amounts of data as well as explore the data using Machine Learning Models. A recent addition to Azure, it is the newest Big Data addition for the Microsoft Cloud. It is freely available to all businesses and helps them realize the full potential of their Data, ELT Procedures, and Machine Learning.

This Apache Spark based Big Data Platform houses Distributed Systems which means the workload is automatically dispersed across multiple processors and scales up & down according to the business requirements. For complex tasks, increased efficiency translates into real-time performance and cost savings. Resources (such as the amount of Compute Clusters) are readily handled, and it only takes a few minutes to get started, as with all other Azure tools.

Key Features of Databricks

Some of the key features of Databricks are as follows:

  • Databricks provides the users with an Interactive Workspace which enables members from different teams to collaborate on a complex project.
  • While Databricks is best suited for large-scale projects, it can also be leveraged for smaller projects for development/testing. Databricks can be utilized as a one-stop-shop for all the Analytics needs. 
  • Databricks is powerful as well as cost-effective. In recent years, using Big Data technology has become a necessity for many firms to capitalize on the Data-Centric Market. Databricks is incredibly adaptable and simple to use, making Distributed Analytics much more accessible.

For further information on Databricks, click here to check out their official website!

Introduction to CSV Files

CSV Files
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A CSV(Comma Separated Values) file is a plain text file that stores tables & Spreadsheet information, and the information is separated by commas. CSV Files can be used with a majority of Spreadsheets programs such as Microsoft Excel or Google Spreadsheets. It can be used to exchange data between multiple applications. Since a CSV file is just a plain text file hence it can be created using any editor.

Benefits of using CSV Files

Some of the benefits of using CSV Files are as follows:

  • Widely Adopted: Other people in your company or organization are likely to be accustomed to working with CSV files. Most importantly, this file type is not limited to Macs or PCs, it can be used with any desktop device on any Operating System.
  • Easy to Organize & Edit: CSV files are editable, and the changes are not locked unless a user restricts editing to a specific set of cells.
  • Compatible with Various Software Programs: For onboarding user data, a wide range of Enterprise Software Programs rely on CSV imports. At the same time, many programs use CSVs as their primary report output. Later in this article. you will learn about Databricks Read CSV.

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How to Perform Databricks Read CSV

Databricks Read CSV is a two-step process. Follow the steps given below to import a CSV File into Databricks and read it:

Step 1: Import the Data

The first step in performing Databricks Read CSV involves importing the data. If you have a CSV file on your workstation that you want to analyze using Databricks, there are two ways by which you can achieve this:

Method A: Import the CSV file to the Databricks File System using the UI

Note: This feature is not enabled by default. To enable this feature, follow the steps given below:

  • Click on the Settings icon on the lower-left corner of the Workspace UI and select the option Admin Console.
  • Now, click on the Workspace Settings tab.
  • Under the Advanced section, turn on the Upload Data using the UI toggle and click on Confirm to proceed with Databricks Read CSV.

Method B: Upload Data to a Table

  • Navigate to the sidebar menu and click on the option Data.
  • Click on the Create Table button.
  • Drag the required CSV File to the file Dropzone or click on the dropdown and browse the CSV File that you wish to upload.
  • Once you have uploaded the file, the path would look something like /FileStore/tables/<filename>-<integer>.<file-type>.
  • Now, click on the Create Table with UI button.
  • The data that you uploaded to a table with the Create Table UI can also be accessed via the Import & Explore Data section on the landing page.
Landing Page for Databricks Read CSV
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Step 2: Modify and Read the Data

Now that you have successfully uploaded data to the table, you can follow the steps given below to modify and read the data in order to perform Databricks Read CSV:

  • Select a Cluster to preview the table and click on the Preview Table button to perform Databricks Read CSV.
Preview of the Table
Image Source: Bigdataprogrammers.com
  • If you look closely at the above image, you can clearly see that the table attributes are of type String by default. You can select the appropriate data type for the attributes from the drop-down menu.
  • The left bar consists of the following options to update the data associated with a table:
    • Table Name: It allows you to change the name of the table that you have created. By default, the table name is the same as your file name.
    • File type: This option allows you to specify the type of file that you have uploaded. In this case, the File type is CSV. The other formats available are JSON and AVRO.
    • Column Delimiter: It represents a field-separating delimiter. In the case of a CSV File ‘,’ acts as a column delimiter.
    • First Row is header: You can select this option if you want to use the first row’s column value as a header.
    • Multi-Line: This option enables you to leverage line breaks in the cell.
Modifying Attributes for Databricks Read CSV
Image Source: bigdataprogrammers.com
  • Once you have all the configurations sorted, click on the Create Table button.
  • To read the data, navigate to the Data section and choose the Cluster where you have uploaded the file. This marks the final step for Databricks Read CSV.
Reading the data
Image Source: bigdataprogrammers.com

Once you follow all the above instructions in the correct sequence, you will be able to perform Databricks Read CSV in a seamless manner!

Conclusion

This article provided a brief introduction of Databricks and CSV Files and also explained their key features. Moreover, it discussed the steps using which you can easily import a CSV File into Databricks and perform Databricks Read CSV. If you want to integrate data from various data sources into your desired Database/destination such as Databricks and seamlessly visualize it in a BI tool of your choice, Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and destinations.

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