Data integration is a crucial way to get intelligent, data-driven insights that your organization needs to beat your competitors and increase customer service. The data integration process uses multiple sources of information, bundled together to make analysis and reporting a lot more painless.

During the data integration process, one of the more common hurdles you may encounter is converting CSV files wehn you're trying to utilize an SQL database.

Here, we are going to discuss some quick and easy ways to convert CSV to SQL, and some tools that will help that process.

Table of Contents:

What Are CSV, SQL, and ETL?
CSV to SQL Methods
What Tools Can You Use to Convert CSV to SQL?
Challenges
Secure, Long-Term Solutions
Solutions to Convert CVS to SQL

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What Are CSV, SQL, and ETL?

One way to integrate data uses a process called "extract, transform, and load", or ETL. You can also use its inverse, ELT (“extract, load, and transform”).

This process extracts the information from its source, loads it into a secure and accessible storage location, and then allows you to run transformations on data to comply with the new standards and/or prepare it for analytics.

If you are converting CSV to SQL, the transform stage of ETL/ELT will prove to be especially beneficial to prepare the data for usage in greater capacities.

Did you know that CSV stands for "comma-separated-values"? This format is used by separating each data point with a comma. Each line in a CSV file equates to a single data record.

A 'structured query language' file, or SQL file, typically contains queries that can be used to modify the structure of a relational database or table. SQL files hold information through SQL statements that direct how to reform a database and the records that are stored inside.

CSV is easy to read but not maximized for analytics. To get the best analytical result out of the data integration process, it is often necessary to convert CSV to SQL.

close up image of numbers in a csv file

CSV to SQL Methods

There are a variety of tools for converting CSV to SQL, depending on the intentions of use by the business.

What Tools Can You Use to Convert CSV to SQL?

The easiest solution may seem to be a simple, online tool like SQlify and Code Beautify. However, they’re not as simple as they seem.

The online tools can be great for simple conversions. However, these tools may not be great to use for larger CSV files, they’re not secure enough to manipulate a CSV file that holds sensitive or confidential information, and depending on the frequency or freshness of your data requirements, there is no way to build an automated data flow.

If you have concerns about the performance, frequency, and privacy of the conversions tools, you may want to check out a piece of software that runs directly on your computer.

Some other concerns to keep in mind are the issue of having duplicate columns and headers that might not transfer correctly to SQL. Or, on the flip side, your CSV may have no headers at all, creating a similar issue. Your CSV may also have no concept of type and therefore creating the issue of not knowing what the data type should be.

Again, there will be pros and cons to using these tools, but a dedicated data integration software that can efficiently perform the conversion for you might be best.

Two employees point to csv files on a computer

Secure, Long-Term Solutions

To save time during the integration process, you can use data integration software. The software can handle a variety of file formats, including SQL and CSV. Mitto is a data staging platform that can be a great solution to this problem. Mitto is designed to make data integration fast, painless, and automated for the end-user.

If you are doing more intricate work with your CSV to SQL conversion, you can make use of Mitto's CSV integration to extract the data from the CSV, allow you to easily define columns and data type,  transform it to SQL, and then store or save the results in a data warehouse. You can then model the newly transformed CSV file with SQL to prepare it for analytics. This entire process allows you to convert source data to the desired target format, or in this example, from CSV to SQL.

Learn exactly how to use Mitto's CSV integration here!

Want to get the most out of your data? Zuar offers data staging services to build data integrations, pipelines, infrastructure, and models.

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Solutions to Convert CVS to SQL

Converting from CSV to SQL is an essential part of the data integration process, no
matter what solution you may choose. There are many options for conversion,
depending on the intended use and frequency of the information.

  1. Mitto
    For the fastest and most efficient way to covert your CSV, Zuar has released the latest version of the Mitto ELT+ solution. You can use our tool to convert your CSV into SQL with the push of a button. Mitto allows for integrations with all the tools you use regularly. Schedule a Mitto demo.
  2. MySQL Console
    If you’re fairly comfortable with MySQL, you may want to use the MySQL console to convert your CSV file to SQL. Open your MySQL console and then follow these steps:  Type this command into the MySQL console query:
    load data infile ‘c:/filename.csv’ into table tablename fields terminated by ‘,’;
    Double-check your data because sometimes the data may need editing.
  3. Microsoft SQL Server
    More of an import rather than a conversion, you can use the Microsoft SQL Server but it may require some manual input. Before you can use this method, you need to have the Microsoft SQL Server installed. Then follow these steps: (a) Connect to your server and create a new database. (b) Using the SQL Server wizard, import the CSV file. (c) Input text qualifiers and the column width for every column.
  4. Excel
    The least desirable method but a method, all the same, you can convert CSV to SQL using Microsoft Excel. Open Excel and follow these steps: (a) Select File > Open. Select your CSV file. (b) Select the Find and Replace option. (c) Set the Find to: , and the Replace to: , . (d) Find: ^ and Replace with: insert insert myTable values(‘   (e) Find: $ and Replace with: ‘);
    You’ll have to go through each row of data with this process. If you have a large dataset, we don’t recommend this process. Using Mitto is going to be much easier and faster.
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As opposed to your typical waterfall strategy, the Data Value Chain has a seamless, agile solution that solves your business roadblocks in a matter of days rather than months.

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Are you ready to give Mitto a test drive? We are excited to show you all the new updates that have come to Mitto, and how it can seamlessly convert CSV to SQL.

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Does your enterprise need a more comprehensive data strategy? Contact Zuar for a data strategy assessment.

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