Introduction

Data integration is an essential part of getting the intelligent, data-driven insights that your organization needs to beat your competitors and serve your customers better. In the data integration process, information from multiple sources is brought together under a single “roof” for more accessible analysis and reporting.

Among the actions to perform during the data integration process, you may need to convert data between different formats, such as converting CSV to SQL. In this article, we’ll discuss the purpose of doing this conversion’ and the best tools to convert CSV to SQL.

Table of Contents:

  1. CSV, SQL, and ETL
  2. Tools to Convert CSV to SQL
  3. Conclusion

CSV, SQL, and ETL

The ETL (extract, transform, load) process is the most common way of performing data integration. Data is first extracted from its source, then transformed to comply with standards, and finally loaded into the target location.

During the transformation stage of ETL, you might find yourself converting CSV to SQL in preparation for the load stage.

Many tools output data as comma-separated values (CSV); it is a standard but straightforward tabular data format of plaintext, which can quickly be processed. Each line represents a single record; a record consists of the same number of fields or columns. Usually, the delimiter between the available fields is either a comma (,), a semi-colon (;), a space, or a tabulator.

Database Management Systems (DBMS) like MySQL, MariaDB, PostgreSQL, and SQLite differ. They store data in a non-plaintext format that is not readable by the eye to extract data. The statements have to be formulated in Structured Query Language (SQL) and evaluated by the DBMS. 

A SQL file contains queries that can modify a relational database or table structure. SQL holds information through the medium SQL statements that direct how to alter and store records within a database.

For example, a simple CSV file for storing customers’ names and ZIP codes may look like:

John,Doe,02184 Jane,Roe,49120

Meanwhile, the SQL file format contains a SQL database that has been exported as a file. The SQL file contains information in SQL statements about how to reconstruct the database and the records inside.

CSV files are straightforward and human-readable but not designed for optimal efficiency and data analytics. To get the best performance during the data integration process, it’s often necessary to convert CSV into SQL.

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Tools to Convert CSV to SQL

There are various tools for converting CSV to SQL, depending on your unique scenario. If you are prototyping and need something immediately, you can use several browser-based tools as a quick hack. See the Simple Solutions section for more information.

And of course, there are secure methods for long term storage, such as using Xplenty's low code solution or utilizing SQL Server Management Studio. For more information on these methods, refer to the Long-term Solution section.

When choosing your method, one must have a general awareness of the data. For example, duplicate columns and headers may corrupt the transfer to correct SQL code.  The CSV may have no headers at all or types, leading to similar problems. Some tools can handle these discrepancies, and some cannot, so choose wisely.

Simple Solutions for Converting CSV to SQL

You may simply want to convert CSV to SQL by taking a CSV file and creating a database from it. 

If you are an Excel wizard and need a quick solution, you can do the following: 

  • Open the CSV within excel and select find and replace.
  • Find, and replace the character with ,.
  • Find ^ and replace the character with  insert myTable values\('
  • And lastly, find $ and replace with '\);

Another quick manual trick is to convert CSV to SQL using the SQL console.

load data in file 'c:/temp.csv' into table tablename fields terminated by ',';

See the official SQL docs for more information.

However, it much wiser to use a ready-to-go tool. Several websites claim to convert CSV into SQL databases, including:

Some of these tools have more settings and customizability than others, so experiment with different ones to see which results you prefer.

The websites listed above may work for simple conversions, but they may not have the optimal performance for larger CSV files. Besides, it’s not wise to use these websites with CSV files that contain sensitive or confidential information.

For instances where performance or privacy is a concern, it’s better to use software tools that run on your computer. Csvsql is an example of a command-line tool that can generate SQL statements from a CSV file. Converting CSV to SQL is also possible in the phpMyAdmin software application. Our favorite is Data Transformer; it keeps your data on your machine and offline. Unlike most other conversion programs and websites, which send your information on the public Internet. It is possible to create SQL scripts from CSV, JSON, XML, or YML.

However, there are certain situations where these more straightforward tools aren't the best option. Suppose you plan to convert from CSV to SQL repeatedly, you have a lot of CSV files to convert, or if you have other file formats to convert as well. It’s a good idea to invest in a dedicated data integration software that can efficiently perform the conversion for you.

Long-term Solutions for Converting CSV to SQL

Several robust tools can perform this job as part of a more extensive automated data pipeline in a drag and drop fashion - this allows one to compose a workflow with a sum of small actions. Alternatively, it is possible to import a CSV directly into a SQL server using a GUI or scripting methods.

Data integration software can streamline the data integration process and handle various file formats, including SQL and CSV. Solutions such as Xplenty make data integration as quick and painless for the end-user as possible.

If you're doing something more involved with your CSV to SQL conversion, you can use Xplenty's services to ingest data in CSV format, transform it to SQL, and then store the result is a data warehouse. Take a look at this article to see how easy it is to get started with Xplenty converting source data to the desired target format, including CSV to SQL.

Import a CSV File directly into SQL Server

Before executing SQL queries on CSV files, one needs to convert CSV files to data tables. There are numerous methods of converting CSV data into a database table format, i.e., create a table and copy all of the data from the CSV file to the table (however, this is time-consuming and not scalable with large datasets). The best way to import a CSV formatted file into your database is to use SQL Server Management Studio.

  • Step one requires creating a table in your database to import the CSV file. On table creation: Log in to the database using SQL Server Management Studio.
  • Right-click on the database and navigate to Tasks -> Import Data.
  • Proceed by clicking the Next > button.
  • Select Flat File Source for the data source and proceed to the Browse button to select the CSV file - configure the data import continuing with the Next > button.
  • Select the correct database provider for the destination (e.g., for SQL Server, you can use the latest driver). 
  • Input the Server name and tick Use SQL Server Authentication
  • Input the Password, Username, and Database, then click the Next > button.
  • Within the Select Source Tables and Views window, it is possible to Edit Mappings before clicking the Next > button.
  • Tick Run immediately and click the Next > button.
  • And finally, tick the Finish button to run the package.

You can now execute SQL queries on the tables generated from the original CSV file.

Data Wrangling with Python

If you are comfortable with python scripting, pandas and SQL alchemy will convert a CSV file to a SQL server in a jiffy.

import pandas as pd

import sqlalchemy

import pyodbc


# set up database connection (with username/pw if needed)

engine = create_engine('mssql+pyodbc://username:password@mydsn')


# read csv data to dataframe with pandas

# datatypes will be assumed

# pandas is smart but you can specify datatypes with the `dtype` parameter

df = pandas.read_csv(r'your_data.csv')


# write to sql table... pandas will use default column names and dtypes

df.to_sql('table_name',engine,index=True,index_label='id')


# add 'dtype' parameter to specify datatypes if needed; dtype={'column1':VARCHAR(255), 'column2':DateTime})

Conclusion

No matter which solution you choose, converting from CSV to SQL is an essential part of the data integration process. There are various options available for this conversion, depending on the exact use case.

How Xplenty Can Help

Want to learn more about the ETL process? Check out this blog post in which we go into more detail. If you’re going to invest in a robust, feature-rich solution for data integration (including capabilities for converting CSV files to SQL), Xplenty's transform stage of ETL/ELT can easily convert CSV to SQL, so you don't have to worry about the nitty-gritty. Get in touch with our team of data integration experts for a demo and risk-free trial.