Subscribe to the Ardan Labs Insider

You’ll get our FREE Video Series & special offers on upcoming training events along with notifications on our latest blog posts.

Included in your subscription
  • Access to our free video previews
  • Updates on our latest blog posts
  • Discounts on upcoming events

Valid email required.

Submit failed. Try again or message us directly at

Thank You for Subscribing

Check your email for confirmation.

Extract, Transform, and Load in Go

Author image

Miki Tebeka


You are about to visit Boston, and would like to taste some good food. You ask your friend who lives there what are good places to eat. They reply with “Everything is good, you can’t go wrong”. Which makes you think, maybe I should check where not to eat.

The data geek in you arises, and you find out that the city of Boson has a dataset of food violations. You download it and decide to have a look.

The data is in CSV format (which you hate). Since you’re going to play around with the data, you decide to load this data into an SQL database. Once the data is inside the database, you can dynamically query it with SQL, or even use fancy tools such as Grafana, or Redash to visualize the data.

This post will focus on the process of loading data from various sources to an SQL database, known as ETL.

Extract, Transform, Load (ETL)

ETL stands for “Extract, Transform, Load”. A lot of data in it’s raw format can be found in files (logs, CSV, JSON …) and it’s common practice to upload this data to a common place (sometimes called data warehouse or a data lake) where data scientists can better analyze it.

The three stages of ETL are:

  • Extract: Data in logs, CSVs and other formats need to be parsed (or extracted). Sometimes we want only parts of the data.
  • Transform: Here we rename field, convert data types, enrich (e.g. geolocation), and more
  • Load: Finally, we load the data to its destination.

Note: Sometimes the order is changed, and we do ELT. First we extract and load, and then transformations are done in the database.

First Look at the Data

CSV data has many, many faults, but it’s easy to look at the data since it’s textual. Let’s look at the header line.

Listing 1: First Look

$ wc -l boston-food.csv 
655317 boston-food.csv

$ head -1 boston-food.csv           

In listing 1, we use the wc command to see how many lines we have and then use the head command to see the first line that contains the column names.

Some names, such as businessname, make sense. Some, such as expdttm are more cryptic. A quick search online finds the data description.

Note: In your company, make sure every column/field is documented. I’m getting paid to poke in companies data, and the number of times they can’t explain a field to me happens way too often.

After reading the data description, you decide to use only some of the fields and rename some of them to clearer names. Naming is very important, do invest time coming with consistent and meaningful names.

  • businessname will become business_name
  • licstatus will become license_status
  • violdesc will become description
  • violstatus will become status
  • viollevel which is either *, ** or *** will become level (1, 2 or 3 - integer)
  • result, comments, address, city and zip will keep their names

You’re going to ignore all the other fields.

Listing 2: Database schema

02     business_name TEXT,
03     license_status TEXT,
04     result TEXT,
05     description TEXT,
06     time TIMESTAMP,
07     status TEXT,
08     level INTEGER,
09     comments TEXT,
10     address TEXT,
11     city TEXT,
12     zip TEXT
13 );

Listing 2 contains the database schema, which is in schema.sql.


Listing 3: go.mod

01 module
03 go 1.17
05 require (
06 v1.3.4
07 v1.5.1
08 v1.14.8
09 )

Listing 3 shows the content of go.mod. To parse the CSV file, we’ll be using csvutil. For the database, we’ll use go-sqlite3 (I love SQLite ☺) and sqlx.

The Program

Listing 4: imports

03 import (
04     _ "embed"
05     "encoding/csv"
06     "fmt"
07     "io"
08     "log"
09     "os"
10     "time"
12     ""
13     ""
14     _ ""
15 )

Listing 4 shows our imports. On line 04, we _ import the embed package. We’re going to write SQL in .sql files and then embed them in the executable with //go:embed directives. On line 14, we _ import go-sqlite, this will register the packages as an sqlite3 driver for database/sql (which sqlx uses).

Listing 5: SQL statements

17 //go:embed schema.sql
18 var schemaSQL string
20 //go:embed insert.sql
21 var insertSQL string

On lines 17-21, we use the //go:embed directive to embed the SQL written in .sql files into our code. This lets us write SQL outside the Go code and still ship a single executable.

Listing 6: Row

23 type Row struct {
24     Business   string    `csv:"businessname" db:"business_name"`
25     Licstatus  string    `csv:"licstatus" db:"license_status"`
26     Result     string    `csv:"result" db:"result"`
27     Violdesc   string    `csv:"violdesc" db:"description"`
28     Violdttm   time.Time `csv:"violdttm" db:"time"`
29     Violstatus string    `csv:"violstatus" db:"status"`
30     Viollevel  string    `csv:"viollevel" db:"-"`
31     Level      int       `db:"level"`
32     Comments   string    `csv:"comments" db:"comments"`
33     Address    string    `csv:"address" db:"address"`
34     City       string    `csv:"city" db:"city"`
35     Zip        string    `csv:"zip" db:"zip"`
36 }

On lines 23-36, we define the Row struct. It is used both by csvutil to parse rows in the CSV file and by sqlx to insert values to the database. We use field tags to specify the corresponding columns in the CSV and the database.

When you look at the viollevel field in the CSV file (You can use shuf boston-food.csv| head, to see few random lines) - you’ll see it’s either *, ** or ***. We’ll use parseLevel below to convert these * to an integer and populate the Level field from line 31.

Listing 7: parseLevel

44 func parseLevel(value string) int {
45     switch value {
46     case "*":
47         return 1
48     case "**":
49         return 2
50     case "***":
51         return 3
52     }
54     return -1
55 }

Listing 7 shows the parseLevel function that converts * to numeric level. On line 54, we return -1 for unknown values. The decision to return -1 and not an error is a data design decision, in this case you’ve decided it’s OK to have invalid (-1) levels in the database.

Listing 8: unmarshalTime

38 func unmarshalTime(data []byte, t *time.Time) error {
39     var err error
40     *t, err = time.Parse("2006-01-02 15:04:05", string(data))
41     return err
42 }

Listing 8 shows unmarshalTime which is used by csvutil to parse time values in the CSV file.

Note: I never remember how to specify a time format. My go-to place is the Constants section in the time package documentation.

Listing 9: ETL

57 func ETL(csvFile io.Reader, tx *sqlx.Tx) (int, int, error) {
58     r := csv.NewReader(csvFile)
59     dec, err := csvutil.NewDecoder(r)
60     if err != nil {
61         return 0, 0, err
62     }
63     dec.Register(unmarshalTime)
64     numRecords := 0
65     numErrors := 0
67     for {
68         numRecords++
69         var row Row
70         err = dec.Decode(&row)
71         if err == io.EOF {
72             break
73         }
74         if err != nil {
75             log.Printf("error: %d: %s", numRecords, err)
76             numErrors++
77             continue
78         }
79         row.Level = parseLevel(row.Viollevel)
80         if _, err := tx.NamedExec(insertSQL, &row); err != nil {
81             return 0, 0, err
82         }
83     }
85     return numRecords, numErrors, nil
86 }

Listing 8 shows the ETL function. On line 57, we see the ETL function receives an io.Reader as the CSV file and a transaction which is used to insert values to the database, ETL returns number of records, number of bad records, and an error value.

Inside the function on line 63, we register unmarshalTime to handle time values. On lines 64 and65, we initialize the number of records and number of errors which are returned by ETL. On line 67, we start a for loop.

Inside the for loop on line 70, we decode a row from the CSV and on line 71, we check if the returned error is io.EOF signaling end-of–file. On line 74, we check for other errors and if there are any, we log them and increase numErrors on line 76. Then on 79, we convert to numerical level and on line 80 we insert the record to the database, again checking for errors. Finally, on line 85 we return number of records, number of errors and signal that there was no critical error.

Listing 10: main

88 func main() {
89     file, err := os.Open("boston-food.csv")
90     if err != nil {
91         log.Fatal(err)
92     }
93     defer file.Close()
95     db, err := sqlx.Open("sqlite3", "./food.db")
96     if err != nil {
97         log.Fatal(err)
98     }
99     defer db.Close()
101     if _, err := db.Exec(schemaSQL); err != nil {
102         log.Fatal(err)
103     }
105     tx, err := db.Beginx()
106     if err != nil {
107         log.Fatal(err)
108     }
110     start := time.Now()
111     numRecords, numErrors, err := ETL(file, tx)
112     duration := time.Since(start)
113     if err != nil {
114         tx.Rollback()
115         log.Fatal(err)
116     }
118     frac := float64(numErrors) / float64(numRecords)
119     if frac > 0.1 {
120         tx.Rollback()
121         log.Fatalf("too many errors: %d/%d = %f", numErrors, numRecords, frac)
122     }
123     tx.Commit()
124     fmt.Printf("%d records (%.2f errors) in %v\n", numRecords, frac, duration)
125 }

Listing 10 shows the main function. On lines 89-93, we open the CSV file. On lines 95-103, we open the database and create the table. On line 105, we create a transaction. On line 110, we record the start time and on line 111, we execute the ETL.

On line 112, we calculate the duration. On line 113, we check for errors and if there is an error we issue a rollback. On line 118, we calculate the fraction of errors and if it’s more than 10% we issue a rollback on line 120. Finally on line 123, we commit the transaction and on line 124 print some statistics.

Using Transactions

Inserting data via a transaction means that either all of the data is inserted or none of it. If we didn’t use transactions, and half of the data went in - we had a serious issue. We need either to restart the ETL from the middle or delete the data that did manage to get it. Both options are hard to get right and will make your code complicated. Transactions are one of the main reasons (apart from SQL) that I love using transactional databases such as SQLLite, PostgreSQL and others.

Running the ETL

Listing 11: Running the ETL

$ go run etl.go

... (many log lines reducted)
2021/09/11 12:21:44 error: 655301: parsing time " " as "2006-01-02 15:04:05": cannot parse " " as "2006"
655317 records (0.06 errors) in 13.879984129s

About 6% of the rows had errors in them, mostly missing time. This is OK since we’ve defined the error threshold to be 10%.

Analysing the Data

Once the data is in the database, we can use SQL to query it.

Listing 12: Query

02     business_name, COUNT(business_name) as num_violations
04     violations
06     license_status = 'Active' AND 
07     time >= '2016-01-01'
08 GROUP BY business_name
09 ORDER BY num_violations DESC
10 LIMIT 20

Listing 12 shows the SQL query to select the top 20 businesses which have had the most violations in the last 5 years. On line 02, we select the business_name column and the count of it. On lines 05-07, we limit the records to ones that are active and the time is after 2016. On line 08, we group the row by the business_name, and on line 09, we order the results by the number of violations and finally on line 10, we limit to 20 results.

Listing 13: Running the Query

$ sqlite3 food.db < query.sql
Dunkin Donuts|1031
The Real Deal|756
Caffe Nero|640
Burger King|537
Dumpling Palace|463
The Upper Crust|453
Dunkin' Donuts|436
Yamato II|435
Anh Hong Restaurant|413
Yely's Coffee Shop|401
India Quality|386
Domino's Pizza|374
Fan Fan Restaurant|362
Pavement Coffeehouse|357

Listing 13 shows how to run the query using the sqlite3 command line utility.

Final Thoughts

Data science and data analysis are dominated by, well - data :) Data pipelines and ETLs are what brings the data to a place where you can query and analyze it. Go is a great fit for running ETL, it’s fast, efficient and has great libraries.

Using transactions and SQL will save you a lot of effort in the long run. You don’t need to be an SQL expert (I’m not) in order to use them, and there’s a lot of knowledge out there on SQL - it’s been around since the 70’s.

As for where not to eat - I’ll leave that to your discretion :)

The code is available here

Go Training

We have taught Go to thousands of developers all around the world since 2014. There is no other company that has been doing it longer and our material has proven to help jump start developers 6 to 12 months ahead of their knowledge of Go. We know what knowledge developers need in order to be productive and efficient when writing software in Go.

Our classes are perfect for both experienced and beginning engineers. We start every class from the beginning and get very detailed about the internals, mechanics, specification, guidelines, best practices and design philosophies. We cover a lot about "if performance matters" with a focus on mechanical sympathy, data oriented design, decoupling and writing production software.

Capital One
Red Ventures

Interested in Ultimate Go Corporate Training and special pricing?

Let’s Talk Corporate Training!

Join Our Online
Education Program

Our courses have been designed from training over 4,000 engineers since 2013 and they go beyond just being a language course. Our goal is to challenge every student to think about what they are doing and why.