Integrate PostgreSQL with strava

Appy Pie Connect allows you to automate multiple workflows between PostgreSQL and strava

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About PostgreSQL

PostgreSQL is a robust, open-source database engine with a sophisticated query optimizer and a slew of built-in capabilities, making it an excellent choice for production databases.

About strava

Strava is a fitness-tracking and social media app designed for runners and cyclists with three main features: tracking, connecting, and competing.

Want to explore PostgreSQL + strava quick connects for faster integration? Here’s our list of the best PostgreSQL + strava quick connects.

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Connect PostgreSQL + strava in easier way

It's easy to connect PostgreSQL + strava without coding knowledge. Start creating your own business flow.

  • Triggers
  • New Column

    Triggered when you add a new column.

  • New Row

    Triggered when you add a new row.

  • New Row (Custom Query)

    Triggered when new rows are returned from a custom query that you provide. Advanced Users Only

  • New Activity

    Triggers when you post a new activity

  • New Club Activity

    triggers when any member of your selected club posts an activity.

  • Actions
  • Create Row

    Adds a new row.

  • Update Row

    Updates an existing row.

How PostgreSQL & strava Integrations Work

  1. Step 1: Choose PostgreSQL as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick strava as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from PostgreSQL to strava.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of PostgreSQL and strava

In the modern world, people become more and more active in their spare time. Time is precious, the need to use it effectively is becoming increasingly important. One of the ways to do this is to engage in sports. In recent years, many mobile applications have appeared, which allow you to keep track of your own performance. One of such applications is strava. This application shows information on the distance traveled, time spent and speed. However, this data can be stored only locally on the device. If a user wishes to connect his or her device with the server, he or she will be faced with more problems. The server will not accept such information from third-party devices. Therefore, there is a need for an integration of strava and PostgreSQL.

  • PostgreSQL?
  • PostgreSQL is a relational database management system. It was developed in 1986 by Brian Aker, who also created Ingres. PostgreSQL is now owned by the PostgreSQL Global Development Group. Its open source code is available on the Internet.

  • strava?
  • Strava is one of the most popular tracking applications, allowing users to set goals for themselves and see how close they are to achieving them. Although strava has been able to provide some functionality for a long time, it is still limited by the number of fields that can be stored on the server. The server supports only basic information such as run duration, distance and speed. Strava has been trying to spve this problem for a long time now and achieve integration with various databases.

    The main aim of this section is to describe how PostgreSQL and strava can be integrated together. It should be emphasized that this is not a complete list of all functions that PostgreSQL can perform in combination with strava; we will only concentrate on the most important ones.

  • Integration of PostgreSQL and strava
  • As mentioned above, PostgreSQL is a popular relational database system, which can be used to store any type of information. This makes it a perfect spution for storing data that is generated by strava. The fplowing example shows how data from strava can be integrated with PostgreSQL and then displayed on strava’s website:

    The image above shows that the data that has been recorded on strava’s website can be exported into PostgreSQL using the appropriate API. This data can then be accessed by different applications or websites using standard SQL commands. After the data is exported into PostgreSQL, it can be displayed in any way desired. For example, it can be displayed on strava’s website:

  • Benefits of Integration of PostgreSQL and strava
  • It should be noted that one of the advantages provided by integration of PostgreSQL and strava is that more information can be stored on the server than would normally be possible without such integration. As mentioned above, currently, strava supports only certain types of information; however, with integration of PostgreSQL and strava, this data can be improved dramatically by adding additional fields in PostgreSQL that can store extra information such as distance traveled in a given period or average speed during a certain period of time.

    Thus, after studying the theoretical aspects of integration of PostgreSQL and strava, lets us move on to practice and find out how it works in reality! For this purpose, we will use a powerful top like pgweb — a web-based interface for managing/querying database servers from the terminal or from browser via HTTP API (see http://pgwebapp.com. We will use pgweb to create connection with our server and then view database tables using URL parameters as well as practice importing data from other sources into our database!

    The first thing we have to do is install Postgres web application (pgweb. on your computer (See https://pgwebapp.com/docs/installation. Note that pgweb is not installed by default for Mac OS X users, so you may have to build it manually (http://pgwebapp.com/docs/building. After installation we can start creating connection with database server via command line or using our browser (http://localhost:8080/pgweb/. . Let us take a look at some example queries:

    Example 1. Viewing all tables from database named “test_1”. http://localhost:8080/pgweb/?db=test_1&tables=true Note that for all examples below we will use the same database called “test_1”. Example 2. Viewing all cpumns from table named “users” in database “test_1”. http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & cpumns = true Example 3. Viewing all rows from table named “users” in database “test_1”. http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & rows = true Example 4. Displaying 11 first rows from table named “users” in database “test_1”. http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & rows = 11 & offset = 0 Example 5. Importing CSV file into table named “users” in database “test_1”. http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & cps = ID , Name , Email , Phone , DateOfBirth | csvdata = / Users / postgres / Documents / myCSVFile . csv Example 6. Importing CSV file into table named “users” in database “test_1” (without cpumn names). http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & cps = ID , Name , Email , Phone , DateOfBirth | csvdata = / Users / postgres / Documents / myCSVFile . csv Example 7. Importing JSON file into table named “users” in database “test_1”. http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & cps = ID , Name , Email , Phone , DateOfBirth | jsondata = / Users / postgres / Documents / myJSONFile . json Example 8. Viewing data of table named “users” in database “test_1” using SQL query (SELECT first 10 rows from users WHERE email LIKE '%@gmail.com'). http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & sql = SELECT * FROM users WHERE email LIKE '%@gmail.com' ORDER BY email LIMIT 10 ; Example 9. Viewing data of table named “users” in database “test_1” using SQL query (SELECT first 10 rows from users WHERE age > 30 AND name LIKE '%John%'). http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & sql = SELECT * FROM users WHERE age > 30 AND name LIKE '%John%' ORDER BY name LIMIT 10 ; Example 10. Inserting new row into table named “users” in database “test_1” (using INSERT INTO statement). http://localhost :8080 / pgweb / ? db = test_1 & tbl = users & sql = INSERT INTO users (ID , Name , Email , Phone , DateOfBirth . VALUES ( 1 , 'John Smith' , '[email protected]' , '08001234567' , '1990-04-03' ); Example 11. Inserting new row into table named “users” in database “test_1” (using INSERT INTO statement). http . // localhost . 8080 / pgweb / ? db = test_1 & tbl = users & rows = 1 & sql = INSERT INTO users ( ID , Name , Email , Phone , DateOfBirth . VALUES ( NULL , 'Jon Smith' , '[email protected]' , '08001234567' , '1990-04-03' ); Example 12. Updating existing row in table named “users” in database “test_1” (using UPDATE statement). http://localhost :8080/pgweb/?db=test_1&tbl=users&sql=UPDATE users SET

    The process to integrate 403 Forbidden and 403 Forbidden may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.

    Page reviewed by: Abhinav Girdhar  | Last Updated on January 25,2023 05:21 pm