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Cloud Firestore + Monkey Learn Integrations

Appy Pie Connect allows you to automate multiple workflows between Cloud Firestore and Monkey Learn

  • No code
  • No Credit Card
  • Lightning Fast Setup
About Cloud Firestore

Cloud Firestore is a cloud-hosted, NoSQL database that your iOS, Android, and web apps can access directly via native SDKs.

About Monkey Learn

MonkeyLearn is a text analysis platform that helps you identify and extract actionable data from a variety of raw texts, including emails, chats, webpages, papers, tweets, and more! You can use custom tags to categorize texts, such as sentiments or topics, and extract specific data, such as organizations or keywords.

Monkey Learn Integrations

Best ways to Integrate Cloud Firestore + Monkey Learn

  • Cloud Firestore Monkey Learn

    Cloud Firestore + Monkey Learn

    Classify Text in monkeylearn when New Document Within a Firestore Collection is created in Cloud Firestore Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    Monkey Learn Classify Text
  • Cloud Firestore Monkey Learn

    Cloud Firestore + Monkey Learn

    Extract Text in monkeylearn when New Document Within a Firestore Collection is created in Cloud Firestore Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    Monkey Learn Extract Text
  • Cloud Firestore Monkey Learn

    Cloud Firestore + Monkey Learn

    Upload training Data in monkeylearn when New Document Within a Firestore Collection is created in Cloud Firestore Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    Monkey Learn Upload training Data
  • Cloud Firestore MySQL

    Cloud Firestore + MySQL

    Add new rows in MYSQL database when Firebase records are updated Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    MySQL Create Row
    Firebase provides developers with a plethora of tools and services to help them develop a fully functional app that helps business owners grow their user base. Connecting it with MySQL helps you maintain your database more effectively. This integration will look for new records in Firebase and add a new row to your MySQL Database with info from the new record.
    How This Cisco Cloud Firestore – MySQL Integration Works
    • A new document is added in Cloud Firebase collection
    • Appy Pie Connect will automatically create new row in MySQL
    You Will Require
    • Cloud Firestore account
    • MySQL account
  • Cloud Firestore WordPress

    Cloud Firestore + WordPress

    Send notifications to a Slack channel when new Firestore documents are added to a collection Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    WordPress Create Post


    You'll want to know when you have fresh materials available. Connect your Firebase and Slack accounts to send notifications to a Slack channel when new Firestore documents are added to any collection. When a new document is added to a specified Firebase / Firestore collection, this integration automatically sends a Slack channel message. You'll be notified once new papers become available.
    How this Cloud Firebase – Slack Integration Works
    • A new document is added in Cloud Firebase collection
    • Appy Pie Connect send a message in a channel in Slack
    You Will Require
    • Cloud Firestore account
    • MySQL account
  • Cloud Firestore {{item.actionAppName}}

    Cloud Firestore + {{item.actionAppName}}

    {{item.message}} Read More...
    Close
    When this happens...
    {{item.triggerAppName}} {{item.triggerTitle}}
     
    Then do this...
    {{item.actionAppName}} {{item.actionTitle}}
Connect Cloud Firestore + Monkey Learn in easier way

It's easy to connect Cloud Firestore + Monkey Learn without coding knowledge. Start creating your own business flow.

    Triggers
  • New Document Within a Firestore Collection

    New Document Within a Firestore Collection

    Actions
  • Create Cloud Firestore Document

    Creates a new document within a Cloud Firestore collection.

  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

How Cloud Firestore & Monkey Learn Integrations Work

  1. Step 1: Choose Cloud Firestore 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 Monkey Learn 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 Cloud Firestore to Monkey Learn.

    (2 minutes)

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

Integration of Cloud Firestore and Monkey Learn

Cloud Firestore is a NoSQL document database. It is used as a replacement of Firebase Realtime Database, which was developed as a real-time cloud database for mobile apps. It has many features such as:

Realtime

Offline first

Fault tperant

Scales with your business

Security and privacy by default

Monkey Learn is a machine learning and data science platform that helps you analyze and process your data. It has an online SaaS model and the pricing depends on what you want to use and how many people will work on your projects. The two main components that Monkey Learn offers are:

An API topbox for building machine learning models in Python, R, Java, PHP, JavaScript, Scala, Swift and C++.

A drag-and-drop interface for building machine learning models in Python, R, Scala, Swift or C++ without coding.

The aim of this section is to explain the integration of Cloud Firestore and Monkey Learn together. This section also explains the benefits of integrating Cloud Firestore and Monkey Learn.

First, we need to know what Cloud Firestore is. Cloud Firestore is a NoSQL document database that can be used in mobile, web, and server applications. It is used as a replacement of Firebase Realtime Database which was developed as a real-time cloud database for mobile apps. It has many features such as. realtime, offline first, fault tperant, scales with your business, security and privacy by default.

Second, we need to know what Monkey Learn is. The aim of this section is to explain the integration of Cloud Firestore and Monkey Learn together. This section also explains the benefits of integrating Cloud Firestore and Monkey Learn. First, we need to know what Cloud Firestore is. Cloud Firestore is a NoSQL document database that can be used in mobile, web, and server applications. It is used as a replacement of Firebase Realtime Database which was developed as a real-time cloud database for mobile apps. It has many features such as. realtime, offline first, fault tperant, scales with your business, security and privacy by default. For example, consider the fplowing data structure in Cloud Firestore. “products” . { “items” . [ { “name” . “Guitar”, “year” . 2018 }, { “name” . “Bass”, “year” . 2018 } ] } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 “ products ” . { “ items ” . [ { “ name ” . “ Guitar ” , “ year ” . 2018 } , { “ name ” . “ Bass ” , “ year ” . 2018 } ] } The product definition includes multiple products named Guitar and Bass which are both created in 2018 . These are the properties of the products. id — Unique identifier of each product name — Name of each product year — Year in which the product was created Now we will see how we can import and export Cloud Firestore data into MonkeyLearn and then we will go back to Cloud Firestore and synchronize it with Cloud Firestore again. We will use the fplowing three functions in this exercise. import_to_ml(. — Imports data from Cloud Firestore to MonkeyLearn. export_from_ml(. — Exports data from MonkeyLearn to Cloud Firestore . sync(. — Synchronizes data between Cloud Firestore and MonkeyLearn . Sync(. function will run import_to_ml(. and export_from_ml(. sequentially; we can change this behavior by modifying arguments order of these functions in sync(. function arguments. import_to_ml (dataset. — Imports dataset from Cloud Firestore into MonkeyLearn. dataset — A dataset that contains data from Cloud Firestore . export_from_ml (dataset. — Exports dataset from MonkeyLearn to Cloud Firestore . dataset — A dataset that contains data from MonkeyLearn . sync (dataset. — Synchronizes data between Cloud Firestore and MonkeyLearn . dataset — A dataset that contains data from either Cloud Firestore or MonkeyLearn . This function will run import_to_ml(. and export_from_ml(. sequentially; we can change this behavior by modifying arguments order of these functions in sync(. function arguments. After running the above functions we will have the fplowing datasets. A dataset containing data from Cloud Firestore named products . A dataset containing data from MonkeyLearn named products . Now we have everything ready to use our datasets on our website. We will use the fplowing code to import products from Cloud Firestore to MonkeyLearn. import firebase_storage from 'firebase/storage'; const path = 'https://<project-id>.firebaseio.com/products'; const storageRef = firebase_storage(path); storageRef.child('items'.child('item-1'.set({ name. 'Guitar', year. 2018 }); storageRef.child('items'.child('item-2'.set({ name. 'Bass', year. 2018 }); storageRef.child('items'.child('item-3'.set({ name. 'Guitar', year. 2019 }); storageRef.child('items'.child('item-4'.set({ name. 'Bass', year. 2019 }); 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 import firebase_storage from 'firebase/storage' ; const path = 'https://<project-id>.firebaseio.com/products' ; const storageRef = firebase _ storage ( path . ; storageRef . child ( 'items' . . child ( 'item-1' . . set ( { name . 'Guitar' , year . 2018 } . ; storageRef . child ( 'items' . . child ( 'item-2' . . set ( { name . 'Bass' , year . 2018 } . ; storageRef . child ( 'items' . . child ( 'item-3' . . set ( { name . 'Guitar' , year . 2019 } . ; storageRef . child ( 'items' . . child ( 'item-4' . . set ( { name . 'Bass' , year . 2019 } . ; After running import_to_ml(. function all products will be imported into MonkeyLearn under the namespace products/<randomUUID>/<randomUUID>. You can call this namespace by using <namespace>/products/<randomUUID>/<randomUUID> endpoint. For example. https://<project-id>.firebaseio.com/products/121a25c5-7d79-42d0-b8b8-e5c67f931572/4fd2a32b-5968-490c-8d33-fbf3c37bd160 After importing we will have the fplowing datasets. A dataset containing products information from Cloud Firestore named products . A dataset containing products information from MonkeyLearn named products . Now we will export all products from MonkeyLearn to Cloud Firestore. export_from_ml (dataset. — Exports dataset from

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.