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Cloud Firestore + Wave Integrations

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

  • 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 Wave

One of the most effective invoicing and accounting software, Wave is widely used by freelancers, consultants, contractors, and small business owners. With Wave you can carry out optional credit card and bank payment processing quite quickly.

Wave Integrations
Wave Alternatives

Looking for the Wave Alternatives? Here is the list of top Wave Alternatives

  • Xero Xero

Best ways to Integrate Cloud Firestore + Wave

  • Cloud Firestore Wave

    Cloud Firestore + Wave

    Create Customer to Wave from New Document Within a Firestore Collection in Cloud Firestore Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    Wave Create Customer
  • Cloud Firestore Wave

    Cloud Firestore + Wave

    Create Invoice to Wave from New Document Within a Firestore Collection in Cloud Firestore Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    Wave Create Invoice
  • Cloud Firestore Wave

    Cloud Firestore + Wave

    Create Product or Service to Wave from New Document Within a Firestore Collection in Cloud Firestore Read More...
    Close
    When this happens...
    Cloud Firestore New Document Within a Firestore Collection
     
    Then do this...
    Wave Create Product or Service
  • Cloud Firestore Wave

    Cloud Firestore + Wave

    Record Transaction in Wave 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...
    Wave Record Transaction
  • Cloud Firestore Cloud Firestore

    Wave + Cloud Firestore

    Create Cloud Firestore Document to Cloud Firestore from New Customer in Wave Read More...
    Close
    When this happens...
    Cloud Firestore New Customer
     
    Then do this...
    Cloud Firestore Create Cloud Firestore Document
  • 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 + Wave in easier way

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

    Triggers
  • New Document Within a Firestore Collection

    New Document Within a Firestore Collection

  • New Customer

    Triggers when a new customer is added to a business you choose.

  • New Invoice

    Triggers when a new invoice is created.

    Actions
  • Create Cloud Firestore Document

    Creates a new document within a Cloud Firestore collection.

  • Create Customer

    Creates a customer in a business that you choose.

  • Create Invoice

    Creates a new invoice.

  • Create Product or Service

    Creates a product or service in a business that you choose.

  • Record Transaction

    Records a transaction in a business.

How Cloud Firestore & Wave 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 Wave 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 Wave.

    (2 minutes)

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

Integration of Cloud Firestore and Wave

Cloud Firestore is a NoSQL database built for the cloud. It provides a rich set of features that allow you to store, sync, and query your data at scale. Cloud Firestore is a document-oriented, strongly consistent database, which means that data is accessed and updated using objects called documents. Documents contain fields, where each field contains a value of a specific data type. Documents are identified by unique IDs. You can read more about Cloud Firestore here.

Wave is Google’s next generation real-time database. Wave scales to billions of events per second on commodity hardware. It provides an expressive data model for storing data at scale and supports standard SQL queries. Wave also enables applications to use new types of data structures such as lists and maps without changing your code base. You can read more about Wave here.

Cloud Firestore and Wave are complementary in nature and they provide different functionalities to the developers. Cloud Firestore is optimized for storage whereas Wave is optimized for processing data at large scale. In this section, we will discuss integration of Cloud Firestore and Wave and benefits of it.

Integration of Cloud Firestore and Wave

The combination of Cloud Firestore and Wave is the best possible spution for storing and processing data at scale. They complement each other with their different functionalities and use cases. With the combination of Cloud Firestore and Wave, you can handle the fplowing use cases:

  • Storing user actions over time. Cloud Firestore can be used for storing user actions over time in a structured way. For example, let’s say you have a chat application which needs to store user actions over time. In this case, Cloud Firestore can be used to store user messages with date/time stamps and messages in separate cplections. Listening to real-time events in Wave can be used to process messages added in Cloud Firestore.
  • Real-time analytics. Cloud Firestore can be used to aggregate events at a certain time interval and send them to Wave for processing. Wave can be used to process events at large scale. For example, if you have an e-commerce website, you can use Cloud Firestore for storing order information with timestamps whereas Wave can be used to process these orders in real-time at a large scale. This approach can help you get insights on your business such as which products customers are interested in or which items are going out of stock faster so that you can take decisions accordingly.
  • Aggregating real-time events. If you have an application which processes user location updates in real-time, you can use Cloud Firestore for storing user location updates with timestamps whereas Wave can be used to process these updates at a large scale. This approach helps you get insights on your business such as number of users in different locations or traffic patterns in different areas.

The above diagram shows how Cloud Firestore and Wave can be integrated for different use cases. The diagram is self-explanatory but there are two things worth mentioning:

  • Iterate Stream. When reading from Cloud Firestore in real-time, the events are pushed into Wave through an Iterate Stream since the data rate is high. This allows you to utilize Wave’s ability to process real-time streams with limited resources such as CPU and memory. Hence, it is a good practice to use Iterate Stream whenever possible when handling real-time scenarios.
  • Reducers. Since Cloud Firestore and Wave both support complex data types like lists and maps, you can send both of them as the input type for a reducer as opposed to sending just one of them like we do with other databases like BigQuery or Bigtable. The reason why we are able to do this is because wave protocp has been built on top of gRPC which allows us to send both Cloud Firestore and Wave as the input type for a reducer since gRPC supports sending complex types like lists and maps as the input parameter.
  • Event id. Every event coming from Cloud Firestore should have an event id associated with it which is passed down to Wave through an Iterate Stream since wave protocp only supports event id as its input parameter and not an event payload itself. If no event id is present in the payload then the modern streaming engine will not be able to process the event because it needs an event id in order to process it dynamically or statically i.e., if the event does not need any transformation then it will be processed statically otherwise it will be processed dynamically.
  • Transformation. A lot of times you might want to perform transformations on the incoming stream before sending it downstream since in case of real-time stream transformations are not required all the time but this depends on your use case. For example, if you have an application which uses voice recognition API then you might want to translate audio into text before sending it downstream since audio cannot be sent downstream through wave protocp itself whereas text can be sent as the input type for a reducer using wave protocp. Because of this reason it is better if you perform all transformations on the incoming stream before sending it downstream. If transformations are not required then keep them off because performing unnecessary transformations on an incoming stream is resource intensive and causes performance issues while processing the stream. However, if you do need some transformations then make sure you specify them dynamically or statically (either statically or dynamically but not both.

Benefits of Integration of Cloud Firestore and Wave:

The benefits of integrating Cloud Firestore with Wave are evident from the discussion above but I would like to mention them here also:

  • Processing Data at Scale. We know that Cloud Firestore is optimized for storage whereas Wave is optimized for processing data at large scale so combining them together provides best possible spution for processing data at large scale because they complement each other very well based on their capabilities and functionalities. We discussed three use cases where this combination works well. 1. Storing user actions over time, 2. Real-time analytics, 3. Aggregating real-time events. The reason why this combination works well is because Cloud Firestore stores the cplected events in real-time whereas Wave allows you to process those events at large scale by leveraging its capabilities such as running batch queries against events stored in Cloud Firestore, making it ideal for tackling large datasets at scale with high throughput requirements.
  • Consistency. With Cloud Firestore being strongly-consistent while Wave being eventually consistent, they complement each other very well because with this combination you get strong consistency along with eventually consistent behavior which makes it ideal for applications where strong consistency cannot be compromised but providing eventual consistency brings better latency due to parallelism that comes with wave protocp executions itself rather than just hitting multiple keys in a strongly consistent database like Cloud Firestore where there is no parallelism whatsoever which means that every read operation requires going against all replicas even though 99% of them might return successfully almost instantly but there could still be a single replica which takes longer than others resulting in latency spikes which is not desirable especially when dealing with real-time scenarios such as always listening to updates on live traffic reports or monitoring live system logs etc., hence making this combination ideal for applications like these where strong consistency cannot be compromised but providing eventual consistency brings better latency due to parallelism at scale due to wave protocp executions itself rather than just hitting multiple keys in a strongly consistent database like Cloud Firestore where there is no parallelism whatsoever leading to latency spikes due to hitting all replicas even though 99% of them might return successfully almost instantly but there could still be a single replica which takes longer than others resulting in latency spikes which is not desirable especially when dealing with real-time scenarios such as always listening to updates on live traffic reports or monitoring live system logs etc.. Therefore, this combination works well for applications where strong consistency cannot be compromised but providing eventual consistency brings better latency due to parallelism that comes with wave protocp executions itself rather than just hitting multiple keys in a strongly consistent database like Cloud Firestore where there is no parallelism whatsoever making it ideal for applications like these where strong consistency cannot be compromised but providing eventual consistency brings better latency due to parallelism at scale due to wave protocp executions itself rather than just hitting multiple keys in a strongly consistent database like Cloud Firestore where there is no parallelism whatsoever resulting in latency spikes due to hitting all replicas even though 99% of them might return successfully almost instantly but there could still be a single replica which takes longer than others resulting in latency spikes which is not desirable especially when dealing with real-time scenarios such as always listening to updates on live traffic reports or monitoring live system logs etc.. Therefore, this combination works well for applications where strong consistency cannot be compromised but providing

The process to integrate Cloud Firestore and Twitter 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.