Firebase Analytics helps businesses track customer behavior on the web and mobile applications. It can track every app event and assist companies in improving their apps. Organizations can store all such app information in a centralized repository like Snowflake for real-time and in-depth analysis. Snowflake can then be integrated with powerful BI tools such as Tableau, PowerBI, Google Data Studio, and more to gain insights into the Firebase data. Businesses must use standard APIs or third-party ETL (Extract, Transform, and Load) tools.
In this article, you will learn how to easily connect Firebase Analytics to Snowflake using 2 different methods.
What is Firebase Analytics?
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Developed in 2011, Firebase is a cross-platform app development platform that assists enterprises in building, improving, and growing their apps. Firebase includes Firebase Analytics which helps businesses understand how Android and iOS users engage with their applications. It includes metrics like average revenue per user (ARPU), active users, retention resorts, event counts, and more for measuring user engagement. These different metrics are combined with varying properties like device type, app version, and OS version to provide businesses with meaningful insights into how users interact with their apps.
Whenever any organization adds Firebase to their app, key events are measured automatically, like the in-app purchases, the number of apps installed, and more. Firebase Analytics can measure suggested and custom events unique to your app with very few lines of code. It can also offer businesses an option to report more than 500 distinct event types with 25 key-value pair parameters to segment the audience based on their preference and update customer data quickly.
Key Features of Firebase Analytics
- Real-time Database: Firebase consists of a real-time NoSQL database that is hosted on the cloud. Therefore, any changes made by the clients can reflect immediately on the other connected clients, which allows your users to access real-time data from anywhere at any time using any device.
- Firebase Authentication: Businesses can authenticate users to their app using firebase analytics, its backend services, pre-designed UI, easy-to-use SDK, and more. They can also sign in using popular platforms like Twitter, Google, Facebook, Github, and more with email-id, password, and phone number.
- Machine Learning Capabilities: Firebase Analytics consists of a machine learning kit and ready-to-use machine learning APIs that help businesses integrate their apps. Companies can use machine learning for various purposes such as identifying faces and texts, labeling images, scanning barcodes, recognizing location landmarks, and more in apps.
- Accelerated Analytics: Firebase can seamlessly integrate with Google Analytics to freely report up to 500 events. It provides a unified platform for businesses to monitor all the essential metrics like app engagement, demographics, retention to average in-app purchase revenues, and more. This process results in gaining meaningful insights into the app by optimizing the advertisements campaigns. Similarly, another feature of Firebase is called Crashlytics, which helps provide a crash reporting solution. It assists businesses in tracking, prioritizing, and fixing stability issues that may lead to eroding the app quality. Crushlytics saves troubleshooting time by grouping crashes and highlighting the situations which lead to them.
- Performance Monitoring: This service in Firebase Analytics allows businesses to learn more about the performance of their Android and iOS apps. Companies can collect performance data from their app by using the performance monitoring SDK and then examining and analyzing it with the Firebase Console.
If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc can change, creating and managing these pipelines can be a soul-sucking exercise.
Hevo’s no-code data pipeline platform lets you connect over 150+ sources like Firebase Analytics in a matter of minutes to deliver data in near real-time to your Snowflake warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes.
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What is Snowflake?
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Developed in 2012, Snowflake is a fully managed data warehouse as a service platform that offers businesses cloud-based data storage and analytics solutions. It consists of a fast and robust engine to store and query data that runs on popular cloud providers such as Amazon web services (AWS), Microsoft Azure, and Google Cloud Platform.
Since Snowflake is a fully managed data warehouse, it is the best choice for businesses that do not want to dedicate resources for their setup, maintenance, and more. Snowflake architecture separates its storage unit from the computation unit. Therefore, companies can leverage and pay for both computation and storage independently.
Key Features of Snowflake
- Support for semi-structured data: Snowflake can combine structured and semi-structured data without using complex technologies such as Hadoop or Hive. It can support semi-structured data in a wide range of formats, including JSON, Avro, ORC, XML, Parquet, and more data types, with a size limit of 16 MB.
- Caching Results: Snowflake provides caching at different levels to speed up queries and reduce costs accordingly. For example, if a query runs, Snowflake will keep its results for 24 hrs. Therefore, if the same query is run for the second time by the same user or the other user, the query results are already immediately available, assuming that the underlying data has not been changed. This process is very beneficial for analysis, as it eliminates the time of rerunning complex queries for accessing previous data.
- Cloning: Cloning is another essential feature of Snowflake. It is used for creating an instant copy of any snowflake objects like databases, schemas, tables, and more in real-time. Cloning is possible due to the Snowflake architecture of storing data as immutables in S3, versioning the changes, and storing the changes as Metadata. Therefore, cloning an object is a Metadata operation that does not duplicate the storage data in Snowflake.
- UNDROP Command: The UNDROP command is one of the unique features of Snowflake. With the UNDROP command, a dropped object can be restored in Snowflake, assuming that the system does not purge the object. Objects like tables, schemas, databases and more can be restored using the UNDROP command.
You can also use the UNDROP command to restore objects to multiple tables. For example, if a table has been dropped three times and has been recreated each time, each drop version can be restored in reverse order so the last dropped version can be restored first. This process works until the existing table with the same name is renamed before UNDROP operation. If the table with the same name already exists in the system, the UNDROP command can be failed. As a result, it is necessary to rename the existing table and execute the UNDROP operation.
How to connect Firebase Analytics to Snowflake? 2 Easy Methods
You can easily connect Firebase Analytics to Snowflake by following any of the 2 methods given below:
Method 1: Using Hevo Data to Automate Firebase Analytics to Snowflake Connection
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Hevo Data is a No-code Data Pipeline solution that can help you seamlessly replicate data in real-time from 150+ data sources(Including 40+ free sources like Firebase Analytics) like to your Data Warehouses such as Snowflake or a destination of your choice in a completely hassle-free & automated manner.
Hevo is fully managed and completely automates the process of not only loading data from 150+ data sources such as Firebase Analytics but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Using manual scripts and custom code to move data from Firebase Analytics to Snowflake is cumbersome. Changing API endpoints and limits, ad-hoc data preparation, and inconsistent schema make maintaining such a system a nightmare. Hevo’s reliable no-code data pipeline platform enables you to set up zero-maintenance data pipelines that just work.
- Wide Range of Connectors – Instantly connect and read data from 150+ sources including SaaS apps and databases, and precisely control pipeline schedules down to the minute.
- In-built Transformations – Format your data on the fly with Hevo’s preload transformations using either the drag-and-drop interface, or our nifty python interface. Generate analysis-ready data in your warehouse using Hevo’s Postload Transformation
- Near Real-Time Replication – Get access to near real-time replication for all database sources with log-based replication. For SaaS applications, near real-time replication is subject to API limits.
- Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
- Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
- 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day free trial.
- Security – Discover peace with end-to-end encryption and compliance with all major security certifications including HIPAA, GDPR, and SOC-2.
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Without the need for manually extracting CSV files & uploading them to Snowflake, you can effortlessly replicate data from Firebase Analytics to Snowflake using Hevo by following the simple steps given below:
- Step 1: To replicate data from Firebase Analytics to Snowflake, you can first configure Firebase Analytics as a source by providing your Firebase credentials such as your authorized Username and Password, etc. You will also need to give a name for your database and a unique name for this Pipeline.
- Step 2: For completing the process to replicate data from Firebase Analytics to Snowflake, you can start by providing your Snowflake credentials such as your authorized Database Username and Password, etc. You will also need to give the name for your Warehouse, Database, Schema, and a unique name for this destination.
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This completes the No-Code & Automated method of connecting Firebase Analytics to Snowflake using Hevo.
Method 2: Manually Connect Firebase Analytics to Snowflake using CSV Files
To manually connect Firebase Analytics to Snowflake, you must first export your data from Firebase Analytics in a CSV file and upload the file into Snowflake.
Exporting Firebase Analytics data
To get started with the Firebase Analytics to Snowflake Integration, follow the below steps to export data from Firebase Analytics.
- Step 1: It is assumed that you have logged in to your Firebase account with valid credentials. Go to your project and then click on the real-time database module.
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- Step 2: Click on the three dots in the top right corner to open the menu.
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- Step 3: Select the Export JSON option, as shown in the below image.
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- Step 4: Your file will then start downloading. You can convert the JSON file into a CSV file using the read_json method.
Importing data to Snowflake
The classic user interface of Snowflake provides a wizard for loading a limited amount of data to a table from a small set of files. The wizard uses PUT and COPY commands to load data at the backend.
Follow the below steps to manually import files from Firebase Analytics to Snowflake.
- Step 1: Open the Load Data Wizard
- Click on Data and then on Databases in the Snowflake user interface.
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- To view the items in the database, click on the link for that database.
- You can click on the table by selecting the row and then loading or clicking the load button after selecting the table name.
- The Load Data wizard appears. It populates the table you have specified with the data.
- Step 2: Select the specific data warehouse.
- From the drop-down list, select the data warehouse to load data into the table.
- Click on Next to load files.
- Step 3: Select files.
- You can load your data from the local machines or existing files in Microsoft Azure, Amazon S3, or Google Cloud Storage using the steps below. In this article, we will load data from cloud storage.
- Load data from Cloud Storage
- From the drop-down list, select the existing name Stage.
- Click on the Next tab to select the format of the file. But, if you do not have existing files in the cloud storage, create a new Stage in the cloud.
- Select the File Format
- Select the existing file format from the drop-down list.
- Click on the Next tab to select the data load options. But, if you do not have files containing the format, you can create a new file format for your required format.
- Select the data load options
- If your data files from Firebase Analytics encounter errors, you should specify how Snowflake should behave. You can check the COPY INTO table section for more details.
- Click on the Load button. Snowflake then loads data into your selected table in your data warehouse. Click on OK, and the Load Data wizard will be closed.
This completes the manual process of connecting Firebase Analytics to Snowflake.
Limitations of Manually Connecting Firebase Analytics to Snowflake
Businesses can integrate Firebase Analytics to Snowflake using standard APIs and manual processes. With the manual processes, companies can easily export data from Firebase Analytics and import it to the Snowflake account. Although the manual process seems easy, you cannot work with real-time data. On the other hand, you can use the standard APIs to integrate Firebase Analytics to Snowflake. However, for standard APIs, you need a strong technical team and establish several teams to maintain the pipelines. Therefore, to avoid such issues, you should use third-party ETL tools like Hevo to integrate and automate data replication from Firebase Analytics to Snowflake.
Conclusion
This article talks about connecting Firebase Analytics to Snowflake. Firebase Analytics offers different features to design mobile applications that are easy to use. At the same time, Snowflake provides a platform where businesses can store and analyze their Firebase Analytics data to gain meaningful insights. By combining the functionalities of both platforms, companies can store their Firebase Analytics data safely and make better business decisions to grow their business.
If you rarely need to send data from Firebase Analytics to Snowflake, then the manual method is sufficient. However, if you need to frequently replicate data from Firebase Analytics to Snowflake with on-the-fly complex data transformations, then Hevo Data is the right choice for you!
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Hevo Data, a No-code Data Pipeline can replicate Data in Real-Time from a vast sea of 150+ sources like Firebase Analytics to a Data Warehouse like Snowflake or a Destination of your choice. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!
If you are using Snowflake as your Data Warehousing & Analytics platform and searching for a seamless alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo’s strong integration with 150+ Connectors (Including 40+ Free Sources), allows you to export, load, transform & enrich your data to make it analysis-ready.
Want to take Hevo for a ride? Sign Up for a 14-day free trial and simplify your Data Integration process. Do check out the pricing details to understand which plan fulfills all your business needs.
Share your experience of connecting Firebase Analytics to Snowflake! Let us know in the comments section below!
Manjiri loves data science and produces insightful content on AI, ML, and data science. She applies her flair for writing for simplifying the complexities of data integration and analysis for solving problems faced by data professionals businesses in the data industry.