AppsFlyer is an attribution platform that helps developers build their mobile apps. Moreover, it also helps them market their apps, track their Leads, analyze their customer behavior and optimize their Sales accordingly.
Amazon Redshift is a Cloud-based Data Warehousing Solution from Amazon Web Services (AWS). It helps you consolidate your data from multiple data sources into a centralized location for easy access and analytical purposes. It extracts the data from your data sources, transforms it into a specific format, and then loads it into Redshift Data Warehouse.
Whether you are looking to load data from AppsFlyer to Redshift for in-depth analysis or you are looking to simply backup Appsflyer data to Redshift, this post can help you out. This blog highlights the steps and broad approaches required to load data from Appsflyer to Redshift.
Introduction to AppsFlyer
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AppsFlyer is an attribution platform for mobile app marketers. It helps businesses understand the source of traffic and measure advertising. It provides a dashboard that analyses the users’ engagement with the app. That is, which users engage with the app, how they engage, and the revenue they generate.
For more information on AppsFlyer, click here.
Introduction to Amazon Redshift
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Amazon Redshift is a Data Warehouse built using MPP (Massively Parallel Processing) architecture. It forms part of the AWS Cloud Computing platform and is owned and maintained by AWS. It has the ability to handle large volumes of data sets and huge analytical workloads. The data is stored in a column-oriented DBMS principle which makes it different from other databases offered by Amazon.
Using Amazon Redshift SQL, you can query megabytes of structured or unstructured data and save the results in your S3 data lake using Apache Parquet format. This helps you to do further analysis using Amazon SageMaker, Amazon EMR, and Amazon Athena.
For more information on Amazon Redshift, click here.
Method 1: Load Data from AppsFlyer to Redshift by Building Custom ETL Scripts
This approach would be a good way to go if you have decent engineering bandwidth allocated to the project. The broad steps would involve: Understanding the AppsFlyer data export APIs, building code to bring data out of AppsFlyer, and loading data into Redshift. Once set up, this infrastructure would also need to be monitored and maintained for accurate data to be available all the time.
Method 2: Load Data from AppsFlyer to Redshift using Hevo Data
Hevo comes with out-of-the-box integration with AppsFlyer (Free Data Source) and loads data to Amazon Redshift without having to write any code. Hevo’s ability to reliably load data in real-time combined with its ease of use makes it a great alternative to Method 1.
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This blog outlines both of the above approaches. Thus, you will be able to analyze the pros and cons of each when deciding on a direction as per your use case.
Methods to Load Data from AppsFlyer to Redshift
Broadly there are 2 methods to load data from AppsFlyer to Redshift:
Let’s walk through these methods one by one.
Method 1: Load Data from AppsFlyer to Redshift by Building Custom ETL Scripts
Follow the steps below to load data from AppsFlyer to Redshift by building custom ETL scripts:
Step 1: Getting Data from Appsflyer
AppsFlyer supports a wide array of APIs that allow you to pull different data points both in raw (impressions, clicks, installs, etc.) and aggregated (aggregated impressions, clicks, or filtering by Media source, country, etc.) format. You can read more about them here. Before jumping on to implementing an API call, you would first need to understand the exact use case that you are catering to. The basis that, you will need to choose the API to implement.
Note that certain APIs would only be available to you based on your current plan with AppsFlyer.
For the scope of this blog, let us bring in data from PULL APIs. PULL APIs essentially allow the customers of AppsFlyer to get a CSV download of raw and aggregate data. You can read more about the PULL APIs here.
In order to bring data, you would need to make an API call describing the data points you need to be returned. The API call must include the authorization key of the user, as well as the date range for which the data needs to be extracted. More parameters might be added to request information like currency, source, and other specific fields.
A sample PULL API call would look like this:
https://hq.appsflyer.com/export/master_report/v4?api_token=[api_token]
&app_id=[app_id]&from=[from_date]&to=[to_date]&groupings=[list]&kpis=[list]
As a response, a CSV file is returned from each successful API query. Next, you would need to import this data into Amazon Redshift.
Step 2: Loading Data into Redshift
As a first step, identify the columns you want to insert and use the CREATE TABLE Redshift command to create a table. All the CSV data will be stored in this table.
Loading data with the INSERT command is not the right choice because it inserts data row by row. Therefore, you would need to load data to Amazon S3 and use to COPY command to load it into Redshift.
In case you need this process to be done on a regular basis, the cron job should be set up to run with reasonable frequency, ensuring that the AppsFlyer data in the Redshift data warehouse stays up to date.
Limitations of Loading Data from AppsFlyer to Redshift Using Custom Code
Listed down are the limitations and challenges of loading data from AppsFlyer to Redshift using custom code:
- Accessing Appsflyer Data in Real-Time: After you’ve successfully created a program that loads data to your warehouse, you will need to deal with the challenge of loading new or updated data. Replicating the data in real-time when a new or updated record is created slows the operation because it’s resource-intensive. To get new and updated data as it appears in the AppsFlyer, you will need to write additional code and build cron jobs to run this in a continuous loop.
- Infrastructure Maintenance: When moving data from AppsFlyer to Redshift, many things can go wrong. For example, AppsFlyer may update the APIs or sometimes the Redshift data warehouse might be unavailable. These issues can cause the data flow to stop, resulting in severe data loss. Hence, we would need to have a team that can continuously monitor and maintain the infrastructure.
Method 2: Load Data from AppsFlyer to Redshift using Hevo Data
Hevo Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources (including 40+ Free Data Sources) including AppsFlyer, etc., for free and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
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Hevo overcomes all the limitations mentioned. You move data in just two steps, no coding is required.
- Authenticate and Connect Appsflyer Data Source as shown in the image below by entering the Pipeline Name, API Token, App ID, and Pull API Timezone.
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- Configure the Redshift Data Warehouse where you want to load the data as shown in the image below by entering the Destination Name and Database Cluster Identifier, User, Password, Port, Name, and Schema.
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Benefits of Loading Data from AppsFlyer to Redshift using Hevo Data
Hevo platform allows you to seamlessly move data from AppsFlyer and numerous other free data sources to Redshift. Here are some more advantages:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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Conclusion
This article introduced you to AppsFlyer and Amazon Redshift. It provided you with 2 methods that you can use to load data from AppsFlyer to Redshift. The 1st method includes Manual Integration between AppsFlyer and Redshift while the 2nd method includes Automated Integration using Hevo Data.
With the complexity involves in manual Integration, businesses are leaning more towards automated Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, Hevo Data is the right choice for you! It will help simplify the Web Analysis process by setting up AppsFlyer Redshift Integration for free.
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Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
What are your thoughts about moving data from AppsFlyer to Redshift? Let us know in the comments.
Eva loves learning about data science, with an intense passion for writing on data, software architecture, and related topics. She enjoys creating an impact through content tailored for data teams, aimed at resolving intricate business problems.