Client Challenge
Our client, a growing brand, was struggling to turn their high number of Instagram followers and likes into actual revenue. They invested heavily in paid advertisement but found it wasn’t enough to bring in a stable flow of organic followers and generate meaningful revenue growth. To complicate matters, the social media landscape is constantly changing, making it challenging to stay on top of the latest trends. Brands that failed to adapt risked being left behind, while those that tried to keep up often resorted to buying followers and likes in a desperate attempt to boost their numbers. Unfortunately, these numbers alone did not translate into revenue growth, and our client realized they needed a more comprehensive and sustainable strategy to succeed on Instagram.
Our Solution
To help the client achieve their goals, we developed a custom Instagram data solution that enabled them to derive meaningful insights and analytics from their data. By leveraging the power of our data engineering expertise and tools, we provided the client with a better understanding of their customer behavior and preferences, and developed a more targeted and effective strategy for driving revenue growth on Instagram. Through our partnership, we were able to help the client achieve their goals and unlock the full potential of their Instagram data.
- We developed a custom data engineering solution based on a combination of AWS tools and services, including S3, Glue, AWS Step functions, lambda, AWS Athena, AWS Redshift, Quicksight, and AWS Cloudwatch.
- Our solution provided quick results by leveraging AWS Glue to automate the data extraction and processing, allowing our client to access their data in real-time.
- We provided a great price-to-quality ratio by leveraging AWS’s scalable and pay-as-you-go infrastructure.
- Our solution was designed with ease of communication in mind, providing fast and responsive communication with our client whenever they needed information or updates.
- By providing a customized and responsive solution, we helped our client achieve their goals and unlock the full potential of their Instagram data.
- Data collection: Collecting Instagram data using the Instagram API and storing the data in AWS S3.
- Data processing: Using AWS Glue to extract, transform, and load the data into AWS Redshift for analysis.
- Data Catalogue & Modeling : Leveraging AWS Glue, we created a data catalog to track metadata and lineage of the data, and developed a data model that enabled easy analysis and visualization.
- Data analysis: Using AWS Athena to query and analyze the data stored in AWS Redshift.
- Data visualization: Using AWS Quicksight to create visualizations of the analyzed data for better understanding and communication of the findings.
- Deployment: Using AWS Step Functions to orchestrate and automate the data processing pipeline, and AWS Lambda to trigger the pipeline when new data is available in S3.
- Monitoring and maintenance: Using AWS CloudWatch to monitor the performance of the pipeline and ensure that it is running smoothly.
Data Collection
To collect our client’s Instagram data, we created a custom API that could extract data from their account and save it to an AWS S3 bucket.
Data Processing & Data Catalogue & Modeling
Our custom API saves data outputs as JSON files to an S3 bucket. However, these files require a valid data format for AWS Athena queries to execute. To address this issue, we convert the JSON files to Parquet format and save them in a separate S3 bucket. We then use AWS Glue jobs to transform and join the Parquet files for our Redshift table.
Data Visualization
For data visualization, we leveraged Power BI, a popular business intelligence and analytics tool. Power BI enabled us to create interactive dashboards and reports that allowed our client to gain deeper insights and understanding from their Instagram data. We used Power BI’s powerful visualization capabilities to transform the data into meaningful and actionable insights, providing our client with the information they needed to make informed business decisions.
Deployment
To deploy our Instagram data pipeline, we used a combination of AWS Step Functions and AWS Lambda. Our data pipeline was designed to trigger automatically when new data was available in our S3 bucket. AWS Lambda then triggered the pipeline and AWS Step Functions orchestrated and automated the data processing pipeline.
To ensure smooth and efficient deployment, we used AWS CloudFormation to create and manage all the necessary resources for our pipeline. This helped to ensure consistency and scalability across our pipeline, making it easier to manage and maintain over time.
Throughout the deployment process, we monitored the pipeline using AWS CloudWatch, which enabled us to quickly identify and troubleshoot any issues that arose. By leveraging these AWS tools and services, we were able to ensure that our Instagram data pipeline was deployed smoothly and effectively, providing our client with a reliable and scalable solution for their data processing and analysis needs.