End-to-end Data Engineering Work for a Leading Nonprofit

After trying to manage the workflow in-house, the client found it too overwhelming to handle it entirely alone. So, they turned to Capital Numbers. The client chose us because we have a solid grasp of data handling solutions.

Capital Numbers delved deep into the criticalities of the client's data engineering needs and jotted down the key areas to focus on. Some of these areas included:

• Mapping data sources to the target database
• Data cleansing
• Data ingestion
• Data synchronization
• Data transformation
• Visualizations at scale

We worked towards moving data from varied sources, like CRMs, third-party lists, mobile apps, etc., to the target database systematically using custom logic.

We didn’t move data from these sources to our target database in bulk because that would cause latencies. Instead, we moved data in batches, which ensured a streamlined migration.

Once we extracted data from different sources and put them in a central repository, we looked for inconsistencies or incorrect values (if any). We discarded duplicate entries. We got rid of all anomalies. We also applied additional custom rules to improve data quality.

After sorting and improving the data quality, we worked towards loading data into our data warehouse. Here, we gave special attention to verify all data belonged to appropriate tables when loaded in the data warehouse. We used custom scripts to ensure the data loading process took place smoothly.

Currently, it’s an ongoing process where our data engineers regularly populate the data warehouse with new data. We also configure components when data is added or changed in the data warehouse.

Every time our data engineers feed new data, our data visualization experts use custom logic and HTML5/CSS3 to visually present that data on the frontend through graphical reports for end users.

Our backend developers used Java for this data engineering project because Java is ideal for ETL environments that handle huge amounts of data volume, data velocity, and data veracity. Java allows flawless extraction, transformation, and loading of big datasets.

Our engineers leveraged Oracle database and MongoDB to sort, structure, and segment massive datasets. Both these database solutions ensure excellent data querying, availability, and security.