Data Pipeline Automation
LeadGenius needed to enhance and automate its data processing pipeline to achieve higher scalability and to improve sales and marketing performance.
LeadGenius is a marketing automation & demand generation company that uses AI and human computation to help clients identify and communicate with targeted leads
LeadGenius’ data processing pipeline was inefficient due to the high amount of manual processes (e.g. data incorporation, verification). The pipeline did not allow to ensure data quality and data consistency.
An automated, scalable, and fault-tolerant data processing & data storage solution was designed and built. The highly scalable infrastructure was optimized to run with elastic applications on AWS.
The automation of data cleaning and data gathering pipeline allowed LeadGenius to improve the quality and to deliver data to users faster while reducing data processing time 2x and optimizing data processing cost 3x.
2x reduction in data processing time
3x reduction in data processing cost
Increased speed of delivery of customer-facing data
Increased accuracy of customer-facing data
Addressing Manual Bottlenecks Through Automation to Ensure Data Consistency and Data Quality
LeadGenius needed to enhance and automate its data processing pipeline, because:
- The data processing pipeline suffered from bottlenecks caused by the high amount of processes that had to be performed manually, which was inefficient
- The data was parsed from varying sources and had to be verified carefully, which, given that performed manually, slowed data delivery to customers
- The pipeline was lacking in terms of data quality and data consistency due to the variety of data sources and the reliance on manual processing
The data processing pipeline had to be fault-tolerant and be able to run on demand should any issues with its components occur. High scalability with elastic apps was required to be run on AWS so that the data can be cleaned and enriched continuously to rapidly deliver results to customers. LeadGenius approached Provectus to ensure that its data processing pipeline processes are automated, and the platform is optimized for further scaling.
Building a Robust, Automated Data Processing and Data Storage Solution
Provectus designed and built an automated, scalable, and fault-tolerant data processing & data storage solution, which utilized cutting-edge algorithms to clean and enrich parsed data.
The data parsing and data processing pipeline is based on Apache Spark managed by Amazon EMR. The data storage solution was designed and built with Amazon S3, Amazon RDS with PostgreSQL, Amazon Redshift, and Amazon Elasticsearch service.
The use of Apache Spark with Amazon EMR allowed accelerating collection and processing of huge swaths of data from varying sources, from third-party websites to publicly accessible government and credit data.
In an effort to optimize object data storage in the cloud, Amazon S3 was used. The service ensured the solution’s reliability (i.e. 24/7 access to data) and compatibility with other AWS services to accelerate and simplify scaling.
Amazon RDS and Amazon Redshift services were used for data storage. Their scalability, fault tolerance, latency allowed for multiple scaling options. Amazon Elasticsearch was used to ensure the timely, uninhibited customer access to the data.
Ensuring Data Quality with Automated Data Processing Pipelines
Provectus delivered a data processing and data storage solution, with an automated data processing pipeline, which allowed LeadGenius collect and process data more rapidly, increase the quality of customer-facing data that empowers sales and marketing teams.
The solution cleans and enriches parsed data continuously, in an automated manner, ensuring that customers fully utilize the power of LeadGenius’ lead identification capabilities.
LeadGenius received a fully automated data processing and data storage solution to collect, process, and manage data in a continuous manner to deliver high-quality customer-facing data to their clients, sales and marketing teams that identify and communicate with targeted leads in B2B.
The solution was optimized to work with elastic applications on AWS and to faultlessly run on demand even if certain components sport issues.
The data processing pipeline and the data storage solution were released ahead of schedule.