ML-Powered Data Warehouse for SERP Analytics
Monitoring website position inside global and local search engines.
COO @ DataRoot Labs
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Summary
- Modern approaches for monitoring the site position inside global and local search engines require huge amounts of different textual queries.
- A leading digital marketing agency wanted a scalable and automated service, dedicated to performing search engine optimization analysis.
- The agency used such engine for day-to-day and long term analytics and monitoring of performance of SEO optimized sites.
- Natural Language Processing and other Machine Learning methods were the foundations of the solution implemented by our team.
Tech Stack
Akka
Apache Spark
Cassandra
GlusterFS
Kafka
PostgreSQL
Python
Scala
TensorFlow
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Timeline
2 Weeks
Data Gathering Parser
Data Engineer
1 Week
Solution Architecture Design
Solution Architect
1 Week
Feature Extraction Pipeline Development
Deep Learning Engineer
Deep Learning Researcher
1 Week
Development of Customised LSH
Deep Learning Researcher
Deep Learning Engineer
1 Week
Clustering Performance Optimization
Deep Learning Engineer
Data Engineer
2 Weeks
Data Warehouse Configuration
Data Engineer
6 Weeks
Web Platform Development
Backend Developer
Frontend Developer
2 Weeks
Integration & Deployment
Backend Developer
Dev Ops
Tech Challenge
- Implementation required removal of the laborious manual tasks from the SEO team, allowing the client to considerably improve quality and revenues of the services.
- It was important that the entire range of analytics, from days to years, is in full disposal of the SEO specialist to adjust the parameters and predict the outcome.
- Parsing of Google Search Console of the websites and then parse google for search queries results taken from GSC.
- Clustering query-results matrix by links where the size of the matrix could be tens of millions squared.
Solution
- Peek queries are formed automatically by our own Natural Language Processing algorithm, applying modern approach to monitoring the site position inside global and local search engines.
- This algorithm considers structure and content of the target site pages and builds huge amounts of different textual queries to get the whole picture of the site’s performance.
- Those queries are performed and stored inside the database on a daily basis for whole range of sites. Each site is then analyzed against the competition, using the tool we have built.
- Different ranges of analytics on day-to-years scale are accessible to the SEO specialist for further iterations.
- Customized and optimized Apache Spark based LSH (Locality Sensitive Hashing) for approaching near linear clustering complexity - O(bn). Avgerage clustering time for 10M x 10M matrix takes near 15min.
Impact
- Our team has built a scalable service, which performs search engine optimization analysis.
- It is used for daily as well as long-term analytics and monitoring the performance of the SEO optimized sites.
- By eliminating the need for manual creation of SEO queries, our solutions has saved the agency at least thousands of working hours allowing to allocate people resources elsewhere.
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Yuliya Sychikova
COO @ DataRoot Labs
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Author
COO @ DataRoot Labs
Yuliya is a co-founder and COO of DataRoot Labs, where she oversees operations, sales, communication, and Startup Venture Services. She brings onboard business and venture capital experience that she gained at a leading tech investment company in CEE, where she oversaw numerous deals and managed a portfolio across various tech niches including AI and IT service companies.
Co-Authors
CTO @ DataRoot Labs
consults, builds and implements custom AI-powered systems across
different verticals to help our clients operate effectively.
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