Lenny Daume
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SEOlytics GmbH Logo

SEOlytics GmbH

Hamburg

SEO Backend Development with Big Data

Backend Development for Scalable SEO Data (BigData)

November 2013 - October 2014
1 year
Full-time
Product work
Hamburg

Relevance

Why this case matters

The case shows how problem understanding, responsibility, decisions, and implementation came together in this context.

Impact

Owned scalable SEO backend systems across Java/Scala, microservices, JMS/HornetQ, multi-stage data processing, databases, AWS/Docker, Google Search Console, release management, and team coaching with end-to-end responsibility from problem definition to operations.

What carries forward today

AI-native systems depend on data quality, interfaces, and reliable backends. This case shows that technical foundation.

Big Data BackendJava/ScalaMicroservicesJMS/HornetQEnd-to-End Product OwnershipYou Build It You Run ItRelease Management

Defensible proof

Multi-DB

Data architecture

Used MongoDB, Cassandra, Neo4j, ArangoDB, and MySQL in an SEO data context.

AWS/Docker

Platform work

Used cloud and container setup for scalable backend development.

JMS/HornetQ

Messaging topology

Used queues, topics, a DLQ, expiry handling, and storage-failover paths for decoupled backend components.

Where this experience creates value

  • For teams with data-intensive products where backend, data model, and delivery need to fit together.
  • For organizations that need technical depth and team coaching inside ongoing product development.

Case context

Overview

SEOlytics needed backend systems for large SEO data flows and faster technical adaptation. Across my full tenure, I owned work from problem definition through product and architecture decisions, implementation, testing, and delivery to production operations. That included microservice architecture, REST/JMS communication with JBoss HornetQ, Daily Rankings data, database reengineering, and team coaching.

The technical work connected Java/Scala, Docker/AWS, Google Search Console, Search Analytics, and own services with TDD, CI, and release management. The point was not only big-data processing, but a backend that could absorb market changes faster while staying maintainable.

Responsibility

Activities

  • Microservice Architecture: New and further development of the microservice landscape, scalable architecture
  • JMS/HornetQ landscape: Decoupled backend applications through queues, topics, and request/response flows; accounted for DLQ, expiry, and storage-failover paths in the messaging topology
  • Evaluated Kafka as an event-processing alternative and deliberately did not introduce it given its maturity at the time
  • Data-pipeline architecture: Analyzed the existing database/HDFS/Hive/Solr batch flow and designed a lower-latency JMS/Cassandra/Solr flow for transformation, enrichment, historical state, and incremental calculation
  • Search platform: Co-owned an Apache Solr cluster including sharding for scalable search and analytics paths
  • SEO data models: Connected Daily Rankings, position dailies, and Google Search Console data to scalable processing
  • Database Reengineering: Performance optimization, data model design, Big Data integration
  • Technical Leadership: Team coaching for implementation, architecture, design, testing, documentation
  • Big Data Processing: Java/Scala, Docker/AWS, Google APIs for SEO data processing
  • End-to-End Ownership: Connected problem definition, product work, architecture, implementation, release, and production operations across the full SEOlytics tenure

Operating mode

Methodology

  • Test-Driven Development: Built-in quality, automated tests, and mocking/stubbing for risky backend changes
  • Continuous Integration: Automated builds, release management, and quality gates as a fast feedback system
  • Visible architecture decisions: Keep data models, microservice boundaries, and performance work inspectable
  • Batch-to-stream modernization: Evaluate the target architecture and transition path against business latency, data volume, persistence needs, and operational failure paths
  • Team enablement: Use coaching, reviews, and documentation so architecture knowledge does not stay with isolated individuals

Technical context

Technology stack

The tools are not the point by themselves. What matters is which system layers had to work together.

10Areas
53Technologies

Databases & Storage

9
Google Guava CacheVarnishAWS S3MongoDBCassandraNeo4jArangoDBMySQLBig Data Storage

DevOps

9
MavenDockerAWS EC2AWS GlacierApache SolrCloud InfrastructureGitSVNJBoss HornetQ

Tools

7
SBTJUnitTestNGSpockMockitoJiraConfluence

Backend

7
Java 8ScalaGroovyPlay FrameworkAkkaGoogle GuiceREST APIs

Frontend

2
JavaScriptJSON/XML

Other

1
Solr Sharding

Data & AI

8
NLPMachine LearningBig Data ProcessingGoogle APIs (Adwords, WebmasterTools)Google Search ConsoleSearch AnalyticsXPathData Enrichment Pipelines

CI/CD & Delivery Pipelines

3
JenkinsCI/CD PipelineNexus

Practices

3
Test-Driven DevelopmentProject ManagementDocumentation

Messaging & Event Streaming

4
JMSJMS Queues & TopicsEvent-driven ArchitectureDead Letter Queues

Next step

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