“Spark jobs are getting slower as data volume and complexity grow.”
All consulting solutions
INGEST → PROCESS → STREAM → SERVE → OPTIMIZE
Data Engineering & Big Data
Architect, modernize and troubleshoot distributed data platforms spanning Apache Spark, Kafka, Trino, orchestration and lakehouse technologies.
Typical client situations
Recognize the problem before choosing the solution.
These are representative situations, not prerequisites for engagement.
“The organization needs to combine batch and streaming workloads without creating separate platform silos.”
“A legacy Hadoop or warehouse-oriented environment is being modernized toward lakehouse architecture.”
“Teams need a practical architecture for Spark, Kafka, Trino, Airflow and open table formats.”
Consulting areas
Data platform architecture
Apache Spark / PySpark
Kafka and event streaming
Trino / distributed SQL
Lakehouse design
Airflow / orchestration
Data quality and observability
Performance and migration
Assessment scope
Workload and data-flow topology
Spark plans, partitioning and shuffles
Cluster sizing / resource use
Kafka partitions and consumer patterns
Storage / table format design
Query architecture
Orchestration and failure handling
Data observability
Representative engagements
Scope the engagement around the engineering decision.
Engagements can be short assessments, focused architecture work, implementation support or retained advisory.
Spark performance assessment
Data-platform architecture
Streaming architecture review
Lakehouse design
Hadoop / warehouse modernization
Trino integration
Production troubleshooting
Deliverables
Leave with decisions, evidence and a path forward.
Deliverables are selected during scoping. Not every engagement needs every artifact.
01
Current-state data-flow map
02
Spark tuning findings
03
Reference data-platform architecture
04
Capacity / partition recommendations
05
Lakehouse / streaming design
06
Migration roadmap
07
Operational and monitoring guidance
Intended outcomes
Faster and more predictable data workloads
Clearer batch / streaming architecture
Reduced platform complexity
Improved data operability
Safer modernization path
Technology context
Apache SparkPySparkKafkaFlinkTrinoAirflowHadoop / HDFSIcebergDelta LakeObject storage
PoCs and selected optimization work can be included, while large-scale migration execution is scoped separately based on environment size and data responsibility.
CONNECTED PATHS
Connect the consulting problem to learning and field guidance.
Explore Knowledge HubTrainingData Engineering
Data Engineering & Big Data
Understand distributed data systems and build practical batch, streaming and lakehouse pipelines with production-oriented performance and operability.
ContinueCase StudyData Engineering
Recovering a slow Spark data pipeline
A representative performance-engineering scenario for a Spark/PySpark workload that becomes slower and less predictable as data volume and transformation complexity grow.
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Data Platform Architecture Review Checklist
A practical checklist for reviewing batch, streaming, Spark, Kafka, lakehouse and distributed-SQL architectures.
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Modern Data Platforms: Batch, Streaming and the Lakehouse
How Spark, Kafka, Trino, Airflow and open table formats can fit together in a modern data-engineering platform.
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Apache Spark for Production Data Engineering
A practical engineering guide to Spark architecture, PySpark pipelines, partitioning, performance, streaming and production operations.
ContinueResourceData Engineering
Apache Spark Production Readiness Checklist
A compact review checklist for Spark and PySpark workloads before they become production data pipelines.
ContinueHow we can engage
Assess
Independent technical assessment of architecture, reliability, security, performance or operational readiness.
Architect
Reference architecture, design decisions, technology selection and implementation roadmap.
Implement
PoCs, integration, migration, automation, optimization and selected hands-on engineering work where scope permits.
Advise
Ongoing technical advisory, architecture reviews, troubleshooting support and engineering guidance.
Start with the problem