Spark Pipeline Development
Building batch and streaming data pipelines on Spark.
Apache Spark Consulting Services
Apache Spark Consulting Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We build and tune Apache Spark to process huge volumes of data fast, for the pipelines, transformations and workloads that other engines cannot handle efficiently. Over a decade of experience, 250+ digital solutions delivered.
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What is Apache Spark consulting?
Apache Spark consulting is expert help to build, optimise and operate data processing on Apache Spark, the distributed engine for large-scale batch and streaming workloads. It covers pipeline development, performance tuning and cost-efficient cluster design. Noseberry builds Spark workloads, in PySpark or Scala, that process big data reliably and fast, whether standalone or on platforms like Databricks.
Key takeaways
- Apache Spark is a distributed engine for large-scale batch and streaming data.
- It powers heavy ETL, transformations and machine learning data prep.
- Performance tuning and cluster design are what keep Spark fast and cost-efficient.
- Spark runs standalone or inside platforms like Databricks.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we do
Our Apache Spark services
PySpark and Scala Engineering
Robust, maintainable Spark code.
Performance Optimisation
Tuning jobs, partitions and memory for speed.
Cluster Design and Cost Efficiency
Right-sized clusters that control spend.
Spark Streaming
Real-time processing with structured streaming.
Migration and Modernisation
Moving legacy processing onto Spark.
Where it delivers value
Where Spark delivers value
Processing very large datasets efficiently
Heavy ETL and transformation workloads
Preparing data at scale for machine learning
Combining batch and streaming processing
Workloads too big for single-node engines
How we work
Our five-phase process
We benchmark a workload and tune it before scaling to full production volumes.
1
Discovery and Audit
We review your workloads, data volumes and costs.
2
Strategy and Roadmap
We design the Spark approach and plan.
3
Rapid Proof of Concept
We benchmark a workload and tune it.
4
Build and Integrate
We build robust pipelines and connect storage.
5
Deploy and Optimize
We scale to production volumes with cost control.
Technology and integrations
Engine
- Apache Spark
- PySpark
- Scala
- Structured streaming
Platforms
- Databricks
- Cloud Spark services
Storage
- Delta Lake
- Cloud data lakes
- Snowflake
Cloud
- AWS
- Azure
- Google Cloud
Security and compliance
Secure, monitored processing
Built with access controls, encryption and monitoring. We align to GDPR, HIPAA and SOC 2, on AWS, Azure and Google Cloud.
GDPRHIPAASOC 2
Real success storiesFinTech · Digital Insurer PropTech · Real-estate marketplace E-Commerce · Retail leader
Outcomes we have driven
Challenge
Large-scale scoring jobs were slow and costly.
Solution
Tuned PySpark pipelines with right-sized clusters.
Impact
Faster processing behind 93% of fraud caught pre-payout.
Challenge
Heavy transformation jobs couldn't keep pace.
Solution
Optimised Spark jobs and partitioning across markets.
Impact
40% faster data processing and valuations.
Challenge
Recommendation data prep didn't scale.
Solution
Distributed Spark processing feeding the engine.
Impact
+28% lift in conversion rate.
Sector-anonymised outcomes shown until named clients are approved.
Why Noseberry
Why choose Noseberry for Apache Spark
Specialist
AI, Cloud and Data is our core, no generalist dilution.
Performance-focused
We tune Spark for both speed and cost.
Platform-flexible
Standalone Spark or on Databricks, your choice.
Proven at scale
250+ solutions delivered across 20+ countries.
Apache Spark, answered.
Processing large-scale data fast, including heavy ETL, transformations, streaming and machine learning data preparation.
Spark is the processing engine. Databricks is a managed platform built on Spark with added lakehouse, governance and ML features. We work with both.
Usually due to poor partitioning, memory settings or cluster sizing. We tune these to cut both runtime and cost.
Both, chosen for your team and workload. PySpark is common for data and ML, Scala for performance-critical jobs.
Yes. Structured streaming processes real-time data on the same engine as batch.
Processing data at scale?
Book your free 30-minute strategy session and we will review your Spark workloads.
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