A+ rating
Average 5.00 ratingIndustry best services by CDU - Databricks and Spark Consulting in Australia
What makes the difference between Spark’s in-memory and disk-based framework is the speed. The computing engine with in-memory in Spark can process the same volume of workload many times faster than the same is done by disk-based frameworks. However, it can process faster only when the cluster, code and design of the pipeline are well-organized. This is the Gap we close with our capabilities. Whether you are building a new Spark Machine Learning Pipeline or wish to fix the one already in production, our team of expert is here to help you out in it.
Spark Streaming Implementation Australia
Real-time analytics pipeline builds on Spark Structured Streaming for low-latency event processing.
Managed Spark Operations & Support
Cluster monitoring, job troubleshooting and on-call support after go-live.
Spark on Kubernetes Australia
Container-native Spark deployment for teams standardizing on Kubernetes over managed platforms.
Hadoop to Spark Migration
Structured migration off legacy MapReduce jobs onto a Spark-based processing layer.
Spark Performance Tuning & Cost Optimization
Cluster sizing, partitioning and shuffle tuning to cut runtime and infrastructure spend.
Batch ETL & Data Pipeline Engineering
Scheduled Spark SQL jobs for warehouse loads, transformations and reconciliation.
Databricks Lakehouse Architecture
Delta Lake, workspace design and governance for teams standardizing on Databricks.
Spark Machine Learning Pipelines
MLlib-based feature engineering, training and inference pipelines at cluster scale.
Much in demand Spark components & the platforms we run them on
The team at CDU is well-versed to work across the full Spark ecosystem and stay neutral on which managed platform sits underneath it.
From Spark Core and Spark SQL to Spark Streaming, MLlib and Delta Lake, we bring together the right technologies for your data processing needs. Our capabilities also extend across Kubernetes, Kafka, HDFS and Airflow, supported by Databricks, Amazon EMR and Azure Synapse. This approach keeps your Spark environment scalable, efficient and aligned with your data and business goals.
Benifit’s the Services
Get the right Apache Spark stack, tailored to your data processing needs.
Tailored Spark Architecture
Flexible Deployment Options
Scalable Data Processing
Connected Spark Ecosystem
Apache Spark Components
Spark Core Spark SQL Spark Streaming MLlib Delta Lake
Orchestration & Storage
Kubernetes Kafka HDFS Airflow
Cloud Platforms We Run Spark On
Databricks Amazon EMR Azure Synapse
Let’s explain how we approach our clients for Our Apache Spark engagement process
A well-structured and thought-out path from the very first assessment to fully managed operations.
Spark architecture review
Roadmap & fixed-scope proposal
Build, tune & migrate
Handover or managed support
Hey 👋 I am Gokul, Sr. Business Consultant
Let's talk about your growthThe Reasons Why choose CDU for Apache Spark
CDU never resells a preferred Spark Platform for the sake convenience. Before making any recommendation, we analyze and make it sure that Databricks, self-managed on Kubernetes, or a hyper-scaler-managed service is based on your team's skills and constraints and it is not a partnership incentive.
What changes after a Spark engagement
The following figures depict the typical ranges our clients have reported to us across our latest engagements, not guarantees. In fact, your results depend much on the point where we start from.
User Training
60%
Faster OnboardingIntegrations
130+
Dealerships ConnectedOperations
99.9%
Platform Availability
Choose how you want to start
Org Health Check
Audit your existing org to find quick wins before investing further.
- Full architecture review
- Security & compliance audit
- Performance analysis
- Recommendations report
Implementation
POPULARFull cloud setup from scoping to go-live with fixed-price delivery.
- Sales or Service Cloud Core
- Custom Apex / LWC logic
- Full data migration & QA
- Comprehensive user training
Managed Services
Dedicated admin support without the overhead of a full-time hire.
- 24/7 technical helpdesk
- Automation & workflow shifts
- Release and health compliance
- Config and report build-outs
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Backed by Results.
Real partnerships. Honest feedback. See why organisations across Australia trust Cloud Downunder to design, build, and support their digital products.
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out of 5Verified Google Reviews
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out of 5Top Software Development Company
4.8
out of 5Frequently Asked Questions
Everything you need to know about Cloud Downunder's services, process, and Australian-local approach.
Still have questions?
We're here to help. Reach out directly and our specialists will respond within 2 business hours.
Apache Spark is an in-memory distributed computing engine used to process large volumes of data for ETL pipelines, real-time analytics pipelines and machine learning at scale, faster than traditional disk-based processing frameworks.
Spark processes data in-memory, which typically makes it significantly faster than Hadoop MapReduce for iterative and interactive workloads. Many Australian organisations still run Hadoop for storage (HDFS) while using Spark as the processing engine on top of it.
Batch processing runs on a scheduled basis over a fixed dataset, while streaming processing continuously handles data as it arrives. Spark supports both through Spark SQL for batch jobs and Spark Structured Streaming for real-time pipelines.
It depends on your team's skills, budget and operational preferences. Databricks adds managed infrastructure, collaborative notebooks and Delta Lake on top of Spark, while self-managed Spark on Kubernetes or cloud services like Amazon EMR can be more cost-effective for teams with strong platform engineering skills. We advise based on your constraints, not a preferred vendor.
Cost depends on whether you need a performance tuning engagement, a new pipeline build, or a full platform migration. Most engagements start with a fixed-price architecture review before a project is scoped and quoted.
Yes. Our managed Spark operations retainer covers cluster monitoring, cost optimisation, job troubleshooting and on-call support during Australian business hours.
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