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A+ rating

Average 5.00 rating
What's included

Industry 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.

Streaming Scalable Optimised
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Spark Streaming Implementation Australia

Real-time analytics pipeline builds on Spark Structured Streaming for low-latency event processing.

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Managed Spark Operations & Support

Cluster monitoring, job troubleshooting and on-call support after go-live.

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Spark on Kubernetes Australia

Container-native Spark deployment for teams standardizing on Kubernetes over managed platforms.

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Hadoop to Spark Migration

Structured migration off legacy MapReduce jobs onto a Spark-based processing layer.

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Spark Performance Tuning & Cost Optimization

Cluster sizing, partitioning and shuffle tuning to cut runtime and infrastructure spend.

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Batch ETL & Data Pipeline Engineering

Scheduled Spark SQL jobs for warehouse loads, transformations and reconciliation.

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Databricks Lakehouse Architecture

Delta Lake, workspace design and governance for teams standardizing on Databricks.

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Spark Machine Learning Pipelines

MLlib-based feature engineering, training and inference pipelines at cluster scale.

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Platforms

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.

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Tailored Spark Architecture

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Flexible Deployment Options

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Scalable Data Processing

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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

How We Deliver

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.

01

Spark architecture review

02

Roadmap & fixed-scope proposal

03

Build, tune & migrate

04

Handover or managed support

Cluster Configuration Job Design Cost Analysis Performance Review

The team of experts at CDU will evaluate and review cluster configuration, job design and cost to identify the highest-impact fixes first.

Cluster Configuration Job Design Cost Analysis Performance Review

In the next stage, our team will come-up with a prioritized plan proposal with what actually we are going to offer such as covering tuning, new pipelines or platform migration, with timeline and pricing.

Cluster Configuration Job Design Cost Analysis Performance Review

Having completed the proposal formalities, our team will conduct Pipeline builds, performance tuning or Databricks/Kubernetes migration in demo-able sprints.

Cluster Configuration Job Design Cost Analysis Performance Review

Full documentation and training, or an ongoing managed Spark operations retainer.

Hey 👋 I am Gokul, Sr. Business Consultant
Hey 👋 I am Gokul, Sr. Business Consultant
Let's talk about your growth
Why CDU for Apache Spark

The 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.

Typical outcomes

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 Onboarding

Integrations

130+

Dealerships Connected

Operations

99.9%

Platform Availability
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Client Testimonials

Trusted by Businesses.
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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We never felt that we were dealing with sales people, but rather with a team that had a deep technical understanding. AppGurus built an app to improve communication between the client and their customers. They also integrated an AI element to make custom recommendations to users.

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These guys are great and easy to work with. Their patience and service has been amazing considering we come from a non tech background. Highly recommend them.

AppGurus provided the outcome what I was expecting. I just shared my idea with their team which I wanted to publish in the market and they guided me and made this product in a very efficient way. My apps are available on stores and I am really happy with the service they provided.

App Gurus delivered a fully-optimized product that went beyond the project's original scope, much to the client's delight. The team was highly communicative, and internal stakeholders were particularly impressed with the vendor's team dynamic, work ethic, and solutions-based approach.

The app has earned a significant amount of downloads since its launch. Thus far, the platform has satisfied expectations and matches the original vision. App Gurus is dedicated to the project, ensuring they’re approachable and timely in the case of issues.

App Gurus listened to client needs and ensured any technical issues were resolved quickly. The cost was higher than expected, and there was a slight staffing mishap early on, but user responses have been good.

App Gurus has delivered a functional and easy-to-use app in a timely manner. The team has ensured stellar communication and efficiently addressed the project requirements. Their receptiveness, design competencies, and innovative approach are key elements of their work.

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When I was looking for a company to take on my app, most companies wanted to charge me more than my budget just to create a proof of concept. App Gurus came up with a plausible solution on how to make my idea work at our first meeting. This was my first app development project and the team walked me through the process to not only build the app, but also assisted with all things Apple and getting it on to the App Store. The result is a very professional and funky product.

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01 / 30

4.8

Average Rating

Across Leading Review Platforms

Independent reviews that speak for us
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Client Testimonials

Frequently 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.

Responds 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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