We build with Apache Spark
We use Apache Spark to process large-scale data for analytics, ETL pipelines, and machine learning workloads that support your business goals. From cluster tuning and job optimization to integration with your data platform, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTDistributed Compute
We process massive datasets in parallel across clusters.
In-Memory Processing
We speed up transformations by keeping data in memory.
Batch & Stream Unified
We run batch jobs and real-time streams in one engine.
BENEFITS OF APACHE SPARK technology
We use Apache Spark to process massive datasets, unify batch and streaming pipelines, and scale machine learning workloads.
BUILD
[01]- Design ETL pipelines
- Model data partitioning
- Configure Spark clusters
- Set execution parameters
ENGAGE
[02]- Process massive datasets
- Power streaming analytics
- Run distributed ML training
- Optimize query execution
GROW
[03]- Scale cluster capacity
- Add streaming pipelines
- Reduce processing costs
- Extend to new data sources
Our Apache Spark Technology Stack
We combine Apache Spark with Kubernetes, Hadoop YARN, Delta Lake, and cloud storage like S3 or HDFS for cluster orchestration, data lake architecture, and pipeline scheduling across your data platform.
Custom Apache Spark development company
With our Apache Spark development services, we build large-scale data processing pipelines, distributed ETL workflows, and real-time streaming analytics, tailored to your boldest business goals. Having years of experience with Spark, our engineers harness its full potential to deliver fast, reliable data infrastructure across industries and company sizes. Whether it's building a new data pipeline or modernizing an existing Hadoop-based system, we design architectures that scale with your data volume. Our range of Apache Spark development services spans consulting, pipeline architecture, development, and ongoing optimization. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep distributed-systems expertise and a pragmatic approach to data engineering and problem-solving. All this to make sure your pipelines stay fast, cost-efficient, and reliable as your data grows, and moves your business forward.
OUR APACHE SPARK SERVICES
We build, modernize, and support Apache Spark pipelines around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Scalable ETL and batch processing pipelines built around your data sources and destinations.
Real-time streaming pipelines for event processing, monitoring, and near-instant business analytics.
Distributed model training and feature engineering pipelines using MLlib across large datasets.
Migrate legacy Hadoop or MapReduce workloads to Spark, or optimize an existing cluster setup.
Meet our Apache Spark experts
A curated selection of senior specialists currently available for new engagements.
VA
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use Apache Spark and what it can bring to your project. Have a specific requirement?
How does SoftDoes use Apache Spark?
We use Apache Spark to build large-scale ETL pipelines, distributed machine learning workflows, and real-time streaming analytics. We design cluster configurations and data architectures around your workload's scale and latency requirements.
What types of workloads do you build with Spark?
We build batch ETL pipelines, data lake transformations, distributed model training jobs, and structured streaming applications for near-real-time analytics.
Can Apache Spark integrate with our existing data infrastructure?
Yes. Spark connects with storage layers like S3, HDFS, and Delta Lake, and can read from or write to most existing databases, data warehouses, and message queues such as Kafka.
Do you work with PySpark or Scala Spark projects?
Yes. We work in both PySpark and Scala depending on your team's existing codebase and skillset, and can help migrate between the two if needed.
Do you deploy Spark on Databricks, EMR, or Kubernetes?
Yes, we deploy Spark on whichever platform fits your environment, including Databricks, AWS EMR, Hadoop YARN, or Kubernetes, and help you choose based on cost and operational overhead.
Can you modernize an existing Hadoop or MapReduce system with Spark?
Yes. We can migrate legacy Hadoop or MapReduce jobs to Spark incrementally, or rebuild the pipeline from scratch, depending on your timeline and data volume.
How do you decide whether Spark fits a project?
We look at your data volume, latency requirements, existing infrastructure, and team experience, then confirm Spark is the right fit before starting development.
