Running Apache Spark shouldn’t mean choosing between speed and cost. With Kamatera’s high-performance cloud infrastructure, you get dedicated resources, lightning-fast NVMe storage, and the flexibility to scale your Spark clusters exactly when you need them—without the enterprise price tag.
Deploy Apache Spark on Kamatera and eliminate the bottlenecks of big data. Whether you are running complex iterative algorithms, real-time stream processing, or massive-scale data transformations, our enterprise-grade hardware ensures your Spark clusters perform at peak efficiency.
Why run Apache Spark on Kamatera?
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Frequently asked questions
Apache Spark is an open-source distributed computing framework designed for processing large datasets quickly across clusters of computers. It provides a unified analytics engine that can handle batch processing, real-time stream processing, machine learning, and interactive queries all within a single platform.
Spark works by distributing data and computations across multiple machines, processing information in parallel to achieve speeds up to 100 times faster than traditional Hadoop MapReduce for certain workloads. Data engineers and scientists use Spark to transform raw data, build ETL pipelines, run complex analytics, train machine learning models, and power data-driven applications. It supports multiple programming languages including Python, Scala, Java, and R, making it accessible to diverse technical teams.
The following are the minimum system requirements for Apache Spark:
· 8GB of storage
· 2 CPU threads per host
· Internet access to download the Canonical Apache Spark image
· Access to a Kubernetes cluster
For more details, refer to the Apache Spark hardware provisioning.
Apache Spark is primarily used to manage and accelerate large-scale data workloads across various domains. Its core use cases include high-speed ETL (Extract, Transform, Load) operations, where it efficiently cleans and prepares massive datasets before moving them to storage or data warehouses. It is also the platform of choice for running complex, interactive SQL queries and deep business intelligence analysis against big data, serving as a powerful, unified data warehousing solution.
Beyond batch processing, Spark is critical for advanced, low-latency analytics. This involves real-time stream processing of live data (like financial trades or IoT sensor readings) using Spark Streaming or Structured Streaming. Furthermore, its built-in MLlib library enables the development and distributed training of large-scale machine learning models, and its GraphX component is used for analyzing relationships in network data for tasks such as social network analysis and fraud detection.
Kamatera’s cloud infrastructure is ideal for organizations that require high discretion and security. Apart from offering a secure virtual environment for operations, our cloud services provide these advantages:
· Reliable and resilient cloud networks, which eliminate the risk of technical failures by collecting resources from the shared infrastructure
· Increased data privacy
· Energy efficiency by using shared electrical resources
Kamatera offers a free 30-day trial period. This free trial offers services worth up to $100 on 1 server. After signing up, you can use the management console to deploy a server and test our infrastructure. You can select a data center, operating system, CPU, RAM, storage, and other system preferences.
All Kamatera servers use NVMe SSD storage, providing the high IOPS and low latency that Spark’s distributed architecture requires. This is particularly important for shuffle operations and when working with large RDDs or DataFrames. You can also attach additional storage volumes as needed for data lake workloads.
You can choose from a wide range of Linux distributions (including Ubuntu, CentOS, and Debian) or Windows Server. Most Spark users prefer our optimized Linux images for the best performance and compatibility with the Hadoop ecosystem.
New servers deploy in under five minutes. You can add nodes to your existing cluster rapidly when workloads increase, or shut down nodes when you’re done processing. The API enables programmatic scaling, so you can build automation that adjusts your cluster size based on your actual workload demands.