Run batch and streaming big data workloads using our developer-friendly cloud platform. Derive insights, delight your customers, and drive business growth.
DigitalOcean is a developer-friendly cloud platform that makes big data accessible to even the smallest of businesses. With managed compute and storage infrastructure, your team can completely control your big data stack, and run workloads reliably, securely, and inexpensively.
You’re going to need substantial compute if you want to crunch terabytes or petabytes of data. DigitalOcean is built with best-in class Intel processors that run your workloads at blazing speeds. With DigitalOcean, you can run your big data jobs directly on VMs or Kubernetes.
Run and manage your app directly on our VMs, or as we call them, Droplets. Choose between Basic, General Purpose, CPU-Optimized, or Memory-Optimized VMs. Spin up Droplets with your choice of Linux OS in 55 seconds or less.
Spin up a managed Kubernetes cluster in minutes, and run your app as microservices using Docker containers. Scale up or down as needed. Pay only for your worker nodes, as the master is free.
It should be easy and inexpensive to store, scale, and retrieve your data. DigitalOcean provides infrastructure flexibility so you can build and operate your big data workload with the best-fit storage technology for your use case and technology stack.
Store vast amounts of data in five global data centers with S3-compatible tools. Cut retrieval times by up to 70% with a built-in CDN that caches data at 25+ points of presence.
All Droplets feature local SSD for super fast operations. With Volumes, you can attach extra highly available and resizable SSD storage as needed.
Easily build, scale, and stream large data pipelines with Managed Kafka which provides cost-effective pricing and simplicity for SMBs to handle multi-node clusters.
After spinning up your infrastructure, you’re free to deploy whatever big data framework is the best fit for your workload. Many DigitalOcean customers utilize Apache Hadoop or Spark.
Apache Hadoop is a processing framework that provides batch processing. Hadoop stores distributed data using the Hadoop Distributed File System (HDFS), and processes data where it is stored using the MapReduce engine.
Apache Spark is a next-generation processing framework with both batch and stream processing capabilities. Spark focuses primarily on speeding up batch processing workloads using full in-memory computation and processing optimization.
We run a Mesos Cluster with HDFS on DigitalOcean. This cluster handles our data pipeline, model generation, databases, and end-user applications, enabling us to process over 200k requests per second.
DigitalOcean’s low-cost servers made it feasible for us to offer a free trial to new customers.
Co-Founder and CTO
We still use some Amazon services, but 95% of our system works with DigitalOcean nodes.
DigitalOcean’s community tutorials and product docs help you quickly get started. Here’s just a small sample of the resources available.