AWS Lake Formation vs HPCC Systems
AWS Lake Formation
aws.amazon.com
HPCC Systems
hpccsystems.comSide by side
| Figure | AWS Lake Formation | HPCC Systems |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 3 |
| Spec score · Data Lake Software | 7.8 (#6) | 8.7 (#2) |
| Founded | 2006 | 2000 |
| Purpose | AWS Lake Formation helps centrally govern, secure, and share data for analytics and machine learning. | HPCC Systems is an open-source, data-intensive supercomputing platform for enterprise big-data problems. |
Plans
AWS Lake Formation
- PermissionsFree
- Governed TablesPriced on request
- Storage APIPriced on request
- Storage optimizerPriced on request
HPCC Systems
- Community EditionFree
- Open sourceFree
- Enterprise subscriptionPriced on request
In their own words
AWS Lake Formation
AWS Lake Formation has no write-up on Specifiction yet; its figures are above.
HPCC Systems
HPCC Systems is a free, open-source platform for enterprise big-data processing and data-lake management. Its core components are ECL, a declarative and modular language, Thor for data refinement, and Roxie for data delivery. ECL's compiler optimizes programs for parallel processing and compiles them into C++. Thor handles ingestion, transformation, linking, and indexing; Roxie serves concurrent queries. Clusters can scale from two computers to more than a thousand commodity-hardware nodes. The cloud-native platform runs on Kubernetes and supports Azure Kubernetes Service and Amazon Elastic Kubernetes Service. Storage options include AWS S3, Azure Blob Storage, AWS Elastic Block Store, Azure Files, and Azure Disks. Security capabilities include end-to-end encryption, service meshes, OAuth 2.0 with Azure AD support, JWT, and configurable controls for data and services. The Machine Learning Library offers ECL-accessible algorithms for building and testing prediction models. Integrations include Spark, Kafka, Couchbase, Redis, Memcached, Pentaho, R, JDBC, Java APIs, and Tableau data connectors. ESP exposes queries through XML, HTTP, SOAP, and REST interfaces, and deployed queries can be called with REST/JSON. The platform is free to use under the Apache 2.0 license and is self-hosted.