Apache Hive
7.4#3 in query engine softwareFree planFree planAPILinuxmacOSSelf-hosted
VisitApache Hive vs Teradata Database
Teradata Database
5.5#19 in data warehouse softwareWebAPILinuxSelf-hosted
VisitWhat both publish
| Fact | Apache Hive | Teradata Database |
|---|---|---|
| Deployment | The official quickstart provides Docker instructions for running HiveServer2 and the Metastore in a container. | Teradata documentation lists Teradata Cloud, public cloud, Teradata hardware, commodity hardware, and hybrid cloud as deployment options. |
| Integrations | The project site names Spark, Presto, and Impala as integrations; it also describes Apache Ranger for authorization and Apache Atlas for lineage and governance. | Teradata’s developer portal demonstrates connections and workflows using JDBC, dbt, Teradata Parallel Transporter, and object storage such as AWS S3, Google GCS, and Azure Blob. |
| Purpose | Apache Hive is a distributed, fault-tolerant data warehouse system for reading, writing, and managing large datasets with SQL. | Teradata Database is the core of the Teradata SQL Engine and supports complex analytic workloads using massively parallel processing. |
| Security | The site describes Kerberos authentication, fine-grained access control, and audit logging; HiveServer2 documentation also lists LDAP, PAM, and custom authentication options. | Teradata says it supports identity and access management, optional customer-managed encryption keys, and TLS 1.3 for client sessions. |
Apache Hive plans
- Apache HiveFree
Teradata Database plans
- Teradata DatabasePriced on request
Pick Apache Hive if…
Hive suits teams managing large datasets with SQL that need a data warehouse with distributed storage and integrations. Its documentation lists Linux for production use and Mac for development, with Windows also among the listed platforms.
Pick Teradata Database if…
Teradata Database suits enterprise teams with complex analytic workloads, including organizations in the listed industries such as financial services, healthcare, retail, and manufacturing. It may also fit teams that need in-database analytics, hybrid deployment, or data lake support.