diff --git a/about/feature-comparison.md b/about/feature-comparison.md index 17daade53f..4fe812d966 100644 --- a/about/feature-comparison.md +++ b/about/feature-comparison.md @@ -1,6 +1,6 @@ --- title: Compare the features in TigerData products -excerpt: Get an overview of features available in Tiger Cloud vs self-hosted TimescaleDB +excerpt: Get an overview of features available in Tiger Cloud on AWS, Tiger Cloud on Azure, and self-hosted TimescaleDB products: [cloud, self_hosted] keywords: [TimescaleDB, Tiger Cloud] --- @@ -9,85 +9,85 @@ keywords: [TimescaleDB, Tiger Cloud] The following table compares the features available in $CLOUD_LONG and self-hosted $TDB_COMMUNITY. -| Feature | $CLOUD_LONG on AWS | $CLOUD_LONG on Azure | $TIMESCALE_DB | -|---------------------------------------------------------------------------------------------------------------------|-------------------------------------------|-------------------------------------|------------------------------| -| **Best-in-сlass $PG performance** | | | | -| Automatic partitioning via hypertables for efficient indexes and faster ingest | ✓ | ✓ | ✓ | -| Continuous aggregates | ✓ | ✓ | ✓ | -| Time/partition-oriented constraint exclusion for faster queries | ✓ | ✓ | ✓ | -| Skip scans, ordered appends, custom optimizations for faster `LIMIT` and `DISTINCT` queries | ✓ | ✓ | ✓ | -| Columnar storage for accelerated scans | ✓ | ✓ | ✓ | -| Vectorized query execution (SIMD) | ✓ | ✓ | ✓ | -| Specialized vector indexes for AI applications | ✓ | ✓ | ✓ | -| $PG_CONNECTOR_CAP | ✓ | ✓ | Manual | -| $S3_CONNECTOR_CAP | ✓ | ✗ | ✗ | -| Source Apache Kafka connector | ✓ | ✗ | ✗ | -| $LAKE_LONG destination connector from $CLOUD_LONG to Iceberg-backed S3 Tables | ✓ | ✗ | ✗ | -| In-Console CSV, Parquet, and text file imports | ✓ | ✗ | ✗ | -| **Flexible analysis with full SQL** | | | | -| Complete $PG ecosystem including all $PG features, connectors, and third-party drivers | ✓ | ✓ | ✓ | -| Cross-table JOINs for time-series and events tables with relational tables | ✓ | ✓ | ✓ | -| Rich timestamp and timezone support | ✓ | ✓ | ✓ | -| Flexible time-bucketing for time-oriented analysis | ✓ | ✓ | ✓ | -| Advanced hyperfunctions including interpolation, approximation, and visualization functions | ✓ | ✓ | ✓ | -| Geospatial and vector data types | ✓ | ✓ | ✓ | -| **Automated data management** | | | | -| Native compression (up to 98% storage savings) | ✓ | ✓ | ✓ | -| Columnar storage format with fast scans | ✓ | ✓ | ✓ | -| Data retention policies | ✓ | ✓ | ✓ | -| Data tiering with automated policies | ✓ | ✗ | ✗ | -| Data reordering for efficient disk scans | ✓ | ✓ | ✓ | -| Data downsampling for efficient historical analysis | ✓ | ✓ | ✓ | -| Background job scheduler and user-defined jobs | ✓ | ✓ | ✓ | -| **Enterprise scalability** | | | | -| Disaggregated compute and storage | ✓ | ✓ | Manual | -| Dynamic compute resizing | ✓ | ✓ | ✗ | -| Dynamic disk storage with usage-based pricing | ✓ | ✓ | ✗ | -| Dynamic I/O provisioning for high-read/ high-write performance | ✓ | ✓ | Manual | -| Low-cost storage with infinite capacity on S3 | ✓ | ✗ | ✗ | -| Transparent queries across high-performance and low-cost tiers | ✓ | ✗ | ✗ | -| Read replicas with load balancing for seamless read scaling | ✓ | ✓ | Manual | -| Connection pooling for connection scaling | ✓ | ✓ | Manual | -| Automated resource-aware parameter tuning | ✓ | ✓ | ✗ | -| Terraform for infrastructure-as-code control | ✓ | ✓ | ✗ | -| **High availability and reliability** | | | | -| Multi-AZ deployments for high availability | ✓ | ✓ | Manual | -| Continuous incremental backup and automated restore | ✓ | ✓ | Manual | -| Cross-region backup | ✓ | 🔜 | ✗ | -| Point-in-time recovery and branching | ✓ | ✓ | Manual | -| Regular database and disk snapshots to enable fast restore | ✓ | ✓ | ✗ | -| Rapid recovery for all services by fast database restart and remote disk remount | ✓ | ✓ | ✗ | -| Memory guard protections to avoid database out-of-memory crashes | ✓ | ✓ | ✗ | -| Decoupled control/data planes for greater resilience | ✓ | ✓ | ✗ | -| Commercial SLAs | ✓ | ✓ | ✗ | -| **Automated upgrades and software patching** | | | | -| Automated upgrades during maintenance windows | ✓ | ✓ | ✗ | -| Phased, zero-downtime $TIMESCALE_DB and $PG minor upgrades | ✓ | ✓ | ✗ | -| $PG major version upgrades with forking workflow and disk snapshots to minimize downtime | ✓ | ✓ | ✗ | -| HA-replica-aware coordinated upgrades | ✓ | ✓ | ✗ | -| Fleet-wide version and stability monitoring with staged roll-out/roll-back upgrades | ✓ | ✓ | ✗ | -| **Security and compliance** | | | | -| SOC 2 Type 2, GDPR, HIPAA certified compliance | ✓ | ✓ | ✗ | -| Data encryption at rest (both disk and backup) | ✓ | ✓ | Manual | -| Data encryption in transit | ✓ | ✓ | Manual | -| Database SSL with fully verifiable certificate chains | ✓ | ✓ | ✗ | -| Control plane role-based access control | ✓ | ✓ | ✗ | -| Database role-based access control | ✓ | ✓ | ✓ | -| Multi-factor authentication | ✓ | ✓ | ✗ | -| Corporate SSO and SAML | ✓ | ✓ | ✗ | -| VPC peering | ✓ | ✗ | ✗ | -| AWS Transit Gateway | ✓ | ✗ | ✗ | -| Layered database "privilege escalation" protections | ✓ | ✓ | ✗ | -| Secure SDLC practices and vulnerability scanning, third-party pen testing | ✓ | ✓ | ✗ | -| **Deep observability** | | | | -| Operational database visibility to understand performance, uncover regressions, optimize performance | ✓ | ✓ | ✗ | -| Automated query analysis and statistics | ✓ | ✓ | ✗ | -| Per-query drill-downs into execution times, row results, plans, memory buffer management, cache performance | ✓ | ✓ | ✗ | -| In-Console metric visualization and system logs | ✓ | ✓ | ✗ | -| Exporters to AWS CloudWatch, Prometheus, Datadog | ✓ | ✗ | ✗ | -| Connection monitoring | ✓ | ✓ | Manual | -| Connection management | ✓ | ✓ | Manual | -| **Production-grade support and operations** | | | | +| Feature | $CLOUD_LONG on AWS | $CLOUD_LONG on Azure | self-hosted $TIMESCALE_DB | +|---------------------------------------------------------------------------------------------------------------------|-------------------------------------------|-------------------------------------|---------------------------------------------| +| **Best-in-сlass $PG performance** | | | | +| Automatic partitioning via hypertables for efficient indexes and faster ingest | ✓ | ✓ | ✓ | +| Continuous aggregates | ✓ | ✓ | ✓ | +| Time/partition-oriented constraint exclusion for faster queries | ✓ | ✓ | ✓ | +| Skip scans, ordered appends, custom optimizations for faster `LIMIT` and `DISTINCT` queries | ✓ | ✓ | ✓ | +| Columnar storage for accelerated scans | ✓ | ✓ | ✓ | +| Vectorized query execution (SIMD) | ✓ | ✓ | ✓ | +| Specialized vector indexes for AI applications | ✓ | ✓ | ✓ | +| $PG_CONNECTOR_CAP | ✓ | ✓ | Manual | +| $S3_CONNECTOR_CAP | ✓ | ✗ | ✗ | +| Source Apache Kafka connector | ✓ | ✗ | ✗ | +| $LAKE_LONG destination connector from $CLOUD_LONG to Iceberg-backed S3 Tables | ✓ | ✗ | ✗ | +| In-Console CSV, Parquet, and text file imports | ✓ | ✗ | ✗ | +| **Flexible analysis with full SQL** | | | | +| Complete $PG ecosystem including all $PG features, connectors, and third-party drivers | ✓ | ✓ | ✓ | +| Cross-table JOINs for time-series and events tables with relational tables | ✓ | ✓ | ✓ | +| Rich timestamp and timezone support | ✓ | ✓ | ✓ | +| Flexible time-bucketing for time-oriented analysis | ✓ | ✓ | ✓ | +| Advanced hyperfunctions including interpolation, approximation, and visualization functions | ✓ | ✓ | ✓ | +| Geospatial and vector data types | ✓ | ✓ | ✓ | +| **Automated data management** | | | | +| Native compression (up to 98% storage savings) | ✓ | ✓ | ✓ | +| Columnar storage format with fast scans | ✓ | ✓ | ✓ | +| Data retention policies | ✓ | ✓ | ✓ | +| Data tiering with automated policies | ✓ | ✗ | ✗ | +| Data reordering for efficient disk scans | ✓ | ✓ | ✓ | +| Data downsampling for efficient historical analysis | ✓ | ✓ | ✓ | +| Background job scheduler and user-defined jobs | ✓ | ✓ | ✓ | +| **Enterprise scalability** | | | | +| Disaggregated compute and storage | ✓ | ✓ | Manual | +| Dynamic compute resizing | ✓ | ✓ | ✗ | +| Dynamic disk storage with usage-based pricing | ✓ | ✓ | ✗ | +| Dynamic I/O provisioning for high-read/ high-write performance | ✓ | ✓ | Manual | +| Low-cost storage with infinite capacity on S3 | ✓ | ✗ | ✗ | +| Transparent queries across high-performance and low-cost tiers | ✓ | ✗ | ✗ | +| Read replicas with load balancing for seamless read scaling | ✓ | ✓ | Manual | +| Connection pooling for connection scaling | ✓ | ✓ | Manual | +| Automated resource-aware parameter tuning | ✓ | ✓ | ✗ | +| Terraform for infrastructure-as-code control | ✓ | ✓ | ✗ | +| **High availability and reliability** | | | | +| Multi-AZ deployments for high availability | ✓ | ✓ | Manual | +| Continuous incremental backup and automated restore | ✓ | ✓ | Manual | +| Cross-region backup | ✓ | 🔜 | ✗ | +| Point-in-time recovery and branching | ✓ | ✓ | Manual | +| Regular database and disk snapshots to enable fast restore | ✓ | ✓ | ✗ | +| Rapid recovery for all services by fast database restart and remote disk remount | ✓ | ✓ | ✗ | +| Memory guard protections to avoid database out-of-memory crashes | ✓ | ✓ | ✗ | +| Decoupled control/data planes for greater resilience | ✓ | ✓ | ✗ | +| Commercial SLAs | ✓ | ✓ | ✗ | +| **Automated upgrades and software patching** | | | | +| Automated upgrades during maintenance windows | ✓ | ✓ | ✗ | +| Phased, zero-downtime $TIMESCALE_DB and $PG minor upgrades | ✓ | ✓ | ✗ | +| $PG major version upgrades with forking workflow and disk snapshots to minimize downtime | ✓ | ✓ | ✗ | +| HA-replica-aware coordinated upgrades | ✓ | ✓ | ✗ | +| Fleet-wide version and stability monitoring with staged roll-out/roll-back upgrades | ✓ | ✓ | ✗ | +| **Security and compliance** | | | | +| SOC 2 Type 2, GDPR, HIPAA certified compliance | ✓ | ✓ | ✗ | +| Data encryption at rest (both disk and backup) | ✓ | ✓ | Manual | +| Data encryption in transit | ✓ | ✓ | Manual | +| Database SSL with fully verifiable certificate chains | ✓ | ✓ | ✗ | +| Control plane role-based access control | ✓ | ✓ | ✗ | +| Database role-based access control | ✓ | ✓ | ✓ | +| Multi-factor authentication | ✓ | ✓ | ✗ | +| Corporate SSO and SAML | ✓ | ✓ | ✗ | +| VPC peering | ✓ | ✗ | ✗ | +| AWS Transit Gateway | ✓ | ✗ | ✗ | +| Layered database "privilege escalation" protections | ✓ | ✓ | ✗ | +| Secure SDLC practices and vulnerability scanning, third-party pen testing | ✓ | ✓ | ✗ | +| **Deep observability** | | | | +| Operational database visibility to understand performance, uncover regressions, optimize performance | ✓ | ✓ | ✗ | +| Automated query analysis and statistics | ✓ | ✓ | ✗ | +| Per-query drill-downs into execution times, row results, plans, memory buffer management, cache performance | ✓ | ✓ | ✗ | +| In-Console metric visualization and system logs | ✓ | ✓ | ✗ | +| Exporters to AWS CloudWatch, Prometheus, Datadog | ✓ | ✗ | ✗ | +| Connection monitoring | ✓ | ✓ | Manual | +| Connection management | ✓ | ✓ | Manual | +| **Production-grade support and operations** | | | | | 24/7 follow-the-sun support with global support team across APAC, EMEA, and Americas | ✓ | ✓ | [Contact sales](mailto:sales@tigerdata.com) | | Production support (severity 1) | ✓ | ✓ | [Contact sales](mailto:sales@tigerdata.com) | | Architectural reviews, data modeling, and query optimization and assistance, feature testing, and migration support | ✓ | ✓ | [Contact sales](mailto:sales@tigerdata.com) | diff --git a/about/pricing-and-account-management.md b/about/pricing-and-account-management.md index baaf802e8f..592dfb1c87 100644 --- a/about/pricing-and-account-management.md +++ b/about/pricing-and-account-management.md @@ -1,6 +1,6 @@ --- title: Billing and account management -excerpt: Manage billing and account information for your Tiger Data account +excerpt: Manage billing and account information for your Tiger Data account for Tiger Cloud on AWS and Tiger Cloud on Azure products: [cloud] keywords: [billing, accounts, admin] tags: [payment, billing, costs] diff --git a/getting-started/index.md b/getting-started/index.md index dceb57131e..08dd2f3422 100644 --- a/getting-started/index.md +++ b/getting-started/index.md @@ -1,6 +1,6 @@ --- title: Get started with Tiger Data -excerpt: Supercharge your real-time analytics on time-series data with Tiger Cloud. Create a free account, launch your first service, and use some of the advanced features +excerpt: Supercharge your real-time analytics on time-series data with Tiger Cloud on AWS and Tiger Cloud on Azure. Create a free account, launch your first service, and use some of the advanced features products: [cloud] content_group: Getting started --- diff --git a/getting-started/run-queries-from-console.md b/getting-started/run-queries-from-console.md index 1c99ed978a..05ca600f43 100644 --- a/getting-started/run-queries-from-console.md +++ b/getting-started/run-queries-from-console.md @@ -1,6 +1,6 @@ --- title: Run your queries from Tiger Cloud Console -excerpt: Choose the right tool to manage your data. Tiger Cloud offers the data mode, the SQL editor, and the SQL Assistant to better address your needs +excerpt: Choose the right tool to manage your data. Tiger Cloud on AWS and Tiger Cloud on Azure offer the data mode, the SQL editor, and the SQL Assistant to better address your needs products: [cloud] content_group: Getting started --- diff --git a/getting-started/services.md b/getting-started/services.md index 697619514a..0ee8fe5bdb 100644 --- a/getting-started/services.md +++ b/getting-started/services.md @@ -1,5 +1,5 @@ --- -title: Create your first Tiger Cloud service +title: Create your first service for Tiger Cloud on AWS and Tiger Cloud on Azure excerpt: Tiger Cloud offers a range of capabilities to accommodate your real-time analytics and AI and vector workloads. Learn more about each of them and create your first service in Tiger Cloud Console products: [cloud] content_group: Getting started diff --git a/use-timescale/compression/decompress-chunks.md b/use-timescale/compression/decompress-chunks.md index 025e256d50..a987fdbb25 100644 --- a/use-timescale/compression/decompress-chunks.md +++ b/use-timescale/compression/decompress-chunks.md @@ -1,6 +1,6 @@ --- title: Decompression -excerpt: While TimescaleDB supports modifying compressed data, for bulk operations you need to decompress it first. Learn to decompress data manually +excerpt: Manually decompress compressed chunks by name, time, or more precise constraints products: [cloud, mst, self_hosted] keywords: [compression, hypertables, backfilling] tags: [decompression]