mirror of
https://github.com/open-metadata/OpenMetadata.git
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Updated number of connectors and other fixes (#19699)
This commit is contained in:
Suresh Srinivas
GitHub
parent
75952889a1
commit
e3f2b78f3c
@ -3,6 +3,7 @@
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{% step srNumber=1 %}
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{% stepDescription title="1. Visit the Services Page" %}
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Click `Settings` in the side navigation bar and then `Services`.
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The first step is to ingest the metadata from your sources. To do that, you first need to create a Service connection first.
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@ -24,7 +24,7 @@ with links to more detailed documentation.
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## Step 1: Set up a Data Connector
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Once you’re able to login to your Collate instance, set up a data connector to start bringing metadata into Collate.
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There are [80+ turnkey connectors](/connectors) to various services: data warehouses, data lakes, databases, dashboards,
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There are [90+ turnkey connectors](/connectors) to various services: data warehouses, data lakes, databases, dashboards,
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messaging services, pipelines, ML models, storage services, and other Metadata Services.
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Connections to [custom data sources](/connectors/custom-connectors) can also be created via API.
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@ -7,7 +7,7 @@ collate: true
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# Getting Started
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Welcome to Collate's unified platform for data discovery, observability, and governance! Our platform centralizes all
|
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the context around your data to help you build high-quality and AI assets. This guide gives you all the information you
|
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the context around your data to help you build high-quality data and AI assets. This guide gives you all the information you
|
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need to set up your Collate environment in 30 minutes.
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## How Does Collate Work?
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@ -15,14 +15,14 @@ need to set up your Collate environment in 30 minutes.
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Collate is designed for both technical and non-technical data practitioners to work together across a broad set of use cases,
|
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including data discovery, lineage, observability, quality, collaboration, governance, and insights.
|
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|
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A library of 80+ turnkey connectors is available to easily ingest metadata into Collate, such as data warehouses, data lakes,
|
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A library of 90+ turnkey connectors is available to easily ingest metadata into Collate, such as data warehouses, data lakes,
|
||||
streaming, dashboards, ML models, and more. APIs are also available to easily ingest metadata from custom data sources.
|
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Metadata from these different sources is organized into a Unified Metadata Graph, which provides a single, comprehensive
|
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source of truth across your entire data estate.
|
||||
|
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This centralized information is surfaced through a unified user interface for all your use cases so that different data
|
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practitioners no longer need to switch between different data catalogs, quality, or governance tools. Additionally,
|
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Collate can be extended through the application ecosystem, such as with AI productivity applications like MetaPilot,
|
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Collate can be extended through the application ecosystem, such as with AI productivity applications like Collate AI,
|
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or with customer-built workflows to integrate Collate with your existing systems. These capabilities are built around
|
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native collaboration capabilities for shared workflows across different teams so that every data practitioner can work
|
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together: data platform, data governance, data scientist/analyst, and business user.
|
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@ -31,7 +31,7 @@ together: data platform, data governance, data scientist/analyst, and business u
|
||||
|
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Before we get started, here’s a quick summary of some of Collate’s main features:
|
||||
|
||||
- **Discovery**: integrated catalog, quality, and glossary; natural language search, filtering, and faceting, 80+ turnkey data connectors, and MetaPilot AI Chatbot.
|
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- **Discovery**: integrated catalog, quality, and glossary; natural language search, filtering, and faceting, 90+ turnkey data connectors, and Collate AI Chatbot.
|
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- **Lineage**: table and column-level lineage, automated data estate mapping and APIs, lineage layers and search, governance and PII automation and manual customization.
|
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- **Observability**: alerting and notifications, incident management, third-party notifications, pipeline monitoring, root cause analysis, anomaly detection, data profiler.
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- **Quality**: table and column test cases, no-code and SQL data quality tests, test suites, test case reporting, quality dashboards, widgets and data quality lineage maps.
|
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@ -7,10 +7,10 @@ slug: /quick-start/getting-started/day-1
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Get started with your OpenMetadata service in a few simple steps:
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1. Set up a Data Connector: Connect your data sources to begin collecting metadata.
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2. Ingest Metadata: Run the metadata ingestion process to gather and push data insights.
|
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3. Invite Users: Add team members to collaborate and manage metadata together.
|
||||
4. Explore the Features: Dive into OpenMetadata's extensive feature set to unlock the full potential of your data.
|
||||
1. **Set up a Data Connector**: Connect your data sources to begin collecting metadata.
|
||||
2. **Ingest Metadata**: Run the metadata ingestion process to gather and push data insights.
|
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3. **Invite Users**: Add team members to collaborate and manage metadata together.
|
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4. **Explore the Features**: Dive into OpenMetadata's extensive feature set to unlock the full potential of your data.
|
||||
|
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**Ready to begin? Let's get started!**
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@ -20,7 +20,7 @@ You should receive your initial OpenMetadata credentials from OpenMetadata suppo
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## Step 1: Set up a Data Connector
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Once you have logged into your OpenMetadata instance, set up a data connector to start ingesting metadata. OpenMetadata provides [80+ turnkey connectors](/connectors) for a wide range of services, including:
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Once you have logged into your OpenMetadata instance, set up a data connector to start ingesting metadata. OpenMetadata provides [90+ turnkey connectors](/connectors) for a wide range of services, including:
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|
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- Databases
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- Dashboards
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@ -11,9 +11,9 @@ Welcome to OpenMetadata's unified platform for data discovery, observability, an
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|
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OpenMetadata is designed to support both technical and non-technical data practitioners across various use cases, including data discovery, lineage, observability, quality, collaboration, governance, and insights.
|
||||
|
||||
The platform includes a library of 80+ turnkey connectors to easily ingest metadata from sources such as data warehouses, data lakes, streaming platforms, dashboards, and ML models. For custom data sources, APIs are available to streamline metadata ingestion. Metadata from these sources is organized into a Unified Metadata Graph, providing a single, comprehensive source of truth for your entire data estate.
|
||||
The platform includes a library of 90+ turnkey connectors to easily ingest metadata from sources such as data warehouses, data lakes, streaming platforms, dashboards, and ML models. For custom data sources, APIs are available to streamline metadata ingestion. Metadata from these sources is organized into a Unified Metadata Graph, providing a single, comprehensive source of truth for your entire data estate.
|
||||
|
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This centralized metadata is accessible through a unified user interface, eliminating the need for practitioners to switch between multiple catalogs, quality, or governance tools. OpenMetadata can also be extended with applications, such as AI-driven productivity tools like MetaPilot, or through custom-built workflows that integrate the platform with existing systems.
|
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This centralized metadata is accessible through a unified user interface, eliminating the need for practitioners to switch between multiple catalogs, quality, or governance tools. OpenMetadata can also be extended with applications, such as AI-driven productivity tools like Collate AI, or through custom-built workflows that integrate the platform with existing systems.
|
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The platform’s native collaboration features support shared workflows, enabling different teams—data platform engineers, governance professionals, data scientists/analysts, and business users—to collaborate effectively in a single environment.
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## Key Features of OpenMetadata
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@ -23,7 +23,7 @@ Before we get started, here’s a quick summary of some of OpenMetadata’s main
|
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### Discovery
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- Integrated catalog, data quality, and glossary
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- Natural language search, filtering, and faceting
|
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- 80+ turnkey data connectors
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- 90+ turnkey data connectors
|
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|
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### Lineage
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- Table and column-level lineage
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@ -542,7 +542,7 @@ To continue pursuing this objective, the application was completely refactored t
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## Ingestion Connectors
|
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|
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80+ connectors to help teams to centralize metadata. We continue to push the boundaries of this mission, in
|
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90+ connectors to help teams to centralize metadata. We continue to push the boundaries of this mission, in
|
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|
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- **Apache Flink** as a Pipeline Connector
|
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- **SAP ERP**, after a long and successful collaboration with our community and SAP experts
|
||||
|
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@ -8,10 +8,10 @@ collate: true
|
||||
|
||||
Get started with your Collate service in just few simple steps:
|
||||
|
||||
1. Set up a Data Connector: Connect your data sources to begin collecting metadata.
|
||||
2. Ingest Metadata: Run the metadata ingestion to gather and push data insights.
|
||||
3. Invite Users: Add team members to collaborate and manage metadata together.
|
||||
4. Explore the Features: Dive into Collate's rich feature set to unlock the full potential of your data.
|
||||
1. **Set up a Data Connector**: Connect your data sources to begin collecting metadata.
|
||||
2. **Ingest Metadata**: Run the metadata ingestion to gather and push data insights.
|
||||
3. **Invite Users**: Add team members to collaborate and manage metadata together.
|
||||
4. **Explore the Features**: Dive into Collate's rich feature set to unlock the full potential of your data.
|
||||
|
||||
**Ready to begin? Let's get started!**
|
||||
|
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@ -24,7 +24,7 @@ with links to more detailed documentation.
|
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## Step 1: Set up a Data Connector
|
||||
|
||||
Once you’re able to login to your Collate instance, set up a data connector to start bringing metadata into Collate.
|
||||
There are [80+ turnkey connectors](/connectors) to various services: data warehouses, data lakes, databases, dashboards,
|
||||
There are [90+ turnkey connectors](/connectors) to various services: data warehouses, data lakes, databases, dashboards,
|
||||
messaging services, pipelines, ML models, storage services, and other Metadata Services.
|
||||
Connections to [custom data sources](/connectors/custom-connectors) can also be created via API.
|
||||
|
||||
|
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@ -7,7 +7,7 @@ collate: true
|
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# Getting Started
|
||||
|
||||
Welcome to Collate's unified platform for data discovery, observability, and governance! Our platform centralizes all
|
||||
the context around your data to help you build high-quality and AI assets. This guide gives you all the information you
|
||||
the context around your data to help you build high-quality data and AI assets. This guide gives you all the information you
|
||||
need to set up your Collate environment in 30 minutes.
|
||||
|
||||
## How Does Collate Work?
|
||||
@ -15,7 +15,7 @@ need to set up your Collate environment in 30 minutes.
|
||||
Collate is designed for both technical and non-technical data practitioners to work together across a broad set of use cases,
|
||||
including data discovery, lineage, observability, quality, collaboration, governance, and insights.
|
||||
|
||||
A library of 80+ turnkey connectors is available to easily ingest metadata into Collate, such as data warehouses, data lakes,
|
||||
A library of 90+ turnkey connectors is available to easily ingest metadata into Collate, such as data warehouses, data lakes,
|
||||
streaming, dashboards, ML models, and more. APIs are also available to easily ingest metadata from custom data sources.
|
||||
Metadata from these different sources is organized into a Unified Metadata Graph, which provides a single, comprehensive
|
||||
source of truth across your entire data estate.
|
||||
@ -31,7 +31,7 @@ together: data platform, data governance, data scientist/analyst, and business u
|
||||
|
||||
Before we get started, here’s a quick summary of some of Collate’s main features:
|
||||
|
||||
- **Discovery**: integrated catalog, quality, and glossary; natural language search, filtering, and faceting, 80+ turnkey data connectors, and MetaPilot AI Chatbot.
|
||||
- **Discovery**: integrated catalog, quality, and glossary; natural language search, filtering, and faceting, 90+ turnkey data connectors, and MetaPilot AI Chatbot.
|
||||
- **Lineage**: table and column-level lineage, automated data estate mapping and APIs, lineage layers and search, governance and PII automation and manual customization.
|
||||
- **Observability**: alerting and notifications, incident management, third-party notifications, pipeline monitoring, root cause analysis, anomaly detection, data profiler.
|
||||
- **Quality**: table and column test cases, no-code and SQL data quality tests, test suites, test case reporting, quality dashboards, widgets and data quality lineage maps.
|
||||
|
||||
@ -7,10 +7,10 @@ slug: /quick-start/getting-started/day-1
|
||||
|
||||
Get started with your OpenMetadata service in a few simple steps:
|
||||
|
||||
1. Set up a Data Connector: Connect your data sources to begin collecting metadata.
|
||||
2. Ingest Metadata: Run the metadata ingestion process to gather and push data insights.
|
||||
3. Invite Users: Add team members to collaborate and manage metadata together.
|
||||
4. Explore the Features: Dive into OpenMetadata's extensive feature set to unlock the full potential of your data.
|
||||
1. **Set up a Data Connector**: Connect your data sources to begin collecting metadata.
|
||||
2. **Ingest Metadata**: Run the metadata ingestion process to gather and push data insights.
|
||||
3. **Invite Users**: Add team members to collaborate and manage metadata together.
|
||||
4. **Explore the Features**: Dive into OpenMetadata's extensive feature set to unlock the full potential of your data.
|
||||
|
||||
**Ready to begin? Let's get started!**
|
||||
|
||||
@ -20,7 +20,7 @@ You should receive your initial OpenMetadata credentials from OpenMetadata suppo
|
||||
|
||||
## Step 1: Set up a Data Connector
|
||||
|
||||
Once you have logged into your OpenMetadata instance, set up a data connector to start ingesting metadata. OpenMetadata provides [80+ turnkey connectors](/connectors) for a wide range of services, including:
|
||||
Once you have logged into your OpenMetadata instance, set up a data connector to start ingesting metadata. OpenMetadata provides [90+ turnkey connectors](/connectors) for a wide range of services, including:
|
||||
|
||||
- Databases
|
||||
- Dashboards
|
||||
|
||||
@ -11,7 +11,7 @@ Welcome to OpenMetadata's unified platform for data discovery, observability, an
|
||||
|
||||
OpenMetadata is designed to support both technical and non-technical data practitioners across various use cases, including data discovery, lineage, observability, quality, collaboration, governance, and insights.
|
||||
|
||||
The platform includes a library of 80+ turnkey connectors to easily ingest metadata from sources such as data warehouses, data lakes, streaming platforms, dashboards, and ML models. For custom data sources, APIs are available to streamline metadata ingestion. Metadata from these sources is organized into a Unified Metadata Graph, providing a single, comprehensive source of truth for your entire data estate.
|
||||
The platform includes a library of 90+ turnkey connectors to easily ingest metadata from sources such as data warehouses, data lakes, streaming platforms, dashboards, and ML models. For custom data sources, APIs are available to streamline metadata ingestion. Metadata from these sources is organized into a Unified Metadata Graph, providing a single, comprehensive source of truth for your entire data estate.
|
||||
|
||||
This centralized metadata is accessible through a unified user interface, eliminating the need for practitioners to switch between multiple catalogs, quality, or governance tools. OpenMetadata can also be extended with applications, such as AI-driven productivity tools like MetaPilot, or through custom-built workflows that integrate the platform with existing systems.
|
||||
The platform’s native collaboration features support shared workflows, enabling different teams—data platform engineers, governance professionals, data scientists/analysts, and business users—to collaborate effectively in a single environment.
|
||||
@ -23,7 +23,7 @@ Before we get started, here’s a quick summary of some of OpenMetadata’s main
|
||||
### Discovery
|
||||
- Integrated catalog, data quality, and glossary
|
||||
- Natural language search, filtering, and faceting
|
||||
- 80+ turnkey data connectors
|
||||
- 90+ turnkey data connectors
|
||||
|
||||
### Lineage
|
||||
- Table and column-level lineage
|
||||
|
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@ -202,7 +202,7 @@ OpenMetadata 1.6 extends Role-Based Access Control (RBAC) to search functionalit
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## Expanded Connector Ecosystem and Diversity
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OpenMetadata's ingestion framework contains 80+ native connectors. These connectors are the foundation of the platform and bring in all the metadata your team needs: technical metadata, lineage, usage, profiling, etc.
|
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OpenMetadata's ingestion framework contains 90+ native connectors. These connectors are the foundation of the platform and bring in all the metadata your team needs: technical metadata, lineage, usage, profiling, etc.
|
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We bring new connectors in each release, continuously expanding our coverage. This time, release 1.6 comes with seven new connectors:
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@ -770,7 +770,7 @@ To continue pursuing this objective, the application was completely refactored t
|
||||
|
||||
## Ingestion Connectors
|
||||
|
||||
80+ connectors to help teams to centralize metadata. We continue to push the boundaries of this mission, in
|
||||
90+ connectors to help teams to centralize metadata. We continue to push the boundaries of this mission, in
|
||||
|
||||
- **Apache Flink** as a Pipeline Connector
|
||||
- **SAP ERP**, after a long and successful collaboration with our community and SAP experts
|
||||
|
||||
@ -8,10 +8,10 @@ collate: true
|
||||
|
||||
Get started with your Collate service in just few simple steps:
|
||||
|
||||
1. Set up a Data Connector: Connect your data sources to begin collecting metadata.
|
||||
2. Ingest Metadata: Run the metadata ingestion to gather and push data insights.
|
||||
3. Invite Users: Add team members to collaborate and manage metadata together.
|
||||
4. Explore the Features: Dive into Collate's rich feature set to unlock the full potential of your data.
|
||||
1. **Set up a Data Connector**: Connect your data sources to begin collecting metadata.
|
||||
2. **Ingest Metadata**: Run the metadata ingestion to gather and push data insights.
|
||||
3. **Invite Users**: Add team members to collaborate and manage metadata together.
|
||||
4. **Explore the Features**: Dive into Collate's rich feature set to unlock the full potential of your data.
|
||||
|
||||
**Ready to begin? Let's get started!**
|
||||
|
||||
@ -24,7 +24,7 @@ with links to more detailed documentation.
|
||||
## Step 1: Set up a Data Connector
|
||||
|
||||
Once you’re able to login to your Collate instance, set up a data connector to start bringing metadata into Collate.
|
||||
There are [80+ turnkey connectors](/connectors) to various services: data warehouses, data lakes, databases, dashboards,
|
||||
There are [90+ turnkey connectors](/connectors) to various services: data warehouses, data lakes, databases, dashboards,
|
||||
messaging services, pipelines, ML models, storage services, and other Metadata Services.
|
||||
Connections to [custom data sources](/connectors/custom-connectors) can also be created via API.
|
||||
|
||||
|
||||
@ -7,7 +7,7 @@ collate: true
|
||||
# Getting Started
|
||||
|
||||
Welcome to Collate's unified platform for data discovery, observability, and governance! Our platform centralizes all
|
||||
the context around your data to help you build high-quality and AI assets. This guide gives you all the information you
|
||||
the context around your data to help you build high-quality data and AI assets. This guide gives you all the information you
|
||||
need to set up your Collate environment in 30 minutes.
|
||||
|
||||
## How Does Collate Work?
|
||||
@ -15,14 +15,14 @@ need to set up your Collate environment in 30 minutes.
|
||||
Collate is designed for both technical and non-technical data practitioners to work together across a broad set of use cases,
|
||||
including data discovery, lineage, observability, quality, collaboration, governance, and insights.
|
||||
|
||||
A library of 80+ turnkey connectors is available to easily ingest metadata into Collate, such as data warehouses, data lakes,
|
||||
A library of 90+ turnkey connectors is available to easily ingest metadata into Collate, such as data warehouses, data lakes,
|
||||
streaming, dashboards, ML models, and more. APIs are also available to easily ingest metadata from custom data sources.
|
||||
Metadata from these different sources is organized into a Unified Metadata Graph, which provides a single, comprehensive
|
||||
source of truth across your entire data estate.
|
||||
|
||||
This centralized information is surfaced through a unified user interface for all your use cases so that different data
|
||||
practitioners no longer need to switch between different data catalogs, quality, or governance tools. Additionally,
|
||||
Collate can be extended through the application ecosystem, such as with AI productivity applications like MetaPilot,
|
||||
Collate can be extended through the application ecosystem, such as with AI productivity applications like Collate AI,
|
||||
or with customer-built workflows to integrate Collate with your existing systems. These capabilities are built around
|
||||
native collaboration capabilities for shared workflows across different teams so that every data practitioner can work
|
||||
together: data platform, data governance, data scientist/analyst, and business user.
|
||||
@ -31,7 +31,7 @@ together: data platform, data governance, data scientist/analyst, and business u
|
||||
|
||||
Before we get started, here’s a quick summary of some of Collate’s main features:
|
||||
|
||||
- **Discovery**: integrated catalog, quality, and glossary; natural language search, filtering, and faceting, 80+ turnkey data connectors, and MetaPilot AI Chatbot.
|
||||
- **Discovery**: integrated catalog, quality, and glossary; natural language search, filtering, and faceting, 90+ turnkey data connectors, and Collate AI Chatbot.
|
||||
- **Lineage**: table and column-level lineage, automated data estate mapping and APIs, lineage layers and search, governance and PII automation and manual customization.
|
||||
- **Observability**: alerting and notifications, incident management, third-party notifications, pipeline monitoring, root cause analysis, anomaly detection, data profiler.
|
||||
- **Quality**: table and column test cases, no-code and SQL data quality tests, test suites, test case reporting, quality dashboards, widgets and data quality lineage maps.
|
||||
|
||||
@ -7,10 +7,10 @@ slug: /quick-start/getting-started/day-1
|
||||
|
||||
Get started with your OpenMetadata service in a few simple steps:
|
||||
|
||||
1. Set up a Data Connector: Connect your data sources to begin collecting metadata.
|
||||
2. Ingest Metadata: Run the metadata ingestion process to gather and push data insights.
|
||||
3. Invite Users: Add team members to collaborate and manage metadata together.
|
||||
4. Explore the Features: Dive into OpenMetadata's extensive feature set to unlock the full potential of your data.
|
||||
1. **Set up a Data Connector**: Connect your data sources to begin collecting metadata.
|
||||
2. **Ingest Metadata**: Run the metadata ingestion process to gather and push data insights.
|
||||
3. **Invite Users**: Add team members to collaborate and manage metadata together.
|
||||
4. **Explore the Features**: Dive into OpenMetadata's extensive feature set to unlock the full potential of your data.
|
||||
|
||||
**Ready to begin? Let's get started!**
|
||||
|
||||
@ -20,7 +20,7 @@ You should receive your initial OpenMetadata credentials from OpenMetadata suppo
|
||||
|
||||
## Step 1: Set up a Data Connector
|
||||
|
||||
Once you have logged into your OpenMetadata instance, set up a data connector to start ingesting metadata. OpenMetadata provides [80+ turnkey connectors](/connectors) for a wide range of services, including:
|
||||
Once you have logged into your OpenMetadata instance, set up a data connector to start ingesting metadata. OpenMetadata provides [90+ turnkey connectors](/connectors) for a wide range of services, including:
|
||||
|
||||
- Databases
|
||||
- Dashboards
|
||||
|
||||
@ -11,9 +11,9 @@ Welcome to OpenMetadata's unified platform for data discovery, observability, an
|
||||
|
||||
OpenMetadata is designed to support both technical and non-technical data practitioners across various use cases, including data discovery, lineage, observability, quality, collaboration, governance, and insights.
|
||||
|
||||
The platform includes a library of 80+ turnkey connectors to easily ingest metadata from sources such as data warehouses, data lakes, streaming platforms, dashboards, and ML models. For custom data sources, APIs are available to streamline metadata ingestion. Metadata from these sources is organized into a Unified Metadata Graph, providing a single, comprehensive source of truth for your entire data estate.
|
||||
The platform includes a library of 90+ turnkey connectors to easily ingest metadata from sources such as data warehouses, data lakes, streaming platforms, dashboards, and ML models. For custom data sources, APIs are available to streamline metadata ingestion. Metadata from these sources is organized into a Unified Metadata Graph, providing a single, comprehensive source of truth for your entire data estate.
|
||||
|
||||
This centralized metadata is accessible through a unified user interface, eliminating the need for practitioners to switch between multiple catalogs, quality, or governance tools. OpenMetadata can also be extended with applications, such as AI-driven productivity tools like MetaPilot, or through custom-built workflows that integrate the platform with existing systems.
|
||||
This centralized metadata is accessible through a unified user interface, eliminating the need for practitioners to switch between multiple catalogs, quality, or governance tools. OpenMetadata can also be extended with applications, such as AI-driven productivity tools like Collate AI, or through custom-built workflows that integrate the platform with existing systems.
|
||||
The platform’s native collaboration features support shared workflows, enabling different teams—data platform engineers, governance professionals, data scientists/analysts, and business users—to collaborate effectively in a single environment.
|
||||
|
||||
## Key Features of OpenMetadata
|
||||
@ -23,7 +23,7 @@ Before we get started, here’s a quick summary of some of OpenMetadata’s main
|
||||
### Discovery
|
||||
- Integrated catalog, data quality, and glossary
|
||||
- Natural language search, filtering, and faceting
|
||||
- 80+ turnkey data connectors
|
||||
- 90+ turnkey data connectors
|
||||
|
||||
### Lineage
|
||||
- Table and column-level lineage
|
||||
|
||||
@ -202,7 +202,7 @@ OpenMetadata 1.6 extends Role-Based Access Control (RBAC) to search functionalit
|
||||
|
||||
## Expanded Connector Ecosystem and Diversity
|
||||
|
||||
OpenMetadata's ingestion framework contains 80+ native connectors. These connectors are the foundation of the platform and bring in all the metadata your team needs: technical metadata, lineage, usage, profiling, etc.
|
||||
OpenMetadata's ingestion framework contains 90+ native connectors. These connectors are the foundation of the platform and bring in all the metadata your team needs: technical metadata, lineage, usage, profiling, etc.
|
||||
|
||||
We bring new connectors in each release, continuously expanding our coverage. This time, release 1.6 comes with seven new connectors:
|
||||
|
||||
@ -770,7 +770,7 @@ To continue pursuing this objective, the application was completely refactored t
|
||||
|
||||
## Ingestion Connectors
|
||||
|
||||
80+ connectors to help teams to centralize metadata. We continue to push the boundaries of this mission, in
|
||||
90+ connectors to help teams to centralize metadata. We continue to push the boundaries of this mission, in
|
||||
|
||||
- **Apache Flink** as a Pipeline Connector
|
||||
- **SAP ERP**, after a long and successful collaboration with our community and SAP experts
|
||||
|
||||
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Reference in New Issue
Block a user