MarkSfik commented on a change in pull request #339:
URL: https://github.com/apache/flink-web/pull/339#discussion_r429982337
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
Review comment:
```suggestion
The latest release of [Apache Zeppelin](https://zeppelin.apache.org/) comes
with a redesigned interpreter for Apache Flink (version Flink 1.10+ is only
supported moving forward)
```
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
Review comment:
I think there is a link missing here.
Maybe you can add the link to the Flink Tutorial/Streaming ETL page so that
users can go directly there from the post?
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
+
+* Step 1. Create source table to represent the source data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_source.png" width="80%"
alt="Create Source Table"/>
+</center>
+
+* Step 2. Create a sink table to represent the processed data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_sink.png" width="80%"
alt="Create Sink Table"/>
+</center>
+
+* Step 3. After creating the source and sink table, we can use insert them to
our statement to trigger the streaming processing job as the following:
Review comment:
```suggestion
* Step 3. After creating the source and sink table, we can insert them to
our statement to trigger the stream processing job as the following:
```
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
+
+* Step 1. Create source table to represent the source data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_source.png" width="80%"
alt="Create Source Table"/>
+</center>
+
+* Step 2. Create a sink table to represent the processed data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_sink.png" width="80%"
alt="Create Sink Table"/>
+</center>
+
+* Step 3. After creating the source and sink table, we can use insert them to
our statement to trigger the streaming processing job as the following:
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-25-flink-on-zeppelin/etl.png"
width="80%" alt="ETL"/>
+</center>
+
+* Step 4. After initiating the streaming job, you can use another SQL
statement to query the sink table to verify your streaming job. Here you can
see the top 10 records which will be refreshed every 3 seconds.
Review comment:
```suggestion
* Step 4. After initiating the streaming job, you can use another SQL
statement to query the sink table to verify the results of your job. Here you
can see the top 10 records which will be refreshed every 3 seconds.
```
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
+
+* Step 1. Create source table to represent the source data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_source.png" width="80%"
alt="Create Source Table"/>
+</center>
+
+* Step 2. Create a sink table to represent the processed data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_sink.png" width="80%"
alt="Create Sink Table"/>
+</center>
+
+* Step 3. After creating the source and sink table, we can use insert them to
our statement to trigger the streaming processing job as the following:
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-25-flink-on-zeppelin/etl.png"
width="80%" alt="ETL"/>
+</center>
+
+* Step 4. After initiating the streaming job, you can use another SQL
statement to query the sink table to verify your streaming job. Here you can
see the top 10 records which will be refreshed every 3 seconds.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/preview.png" width="80%"
alt="Preview"/>
+</center>
+
+# Summary
+
+In this post, we explained how the redesigned Flink interpreter works in
Zeppelin 0.9.0 and provided some examples for performing streaming ETL jobs
with
+Flink and Zeppelin. You can find additional tutorial for batch processing with
Flink on Zeppelin as well as using Flink on Zeppelin for
Review comment:
```suggestion
Flink and Zeppelin. You can find an additional tutorial for batch processing
with Flink on Zeppelin as well as using Flink on Zeppelin for
```
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
+
+* Step 1. Create source table to represent the source data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_source.png" width="80%"
alt="Create Source Table"/>
+</center>
+
+* Step 2. Create a sink table to represent the processed data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_sink.png" width="80%"
alt="Create Sink Table"/>
+</center>
+
+* Step 3. After creating the source and sink table, we can use insert them to
our statement to trigger the streaming processing job as the following:
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-25-flink-on-zeppelin/etl.png"
width="80%" alt="ETL"/>
+</center>
+
+* Step 4. After initiating the streaming job, you can use another SQL
statement to query the sink table to verify your streaming job. Here you can
see the top 10 records which will be refreshed every 3 seconds.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/preview.png" width="80%"
alt="Preview"/>
+</center>
+
+# Summary
+
+In this post, we explained how the redesigned Flink interpreter works in
Zeppelin 0.9.0 and provided some examples for performing streaming ETL jobs
with
+Flink and Zeppelin. You can find additional tutorial for batch processing with
Flink on Zeppelin as well as using Flink on Zeppelin for
Review comment:
```suggestion
Flink and Zeppelin. You can find an additional [tutorial for batch
processing with Flink on
Zeppelin](https://medium.com/@zjffdu/flink-on-zeppelin-part-2-batch-711731df5ad9)
as well as using Flink on Zeppelin for
```
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
+
+* Step 1. Create source table to represent the source data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_source.png" width="80%"
alt="Create Source Table"/>
+</center>
+
+* Step 2. Create a sink table to represent the processed data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_sink.png" width="80%"
alt="Create Sink Table"/>
+</center>
+
+* Step 3. After creating the source and sink table, we can use insert them to
our statement to trigger the streaming processing job as the following:
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-25-flink-on-zeppelin/etl.png"
width="80%" alt="ETL"/>
+</center>
+
+* Step 4. After initiating the streaming job, you can use another SQL
statement to query the sink table to verify your streaming job. Here you can
see the top 10 records which will be refreshed every 3 seconds.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/preview.png" width="80%"
alt="Preview"/>
+</center>
+
+# Summary
+
+In this post, we explained how the redesigned Flink interpreter works in
Zeppelin 0.9.0 and provided some examples for performing streaming ETL jobs
with
+Flink and Zeppelin. You can find additional tutorial for batch processing with
Flink on Zeppelin as well as using Flink on Zeppelin for
+more advance operations like resource isolation, job concurrency &
parallelism, multiple Hadoop & Hive environments and more on our series of post
on Medium.
Review comment:
```suggestion
more advance operations like resource isolation, job concurrency &
parallelism, multiple Hadoop & Hive environments and more on our series of
posts on
[Medium](https://medium.com/@zjffdu/flink-on-zeppelin-part-4-advanced-usage-998b74908cd9).
```
##########
File path: _posts/2020-05-25-flink-on-zeppelin.md
##########
@@ -0,0 +1,83 @@
+---
+layout: post
+title: "Flink on Zeppelin Notebooks for Interactive Data Analysis"
+date: 2020-05-25T08:00:00.000Z
+categories: news
+authors:
+- zjffdu:
+ name: "Jeff Zhang"
+ twitter: "zjffdu"
+---
+
+The latest release of Apache Zeppelin comes with a redesigned interpreter for
Apache Flink (version Flink 1.10+ is only supported moving forward)
+that allows developers and data engineers to use Flink directly on Zeppelin
notebooks for interactive data analysis. In this post, we explain how the Flink
interpreter in Zeppelin works,
+and provide a tutorial for running Streaming ETL with Flink on Zeppelin.
+
+# The Flink Interpreter in Zeppelin 0.9
+
+The Flink interpreter can be accessed and configured from Zeppelin’s
interpreter settings page.
+The interpreter has been refactored so that Flink users can now take advantage
of Zeppelin to write Flink applications in three languages,
+namely Scala, Python (PyFlink) and SQL (for both batch & streaming
executions).
+Zeppelin 0.9 now comes with the Flink interpreter group, consisting of the
below five interpreters:
+
+* %flink - Provides a Scala environment
+* %flink.pyflink - Provides a python environment
+* %flink.ipyflink - Provides an ipython environment
+* %flink.bsql - Provides a stream sql environment
+* %flink.ssql - Provides a batch sql environment
+
+Not only has the interpreter been extended to support writing Flink
applications in three languages, but it has also extended the available
execution modes for Flink that now include:
+* Running Flink in Local Mode
+* Running Flink in Remote Mode
+* Running Flink in Yarn Mode
+
+
+You can find more information about how to get started with Zeppelin and all
the execution modes for Flink applications in Zeppelin notebooks in this post.
+
+
+# Flink on Zeppelin for Stream processing
+
+Performing stream processing jobs with Apache Flink on Zeppelin allows you to
run most major streaming cases,
+such as streaming ETL and real time data analytics, with the use of Flink SQL
and specific UDFs.
+Below we showcase how you can execute streaming ETL using Flink on Zeppelin:
+
+You can use Flink SQL to perform streaming ETL by following the steps below
+(for the full tutorial, please refer to the Flink Tutorial/Streaming ETL
tutorial of the Zeppelin distribution):
+
+* Step 1. Create source table to represent the source data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_source.png" width="80%"
alt="Create Source Table"/>
+</center>
+
+* Step 2. Create a sink table to represent the processed data.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/create_sink.png" width="80%"
alt="Create Sink Table"/>
+</center>
+
+* Step 3. After creating the source and sink table, we can use insert them to
our statement to trigger the streaming processing job as the following:
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-25-flink-on-zeppelin/etl.png"
width="80%" alt="ETL"/>
+</center>
+
+* Step 4. After initiating the streaming job, you can use another SQL
statement to query the sink table to verify your streaming job. Here you can
see the top 10 records which will be refreshed every 3 seconds.
+
+<center>
+<img src="{{ site.baseurl
}}/img/blog/2020-05-25-flink-on-zeppelin/preview.png" width="80%"
alt="Preview"/>
+</center>
+
+# Summary
+
+In this post, we explained how the redesigned Flink interpreter works in
Zeppelin 0.9.0 and provided some examples for performing streaming ETL jobs
with
+Flink and Zeppelin. You can find additional tutorial for batch processing with
Flink on Zeppelin as well as using Flink on Zeppelin for
+more advance operations like resource isolation, job concurrency &
parallelism, multiple Hadoop & Hive environments and more on our series of post
on Medium.
Review comment:
```suggestion
more advanced operations like resource isolation, job concurrency &
parallelism, multiple Hadoop & Hive environments and more on our series of
posts on
[Medium](https://medium.com/@zjffdu/flink-on-zeppelin-part-4-advanced-usage-998b74908cd9).
```
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
