{% include JB/setup %}

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{% include JB/setup %}

[Elasticsearch](https://www.elastic.co/products/elasticsearch) is a highly scalable open-source full-text search and analytics engine. It allows you to store, search, and analyze big volumes of data quickly and in near real time. It is generally used as the underlying engine/technology that powers applications that have complex search features and requirements.

{% include JB/setup %}

[Apache Flink](https://flink.apache.org) is an open source platform for distributed stream and batch data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. Flink also builds batch processing on top of the streaming engine, overlaying native iteration support, managed memory, and program optimization.

{% include JB/setup %}

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<th>Name</th>

This [Video Tutorial](https://www.youtube.com/watch?v=zvzzA9GXu3Q) illustrates some of the features provided by the `Geode Interpreter`.

By default Zeppelin creates one `Geode/OQL` instance. You can remove it or create more instances.

Multiple Geode instances can be created, each configured to the same or different backend Geode cluster. But over time a `Notebook` can have only one Geode interpreter instance `bound`. That means you _cannot_ connect to different Geode clusters in the same `Notebook`. This is a known Zeppelin limitation.

In the `Notebook` click on the `settings` icon in the top right corner. The select/deselect the interpreters to be bound with the `Notebook`.

You can modify the configuration of the Geode from the `Interpreter` section. The Geode interpreter expresses the following properties:

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The OQL specification does not support [Geode Regions](https://cwiki.apache.org/confluence/display/GEODE/Index#Index-MainConceptsandComponents) mutation operations. To `create`/`destroy` regions one should use the [GFSH](http://geode-docs.cfapps.io/docs/tools_modules/gfsh/chapter_overview.html) shell tool instead. In the following it is assumed that the GFSH is colocated with Zeppelin server.

Above snippet re-creates two regions: `regionEmployee` and `regionCompany`. Note that you have to explicitly specify the locator host and port. The values should match those you have used in the Geode Interpreter configuration. Comprehensive list of [GFSH Commands by Functional Area](http://geode-docs.cfapps.io/docs/tools_modules/gfsh/gfsh_quick_reference.html).

%geode.oql

SELECT count(*) FROM /regionEmployee

Use the Shell Interpreter (`%sh`) to run OQL commands form the command line:

gfsh -e "connect" -e "list members"

You can leverage [Zeppelin Dynamic Form](../manual/dynamicform.html) inside your OQL queries. You can use both the `text input` and `select form` parameterization features

%geode.oql

SELECT * FROM /regionEmployee e WHERE e.employeeId > ${Id}

To list the defined regions you can use the [Geode REST API](http://geode-docs.cfapps.io/docs/geode_rest/chapter_overview.html):

{% include JB/setup %}

[HBase Shell](http://hbase.apache.org/book.html#shell) is a JRuby IRB client for Apache HBase. This interpreter provides all capabilities of Apache HBase shell within Apache Zeppelin. The interpreter assumes that Apache HBase client software has been installed and it can connect to the Apache HBase cluster from the machine on where Apache Zeppelin is installed.

By default, Zeppelin is built against HBase 1.0.x releases. To work with HBase 1.1.x releases, use the following build command:

mvn clean package -DskipTests -Phadoop-2.6 -Dhadoop.version=2.6.0 -P build-distr -Dhbase.hbase.version=1.1.4 -Dhbase.hadoop.version=2.6.0

To work with HBase 1.2.0+, use the following build command:

put 'test', 'row1', 'cf:a', 'value1'

{% include JB/setup %}

[Hadoop File System](http://hadoop.apache.org/) is a distributed, fault tolerant file system part of the hadoop project and is often used as storage for distributed processing engines like [Hadoop MapReduce](http://hadoop.apache.org/) and [Apache Spark](http://spark.apache.org/) or underlying file systems like [Alluxio](http://www.alluxio.org/).

In a notebook, to enable the **HDFS** interpreter, click the **Gear** icon and select **HDFS**.

You can confirm that you're able to access the WebHDFS API by running a curl command against the WebHDFS end point provided to the interpreter.

Here is an example:

$> curl "http://localhost:50070/webhdfs/v1/?op=LISTSTATUS"

{% include JB/setup %}

Hive Interpreter will be deprecated and merged into JDBC Interpreter. You can use Hive Interpreter by using JDBC Interpreter with same functionality. See the example below of settings and dependencies.

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This interpreter provides multiple configuration with `${prefix}`. User can set a multiple connection properties by this prefix. It can be used like `%hive(${prefix})`.

Basically, you can use

{% include JB/setup %}

[Apache Ignite](https://ignite.apache.org/) In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies.


You can use Zeppelin to retrieve distributed data from cache using Ignite SQL interpreter. Moreover, Ignite interpreter allows you to execute any Scala code in cases when SQL doesn't fit to your requirements. For example, you can populate data into your caches or execute distributed computations.

In order to use Ignite interpreters, you may install Apache Ignite in some simple steps:

1. Download Ignite [source release](https://ignite.apache.org/download.html#sources) or [binary release](https://ignite.apache.org/download.html#binaries) whatever you want. But you must download Ignite as the same version of Zeppelin's. If it is not, you can't use scala code on Zeppelin. You can find ignite version in Zeppelin at the pom.xml which is placed under `path/to/your-Zeppelin/ignite/pom.xml` ( Of course, in Zeppelin source release ). Please check `ignite.version` .<br>Currently, Zeppelin provides ignite only in Zeppelin source release. So, if you download Zeppelin binary release( `zeppelin-0.5.0-incubating-bin-spark-xxx-hadoop-xx` ), you can not use ignite interpreter on Zeppelin. We are planning to include ignite in a future binary release.

At the "Interpreters" menu, you may edit Ignite interpreter or create new one. Zeppelin provides these properties for Ignite.

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After configuring Ignite interpreter, create your own notebook. Then you can bind interpreters like below image.


For more interpreter binding information see [here](http://zeppelin.apache.org/docs/manual/interpreters.html).

In order to execute SQL query, use ` %ignite.ignitesql ` prefix. <br>

Supposing you are running `org.apache.ignite.examples.streaming.wordcount.StreamWords`, then you can use "words" cache( Of course you have to specify this cache name to the Ignite interpreter setting section `ignite.jdbc.url` of Zeppelin ).

For example, you can select top 10 words in the words cache using the following query