# Free Trial Quick Start
DeltaStream provides a relational model on top of your streaming data. Similar to other relational systems, DeltaStream uses databases and namespaces for organizing your data.
Using DeltaStream’s free 14-day trial? Follow this guide to build an end-to-end streaming application in minutes. We provide you with a default organization – named after the email address you used to sign on – and a default Kafka store with synthetic data. You’ll use these resources to:
1. Inspect the data in the streaming trial store.
2. Create a database.
3. Create a stream and changelog for your Kafka topics.
4. Enrich your data and query it.
{% hint style="info" %}
**Note** The trial version limits you to 3 queries. Also, user-defined functions aren’t supported, and there are no materialized views. You can add your own external store, but it must be available via the Internet. Contact DeltaStream support if you wish to set up a private store.
{% endhint %}
## 1. Inspect Data in Your Trial Store
You receive access to a pre-defined DeltaStream `trial_store` when you sign in to your trial account. This store is a discrete AWS MSK (Managed Streaming for Kafka) cluster that includes several topics with synthetic data producers; the producers continuously publish messages into these topics.
{% hint style="info" %}
**Note** In DeltaStream you define stores to represent each Kafka cluster. DeltaStream [also works with other stores](https://docs.deltastream.io/getting-started/broken-reference), such as AWS Kinesis and Postgres.
{% endhint %}
The trial store displays in several places:
* The **Welcome** page
* The **Workspace** page
* The **Resources** page
When you log on, DeltaStream displays the **Workspace** page. This page provides an at-a-glance dashboard view of your overall DeltaStream organization.
To begin exploring your trial store, in the lefthand navigation click **Resources** (  ). The **Resources** page displays with the **Data Stores** tab active and your trial store listed beneath it.
To display the topics contained in the trial store, click anywhere in the trial store row and open the **trial\_store** page.
Now confirm the store connectivity and inspect the data in a topic. To do this:
1. Click anywhere in the row of the topic you want. The topic **Details** pane slides open.
* You can also display the topic **Details** pane by clicking  under the **Actions** column.
2. Click **Print**. This displays the live stream of data flowing to the topic.
Here is an image of the data flowing into the pageviews topic.
{% hint style="success" %}
**Tip** After you verify that data is streaming into your trial store, you may wish to click **Stop** to halt the stream.
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There's a range of additional information you can view. For more details, please see [explore-data-store-and-topic-details](https://docs.deltastream.io/how-do-i.../create-and-manage-data-stores/explore-data-store-and-topic-details "mention").
## 2. Create a Database
Now it’s time to declare a database and [DeltaStream objects](https://docs.deltastream.io/how-do-i.../relation#defining-deltastream-objects) and write queries on the streaming data. Databases present a logical organization layer for your streaming data. They make it possible to provide access controls and governance across all your data.
{% hint style="info" %}
**Note** DeltaStream objects are the building blocks of user applications and pipelines. qYou must create an object to represent each Kafka topic you wish to include in a query.
{% endhint %}
**To create a new database**
1. From the lefthand navigation, click **Databases** (  ) and then click **+ Add Database**.
2. Enter the database name. In this guide we name it `DemoDB`.
3. Click **SAVE**.
The newly-created database displays all the topics in the Kafka cluster to which you have access.
For this guide, we named our database `DemoDB`.
You can create as many databases as you wish. Each new database includes a namespace named `public`, but you can add more namespaces if you wish.
**To add a new namespace**
1. Click , click the database you want, and towards the right click **+ Add Namespace**.
2. At the promt, enter the namespace name. Then click **SAVE**.
## 3. Create Streams and Changelogs
Your goal here is to understand pageviews by users over time. You do this by joining the pageviews and users topics.
Start by creating relations backed by Kafka topics. Use DeltaStream’s DDL statements to define your streaming data in a topic as an append-only stream.
{% hint style="info" %}
**Note** In DeltaStream, a stream is simply one type of object.
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**To create a stream**
1. Navigate to the main workspace by clicking .
2. Copy the SQL DDL statement below, and paste it into the SQL pane (above the **Results** pane).\
This creates a discrete stream backed by the pageviews topic from the Kafka cluster; each pageview is an independent event. This stream reflects the view time of each page by user.
3. Click **Run**.
```sql
CREATE STREAM pageviews (
viewtime BIGINT,
userid VARCHAR,
pageid VARCHAR
)WITH (
'topic'='pageviews',
'value.format'='JSON'
);
```
DeltaStream displays a **Success** message in the Results pane, followed by details of the stream you just created.
{% hint style="success" %}
**Tip** You may need to expand the Results pane to see all of the details. To do this, click and drag the pane handle (  ). See below for more tips on [modifying your DeltaStream workspace](#modifying-your-workspace).
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{% hint style="info" %}
**Note** The above stream is created in the currently-used database and namespace – `DemoDB` and `public`, respectively. DeltaStream uses the default store declared above as the store for the pageviews topic. To specify another store, use the WITH clause.
{% endhint %}
Next, declare a changelog backed by the users topic and ordered by UserID. A changelog enables you to interpret events in a topic as UPSERT events. (In DeltaStream, changelogs are simply another type of object.) Events require a primary key; DeltaStream interprets each event as an insert or update for the given primary key. In this case, the changelog relation reflects specific details by user, such as gender and interests.
To declare the users changelog, paste the following statement in the SQL pane and then click **Run**:
```sql
CREATE CHANGELOG users_log (
registertime BIGINT,
userid VARCHAR,
regionid VARCHAR,
gender VARCHAR,
interests ARRAY,
contactinfo STRUCT,
PRIMARY KEY(userid)
)WITH (
'topic'='users',
'key.format'='json',
'key.type'='STRUCT',
'value.format'='json'
);
```
As with the pageviews stream, the users changelog displays in the `DemoDB` public schema. To view the streams, in the lefthand navigation click **Databases** (  ), and in the **Databases** pane click to expand the `DemoDB` database and public namespace.
## 4. Run Queries
Now you can write a continuous query in SQL to process this streaming data in real time.
Let’s start with an **interactive query**, in which the query results stream back to you. You can use such queries to:
* inspect your streams and changelogs
* build queries iteratively by inspecting the query result.
Let’s inspect the pageviews stream. To do this, enter the following interactive query and then click **Run**:
```sql
SELECT * FROM pageviews;
```
DeltaStream compiles your query into a streaming job, runs the job, and streams the result into the **Results** pane, as per the below image:
{% hint style="success" %}
**Tip** After you verify that data is streaming in you may wish to click **Stop Query**.
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While interactive query results stream in, DeltaStream provides persistent queries. These are continuous queries wherein the query results are written continuously either to a store or a materialized view.
Let’s write a persistent query that joins the pageviews stream with the `users_log` changelog relations. This creates a third object called an enriched pageviews stream that provides user details for each pageview event, including view time of each page by user and detailed user information.
While we’re at it, we also convert the epoch time to the timestamp with a timezone using the `TO_TIMESTAMP_LTZ` function.
Start by creating a stream called **enriched\_pv**. Then join the **pageviews** stream with data from the **users\_log** changelog and write the results to the **enriched\_pv** stream.
```sql
CREATE STREAM enriched_pv
AS SELECT
TO_TIMESTAMP_LTZ(viewtime, 3) AS viewtime,
p.userid AS userid,
pageid,
TO_TIMESTAMP_LTZ(registertime, 3) AS registertime,
regionid,
gender,
interests,
contactinfo
FROM pageviews p WITH ( 'starting.position'='latest')
JOIN users_log u WITH ( 'starting.position'='latest')
ON u.userid = p.userid;
```
{% hint style="info" %}
**Note** The above **persistent query** creates a new topic in the trial store DB. When you create a new topic, DeltaStream adds a prefix name to the topic name based on your trial email and some unique random characters. For example, for the email , DeltaStream creates a topic prefix like `t_testgmailcom_4evmsyg_`. Creating the topic `enriched_pv` in turn creates the topic `t_testgmailcom_4evmsyg_enriched_pv`. You can view these topics in the trial store topics list.
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{% hint style="warning" %}
**Important** Topic name prefixes are a requirement only for the trial store we have set. Prefixes are not added if you use any other store, such as your own Apache Kafka or AWS Kinesis.
{% endhint %}
{% hint style="info" %}
**Note** DeltaStream compiles and launches the query as an Apache Flink streaming job. You can view the query along with its status in the **Query Management** page; to do this, in the lefthand navigation click **Queries** (  ).
{% endhint %}
When the query finishes, you have a new Kafka topic named `enriched_pv` in your Kafka cluster and a new stream added to the streams in your TestDB database.
Finally, examine the contents of the new stream. Run the following simple query in the SQL pane:
```sql
SELECT * FROM enriched_pv;
```
The result of this interactive continuous query is an enriched pageviews stream that streams to the client as shown below:
That’s it. In just a few steps you’ve used DeltaStream to connect two different Kafka topics and persist the enriched data, either to query in real time or write out to its final destination. In so doing you avoid the extra steps and expense that might be the case in a data warehouse.
## 4. Clean Up
When your task is completed, it’s time to clean up your environment. To do this:
1. Terminate the queries. To do this, click  to display the **Queries** page. Then, next to the query you wish to terminate, click .
2. Follow the instructions in the prompt, and then click **Terminate**. The system displays a message indicating you've marked that query for termination.
3. Drop the created streams, changelogs, and materialized views. To do this, click  to navigate to the corresponding database and schema, and as with the query, click  and follow the prompt to drop the streams, changelogs, and materialized views.
{% hint style="warning" %}
**Important** If you have a query that uses a stream, changelog, or materialized view, you must terminate the query before dropping the relation.
{% endhint %}
## Modifying Your Workspace
This quickstart guide used simple examples to get you up and running quickly. But the DeltaStream workspace is customizable. If you begin using more extensive queries or a greater number of objects, you may find it more efficient to modify the size of your workspace panes, or even toggle on or off specific sections, to focus on the parts of the workspace that matter most at any given time. You can:
* Hide the **Results** pane. This gives you more room in the SQL pane to work with more extensive SQL. To do this, at the top of your workspace click . Click it a second time to re-display the **Results** pane.
* Hide the **SQL** pane. This gives you more room to examine query results. To do this, at the top of your workspace click . Click it a second time to re-display the **SQL** pane.
* Hide the lefthand (**Database** and **Stores**) panes. This gives you more horizontal screen real estate and creates a cleaner, more expansive workspace. To do this, at the top of your workspace click . Click it a second time to re-display the lefthand pane.
When activated, the icons display in color – for example, .
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**Tip** You can click any two or all three of these icons at once to isolate the precise workspace you wish. For example, if you click  and  you have almost the entire screen to work with SQL.
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Finally, you can manually re-size your panes without hiding them altogether. To resize the SQL and Results panes, or the **Database** and **Stores** panes, click and drag . To resize the left and right panes, click and drag .
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When the query finishes, you have a new Kafka topic named `enriched_pv` in your Kafka cluster and a new stream added to the streams in your TestDB database.
Finally, examine the contents of the new stream. Run the following simple query in the SQL pane:
```sql
SELECT * FROM enriched_pv;
```
The result of this interactive continuous query is an enriched pageviews stream that streams to the client as shown below:
