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.

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.

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.

There's a range of additional information you can view. For more details, please see Explore Data Store and Topic Details.

2. Create a Database

Now it’s time to declare a database and 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.

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.

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.

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.

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:

CREATE CHANGELOG users_log (
    registertime BIGINT, 
    userid VARCHAR, 
    regionid VARCHAR, 
    gender VARCHAR, 
    interests ARRAY<VARCHAR>, 
    contactinfo STRUCT<phone VARCHAR, city VARCHAR, "state" VARCHAR, zipcode VARCHAR>, 
    PRIMARY KEY(userid)
)WITH (
    'topic'='users', 
    'key.format'='json', 
    'key.type'='STRUCT<userid VARCHAR>', 
    '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:

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:

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.

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;

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:

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.

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, .

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 .