Getting Started with Streambased A.S.K.

Looking to enhance your analytical experience with Kafka data? Follow this guide to be connected to your data and querying in your tools in minutes.

pREREQUISITES

Before using A.S.K.
make sure you have:

Publicly available 
KafkaEnsure your Kafka instance is accessible and configured for your needs.
Schema Registry with
TopicNameStrategySet up your Schema Registry to use TopicNameStrategy for managing schemas.
Data in JsonSchema
or Avro formatVerify that your data is formatted correctly using either JsonSchema or Avro.
“backwards” or "backwards transitive” 
compatibility.To ensure the smooth evolution of table structures in A.S.K. we support backwards (the default) and backwards transitive compatibility types.

Download the driver and connect
your tool of choice to ASK

Run some queries against our
lobby data set:

SHOW TABLES;				
SELECT line FROM readme ORDER BY lineno;

Convert your Kafka connection
properties to JSON:

Before:

After:

Apply our new
connection:

SET SESSION streambased_connection =

Explore!

SELECT a.accounttype, 
  count(t.transactionamount) as deposit_count, 
  sum(t.transactionamount) as deposit_total 
FROM 
  demo_transactions t 
JOIN
  demo_accounts a on t.accountId = a.accountId 
WHERE
  t.transactiontype='Deposit' 
GROUP BY
  a.accounttype;

Our Blogs

Articles & Resources

Blog post

Bridging the Gap Between Data Science and Real-Time Insights

In the world of AI and data science, timely data is crucial for generating actionable insights. Traditional workflows often involve delays between data requests and analysis, leading to outdated information. This “insights loop” hinders real-time decision-making and limits the effectiveness of AI models. By leveraging real-time data access, such as through Streambased, data scientists can break this loop, query live data streams, and integrate them with historical data.

3 Reasons counting records on a Kafka topic is hard

Counting records on a Kafka topic is difficult due to log compaction removing messages, aborted transactions leaving ignored offsets, and unreliable timestamp ordering misaligning messages with their offsets.

Want to go faster?

The zero signup nature of A.S.K. means that we cannot apply Streambased super fast acceleration technology. Contact us today to get a dedicated Streambased cluster and experience up to 30x speed up on your queries.

Getting Started with Streambased A.S.K.

Before using A.S.K. make sure you have:

Publicly available Kafka

Schema Registry withTopicNameStrategy

Data in JsonSchemaor Avro format

“backwards” or "backwards transitive” compatibility.

Download the driver and connectyour tool of choice to ASK

Connecting to A.S.K. with Superset

Connecting to A.S.K. with Generic JDBC

Connecting to A.S.K with Generic ODBC

Connecting to A.S.K with Jupyter

Connecting to A.S.K. with DBT

Run some queries against our lobby data set:

Convert your Kafka connectionproperties to JSON:

Apply our newconnection: