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Category: Streaming

Streaming Data To Power BI

Published 2016-08-18 by Kevin Feasel

Reza Rad shows how to hook up streaming data sources to Power BI:

As I mentioned before, download the sample project. and then select the project under this path:

samples/consoleapp/getting-started-for-dotnet-real-time/PBIRealTimeStreaming

And open the PBIRealTimeStreaming solution in Visual Studio. This project creates a data set including a datetime value and a numeric value and will pass that through Power BI API (which will be discussed later in another post) to Power BI service.

Real-time dashboards are great for making it look like you’re doing Very Important Things.  They can also be useful in other ways too.  Read the whole thing.

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Kinesis Analytics

Published 2016-08-15 by Kevin Feasel

Ryan Nienhuis shows how to write SQL against Amazon Kinesis queues:

In your application code, you interact primarily with in-application streams. For instance, a source in-application stream represents your configured Amazon Kinesis stream or Firehose delivery stream in the application, which by default is named “SOURCE_SQL_STREAM_001”. A destination in-application stream represents your configured destinations, which by default is named “DESTINATION_SQL_STREAM”. When interacting with in-application streams, the following is true:

  • The SELECT statement is used in the context of an INSERT statement. That is, when you select rows from one in-application stream, you insert results into another in-application stream.
  • The INSERT statement is always used in the context of a pump. That is, you use pumps to write to an in-application stream. A pump is the mechanism used to make an INSERT statement continuous.

There are two separate SQL statements in the template you selected in the first walkthrough. The first statement creates a target in-application stream for the SQL results; the second statement creates a PUMP for inserting into that stream and includes the SELECT statement.

This is worth looking into if you use AWS and have a need for streaming data.

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Structured Streaming

Published 2016-08-03 by Kevin Feasel

Matei Zaharia, et al discuss how to use structured streaming in Apache Spark 2.0:

In Structured Streaming, we tackle the issue of semantics head-on by making a strong guarantee about the system: at any time, the output of the application is equivalent to executing a batch job on a prefix of the data. For example, in our monitoring application, the result table in MySQL will always be equivalent to taking a prefix of each phone’s update stream (whatever data made it to the system so far) and running the SQL query we showed above. There will never be “open” events counted faster than “close” events, duplicate updates on failure, etc. Structured Streaming automatically handles consistency and reliability both within the engine and in interactions with external systems (e.g. updating MySQL transactionally).

If you want to learn more about streaming data using Spark, check this out.

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Detecting Web Traffic Anomalies

Published 2016-06-27 by Kevin Feasel

Jan Kunigk combines a few Apache products to perform near-real-time analysis of web traffic data:

meinestadt.de web servers generate up to 20 million user sessions per day, which can easily result in up to several thousand HTTP GET requests per second during peak times (and expected to scale to much higher volumes in the future). Although there is a permanent fraction of bad requests, at times the number of bad requests jumps.

The meinestadt.de approach is to use a Spark Streaming application to feed an Impala table every n minutes with the current counts of HTTP status codes within the n minutes window. Analysts and engineers query the table via standard BI tools to detect bad requests.

What follows is a fairly detailed architectural walkthrough as well as configuration and implementation work.  It’s a fairly long read, but if you’re interested in delving into Hadoop, it’s a good place to start.

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Kafka And MapR Streams

Published 2016-05-18 by Kevin Feasel

Ellen Friedman compares and contrasts Apache Kafka with MapR streams:

What’s the difference in MapR Streams and Kafka Streams?

This one’s easy: Different technologies for different purposes. There’s a difference between messagingtechnologies (Apache Kafka, MapR Streams) versus tools for processing streaming data (such as Apache Flink, Apache Spark Streaming, Apache Apex). Kafka Streams is a soon-to-be-released processing tool for simple transformations of streaming data. The more useful comparison is between its processing capabilities and those of more full-service stream processing technologies such as Spark Streaming or Flink.

Despite the similarity in names, Kafka Streams aims at a different purpose than MapR Streams. The latter was released in January 2016. MapR Streams is a stream messaging system that is integrated into the MapR Converged Platform. Using the Apache Kafka 0.9 API, MapR Streams provides a way to deliver messages from a range of data producer types (for instance IoT sensors, machine logs, clickstream data) to consumers that include but are not limited to real-time or near real-time processing applications.

This also includes an interesting discussion of how the same term, “broker,” can be used in two different products in the same general product space and mean two distinct things.

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