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Category: Streaming

Creating a Power BI Streaming Dataset

Published 2020-07-16 by Kevin Feasel

Rob Farley takes us through the process of creating and using a Power BI streaming dataset:

Real-time Power BI sets are a really useful feature, and there’s a good description of them at https://docs.microsoft.com/en-us/power-bi/connect-data/service-real-time-streaming. I thought I’d do a quick walkthrough specifically around the Push side, and show you – including the odd gotcha that you might not have noticed.

To create a dataset that you want to push data into, you need to go to the Power BI service, go to your Workspace, and create a Streaming dataset. Even if you’re not wanting to use it with a streaming service, this is the one you need.

Rob has plenty of animated GIFs to walk you through the process, as well as a couple of caveats if you want to play along at home.

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Apache Flink 1.1.0 Released

Published 2020-07-09 by Kevin Feasel

Marta Paes announces Apache Flink version 1.11:

Change Data Capture (CDC) has become a popular pattern to capture committed changes from a database and propagate those changes to downstream consumers, for example to keep multiple datastores in sync and avoid common pitfalls such as dual writes. Being able to easily ingest and interpret these changelogs into the Table API/SQL has been a highly demanded feature in the Flink community — and it’s now possible with Flink 1.11.

Click through for the full list of updates.

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FlinkSQL in Cloudera Streaming Analytics

Published 2020-07-08 by Kevin Feasel

Marton Balassi announces support for FlinkSQL in Cloudera Streaming Analytics:

Our 1.2.0.0 release of Cloudera Streaming Analytics Powered by Apache Flink brings a wide range of new functionality, including support for lineage and metadata tracking via Apache Atlas, support for connecting to Apache Kudu and the first iteration of the much-awaited FlinkSQL API.

Flink’s SQL interface democratizes stream processing, as it caters to a much larger community than the currently widely used Java and Scala APIs focusing on the Data Engineering crowd. Generalizing SQL to stream processing and streaming analytics use cases poses a set of challenges: we have to tackle expressing infinite streams and timeliness of records. 

All is happening as Feasel’s Law foretold.

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The Basics of Spark Streaming

Published 2020-06-29 by Kevin Feasel

Muskan Gupta gives us an introduction to Spark Streaming:

Spark Streaming is an extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It was added to Apache Spark in 2013. We can get data from many sources such as Kafka, Flume etc. and process it using functions such as map, reduce etc. After processing we can push data to filesystem, databases and even to live dashboards.

In Spark Streaming we work on near real time data. It divides the received input stream into batches. The Spark Engine processes the batches and generate final output in batches.

Read on to understand the key mechanisms behind Spark Streaming.

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Using Apache Flink in Zeppelin Notebooks

Published 2020-06-16 by Kevin Feasel

Jeff Zhang walks us through reviewing data streamed through Apache Flink in an Apache Zeppelin notebook:

In this post, we explained how the redesigned Flink interpreter works in Zeppelin 0.9.0 and provided some examples for performing streaming ETL jobs with Flink and Zeppelin. In the next post, I will talk about how to do streaming data visualization via Flink on Zeppelin. Besides that, you can find an additional tutorial for batch processing with Flink on Zeppelin as well as using Flink on Zeppelin for more advance operations like resource isolation, job concurrency & parallelism, multiple Hadoop & Hive environments and more on our series of posts on Medium. And here’s a list of Flink on Zeppelin tutorial videos for your reference.

Click through for the demo, and stay tuned for part 2.

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Smoothing Out Write Behavior in Apache Flink

Published 2020-06-15 by Kevin Feasel

Dmitry Tolpeko solves an interesting problem:

It would be nice to smooth S3 write operations between two checkpoints. How to do that?

You may have already noticed there are 3 single PUT operations above made at 37:02, 37:06 and 37:09 before the checkpoint. The write size can give you a clue, it is a single part of multi-part upload to S3.

So some data sets were quite large so their data spilled before the checkpoint. Note that this is the internal spill in S3, data will not be visible until committed upon the successful Flink checkpoint.

So how can we force more writes to happen before the checkpoint so we can smooth IOPS and probably reduce the overall checkpoint latency? 

Read on for the answer.

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Building a Stream Processing App with ksql

Published 2020-06-05 by Kevin Feasel

The Hadoop in Real World team walks us through event streaming with ksql:

ksqlDB is an event streaming database that enables creating powerful stream processing applications on top of Apache Kafka by using the familiar SQL syntax, which is referred to as KSQL. This is a powerful concept that abstracts away much of the complexity of stream processing from the user. Business users or analysts with SQL background can query the complex data structures passing through kafka and get real-time insights. In this article, we are going to see how to set up ksqlDB and also look at important concepts in ksql and its usage.

Event streaming has become a lot easier over the past couple of years, as Kafka, Spark, and Flink have all matured.

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Bot-Building with ksqlDB

Published 2020-05-14 by Kevin Feasel

Robin Moffatt has an interesting project for us:

But what if you didn’t need any datastore other than Kafka itself? What if you could ingest, filter, enrich, aggregate, and query data with just Kafka? With ksqlDB we can do just this, and I want to show you exactly how.

We’re going to build a simple system that captures Wi-Fi packets, processes them, and serves up on-demand information about the devices connecting to Wi-Fi. The “secret sauce” here is ksqlDB’s ability to build stateful aggregates that can be directly accessed using pull queries. This is going to power a very simple bot for the messaging platform Telegram, which takes a unique device name as input and returns statistics about its Wi-Fi probe activities to the user:

Click through for the tutorial.

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