A Fine Slice Of SQL Server
Jaydeep Patil adds a NuGet package:
We will go over Apache Kafka basics, installation, and operation, as well as a step-by-step implementation using a .NET Core 6 web application.
Click through for a demo-heavy walkthrough. And if you’re interested in ksqldb or Kafka Streams, also check out Tomas Fabian’s library. Both are under active development and each forms a piece of the whole Kafka solution.
Comments closedMartijn Visser announces Apache Flink version 1.16.1:
The Apache Flink Community is pleased to announce the first bug fix release of the Flink 1.16 series.
This release includes 84 bug fixes, vulnerability fixes, and minor improvements for Flink 1.16. Below you will find a list of all bugfixes and improvements (excluding improvements to the build infrastructure and build stability). For a complete list of all changes see: JIRA.
We highly recommend all users upgrade to Flink 1.16.1.
Read on for the release notes, including links to all of the closed tickets.
Comments closedJingsong Lee announces a new version of Flink Table Store:
Sometimes users only care about aggregated results. The aggregation merge engine aggregates each value field with the latest data one by one under the same primary key according to the aggregate function.
Each field that is not part of the primary keys must be given an aggregate function, specified by the
fields.<field-name>.aggregate-functiontable property.
Read on for the full changeset.
Comments closedIn this post we are going to look at an example of streaming IoT temperature data in Synapse Spark. I have an IoT device that will stream temperature data from two sensors to IoT hub. We’ll use Synapse Spark to process the data, and finally write the output to persistent storage. Here is what our architecture will look like:
Click through for the architectural diagram and step-by-step on how to put the demo together.
Comments closedDustin Vannoy answers some questions:
I received many questions on my tutorial Ingest tables in parallel with an Apache Spark notebook using multithreading. In this video and post I address some of the questions that I couldn’t just answer in the YouTube comments. Watch the video for more complete answers but here are quick responses with links to examples where appropriate.
Click through for the video and some text versions. Dustin includes examples for Synapse and Databricks.
Comments closedShalu Ganotra Chadra, et al, explain what Synapse Genie is:
The Genie framework is a metadata driven utility written in Python. It is implemented using threading (ThreadPoolExecutor module) and directed acyclic graph (Networkx library). It consists of a wrapper notebook, that reads metadata of notebooks and executes them within a single Spark session. Each notebook is invoked on a thread with MSSparkutils.run() command based on the available resources in the Spark pool. The dependencies between notebooks are understood and tracked through a directed acyclic graph.
Read on for more information about how you can use it and what the setup process looks like.
Comments closedAshish Chaudhary does a bit of swapping around:
In this blog, we will be talking about Spark RDD, Dataframe, Datasets, and how we can transform RDD into Dataframes and Datasets.
At this point, most of the libraries I know of accept and produce DataFrames. Occasionally you might need to “downshift” to an RDD to work with some specialty library. But in the event you do have one but want to get to another, Ashish has you covered.
Comments closedRituraj Khare makes some connections:
In Apache Spark, we can use the following types of joins in SQL:
Inner join: An inner join in Apache Spark is a type of join that returns only the rows that match a given predicate in both tables. To perform an inner join in Spark using Scala, we can use the
joinmethod on a DataFrame.
The set of options is the same as you’d see in a relational database: inner, left outer, right outer, full outer, and cross. The examples here are in Scala, though would apply just as easily to PySpark and, of course, writing classic SQL statements.
Comments closedMeagan Longoria adds external tables and views to an Azure Databricks Unity Catalog:
I’ve been busy defining objects in my Unity Catalog metastore to create a secure exploratory environment for analysts and data scientists. I’ve found a lack of examples for doing this in Azure with file types other than delta (maybe you’re reading this in the future and this is no longer a problem, but it was when I wrote this). So I wanted to get some more examples out there in case it helps others.
I’m not storing any data in Databricks – I’m leaving my data in the data lake and using Unity Catalog to put a tabular schema on top of it (hence the use of external tables vs managed tables. In order to reference an ADLS account, you need to define a storage credential and an external location.
Read on for examples of what you can do with this.
Comments closedAmit Kumar shares a few tips with us:
RDD does serialisation and de-serialisation of data whenever it distributes the data across clusters such as during repartition and shuffle, and we all know that serialisation and de-serialisation are very expensive operations in spark.
On the other hand, DataFrame stores the data as binary using off-heap storage, no need for deserialization and serialization of data when it distributes to clusters. We see a big performance improvement in DataFrame over RDD
Click through for several additional tips.
Comments closed