Data Streaming
Streaming analytics continues to become more important because it lets businesses learn new things and make decisions in almost real time. This is especially relevant in fields like finance, health care, and manufacturing where the amount of time needed to make decisions is very critical. By the way, when measuring time in streaming analytics, you’ll […]
Apache Kafka includes a command-line tool named kafka-configs.sh used to obtain configuration values for various types of entities such as topics, clients, users, brokers, and loggers. But, using this tool to determine current configuration values at runtime can be more difficult to use than you might expect. This can be especially true if you want […]
Knowing the Kafka version you are using may not be as straightforward as you might think. For example, if you search for “kafka version” in your favorite search engine or chatbot, there are all kind of results. But, I took a look at many of the top results and became concerned because the answers provided […]
Kafka Access Control Lists (ACLs) are a way to secure your Kafka cluster by specifying which users or client applications have access to which Kafka resources (topics, clusters, etc.). The process of authorizing or refusing access to particular resources or functions within a software application is referred to as “authorization” in software. It is the […]
Apache Spark broadcast variables are available to all nodes in the cluster. They are used to cache a value in memory on all nodes, so it can be efficiently accessed by tasks running on those nodes. For example, broadcast variables are useful with large values needing to be used in each Spark task. By using […]
Although Apache Kafka is written in Java, there are Python Kafka clients available for use with Kafka. In this tutorial, let’s go through examples of Kafka with Python Producer and Consumer clients. Let’s consider this a “Getting Started” tutorial. After completing this, you will be ready to proceed to more complex examples. But we need […]
Let’s consider three open source change data capture (CDC) options ready for production in the year 2023. Before we begin, let’s confirm we all see the CDC trend. To me, it seems everywhere you look these days is all about change data capture. From my perspective that wasn’t the case for many years. Do you […]
In this post, let’s answer the question of Kafka and Dead Letter Quest. But first, let’s start with an overview. A dead letter queue (DLQ) is a queue, or a topic in Kafka, used to hold messages which can not be processed successfully. The origin of DLQs are traditional messaging systems which were popular before […]
PySpark SQL functions are available for use in the SQL context of a PySpark application. These functions allow us to perform various data manipulation and analysis tasks such as filtering and aggregating data, performing inner and outer joins, and conducting basic data transformations in PySpark. PySpark functions and PySpark SQL functions are not the same […]
PySpark DataFrames are a distributed collection of data organized into named columns. It is conceptually equivalent to a table in a relational database or a data frame in R, but with richer optimizations under the hood. DataFrames can be constructed from a wide array of sources such as: structured data files, tables in Hive, external […]
Apache Kafka Connect is a development framework for data integration between Apache Kafka and other systems. It facilitates moving data between Kafka and other systems, such as databases, message brokers, and file systems. A connector which moves data INTO Kafka is called a “Source”, while a connector which moves data OUT OF Kafka is called […]
The PySpark withColumn function is used to add a new column to a PySpark DataFrame or to replace the values in an existing column. To execute the PySpark withColumn function you must supply two arguments. The first argument is the name of the new or existing column. The second argument is the desired value to […]
A PySpark UDF, or PySpark User Defined Function, is a powerful and flexible tool in PySpark. They allow users to define their own custom functions and then use them in PySpark operations. PySpark UDFs can provide a level of flexibility, customization, and control not possible with built-in PySpark SQL API functions. It can allow developers […]
Kafka provides multiple authentication options. In this tutorial, we will describe and show the authentication options and then configure and run a demo example of Kafka authentication. There are two primary goals of this tutorial: There are a few key subjects which must be considered when building a multi-tenant cluster, but it all starts with […]
In PySpark, the DataFrame filter function, filters data together based on specified columns. For example, with a DataFrame containing website click data, we may wish to group together all the platform values contained a certain column. This would allow us to determine the most popular browser type used in website requests. Solutions like this may […]
In PySpark, the DataFrame groupBy function, groups data together based on specified columns, so aggregations can be run on the collected groups. For example, with a DataFrame containing website click data, we may wish to group together all the browser type values contained a certain column, and then determine an overall count by each browser […]
The Kafka Connect REST API endpoints are used for both administration of Kafka Connectors (Sinks and Sources) as well as Kafka Connect service itself. In this tutorial, we will explore the Kafka Connect REST API with examples. Before we dive into specific examples, we need to set the context with an overview of Kafka Connect […]
How do Kafka administrators perform administrative and diagnostic collection actions of Kafka Consumer Groups? This post explores a Kafka Groups operations admin tool called kafka-consumer-groups.sh. This popular, command-line tool included in Apache Kafka distributions. There are other examples of both open source and 3rd party tools not included with Apache Kafka which can also be […]
Use PySpark joins with SQL to compare, and possibly combine, data from two or more datasources based on matching field values. This is simply called “joins” in many cases and usually the datasources are tables from a database or flat file sources, but more often than not, the data sources are becoming Kafka topics. Regardless […]
PySpark joins are used to combine data from two or more DataFrames based on a common field between them. There are many different types of joins. The specific join type used is usually based on the business use case as well as most optimal for performance. Joins can be an expensive operation in distributed systems […]
If you’re looking for an application for change data capture which includes speed, durability, significant history in production deployments across a variety of use cases, then Debezium may be for you. This open-source platform provides streaming from a wide range of both relational and NoSQL based databases to Kafka or Kinesis. There are many advantages […]
As a streaming data engineer, we face many data integration challenges such as “How do we integrate this SaaS with this internal database?”, “Will a particular integration be real-time or batch?”, “How does the system we design recovery from possible failures?” and “If anyone has ever addressed a situation similar to mine before, how did […]
A schema registry in data streaming use cases such as micro-service integration, streaming ETL, event driven architectures, log ingest stream processing, etc., is not a requirement, but there are numerous reasons for implementing one. The reasoning for schema registries in data streaming architectures are plentiful and have been covered extensively already. I’ve included some of […]
Why “Joinable” Streaming Test Data for Kafka? When creating streaming join applications in KStreams, ksqldb, Spark, Flink, etc. with source data in Kafka, it would be convenient to generate fake data with cross-topic relationships; i.e. a customer topic and an order topic with a value attribute of customer.id. In this example, we might want to […]
In this post, let’s explore an example of updating an existing Spark Streaming application to newer Spark Structured Streaming. We will start simple and then move to a more advanced Kafka Spark Structured Streaming examples. My original Kafka Spark Streaming post is three years old now. On the Spark side, the data abstractions have evolved […]
One of the many benefits of running Kafka Connect is the ability to run single or multiple workers in tandem. Running multiple workers provides a way for horizontal scale-out which leads to increased capacity and/or an automated resiliency. For resiliency, this means answering the question, “what happens if a particular worker goes offline for any […]
Kafka Streams presents two options for materialized views in the forms of GlobalKTable vs KTables. We will describe the meaning of “materialized views” in a moment, but for now, let’s just agree there are pros and cons to GlobalKTable vs KTables. Need to learn more about Kafka Streams in Java? Here’s a pretty good option […]
Before we begin diving into event logs, let’s start with a quote from one of my software heroes. “The idea of structuring data as a stream of events is nothing new, and it is used in many different fields. Even though the underlying principles are often similar, the terminology is frequently inconsistent across different fields, […]
In this Azure Kafka tutorial, let’s describe and demonstrate how to integrate Kafka with Azure’s Blob Storage with existing Kafka Connect connectors. Let’s get a little wacky and cover writing to Azure Blob Storage from Kafka as well as reading from Azure Blob Storage to Kafka. In this case, “wacky” is a good thing, I […]
We choose Stream Processing as a way to process data more quickly than traditional approaches. But, how do we do Stream Processing? Is Stream Processing different than Event Stream Processing? Why do we need it? What are a few examples of event streaming patterns? How do we implement it? Let’s get into these questions. As […]
If you are considering Kafka Certification, this page describes what I did to pass the Confluent Certified Developer for Apache Kafka Certification exam. You may see it shortened to “ccdak confluent certified developer for apache kafka tests“. Good luck and hopefully this page is helpful for you! There are many reasons why you may wish […]
In this GCP Kafka tutorial, I will describe and show how to integrate Kafka Connect with GCP’s Google Cloud Storage (GCS). We will cover writing to GCS from Kafka as well as reading from GCS to Kafka. Descriptions and examples will be provided for both Confluent and Apache distributions of Kafka. I’ll document the steps […]
After you start working with Kafka, you will soon find yourself asking the question, “how can I generate test data into my Kafka cluster?” Well, I’m here to show you have many options for generating test data in Kafka. In this post and demonstration video, we’ll cover a few of the ways you can generate […]
In this Kafka Connect S3 tutorial, let’s demo multiple Kafka S3 integration examples. We’ll cover writing to S3 from one topic and also multiple Kafka source topics. Also, we’ll see an example of an S3 Kafka source connector reading files from S3 and writing to Kafka will be shown. Examples will be provided for both […]
Kafka Streams Transformations provide the ability to perform actions on Kafka Streams such as filtering and updating values in the stream. Kafka Stream’s transformations contain operations such as `filter`, `map`, `flatMap`, etc. and have similarities to functional combinators found in languages such as Scala. And, if you are coming from Spark, you will also notice […]
When moving to stream processing architecture or building stream processors, you will soon face two choices. Will you process streams on an individual, per event basis? Or, will you collect and buffer multiple events/messages first, and then apply a function or join results to this collection of events? Examples of single event processing might be […]
Kafka Producers are one of the options to publish data events (messages) to Kafka topics. Kafka Producers are custom coded in a variety of languages through the use of Kafka client libraries. The Kafka Producer API allows messages to be sent to Kafka topics asynchronously, so they are built for speed, but also Kafka Producers have the ability […]
In this Kafka Consumer tutorial, we’re going to demonstrate how to develop and run an example of Kafka Consumer in Scala, so you can gain the confidence to develop and deploy your own Kafka Consumer applications. At the end of this Kafka Consumer tutorial, you’ll have both the source code and screencast of how to […]
Kafka Consumer Groups are the way to horizontally scale out event consumption from Kafka topics… with failover resiliency. “With failover resiliency” you say!? That sounds interesting. Well, hold on, let’s leave out the resiliency part for now and just focus on scaling out. We’ll come back to resiliency later. When designing for horizontal scale-out, let’s […]
Performing Kafka Streams Joins presents interesting design options when implementing streaming processor architecture patterns. There are numerous applicable scenarios, but let’s consider an application might need to access multiple database tables or REST APIs in order to enrich a topic’s event record with context information. For example, perhaps we could augment records in a topic with sensor […]
After experimenting with Kafka Streams with Scala, I started to wonder how one goes about Kafka Streams testing in Java or Scala. How does one create and run automated tests for Kafka Streams applications? How does it compare to Spark Streaming testing? In this tutorial, I’ll describe what I’ve learned so far. Also, if you […]
If you’re new to Kafka Streams, here’s a Kafka Streams Tutorial with Scala tutorial which may help jumpstart your efforts. My plan is to keep updating the sample project, so let me know if you would like to see anything in particular with Kafka Streams with Scala. In this example, the intention is to 1) provide an SBT project you […]
Apache Kafka offers message delivery guarantees between producers and consumers. For more background or information Kafka mechanics such as producers and consumers on this, please see Kafka Tutorial page. Kafka delivery guarantees can be divided into three groups which include “at most once”, “at least once” and “exactly once”. at most once which can lead to […]
Have you struggled to configure debugging in IntelliJ for your Spark programs? Yeah, me too. Debugging with Scala code was easy, but when I moved to Spark things didn’t work as expected. So, in this tutorial, let’s cover debugging Scala based Spark programs in IntelliJ tutorial. We’ll go through a few examples and utilize the occasional help […]
Change Data Capture is a mechanism to capture the changes in databases so they may be processed someplace other than the database or application(s) which made the change. This article will explain what change data capture (CDC) is, how it works, and why it’s important for businesses. Why? Why would we want to capture changes […]
In this Kafka Connect mysql tutorial, we’ll cover reading from mySQL to Kafka and reading from Kafka and writing to mySQL. Let’s run this on your environment. Now, it’s just an example and we’re not going to debate operations concerns such as running in standalone or distributed mode. The focus will be keeping it simple and get it working. We […]
If you are looking for Spark with Kinesis example, you are in the right place. This Spark Streaming with Kinesis tutorial intends to help you become better at integrating the two. In this tutorial, we’ll examine some custom Spark Kinesis code and also show a screencast of running it. In addition, we’re going to cover […]
Which Spark performance monitoring tools are available to monitor the performance of your Spark cluster? In this tutorial, we’ll find out. But, before we address this question, I assume you already know Spark includes monitoring through the Spark UI? And, in addition, you know Spark includes support for monitoring and performance debugging through the Spark […]
Scheduling in Spark can be a confusing topic. When someone says “scheduling” in Spark, do they mean scheduling applications running on the same cluster? Or, do they mean the internal scheduling of Spark tasks within the Spark application? So, before we cover an example of utilizing the Spark FAIR Scheduler, let’s make sure we’re on […]
In this Apache Spark tutorial, we will explore the performance monitoring benefits when using the Spark History server. This Spark tutorial will review a simple Spark application without the History server and then revisit the same Spark app with the History server. We will explore all the necessary steps to configure Spark History server for […]
Wondering how to do perform stress tests with Apache Spark Thrift Server? This tutorial will describe one way to do it. What is Apache Spark Thrift Server? Apache Spark Thrift Server is based on the Apache HiveServer2 which was created to allow JDBC/ODBC clients to execute SQL queries using a Spark Cluster. From my […]
With the Spark Thrift Server, you can do more than you might have thought possible. For example, want to use `joins` with Cassandra? Or, help people familiar with SQL leverage your Spark infrastructure without having to learn Scala or Python? They can use their existing SQL based tools they already know such as Tableau or […]
Spark Streaming with Kafka is becoming so common in data pipelines these days, it’s difficult to find one without the other. This tutorial will present an example of streaming Kafka from Spark. In this example, we’ll be feeding weather data into Kafka and then processing this data from Spark Streaming in Scala. As the data […]
The primary reason why we want to use Spark submit command line arguments is to avoid hard-coding values into our code. As we know, hard-coding should be avoided because it makes our application more rigid and less flexible. For example, let’s assume we want to run our Spark job in both test and production environments. […]
Spark is distributed with the Metrics Java library which can greatly enhance your abilities to diagnose issues with your Spark jobs. In this tutorial, we’ll cover how to configure Metrics to report to a Graphite backend and view the results with Grafana for Spark Performance Monitoring purposes. Spark Performance Monitoring Background If you already know […]
On this site, we’ve learned about distributing processing tasks across a Spark cluster. But, let’s go a bit deeper in a couple of approaches you may need when designing distributed tasks. I’d like to start with a question. What do we do when we need each Spark worker task to coordinate certain variables and values with […]
IntelliJ Scala and Spark Setup Overview In this tutorial, we’re going to review one way to setup IntelliJ for Scala and Spark development. The IntelliJ Scala combination is the best, free setup for Scala and Spark development. And I have nothing against ScalaIDE (Eclipse for Scala) or using editors such as Sublime. I switched from […]
Spark Streaming Testing How do you create and automate tests of Spark Streaming applications? In this tutorial, we’ll show an example of one way in Scala. This post is heavy on code examples and has the added bonus of using a code coverage plugin. Are the tests in this tutorial examples unit tests? Or, are […]
Apache Kafka vs. Amazon Kinesis The question of Kafka vs Kinesis often comes up. Let’s start with Kinesis. *** Updated Spring 2020 *** Since this original post, AWS has released MSK. I think this tells us everything we need to know about Kafka vs Kinesis. Also, since the original post, Kinesis has been separated into […]
Apache Spark with Cassandra is a powerful combination in data processing pipelines. In this tutorial, we will build a Scala application with Spark and Cassandra with battle data from Game of Thrones. Now, we’re not going to make any show predictions! But, we will show the most aggressive kings as well as kings which […]
What is Spark RDD? Spark RDD is short for Apache Spark Resilient Distributed Dataset. A Spark Resilient Distributed Dataset is often shortened to simply Spark RDD. RDDs are a foundational component of the Apache Spark large scale data processing framework. Spark RDDs are an immutable, fault-tolerant, and possibly distributed collection of data elements. RDDs may […]
In this Apache Spark Machine Learning example, Spark MLlib is introduced and Scala source code analyzed. This post and accompanying screencast videos demonstrate a custom Spark MLlib Spark driver application. Then, the Spark MLLib Scala source code is examined. Many topics are shown and explained, but first, let’s describe a few machine learning concepts. Machine […]
In this Apache Spark cluster troubleshooting tutorial, we’ll review a few options when your Scala Spark code does not deploy as anticipated. For example, does your Spark driver program rely on a 3rd party jar only compatible with Scala 2.11, but your Spark Cluster is based on Scala 2.10? Maybe your code relies on a […]
Overview In this Apache Spark cluster deploy tutorial, we’ll cover how to deploy Spark driver programs to a Spark cluster when the driver program utilizes third-party jars. In this case, we’re going to use code examples from previous Spark SQL and Spark Streaming tutorials. At the end of this tutorial, there is a screencast of […]
Spark Machine Learning is contained with Spark MLlib. Spark MLlib Spark’s library of machine learning (ML) functions designed to run in parallel on clusters. MLlib contains a variety of learning algorithms. The topic of machine learning itself could fill many books, so instead, this chapter explains ML in Apache Spark. This post is an excerpt […]
Let’s write a Spark Streaming example which streams from Slack in Scala. This tutorial will show how to write, configure and execute the code, first. Then, the source code will be examined in detail. If you don’t have a Slack team, you can set one up for free. We’ll cover that too. Sound fun? […]
Let’s start Apache Spark Streaming with Scala with small steps to build up our skills and confidence. These small steps will create the forward momentum needed when learning new skills. The quickest way to gain confidence and momentum in learning new software development skills is executing code that performs without error. Right? I mean, right!? […]
After you have a Spark cluster running, how do you deploy Python programs to a Spark Cluster? It’s not as straightforward as you might think or hope, so let’s explore further in this PySpark tutorial. PySpark Application Deploy Overview Let’s deploy a couple of examples of Spark PySpark program to our cluster. Let’s start with […]
Let’s cover how to use Spark SQL with Python and a mySQL database input data source. Shall we? Yes, yes we shall. Consider this tutorial an introductory step when learning how to use Spark SQL with a relational database and Python. If you are brand new, check out the Spark with Python Tutorial. PySpark MySQL […]
This short PySpark SQL tutorial shows analysis of World Cup player data using PySpark SQL with a JSON file input data source from Python perspective. PySpark SQL with JSON Overview We are going to load a JSON input source to Spark SQL’s SQLContext. This Spark SQL JSON with Python tutorial has two parts. The first […]
In this pyspark reading csv tutorial, we will use Spark SQL with a CSV input data source using the Python API. We will continue to use the Uber CSV source file as used in the Getting Started with Spark and Python tutorial presented earlier. Also, this Spark SQL CSV tutorial assumes you are familiar with using […]
In this post, let’s cover Apache Spark with Python fundamentals to get you started and feeling comfortable about using PySpark. The intention is for readers to understand basic PySpark concepts through examples. Later posts will deeper dive into Apache Spark fundamentals and example use cases. Spark computations can be called via Scala, Python or Java. There […]
This post will show ways and options for accessing files stored on Amazon S3 from Apache Spark. Examples of text file interaction on Amazon S3 will be shown from both Scala and Python using the spark-shell from Scala or ipython notebook for Python. To begin, you should know there are multiple ways to access S3 […]
This post will quickly cover how to connect an ipython notebook to two kinds of Spark Clusters: Spark Cluster running in Standalone mode and a Spark Cluster running on Amazon EC2. What is ipython? An interactive computing environment called IPython Notebook enables users to create and share documents with real-time code, equations, visuals, and text. […]
How to set up and run an Apache Spark Cluster on EC2? This tutorial will walk you through each step to get an Apache Spark cluster up and running on EC2. The cluster consists of one master and one worker node. It includes each step I took regardless if it failed or succeeded. While your […]
PySpark action functions produce a computed value back to the Spark driver program. This is different from PySpark transformation functions which produce RDDs, DataFrames or DataSets in results. For example, an action function such as count will produce a result back to the Spark driver while a collect transformation function will not. These may seem […]
If you’ve read the previous PySpark tutorials on this site, you know that Spark Transformation functions produce a DataFrame, DataSet or Resilient Distributed Dataset (RDD). Resilient distributed datasets are Spark’s main programming abstraction and RDDs are automatically parallelized across the cluster. As Spark matured, this abstraction changed from RDDs to DataFrame to DataSets, but the […]
Using ipython notebook with Apache Spark couldn’t be easier. This post will cover how to use ipython notebook (jupyter) with Spark and why it is best choice when using python with Spark. Requirements This post assumes you have downloaded and extracted Apache Spark and you are running on a Mac or *nix. If you are […]
In this tutorial, we will cover using Spark SQL with a mySQL database. Overview Let’s show examples of using Spark SQL mySQL. We’re going to use mySQL with Spark in this tutorial, but you can apply the concepts presented here to any relational database which has a JDBC driver. By the way, If you are […]
This tutorial covers using Spark SQL with a JSON file input data source in Scala. If you are interested in using Python instead, check out Spark SQL JSON in Python tutorial page. Spark SQL JSON Overview We will show examples of JSON as input source to Spark SQL’s SQLContext. This Spark SQL tutorial with JSON […]
In this Spark SQL tutorial, we will use Spark SQL with a CSV input data source. We will continue to use the baby names CSV source file as used in the previous What is Spark tutorial. This tutorial presumes the reader is familiar with using SQL with relational databases and would like to know how […]
How do you deploy a Scala program to a Spark Cluster? In this tutorial, we’ll cover how to build, deploy and run a Scala driver program to a Spark Cluster. The focus will be on a simple example in order to gain confidence and set the foundation for more advanced examples in the future. To […]
Running an Apache Spark Cluster on your local machine is a natural and early step towards Apache Spark proficiency. As I imagine you are already aware, you can use a YARN-based Spark Cluster running in Cloudera, Hortonworks or MapR. There are numerous options for running a Spark Cluster in Amazon, Google or Azure as well. […]
Spark Action Examples in Scala When using Spark API “action” functions, a result is produced back to the Spark Driver. Computing this result will trigger any of the RDDs, DataFrames or DataSets needed in order to produce the result. Recall Spark Transformations such as map, flatMap, and other transformations are used to create RDDs, DataFrames […]
Spark Transformations in Scala Examples Spark Transformations produce a new Resilient Distributed Dataset (RDD) or DataFrame or DataSet depending on your version of Spark. Resilient distributed datasets are Spark’s main and original programming abstraction for working with data distributed across multiple nodes in your cluster. RDDs are automatically parallelized across the cluster. In the Scala […]
Becoming productive with Apache Spark requires an understanding of a few fundamental elements. In this post, let’s explore the fundamentals or the building blocks of Apache Spark. Let’s use descriptions and real-world examples in the exploration. The intention is for you is to understand basic Spark concepts. It assumes you are familiar with installing software […]
